Modelling Financial Derivatives Markets in a Firm Based Evolutionary Macro Model (MOSES)-On the market integration of computing, communications, and financial services

The theory of the EOE is presented as the antithesis of the Walras – Arrow – Debreu (WAD) model. The notion of a knowledge based information economy provides some necessary prior modifications of the WAD model, notably about the state space. Competence bloc theory explains the market selection of projects within the vast state space, or business opportunities space of the EOE.³

In the EOE

1. free competitive entry (Item 1 in Table 1) into, and exploration of the opportunities space, keep incumbent agents under competitive pressure to perform. Incumbent firms respond by reorganizing and/or rationalizing (Items 2 and 3) upgrading their productivities. They are all under constant threat of being competed down along a Salter (1960) performance curve, or out of business (item 4). Because of competition and selection aggregate productivity performance increases.

2. choices of market and technology determine the fate of the individual agent, which often fails. But choices must be prematurely made to avoid ultimate failure and exit, and hence cannot be avoided. Business mistakes become a normal cost for economic development.

Having said that we have also introduced the four fundamental growth generating mechanism in the EOE that are coded into the MOSES model, or what we call the four elements of Schumpeterian Creative Destruction of Table 1. They are universal (Eliasson, 1996a), since all four categories can be defined and observed at all levels of aggregation, even within the firm.⁴

Using the concept of a Salter curve to describe the dynamics of the EOE we can (1) decompose economic growth into the four investment categories of Tables 1 and 2 illustrate the huge synergistic productivity improvements that can be achieved at all levels of aggregation through "organizational change" as illustrated in Table 2. This amounts to classifying firms into growing, contracting, and exiting agents, together shifting the Salter curves in Figure 1 out- and upwards. The main point made is that the large effects of C&C technology are systemic through reorganization of both information processing and physical production (item 5) and that the realization of these systemic effects requires considerable management organizational competence within the firm. At higher levels of reorganization among firms as in Table 1 this, as we have already concluded, becomes a matter of how markets and competition work.⁵

Figure 1

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The initial state description of the MOSES model economy consists of several performance distributions (Salter curves) over firms. The labor productivity and profitability distributions in Figure 1 of real initial data sets for the years 1983 and 1990 illustrate. The horizontal axis measures performance (labor productivity in Figure 1B) in descending order to the right, each step or column representing one firm, its size being expressed in percent (of total capital stock) of the entire population of firms.

In the ranking of Figure 1 each firm is challenged by superior firms to its left, with a greater capacity to outbid itself in both product and labor markets and perhaps also in financial markets to obtain funds to invest and become more profitable. This firm therefore has to (Eliasson, 1996a) take innovative counter action to prevent being overtaken by competition. But the superior firm to the left also knows that firms on its right may be preparing strategic counter action, to leap frog its performance disadvantage by risky innovation and consequently also has to take action to offset that risk. In an open market economy each firm along the Salter ranking finds itself in such a more or less precarious competitive situation. And this is not all.

In the wings of the market theatre eager challengers wait for an opportunity to enter the scene, and if allowed ("free entry") will do it any time. Entrepreneurial entrants are not only optimistic and eager to enter the market fray. Even though on the average less productive than incumbents the group often includes some super performers. So free entry is a real threat to incumbents (Eliasson, 1991b). In principle therefore all agents in an open economy find themselves more or less under competitive threat to innovate themselves out of a perceived precarious position. This "innovation pushed by fear" is sufficient to keep the Schumpeterian creative destruction process of Table 1 in perpetual motion (Eliasson, 1995b), and it is modelled as an endogenous competitive process by the entry algorithms of the MOSES model. But the economy wide outcome of that process depends on the character of the innovation response, incentives in markets and the efficiency of project selection and scale up in markets.

With competitive market selection of business experiments being the main vehicle for economic growth, the main concern of policy makers concerned with growth should be how these markets are organized for dynamically efficient selection. This means making sure that the minimum of market agents being in place to minimize the negative economic incidence of (1) keeping losers on the books for too long (Economic mistake Type I) and of (2) terminating winners prematurely (Economic mistake Type II). What is needed as a minimum for such dynamically efficient project selection is explained by competence bloc theory (Eliasson and Eliasson, 1996; Eliasson, 2000b). (Before we go on it must be understood that the agents of the competence bloc can in principle be found in both the market and internally within the business hierarchy of a large firm. Since we do not yet model project selection within the firm in the context of the MOSES model that observation need not concern us here).

The competence bloc is not yet introduced in the MOSES model. Hence it will not make sense to present it in full here. For that the reader is referred to the references above. Suffice it to mention that completeness of the competence bloc is one criterion for efficient selection. The completeness of the competence bloc defines the incentives for innovation and entrepreneurship and for new entry in particular. New entry in turn initiates competition, that forces incumbent agents to innovate (previous section) and enforces the Schumpeterian creative destruction of Table 1, and the selection that drives economic growth. For incentives to be credible, institutions that reduce the uncertainty surrounding the (property) rights to the expected present value of investments today as valued in financial markets must be in place (Eliasson, 1998a).

The main micro agent of the MOSES model is the firm, and endogenous entering and exiting firms keep the markets of the model economy "alive". The firm population is therefore endogenous through market selection, and firms come in different sizes and structures and with different profit seeking agendas. Aggregation is explicit over dynamic markets governed by competing agents with autonomous price setting capacity, up to the level of a national economy. Behavior is mainly deterministic. Selection makes the MOSES model highly non linear and initial state dependent, exhibiting therefore now and then typical "chaotic" characteristics. The statistical resolution of the model is set at the firm or division level and the endogenization of growth should be seen as dependent upon market selection among those entities as they are kept in competitive motion bin the Schumpeterian creative destruction process of Table 1.

The MOSES model is empirical in that its specification, notably the firm model has been based on careful case studies (Eliasson, 1976a), its initial state measured annually since 1975 on a separate survey (the Planning Survey) of the Federation of Swedish Industries (Albrecht et al., 1992; Taymaz, 1992b), and all being placed for chosen initial years within a stock flow consistent complete micro to macro Swedish National Accounting framework. The latter accounting system has required a major statistical effort, since public sector data have had to be reclassified on the market taxonomy firms use in their internal statistical system, from the production taxonomy of public statistics,¹⁹ ,and in initial state dependent non linear selection based models like MOSES eerrors of measurement tend to cumulate (for 1982, see Taymaz, 1992a). The manufacturers of the model have then been calibrated against Swedish macro data and distributional characteristics, for instance the Salter curves of Figure 1, first using the ad hoc method described in Eliasson and Olavi (1978), then the unique computerized calibration method developed by Taymaz (1991b). We have taken the empirical credibility of MOSES seriously. Even though we believe we have got the specification, the statistical measurements and the calibrated parameters right we talk about MOSES as representing a Sweden like industrial economy, which is sufficient for making simulation experiments empirically interesting.

All simulation experiments reported on in this paper use the parameter set calibrated against historic Swedish test variables for the period 1982 -1990.

It should be emphasized that well researched empirically relevant specification combined with detailed measurement has to a significant extent relieved us of a number of parameter estimation problems. This is one of the many advantages of the micro simulation method.

Quantitative control of economy wide dynamics has been particularly difficult. This includes calibrating parameters such that an empirically reasonable endogenous growth cycle emerges. About a dozen parameters regulating agents’ behavior across markets and over time, notably those relating to the Stockholm School ex ante plan ex post outcome feedbacks, are critical for economy wide systems dynamics. These are also the market mechanims that make the MOSES model depart fundamentally from general equilibrium type models of Walras-Arrow-Debreu (WAD) descent.

The MOSES model endogenizes aggregation over dynamic markets under non- price taking conditions and is market self-coordinated without a need for an exogenous Walrasian auctioneer. It is therefore the ideal tool for analyzing the economy wide long-term consequences of events occurring at the micro level, such as market interactions between firms, or organizational changes within firms and divisions. The model has also been used frequently to generalize from case studies to macro, under the assumption that its parameters reasonably well represent the dynamic market aggregation of consequences up to macro. Dynamic cost benefit analyses of major investment programs would be one example, for instance technical spillovers from the Swedish fighter aircraft Gripen (Eliasson, 1995a). The synergistic economy wide productivity effects of the introduction of generic new technologies, such as C&C technologies and the Internet is another case, as are the consequences of C&C technology for both new product development in, and for raising the allocational capacities of financial markets that we will study in the following section of this paper (Eliasson and Johansson, 1999; Eliasson and Wihlborg, 1998). Only a model of general monopolistic competition, like MOSES, can be used for such purposes.²⁰

Well through the 1950s financial economics was almost all a matter of empirically based rules of thumb in firm decision making. In this subfield of business administration theory business practice encoded as rules of thumb dominated over systematic and consistent analysis. This field was suddenly broken up by the successive entry of new, innovative intellectual tools of analysis based on the WAD tradition and optimized for that same economic environment, a disruption of the entire field of business administration that has gradually moved financial analysis away from business administration practices into the abstractions of economic theory.

It began as an engineering product development by (Markowitz, 1952) who formulated the theory of portfolio choice , a path breaking new financial product that since then has radically changed ways of thinking among financial asset managers. Next, Franco Modigliani and Merton Miller (1958) demonstrated theoretically that large parts of the flora of rules of thumb would be made irrelevant if it could be assumed that financial risks could be separated from other financial considerations and traded in separate markets for risks. This theoretical "insight" generated a mounting controversy in academe and in the financial community. Above all the famous Fisher separability proposition was questioned. It stated that decisions in real product markets and financial markets could be kept separate. Already here those new abstractions clashed with the economics of MOSES firms in that price expectations in product, labour and financial markets are integrated in the business plans of individual firms, plans that are then from period to period tested against each other "for consistency" in markets, revised and then tested again. Another point of criticism was that financial transactions costs in the new theory were assumed to be zero. None of this is true in the MOSES economy, so we have an interesting "empirical" problem to address below in that (1) financial and real markets are explicitly integrated by management in the business plans of MOSES firms, and (2) that business mistakes of types I and II (see Section 2.3) become market transactions costs with fundamental economy wide consequences.

The next round in financial product development occurred when William Sharpe (Sharpe, 1964) introduced the Capital Asset Pricing (CAP) model and demonstrated how risks could be systematically and rationally (under the WAD assumptions) priced and then traded in separate markets. The CAP formulae established tradability and constituted a theoretical underpinning of the rapid development of new markets in risks that in fact soon took place. In the CAP algorithm estimated beta values indicate the individual contributions of shares to the risk exposure of the entire portfolio, and therefore also prices.

The next phase in financial product development soon arrived with the algorithms for options pricing developed by Black and Scholes (1973), which made the markets for financial derivatives a practical reality. In a couple of decades the B-S/M options pricing formula had made such markets a dominant reality, not only refashioning national and global financial systems, but also the global economy, and in passing presenting national policy makers with a much more restricted agenda of political choices than before.

Before we go on it should be recalled that the MOSES model, much as academic finance before its scientific revolution in the 1950s, was built bottom up on observations of business practices and rules of thumb decision making, as studied in Eliasson (1976a), and then conded in Eliasson (1976b). It still remains that way. To be noted also is that when those observed practices are integrated this whole economy wide market model comes out not only incompatible with the fundamentals of neocclassical or neo walrasian equilibrium orthodoxy (Eliasson, 1985; Eliasson, 1991a; Eliasson, 1991b; Eliasson, 1992a), but the rules of thumb and information proxies used in business practice, suddenly appear very much rationally founded when viewed as they should as ex asnte expectationally founded - the Stockholm School connection. Once ex ante plans are confronted in markets and ex post sorted out he world of economics appears quite different. Since the fundamentals of modern financial economics occupy a very special corner of the MOSES economic system, it would in fact the very interesrting, now that this new monetary/ financial part has been integrated within the model, to test the reliability of the new decision rules introduced, for instance the Capital Asset Pricing Model (CAPM) of Sharpe 1964, in the artificial economic world of the MOSES economy.

One could finally say, using Rybsczynski’s (1993) categorization mentioned above that introducing (in this paper) portfolio choice moved the financial system of the MOSES model from a bank- oriented system to a (capital) market oriented system. Introducing the CAP and B-S/M options pricing formula introduced the securitized financial system.

Financial products are introduced in the MOSES model economy in seven steps:

Step I: A market valuation of net worth of individual firms, efficient market type and the possibility for (only) households and specialized investment firms to hold portfolios

Step II: Portfolio holders also include manufacturing firms that can:

• – repurchase own stock (in own firm)

• – buy shares in other firms

These investors can buy stock, sell stock, pay back debt or invest , and deposit money in the bank at the offered interest rate.

Step III: A market valuation of debt equity ratios that determine individual firm interest rates

Step IV: Firm financing through stock issues

Step V: Options and derivatives

Step VI: Wealthy individuals can become industrially experienced venture capitalists.²²

Venture capitalists can build industrial experience from learning from their own experience of investing, and thus acquire a unique understanding of innovative projects that lowers their risk. This will be done through introducing a competence block module (Eliasson and Eliasson, 1996; Eliasson, 2000b) in the MOSES model. By formulating pricing algorithms for the financial products we establish tradability in those products.

Step VII: Insiders and outsiders in the stock market.²³

Insiders will be defined as both particularly well informed individuals, who happen to (1) know before the market about an imminent occurrence that will affect the price of the stock, and (2) who have the industrial competence to understand the commercial opportunities associated with particular technologies. The second type of industrial competence is what make venture capitalists competent. Modelling that competence draws on the genetic learning mechanisms introduced in MOSES by Ballot and Taymaz (1998). In fact, learning and diffusion of knowledge through venture capital and insider activity will create in MOSES the type of positive externalities that are associated with new growth theory. An interesting question that we will come back to later is whether more efficient financial markets, because of new financial products, will reinforce these positive externalities, or not.

We introduce a simple stock market in MOSES and superimpose investment companies that manage their portfolios and then engage in derivatives trading in stock options, or algorithms representing their perceived future value.

The value of the firm to the owners of its equity has earlier been represented by its replacement value, or total assets replacement valued minus nominal debt, as described in Eliasson (1992b), Eliasson (2000a). We now allow for market valuations of that equity using different market pricing formula as follows:

Insiders use internal profit projections in firms to derive the present value of future expected profits.

where Rⁿ is the current or expected nominal rate of return on replacement valued net worth and δᵢ the insider’s discount rate (see Eliasson, 1996a).

Outsiders use dividend projections to determine the firms value according to the standard dividend valuation formula:

We make outsiders rather simple minded and expect them to believe in a rational expectations (efficient market) type valuation formula:²⁴

which is (by Gordon’s formula) the sum of all discounted future dividends, assuming static WAD type expectations. δ₂ is the outsider’s discount rate. DDIV stands for the rate of change in dividends (DIV). The outsiders have more difficulties assessing the dividend income stream than insiders. Hence, in general

Differences between insider and outsider valuations occur because of (1) uncertainties relating to the fundamentals behind dividend policies, and (2) different risk premia in the discount factor.

We specify an initial distribution of shares over portfolio holders who act individually in the stock market. They however mix with the aggregate when it comes to consumption spending. Initially we create shares with an arbitrary nominal value of 1000 SEK. (The number of shares then becomes the initial value of net worth divided by that nominal value).

Firms can issue new shares to finance expansion. The firm does so (1) when its investment plan must be reduced because its borrowing has driven up its interest rate, but (2) its effective rate of return on its shares (ER) is still larger than its borrowing rate i:

The effective rate of return is defined as:

ΔMV/MV + DIV/MV is the rate of growth in the market value (MV) of the firm (see below) plus is the rate of dividend payout of that market value or DIV/MV. If (Equation 4) holds the market is willing to pay a positive premium to the incumbent shareholders.

Holders of stock are of four kinds:

1. Outsiders; Wealthy people invest in non-liquid holdings in stock rather than deposit them in the bank. Outsiders are solely concerned with dividends. They rarely change the composition of their portfolio through speculative purchases and sales.

2. Professionals/speculators analyze external firm data. The pure speculator only looks at stock prices (Taymaz, 1999). Professional speculators may however use a variety of analytical devices. They may obtain information on profits and the balance sheet, but with a delay compared to the insider. They compare firm profits and firm dividends and consider dividends as predictors of future profits. The better the historic fits the more confidence they place on the projections. Unexplained variations enter the discount factor as a risk premium.

Most actors in MOSES financial markets are hybrids of outsiders and professional speculators. They convert dividends into stock prices using (Equation 2) and (Equation 3) and compute weighted averages, the weights telling how speculative they are.

Professional speculators will be specified to be (sometimes) superior investors compared to insiders because of their superior understanding of the macro economy.

1. Insiders know everything about their own firm (all that is in the MOSES data base). They act as professional outsiders when they buy shares in other firms.

2. Firms act as professional outsiders when they buy stock in other firms, and as insiders when they buy back their own stock. This means that firms only figure in MOSES as arm’s length investors in other firms, and do not acquire firms strategically.

Insiders (in other firms) and professional outsiders evaluate the dividend behavior of each firm over the past in terms of observed profitability and dividend behavior. The better the fits, the more confident they are in their dividend evaluation algorithms as proxies for profits and future stock prices. Deviations enter as an extra risk premium in their discount factor. But the professionals may also worry about the ability of managers/insiders to assess the external profit situation of their own firm. They therefore consult outside experts for predictions. Here we can use the method of Antonov and Trofimov (1993), who introduced outside experts/consultants in the MOSES economy to help firms understand what was going on in the model economy. Their two "statistical bureaus" each used their favorite theory (a Keynesian and a neoclassical model) to predict. In their two central planning scenarios firm management had to build their plan on the predictions of one of the two. In a third free market scenario they were allowed to rely on any of the two or any correlation they happened to believe in. The point is that the outsider and insider evaluations of stocks or stock options can be based on a variety of principles.

Both insiders and outsiders need price and market (sales) forecasts. Insiders feed them into their own firm model as expectations, and normally use the forecasts of their own firm. Therefore, insiders may also be wrong, since outsiders (because of superior competence or chance) may have a better understanding of the macro economy.

Outsiders use a simple model to predict firm profit margins, for instance:

Where P and W are firm prices and wages respectively and D are operators that stands for relative change in the same variables. Hence, they can compute the nominal rate of return Rⁿ in (1), using externally available balance sheet data and the definition:

assuming S/K to be roughly constant.

M stands for the gross profit margin of sales (S) and ρ is the depreciation rate on capital K. The firm value can then be computed using (Equation 1).

Professionals and insiders are bold speculators. They buy and sell according to their analyses but revise their assessments every quarter to buy and sell again according to their revised assessments. This is in principle quite like computerized trading already being practiced in the market. To get leverage on such speculative activity we introduce term contracts and options.

Every professional trader should in principle be willing to offer term contracts and options based on their expectations. If expectations among traders are very varied, they should have a stabilizing influence on market pricing. If they are first very differentiated and then suddenly shift uniformly in one direction (which can easily be engineered in the model) very interesting macro behavior will erupt.

To increase the leverage even further professional traders may be allowed to borrow. They borrow as firms do, and pay an interest rate that depends positively on the correlation between the debt market value ratio of their portfolios. If market values drop suddenly and interest rates shoot up on highly leveraged portfolios interesting market behavior should be released.

Now what can we do with this?

With highly leveraged professional traders in both spot and futures markets uniform changes in expectations should cause significant reallocations of funds between firms, traders and the bank of the MOSES model if local interest rates shoot up and firms’ borrowing capacity is affected. Since the banking system can create money, credit available for investment will also be affected.

The portfolio investment possibility of individual firms introduces one additional potentially important allocator of resources through the entry and exit process. A firm’s net worth is defined as:

Where K₁ stands for hardware production capital, K₂ is the portfolio of financial assets, and K₃ current trading assets including inventories of inputs and own products. Now let

where ST is investments in shares that are allowed up to a share ẫ of the portfolio of financial assets, then

When a firm expects a better return on investing in other firms’ shares than in its own production ST investments increase and appear in the balance sheet at current market values. Since the firm may make investment mistakes in its financial portfolio it becomes perfectly possible that a highly leveraged (high D/NW) firm with large investments in ST, after a stock market slump may find itself in a bankruptcy situation with a NW ≤ 0. This situation may thus be entirely unrelated to its own manufacturing performance. A previously well managed manufacturing firm with a good profit record may therefore be forced to shut down because of purely financial mistakes.

Stockholders hold portfolios of shares that they compose according to the relative risk-free returns on deposits in the bank.

Cash proceeds from stock sales can be used to buy more shares in other firms, to deposit in the bank or be added to household disposable income. Professional outsiders and firms can borrow to buy more shares. Professional outsiders/borrowers pay an interest that is positively related to their debt equity ratios. We could in fact define a "capitalist class" that holds wealth and lives on capital income. That capitalist class would then coincide with the insider or venture capital definitions (see above).

Tradability in financial asset markets depends on the possibility of linking a property right to the asset (an institutional legal problem, Eliasson, 1993; Eliasson, 2000a) and on formulating an efficient pricing algorithm. This is what the Markowitz – Modigliani – Miller – Sharpe innovative financial product developments have accomplished, and even more so the Black and Scholes (1973) and Merton (1973) options pricing model. Trading, however, must also be identified as individual transactions between a portfolio holder and shares in one firm to determine individual stock prices. Also, the forming of expectations must be explained. Sellers and buyers of individual shares find (through selective search) each other in different ways. Contrary to the product and labor markets in MOSES, where actual bargaining takes place, when it comes to financial assets demand and supply agents find each other stochastically and clinch rational deals. This is explained in Taymaz (1999).

Firms, households, and private wealthy investors choose between investing in their portfolio or depositing in the bank according to expected after-tax and risk relative returns.

Derivatives are modeled as contracts based on agreements about future trading in the stocks as defined above. There are three kinds of derivatives:

1. Forward/ Future (term contracts)

2. Options

3. Swaps (a series of term contracts)

In principle all financial contracts, including shares, can be seen as options. For the time being we will, however, introduce them in the traditional way. The MOSES model recognizes the following contracts.

1. Term contracts

Professional outsiders and firms (as professional outsiders) are allowed to take forward positions in stocks. With a forward contract the investor offers to buy or sell at a preset price at some future date. This is a commitment. The futures contract differs from the forward contract in that you offer to deliver a financial asset at a preset price at a specific date. If you want to get out of your commitment you must sell the contract in the market.

1. Options

An option is the same as a term contract, only that you don’t have to buy or sell. You have purchased an option to buy or sell at a preset price at a specific date.

1. Swaps

A swap means that the financial transaction takes place by way of a third party.

Originally swaps were created to bypass currency regulations. A US firm had dollars and wanted SEK in Sweden. A Swedish firm had SEK but wanted dollars in the US. The Swedish firm was not allowed to buy dollars. Then the Swedish firm arranged through a bank to buy the dollars of the American firm and to pay the American firm in SEK in Sweden. When authorities used regulation to prevent Swedish firms from transferring money out of the country, regulations could be bypassed because of swapping. No direct transfer of currencies occurred. Swapping meant that the outcome for the parties was anyhow the same. Regulators had created a "semi barter" market, forcing parties to resort to more costly transactions forms (swaps).

Swap transactions have of course been possible all the time, only that modern C&C technologies have made the matching of potential trading and swapping partners more efficient and more complete. Today you may not even need a bank to arrange the transfer.

Standard financial transactions in the Swedish currency market aimed at facilitating access and making the market more complete can take place in a similar fashion. We are talking about firms with different access to the capital market because of credit controls, a bad credit rating or simply incompetent financial institutions. A bank may not want to lend to a small firm that it does not understand, or to a firm with a bad credit record. Another firm which understands the firm in need of cash and has a good credit rating may then borrow from the bank and extend a credit to the firm that needs it. Through increasing access this intermediary firm has made the market more complete through a swap. Obviously new C&C technologies will soon subject an incompetent, legally tied down and over bureaucratized commercial banking system to devastating competition if it does not shape up. Microsimulation method is ideal for studying the economy wide consequences of such institutional competition in financial markets. The venture capital market is one example where banks for legal and organizational reasons are great underperformers (Eliasson, 1997).

Swaps can be used to convert bank lending into venture capital. To do that MOSES requires that inter firm borrowing is explicitly modelled which is currently not possible. We may however limit swaps to professional outsiders which would then appear in the market as more competent competitors to the bank in providing venture capital.

Term contracts make it possible to model pluralism or singularity and everything in between. We can generate a bewildered and perhaps stabilizing expectational future, or a single valued future "compressed immediately into today" that may be heavily destabilizing in a dynamic model economy such as MOSES.

The Black and Scholes (1973) options pricing formula (also see Bookstaber, 1987) determines the price of an option for a given risk level. It reads:

where

N(d₁) = the probability that a normally distributed random variable will be less or equal to d₁, the option delta.

P = price of stock now

EX = Exercise price of option

PV(EX) = present value of EX, calculated using the risk-free interest rate

t = number of periods to exercise date

σ = standard deviation per period of rate of return on stock. σ² is the security’s return volatility.

The B-S formula thus tells the price of a call option that eliminates profit opportunities from trading.

Suppose no risk is associated with the future security price. Then σ² will be close to 0, and both d₁ and d₂ will become large making N(d₁) and N(d₂) equal to 1. Hence, the price of the call option becomes equal to

or the current price minus the discounted future price. Whenever there is volatility in the market or uncertainty about the future value of the security (σ > 0) you take on a risk in buying an option, and therefore pay a lower price.

because the EX may not be what is expected. Since expectations about the future value of a security vary, or the competence to evaluate risks varies, a rational foundation for a market in options exists. A theoretical note is in place here. Modern financial economics has done away with Knight’s (1921) distinction between uncertainty and calculable risks. The B-S (1983) pricing algorithm assumes calculable risks, as in (7) above. Pricing becomes an insurance or lottery business. The assumption about the N() functions above is however an empirical assumption that may not hold up in reality, and probably not in the MOSES model economy, which does not have the properties of a stationary process which both the B-S algorithms , and rational expectations (RE) models (see above) require. The distinction between uncertainty and calculable risks has a meaning in MOSES. Our guess is that the N functions are quite unstable over time which should make the Black and Scholes (1973) pricing formula unreliable, a proposition that is perfectly testable through simulations on the MOSES model.

Volatility (σ²) can be calculated ex post. In an assumed walrasian lottery setting repeated experiments ("trades") in the options market would then produce calculable risks and predictable outcomes in expectation. If the stochastic functions underlying fundamentals are not stable, as should be expected in MOSES type non - linear environments, then stochastic predictability, being assumed in the B-S pricing formula, may not be there. Thus, using the B-S formula in the non-linear MOSES economy may be highly risky (due to uncertainty), not to say failure prone.

A confused and incomplete, but fast market process may both stabilize and destabilize market fundamentals. If correct in expectation in their perceptions of the external desired economic optimum stochastic equilibrium conditions may be attained. Otherwise, market chaos may be created when the whole army of producers march in the same, but the wrong direction, and market self – coordination fails. We have already seen what happens in the MOSES economy when market transactions are speeded up pushing the entire model economy too close to a market clearing situation that does not exist as an operating domain of the MOSES economy. Near economy wide systems collapse is then the outcome (Eliasson, 1991a). The MOSES model economy, enriched with modern financial services technology, should therefore be able to tell whether the optimum desired by policy makers is an economically desirable target to aim for.

(For instance, you may think you know best about the future. You should then act on that insight, taking a speculative risk, that you do not consider a risk because you know better. This is a commendable entrepreneurial attitude that economies need in large numbers to prosper (Eliasson, 1996a; Eliasson, 1997). The definition of an entrepreneur is that s/he believes in, or understands something no other does and dares to act on that understanding.

A professional trader may be convinced, because of his or her superior understanding of the macro economic situation, that the stock of a particular group of firms will decrease significantly in value. If this belief runs counter to current beliefs in the market, the professional trader goes short, offering to sell stock (that he does not have) at a price below the current price a year from now.

Genetic network learning (Ballot and Taymaz, 1998) in MOSES makes it possible for insiders to know more about the competitive situation of a firm than others. Speculators can search, using genetic algorithms, for correlation patterns in the MOSES economy that have been stable for some time for good or spurious reasons, and when identified, act on them).

Derivatives are introduced in the MOSES economy in four steps.

First (Step 1) we introduce tradable stock certificates and firms and households as investors in such certificates. Second (Step 2) we make the market value of a firm’s net worth (rather than the replacement accounting value) dependent on the individual firm debt equity ratios that determine a firm’s interest rate. Third (Step 3) we model markets for stocks in which traders determine the market value of individual firm shares. For this step the information and expectations of individual traders must be specified (Taymaz, 1999).

Step 4 introduces the various options defined above, issuers and holders and the markets for such options.

Insider trade is based on superior information or knowledge. Trading insiders therefore make the market more informed. By not being allowed to trade the market will remain uninformed. With insiders trading in the market financial resources will be allocated more efficiently. The MOSES world of an Experimentally Organized Economy distinguishes between insiders happening to know about specific market determining events and insiders being particularly knowledgeable about the business. Prohibiting both insiders from trading may therefore be bad economic policy (Eliasson, 1990a). Instead of outlawing it policy makers can help making markets more complete (read informed) by instituting more speedy disclosure mechanisms, something modern C&C technology is especially suitable for, maybe revealing a trade even before it has been executed, rather than after several weeks of bureaucratic paperwork which was the case in Sweden not long ago.

The financial investor is concerned with the real rate of return on his or her portfolio, standardized for risks. The higher the estimated risk the higher the return demanded.

We measure the performance of the portfolio by the so-called Sharpe index:

S is the average excess return of the portfolio compared to the standard error of the return (= σ). R stands for the average return, and r for the average "risk free" return (say the interest rate),²⁵ all for the same measurement period. σ can be called a measure of price volatility.

This return over a risk-free investment can be computed for any portfolio. When all investments are in risk - free bonds the Sharpe index is zero. The financial investor wants to maximize the Sharpe index over the longer term. The ex post outcome then depends on his or her ability to foresee the risks taken on. The competence to do that may raise the S even though other, but less competent investors think the risks they have taken on are much higher than they really are.

It is of interest to compare the individual Sharpe index with that of a competitive portfolio, for instance that of another portfolio manager or a stock market index. Such a useful error tracking index (T) is:

where R* is the average return on the reference portfolio and σׇָ the standard deviation of the difference between the portfolio return (=R) and the reference return (=R*).

The five inputs in the B-S/M (1973) options pricing formula (Equations 7; 8) are (a) the current price of the (underlying) security (=P), (b) the exercise price of the option (=EX), (c) time of expiration (=t) , (d) the risk free interest (discount) rate and (e) volatility. Since the first four variables can be determined, the expected volatility in the market can be estimated by introducing the market price of the option and then solving for volatility (=σ) to determine the price of the option. Since an estimate of volatility is an input in the Black-Scholes options price it is perfectly possible to define a market for trading in volatility estimates. Then call options traders can outsource the risks associated with predicting volatility in the market, and price the call option according to Equation (7), conditional on that given volatility estimate. Alternatively, s/he may carry the risk on his/her own and use past measures of volatility of the computed variance (= σ²) in actual prices over time, and some econometric model to predict σ, or simply plug in a guess.

To simplify for an outside participant to engage in the options market of the model we can request for each stock an estimate on all current stock and options prices (last period) and a past record of expected and actual volatility (= σ²). The investor can then determine what s/he is willing to pay for a given option or offer to buy options with this exercise price and time to expiration for so and so much.

Any investor in the real economy would assess the future on the basis of what s/he knows, but that is always only a fraction of everything there is to know about. A limited understanding of the whole makes it necessary (for the investor) to invent personal interpretation models ("theories", Eliasson, 1992a) to "understand" what is going on, and those will be different depending on background circumstances, and more or less mistaken. It is possible to prearrange "analytical" data mining devices for participants in the game to support their personal interpretations. These devices may also be made part of the theoretical specification of actors’ behavior. Antonov and Trofimov (1993) have done something like that on the MOSES model. They demonstrated that diverse and seemingly inconsistent behavior of all agents may improve macro-economic growth compared to following mandatory directives/forecasts from a central policy or planning authority supposed to possess superior overview. The reason is that the odd traders discover investment opportunities that those subjected to centrally directed search miss.

One interesting possibility: MOSES exhibits the "chaotic" property typical of non - linear initial state dependent models that some variables may for a long time converge monotonically towards a particular constellation of values, giving the illusion of predictability, only to suddenly "explode" into unexpected gyrations. With such a phase shift any traditional analytic estimate of volatility in the pricing of options by the B-S/M algorithm will be completely misleading, and result in sudden unpredicted wealth transfers between investors. One idea would be to make traders use efficient data mining in the MOSES economy to discover "correlations to trade on". This search could dig deeply into MOSES microstructures relating volatility measures to the interior performance of firms, for instance. If such search for correlations starts from different ends different traders will find different correlations, and belief in those correlations will increase with the number of "confirming" observations (as discussed in statistical learning theory). In a classical general equilibrium environment with costless search and optimum search principles designed for the same equilibrium environment, traders will eventually find a desired equilibrium "point" (Lindh, 1993). If the environment does not have those nice equilibrium properties, such as in MOSES, and if search is not costless, correlations compatible with a different theory, or any "nonsense" correlation (used by "noise traders") will at best be temporarily reliable, but soon guide traders into unexpected and not desired chaotic market circumstances. In a healthy market economy this would now and then be a normal experience for individual traders, and a normal cost for economic development in an Experimentally Organized Economy (EOE, Eliasson, 1991a), but economy wide disaster if a very large number of agents, or central government commit the same mistake. Differential expectations are therefore a market stabilizing element of an EOE. Differential expectations could be created in the MOSES economy in a market game with many outsider investors with different background experiences. .

New C&C technologies will increase the speed of financial market arbitrage, make markets more integrated and raise competition (Genberg, 1999). Markets will be more complete in reacting to available information.

Active portfolio management took off with the advent of advanced computing. Financial derivatives are fictious products based on mathematical algorithms. While future contracting was conducted hundreds of years ago, general trade in such contracts had to await the integration of computing and communications technologies in the late 1980s (Eliasson, 1996a).

The 1980s is also the time when the macroeconomic effects of such new financial instruments and trading became statistically visible. A growing political discussion has been concerned with the perceived consequences in the form of limitations on monetary policy and financial regulation caused by more efficient financial markets. Speedy and efficient international financial arbitrage has frequently, since the 1980s, forced reluctant national authorities, unable to close their economies off financially, to change domestic policies, interest rates and exchange rates. Badly managed and corrupt economies in the developing world have been particularly affected.

As far as our understanding of experience from MOSES simulations goes, it is only a matter of time before the limited capacity to pursue autonomous monetary policies will also prevent governments from pursuing autonomous fiscal policies, for instance when it comes to taxes. The hypothesis we venture at this point is that the less potent and the more ambitious as policy maker the government the more error prone its activities (Eliasson and Taymaz, 1992). Understanding and quantifying the indirect economy wide side effects of ambitious policy making on financial market performance, the allocation of resources and long term economic growth is what this paper is about. A market self-coordinated agent based model such as MOSES is needed for such analysis.

Markets integrate the real and financial parts of the economy. In MOSES firms integrate financial, product and labor market considerations in their business plans. The stock market influences the real economy through (1) the interest rate and investment, (2) the allocation of funds to firms, and (3) the credit multiplier and credit market expansion and contraction.

For firms with shares being traded in the stock market the market value of their equity should be entered in the leverage ratio that determines their (borrowing) rate, which thus becomes negatively dependent on the market value of the firm, which outside lenders can observe. The market value is however dependent both on the firm’s operational performance and on its financial portfolio management. Sudden portfolio losses may put the entire business of a firm in a precarious situation.

If such a precarious situation, caused by financial portfolio losses, or from deteriorating profitability persists, the consequences will eventually result in declining dividends. Dividends can however be temporarily stabilized over time to hide a deteriorating business situation from the outsider investor. The state of information in the market suffers. Overall, higher interest rates and/or lower market valuation of firms will affect its external financing capacity through restricting credits and stock issues.

Variability in the expected prices of an option is called volatility. It affects the estimated price of an option indirectly in the Black-Scholes formula (Equation 8) via the σ factor.²⁶ One may also be interested in the reverse influence, or how variability in options pricing influences interest rates and investments.

Volatility also tells how bankruptcy prone an individual company is. It should affect its individual interest rate and can therefore substitute for the market valued debt equity ratio in the interest determination (Taymaz, 1999). Volatility estimates on historic data according to the Black-Scholes’ formula are indirect. The latter also includes information for the actors in the market on the bankruptcy propensity of the firm. All this is standard economics. The point is that its economy wide consequences can be represented in, and understood through MOSES simulations.

All experiments in this study assume foreign relative (export) prices to be exogenous and fluctuating around a steady exponential trend. These price trends and the (also exogenous) foreign interest rate have been set such that firms bringing in best practice globally available exogenous technologies (endogenously through their investments) should be capable by adjusting their wage offers to earn a long term return on the new investment equal to the foreign interest rate. This fairly orderly exogenous global market scenario is, however, complicated in the local Swedish MOSES economy by its initial state that defines numerically how individual firms are positioned at the start of a simulation experiment relative to global markets.²⁷ While the orderly predictable global market environment assumed significantly dampens the resource allocation outcomes of the simulations in ways we did not anticipate when the experiments were designed, the initial state, on the other hand, raises the complexity of interpreting what is happening in the model enormously. Since the MOSES model economy is propelled forward by firms’ price expectations, investments and production plans made up under the upper constraint defined by the globally available best practice technologies its selection based non linear properties tend to make small initial circumstances cumulate long term into very different directions, a common "chaotic" property of such models.²⁸ The economy wide outcomes are leveraged up further by the endogenous population of firms, a unique property of the Moses model economy (Eliasson, 1996b). While the global economic environment can therefore be said to be rather stable and predictable by exogenous assumption, the internal dynamics of the Moses model economy are tremendously more unpredictable.

The experiments (Ex₁x₂x₃x₄) have been designed as follows:

1. Stock/options market:

   • – No stock market (x₁=0)

   • – Stock market exists (x₁=1)

   • Options market exits (x₁= 2)

2. Interest rates:

   • – Same for all firms (x₂ = 0)

   • – Firm specific interest rates depend on firms’ debt equity ratio from books (x₂ = 1)

   • – Firm specific dependent on market valued debt equity ratio (x₂ = 2)

3. – Speed of interest adjustment (Taymaz, 1999):

   • – Slow (x₃ = 0)

   • – Fast (x₃ = 1)

4. Financing through new stock issues:

   • – Not possible (x₄ =0)

   • – Possible (x₄ =1)

The target variable is long term (60 years) GNP growth. As the model is built manufacturing, including the IT industry (Eliasson and Johansson, 1999), is the economic engine that drives growth. In all experiments in this paper tax policies, public sector recruiting, and expansion (Eliasson, 1980) are the same. Comparing GNP levels on the 60-year horizon is therefore synonymous with studying how the introduction of different financial market regimes influence long term growth, everything else the same. Since population growth is exogenous and the same in all experiments GNP level and GNP/capita comparisons are identical.

Experiments with no stock market finance and a general and slow interest determination combination (E0000, E1000 and E0100) represent an industrial bank financing system, dominated by internal finance in firms. Add stock market finance and a fast and individual firm interest determination in (E1211) or (E1111) and we have moved into a capital market financial regime. We can then distinguish between two market regimes; one with only a stock market and portfolio choice (E1211), and the other also featuring a securitized (options) market (E2211).

We begin by introducing an increasingly more sophisticated financial market regime, then carry out some special inquiries.

The second question on initial conditions is more intriguing. It is simply neglected by assumption in comparative statics modelling that dominates the field.

Standard practice in static equilibrium modelling is to compare two equilibrium states (comparative statics), before and after a regime change, and ignoring the structural adjustments involved in moving the economy between the two. Since no such determinate equilibrium state exists in the Moses model economy comparative statics does not make sense. Neither does the Moses economy operate on a neoclassical steady state equilibrium growth path. Comparing such steady states is sometimes called comparative dynamics, even though there is no market dynamics involved in determining their diverging paths. MOSES is selection based, highly non linear and initial state dependent. So, attention has to be paid to what its initial state means for its long term evolution. Only then can we talk about "true comparative dynamics". Even though the initial states are the same in all experiments for this study the paths the model economy takes into the future apparently are very different depending on which market regime we enact. And, by accident and discovered late, even small changes in initial conditions between these experiments and the same market experiments conducted in Taymaz (1999) tend to cumulate, as a comparison of the numbers within, and not within brackets show in Table 3. This complicates the evaluation of different market regimes for long term economic evolution considerably. Since this is a study in microsimulation method, and not an attempt to empirically evaluate the merits of different market regimes in a Sweden like industrial economy, we leave this observation as an experience for a future study, and then hopefully with more available computer capacity.

Paradoxically, however, the more primitive the financial system, and the faster the interest rate adjustment the faster the economy grows (Cf E0110 > E0100 > E0000). When a stock market (with stock market financing)³⁰ is combined with passive commercial banking into a capital market regime (E1001) long term growth shoots up to maximum levels , but when a market for derivatives is introduced on top (E2001) entrepreneurial entry increases, but exits even more. With a smaller firm population on the horizon, and apparently a longer trail of economic mistakes Type II, long term growth suffers. Individualize firm interest rates (E2101) and long term growth recovers, and even more so when interest rates are made dependent on market valued debt equity ratios (E2201), because of a more efficient market sorting of firms.

Speeding up market adjustments of interest rates, however, does not stimulate long term GNP growth (cf E1001 > E1011 and E2001 > E2011). Too fast and too deep adjustments destabilize markets. When a derivatives market (with or without fast interest rate arbitrage) is added to a stock market regime with stock market financing and firm specific interest rates dependent on market valued debt/equity ratios, and hence very keen project selection, firm turnover increases. Apparently keener selection among more firms and derivatives do not combine well in supporting long term growth (Cf E1201 > E2201, E1211 ≈ E2211). In the fast interest adjustment case a more intense competitive elimination of firms apparently leaves on average equally productive firms, but fewer and larger. With more concentration fewer firms generates roughly the same long term growth outcome.

Individual interest determination sometimes reduces long term growth, and the more so the more sophisticated the financial system. except, however, when individual interest rates are based on replacement valued debt/equity ratios. The reason for this odd result is that manufacturing firms have then lost money on portfolio speculation, suffered lost borrowing capacity and been forced to pay higher interest rates. These firms have then invested less and become more at risk of exiting. In principle such negative occurrences could also be the outcome of mistaken strategic acquisitions, of which there are plenty of real examples (Eliasson and Eliasson, 2000). Apparently, this negative consequence of strategic mistakes occurs more frequently in sophisticated financial systems that offer better opportunities for financial mismanagement, and has been sufficiently strong in our simulation experiments to affect long term economy wide outcomes. Allowing manufacturing firms to speculate in financial markets, which they are not good at, will therefore introduce noise in the market resource allocation machinery with adverse effect on long term growth.³¹ This effect is perfectly possible to understand from such experiments as an empirical phenomenon. To judge whether it is a serious social (economy wide) problem, however, requires more serious estimation work on the MOSES model, and careful sensitivity testing for the role of initial conditions.

While general interest rate determination combined with stock market financing increases long term growth (E1001 > E1011 > E1010), a fast interest rate arbitrage reduces that positive effect, as does a firm specific interest. Changes into a firm specific market valued debt equity ratio, on the other hand, does not change that outcome much,³² and if specifications are changed to individual interest determination with replacement valued equity and slow interest rate arbitrage there is no change in long term GNP growth. A negative outcome is however recorded if the interest rate adjustment is speeded up (E1001 >E1011).

The entrepreneurial entry and exit characteristics of different financial regimes are particularly interesting. The peak performing regimes (E1000, E0110 and E1001) are characterized by a balanced and not particularly fast firm turnover. The long run low performing experiments exhibit a (much) higher exit than entry rate, caused by a faster interest arbitrage that in turn raises the rate of premature exits of Type II (see Sect. 2.3). Market selection thus matters for long term economy wide performance.

But the experimental picture is not uniform. Introducing options in a stock market with fast adjusting individually determined interest rates dependent on market valued debt equity ratios, long term growth did not change much. The increase in firm entrants and exits was, however, significant (E2211> E1211). When slowing the adjustment speed and removing stock market financing (ceteris paribus) long-term performance dropped sharply (E2211 > E2200) to the tune of an even larger increase in firm turnover. When interest arbitrage was instead speeded up (ceteris paribus), the rate of firm turnover shot up even more, and long term growth increased but much less so (E2210 > E2200).

A stock market regime combined with a slow interest arbitrage raises the exit rate, because of:

1. Volatile stock market valuations and/or

2. Losses in financial portfolios of manufacturing production firms

A generally competent and well managed manufacturing operation may thus crash because of unsuccessful financial portfolio management. This effect is leveraged up if the firm’s interest rate is made dependent on a market valued debt equity ratio.

The rates of entry and exit furthermore seem to be positively correlated suggesting that entrepreneurial entry raises (through increased competition) exits of incumbents and overoptimistic entrants. This correlation pattern has been documented in empirical studies (Geroski, 1995), but the model simulations add a plausible causal explanation to the same complementarity. Long term growth performance, however, is not necessarily positively correlated with the rate of turnover of firms (Cf E0110 and E1001), indicating that an efficient reallocation of resources over the credit market can occur without excessively killing low performing firms. We know however from parallel studies on MOSES (Eliasson and Taymaz, 2000; Eliasson et al., 2001) that fast entry must be accompanied by a corresponding fast exit of low performers and labor market mobility to keep factor price inflation from holding back growth in new expansive firms.

Summarizing so far, the industrial bank (E0110) and capital market (E1001) regimes individually improve the macro outcomes significantly compared to the base run (E0000) which is predominantly backed by interal finance. Mixing the two (in E1111) however lowers long term macro output. On the face of it entry is lowered somewhat compared to both, and exits are increased . The most probable reason is that markets have been speeded up further from the already efficient "static" allocation achieved separately in E1001 and E0110. Markets have been excessively excited, discouraging new entry and forcing more firms to exit, probably including long term winners that survived in the other two experiments. Changing from either into a securitized financial market system (E2002 and E2110) appears not to be a good idea. The negative long term growth outcomes of introducing options in an efficient industrial banking or capital market regime may be a surprise compared to the standard theoretical predictions of financial models. For one thing, however, we have only tried a primitive version of a securitized financial market. Secondly, however, and more to the point, external world market (export) prices by exogenous assumption have been almost in "stochastic equilibrium" in the sense that relative exogenous change in prices in the four subindustries are fluctuating around a slow inflation trend that under the adaptive expectations regime of the model firms will be quite predictable "in expectation". Hence also domestic markets will be predictable, meaning that an industrial market regime with firms being dominantly self-financed will work quite well, and a capital market regime with stock market financing should not, as demonstrated, be more than a marginal improvement. When, on the other hand, global markets are subjected to wrenching unpredictable change in prices, as in the price pivoting experiments on MOSES in Eliasson (1983), the improved flexibility in financial market resource reallocations associated (theoretically) with financial derivatives, should make for improvements in long term growth outcomes. In our experiments, on the other hand, with predictable global markets and a global economy operating close to market clearing with a minimum of slack, we have probably experienced again, what we have learned from many previous experiments, that an economy close to predictable market clearing, is sensitive to speedy market transactions, and that this may explain the negative outcomes of speeding up financial market transactions in our model experiments under such conditions. This is at least a reasonable hypothesis for future experiments on the Moses economy.

The strategic R&D investment module is one of the four modules where stochastic specifications enter (see Eliasson, 1985; Ballot and Taymaz, 1998). The firm faces a spectrum of R&D investment choices offering a tradeoff between a high probability of a normal return and a low probability of a very high return. This module was originally devised to allow individual firms to participate in a MOSES based business game, and to assign their own strategies, or for us (as outsiders) to characterize individual firms exogenously. In the simple version that we use here all firms adopt the same strategy, and outcomes become a chance drawing from the same distribution. For practical computer capacity reasons, we have had to limit the number of experiments to three:

1. No stock market – to be compared with E0000 or E0110

2. With stock market financing – to be compared with E1201 or E1001

3. Also with options – to be compared with E2201

The results (Table 4) are interesting. First, economy wide long-term outcomes are generally lower than in Table 3, but this is a technical result of scaling. The strategic module changes the average R&D pay off for all firms. Second, however, and important, going from an industrial bank regime dominated by internal financing and bank loans (E0000 and E0110) towards a capital market regime with stock market financing and a firm specific interest rate dependent on a market valued debt equity ratio the GNP outcome on the 60-year horizon more than doubles, as does the rate of exits of low performing firms. The reason for the strong performance is a substantial reallocation of resources to a smaller number of high-performance firms. The consequent higher concentration that reduces competition (a negative factor), has however been overshadowed by the market induced reallocation of resources and a flexible labor market to accommodate the change.

Shifting into a securitized financial market regime generates a much better long run outcome than that for the industrial banking regime but below that of the capital market regime. Contrary to the options results in Table 3 exits are lower this time. There are two explanations: (1) a worse selection of firms in the options case than in the capital market regime and/or (2) a consistently lower rate of return and less investments.

A more detailed comparison of the experiments showed that rates of return – as should be expected – were higher in the options experiment than in the stock market case. Still, the stock market experiment shows significantly larger investments, notably towards the end of the period when rates of return increase and an investment boom occurs. Apparently, this is the outcome of a series of circumstances we have observed before (e.g. in Eliasson and Lindberg, 1986) when firms in an early phase of the options experiment are forced to raise rate of return requirements because of the increase in interest rates (a result of the introduction of options). When finding fewer profitable investments, firms choose to lower their investment spending and reduce employment. (This does not necessarily have to be associated with an increase in exits forced by financial portfolio losses by manufacturing firms, as in earlier experiments). The opposite dynamic occurs in the stock market case. With softer rate of return requirements in the first part of the period firms slowly gear up for expansion that culminates with an investment boom during the second half of the of the 60-year simulation. With the higher exit rate of low performing firms in this case, the boom should be sustainable beyond the 60-year horizon. Again, however, a proper comparison of the outcomes of the different market regimes would require a series of complementary and longer run experiments than our current computer capacities allow. We therefore must give up on the ambition to explain the details of our results.

Complex circumstances surrounding the experiments, notably initial conditions, can tilt the outcome of one set of experiments (for instance the creation of a derivatives market) in very different ways. For the time being we cannot say that the "picture", emerging from the introduction of derivatives markets in the MOSES model (see above), namely that positive changes are small and mostly negative, carry any general implications, except that negative results from speeding up financial market arbitrage are likely to occur in an economy that is already structurally adjusted to competition in global markets. A structurally badly adjusted production system, on the other hand, should be forced through increased competition and turnover of firms to become globally competitive.

Modern insider legislation is a peculiar piece of legislation by which lack of information among some market players, for ethical reason has been the motivation for preventing informed market players to inform the market by profitable trading on their information, most likely at the social cost of reduced macro economic growth (Eliasson, 1990a; Antonov and Trofimov, 1992).

In the current calibrated version of the MOSES model insiders have no defined role. Dividends are proportional to profits by assumption (see Equation 1 and Equation 2 in section 4.3). Insiders hence know no more than outsiders. In the following experiments we introduce insiders with unique information and allow them to trade freely on their superior knowledge of, for instance successful outcomes of strategic R&D decisions (see previous section), and study the economy wide outcomes of how this new information affects the prices of shares, and indirectly makes the market more informed.³⁶ Each firm has one insider (= the CEO operating as an investment firm) who can revise his private profit forecasts when he has made decisions on behalf of his firm, that will affect the market value of the firm, and buy or sell.

We should remember that "historically", for instance in Sweden, there were no restrictions preventing insiders from trading on their unique information. Today, market transparency and fairness requirements have made insider trading illegal. Several principal problems are however associated with insider legislation. First, there is the difficult distinction between trades based on particular information (a merger next Monday) and the unique understanding of a business associated with insidership. Second, while buying or selling on a piece of insider information is not allowed, abstaining from buying and selling privately on that same information has the same consequences, but cannot be legally enforced. Preventing insider trading thus not only deprives the market of information but also biases information flows (Eliasson, 1990a). It is therefore of interest to know what such artificial lack of information means for economy wide economic performance.

Beginning with the previous simulation experiments on strategic R&D investments we may observe that insiders involved in the decisions should in principle invest in the company conducting the R&D investment as owners and pilots telling the market that they believe in the strategy. For years, their continued investment in the firm will probably be based on their insider understanding and personal judgment of the consequences of the R&D venture. At some point, for instance advance information on the success of clinical tests of a medical substance, that understanding of the insider changes its legal character and becomes a piece of insider information and is outlawed.

We have no reason to expect that insiders in a firm have a better economy wide understanding than anybody else. We should however expect the insider on a strategic R&D project to have a better understanding of how the economy wide future will interact positively with the outcome of the R&D project, this being an important part of the business idea.

We have studied the role of insiders in an economy on the MOSES model. In terms of the MOSES firm a strategic R&D project will have a positive effect on the capital and labor productivities of the firm (INVEFF and MTEC respectively).³⁷ The insiders upgrade the productivity characteristics of their firms by entering the new R&D based productivity data into the production modules of their firm,³⁸ to be able to compute a new expected RN to be inserted in Equation 1 in Section 4.3. The insider then constantly buys shares in his company at a constant rate if the market price is lower than (say 80 percent of) what the insider has computed. Other agents (speculators and firms) in the model form "short run" and "long run" stock price expectations, P(SR) and P(LR). P(SR) is formed by regressing the stock price on lagged values (short run behavior), and P(LR) is based on the discounted value of expected dividend payments in the future. Expected dividend payments are calculated from the forecasts based on past dividends. The bid price of agents is a weighted average of P(SR) and P(LR) where the weights may differ between agents.

If an agent has insider information about a firm, it will use that information in determining its bid price. In our experiments we assume that insiders can get information about radical and incremental innovations of the firm. The insider then revises its "long run" stock price expectation as:

P̿(LR) = P(LR)· (1+y│∆T - ∆Tᴹ│)

where y is the adjustment parameter, ∆T the change in technical level of the firm in question, and ∆Tᴹ average technical change in the market (assumed to be observed as changes in productivity at industry level). The "expected "short run" stock price is not revised because agents assume that it is driven entirely by short run changes in stock prices. An upward revision of the expected "long run" stock price may of course increase the demand for the stocks of that firm, and that stock price may in turn increase the expected next period "short run" stock price.

We have run three sets of insider experiments. In the first set there are no insiders. In the second agents have a 1 percent probability to obtain insider information about any given firm. We assume that the positions of agents are determined at the beginning of the simulation, and do not change over time. In the third set the probability of being an insider is 10 percent.

All experiments are run for 60 years by quarter. Results are summarized in Table 5. They are quite interesting. In general, but not always, the presence of insiders increases long term economic growth by making the market more informed, and the allocation of resources more efficient. With stock market financing of investments, individual interest rates based on market evaluation of risks (debt equity ratios), and high speed interest arbitrage (E1211) insider information appears most potent in improving long run outcomes. Apparently, insiders do the positive information and growth jobs we expected the derivatives market to do.

To make a point of the magnitudes involved thus moves the analysis into difficult empirical terrain. Then the empirical relevance of calibrated parameters, notably those related to dynamic Stockholm School market feed backs, becomes important, and we are not yet ready to formulate strong conclusions on that. Even so, let us go through the quantitative differences between the many experiments, and discuss to what extent the absolute magnitudes and the differences between the experiments carry empirical information on a Sweden like industrial economy.

The difference between the minimum (E1110, E2100, E2200) and maximum performing market regimes (E0110, E1000,E1001) is about 1 to 2.5 corresponding to an annual difference in GNP growth over the entire 60 year period of about 1.5 percentage points, and these differences have been caused by only switching between two pairs if financial market parameters. But even seemingly minor such changes can cause dramatic drops in long term economy wide performance (E2211 >E2210 or E1111 > E1110). We have already warned about the importance of controlling for biases caused by stochastic events through Monte Carlo sensitivity tests. The very possibility furthermore of seemingly insignificant circumstances cumulating into major economy wide change with time, typical of nonlinear initial state dependent and selection-based models, are characteristics likely to also characterize real economies, and should be taken seriously when showing up in model simulations. Speeded up markets in an already efficient model economy are likely to prematurely kill more winners or to generate more Type II economic mistakes the consequences of which then cumulate over the sixty year simulation. It is our belief that such consequences explain the seriously worsened economy wide outcome when introducing securitized financial instruments. It is again possible in principle to check back through frequent reruns of the model to what extent this is the reason. This would however be a major new study that would require computing capacity that we currently don’t have.

To what extent the higher complexity of the real economy compared to the MOSES model adds robustness and makes it less sensitive to small perturbances is a different and interesting, but difficult possibility that we leave for later studies.

The original problem of this essay was whether the new C&C technologies and the new financial products made possible by these new technologies, will improve the allocation of resources to the benefit of long-term economy wide development, and to what extent they will have a stabilizing or destabilizing influence on markets and economic systems behavior. Simulation analyses on the Swedish Moses model have given numerically precise answers to those questions. The corollary question however is how empirically credible they are. A first such question is to what extent these new financial products have helped to pull resources out of badly managed firms and reallocated them over markets to more promising firms. Will the result be more efficient investments and faster long-term growth in the Sweden like Moses model economy? One result seems clear. If the economy is already operating on an efficiently allocated structure of resources the scope for fast reallocational gains is limited and if pushed for too ambitiously, by for instance policy, is likely to destabilize markets and reduce liong run economy wide outcomes.

A second question is to what extent the global product market assumptions have limited the generality of these results. On this we have opted for a partial experimental design with a predictable global product market scenario for firms with adaptive price expectations. Such an experimental design, we realized late, would benefit an economy populated with profitable globally competitive firms, so shaped by having been subjected to global competition for a long time, as was the case with Swedish manufacturing industry, and hence the Sweden like model economy Moses. Under such circumstances profit plow back into existing firms complemented by (industrial) bank finance should generate satisfactory long term growth, as has also been borne out by the experiments. Under those assumptions speeded up financial market arbitrage would not necessarily benefit growth. As the experiments indicate it might rather disturb markets, lower price predictability under adaptive expectations. Resource allocations will be less informed and worsen long run growth outcomes, an interpretation that is supported by previous model experiments, for instance in Eliasson (1983); Eliasson (1984) and Eliasson (1991a). This adds a third concern for the policy maker, namely to what extent the model economy has been sufficiently diverse and robust to benefit from faster and more efficient financial arbitrage. Deficient diversity of structures is likely to be a deficiency of all micro based model abstractions of a real economy. The quantitative importance of this deficiency can however in principle be tested for by adding more firms to the Moses model and making entrepreneurial entry more vigorous which the early experiments in Eliasson (1991b) also support.

We began with a crude industrial banking financial market regime (only one bank), with internal financing being the dominant form of firm finance. Such a system, lacking open financial markets for across firm resource transfer makes it possible for Government to control to some extent, through bank regulation, the allocation of financial resources in the economy. Such was the financial market regime in Sweden during the early post WWII period through much of the 1980s. The Swedish Government used credit market controls to provide cheap financing for public sector growth, and to patronize a select group of large export firms. This worked OK (1) until the market destabilizing influence of counteracting savers that were earning large negative real interests after inflation and taxes, and increasing financial across border leakage forced a policy reversal (Eliasson and Taymaz, 1992) and (2), until global technological change subjected Swedish export producers to new and radical demands for structural adjustments during the 1990s. New C&C technology based financial products and the introduction of a capital market financial regime helped a new and more flexible financial market scenario materialize. We therefore introduced first a capital market regime in MOSES of the type gradually introduced in Sweden from the second half of the 1980s, and then some rudiments of a securitized financial system. Again, such a securitized financial regime became an increasing reality in Sweden in the first decade of the new millennium. The three levels of financial market sophistication and regimentation in fact correspond to the situation in Europe in the 1960s (industrial banking backed up by exchange controls), in the 1970s and part of the 1980s (a gradual break up of exchange controls caused by international market arbitrage, "euro dollars"), and from the late 1980s an increasing securitization of the financial systems. While the experiments in this study have been designed to represent political and institutional development, unfortunately the external global market was assumed to be the same, which reduced the generality of the experimental designs.

Simulation results tell the following:

1. Introducing stock market financing associated with a capital market regime generally improves profitability filtering of investment projects (E1001) and hence the long run allocation of resources and economic growth.

2. The surprise result, however, is that an industrial banking regime with very limited resources being reallocated between firms through the bank functions equally well (E0110) and that mixing the two regimes (E1111) does not seem to work at all. One reason for the industrial bank regime, with investments predominantly based on internal firm saving feed backs and bank loans, to function well probably³⁹ is that the external market conditions were stable and predictable and did not require fast reallocations of resources between firms. Faster and more efficient financial market arbitrage did rather disturb markets and destabilize the economy.

3. Introducing a primitive market for derivatives gave no clear results. In some scenarios (for instance E2211 > E2210) improvements in the allocation of funds over the stock market raised firm turnover and long-term growth somewhat. In general, however, the introduction of a market for options appeared to disturb a delicate financial allocation machinery with long term negative consequences.

4. The long term consequences of raising the level of financial market sophistication appeared to be sensitive to particular circumstances, notably initial conditions, for instance an initially inefficient production structure.

5. So much said, policy interventions in a fast securitized regime can tilt the long term growth outcome in very unpredictable directions.

One hypothesis emerging from the experiments therefore is that the change into a capital market regime in the volatile global product market conditions from the late 1970s would have contributed to long term economic growth through making transfers of funds between firms over the market possible. Introducing an options market however appeared to cause few such positive effects, and rather the opposite in that a sensitive financial allocation machinery was disturbed.

The implications for policy for the securitization of financial markets may be far reaching. Policy authorities appear to lose control rather than being able to benefit from the faster market arbitrage. Since new C&C technologies allow securitization to occur whether policy makers like it or not, there appears not to be much choice. True, options are only part of a fully securitized market of the kind already introduced in the US, but since they alone seem capable of generating undesirable unpredictable outcomes one would expect a fully securitized system to have an even stronger long term economy wide impact. Again, however, the diversity of structures and robustness of the Moses model compared to the real economy it is supposed to represent, warrants caution of interpretation. The real Swedish economy should be more robust. The extent to which that difference matters can be tested in two ways; (1) by rerunning the experiments in a more volatile global market environment that would require flexible responses of both firms and financial markets, and (2), given (1), increasing the population of firms initially and through entrepreneurial entry.

In general, however, the economies "worst positioned" for a securitized financial system are those of the industrially underdeveloped world with raw material based economies, primitive markets and often corrupt politicians. When they complain (as Mahathir of Malaysia has done frequently⁴⁰) they have a reason, because their economies are so undiversified, and their producers so protected from external competition that even minor changes in status quo may cause severe disruption.

Also, European economies may be in for trouble as C&C based securitized financial markets gain terrain. Many countries overprotect their producers, and are weighed down by very large and inflexible non-market public sectors protected from external market competition and dependent on easy and cheap external finance. With large inflexible, often politicized non market systems, structural adjustments caused by market competition are "transferred" by markets to the flexible private sector causing instabilities, if too fast.⁴¹

The US economy is the opposite example. Stable and fast long term growth appears to be supported by innovative entrepreneurial entry, and the success of the US economy through the 1990s up to the present appears to rest not only on its entrepreneurial culture and people but also entrepreneurs that have been supported by a uniquely innovative financial system, and fairly complete competence blocs with industrially competent venture capitalists (Eliasson, 1997). Not to be missed is that US politicians have allowed foreign competition to wipe out low productivity manufacturing businesses to an extent not seen in other parts of the world. Workers have been forced to move on to higher productivity jobs. Under such demanding market circumstances and strong flows of disruptive mew technologies, securitization of financial markets should generally have positive long term outcomes, an interpretation in keeping with Rybsczynski (1993).

Reasoning based on theory or model abstractions can be no more than tentative hypotheses to be subjected to further empirical testing. Hopefully, however, the hypotheses should be sufficiently empirical to mean something in a (political) decision context. We may have taken some liberties here in generalizing from the simulation experiments. On the other hand, the propositions made are quite in keeping with what we know, and perfectly testable on new data and (!!!) additional simulation experiments on the Moses model. The latter is a great advantage of complex micro simulation modelling. Very much as you try to explain observed empirical phenomena by collecting more statistics, you can ask the Moses model for more information about what it has told you.

The empirical credibility of Moses has been a concern in many studies. It rests on four pillars; (1) a well-researched and relevant prior specification, which sets it apart from most economic models (Eliasson, 1976a; Eliasson, 1976b), (2) an economy wide specification that integrates at least four well known partial models, two of which (Keynesian macro demand, and Schumpeterian innovation push) have been widely used individually for policy prescription, (3) the collection of a customized firm database, that besides being a high quality micro to macro and stock flow consistent description of the initial state, and integrated with the Swedish national accounts (Albrecht et al., 1992), also allowed us to (4) substitute direct measurement in many places for parameter estimation. The latter is important in a microsimulation context.

Complex model specification made possible by the micro simulation method has the great advantage of moving model design closer to direct observation and measurement, in many places doing away with the need for parameter estimation and stochastic assumptions. Thus, for instance, exit of an agent has been directly modelled on bankruptcy law. Firms exit individually when their equity has lost too much in value, and at the latest when it has been reduced to zero, and whatever asset values remaining at exit are returned to owners and the market. There is then no need to estimate artificial "probability of exit functions". Workers are laid off according to Swedish labor market law, and leave unemployment when wage offers exceed their unemployment benefits by a margin (this reservation wage threshold has been taken directly from Swedish labor market studies. See Eliasson, 1977). The Planning Survey of the Federation of Swedish Industries has made it possible to "measure" the initial state production structures of firms directly, such that they can then be upgraded by engineering calculations from period to period (Eliasson, 1976b).

All this means that the number of parameters to be estimated has been reduced to about a dozen critical factors that figure importantly as market determining time reaction and firm behavioral parameters. They are the parameters that regulate intertemporal behavior of the entire model economy from period to period, notably those that regulate Stockholm School ex ante ex post dynamics. Most of them are operational in that they correspond to "control levers" firm managers recognize. Even though we currently calibrate the model assuming these time reaction parameters to be the same for all firms, it is perfectly possible, using the panel data of the annual Planning Survey to "estimate" them individually, or better, to call in real firm managers for a gigantic simulation game to set their own firm parameters (Eliasson, 1985). Above all, however, whether estimated or calibrated the parameters will be surrounded by sizable confidence intervals. In empirical studies we therefore conduct Monte Carlo studies to see how sensitive our results are to small variations in parameter estimates. Unfortunately for this study computer capacity has not been sufficient for much in the form of such systematic evaluations.

Still, considering all empirical inputs in the MOSES model we regard it as an empirically credible representation of a Sweden like industrial economy. This means that we, being responsible for its design and construction, including its prior assumptions, take even surprise outcomes of model simulations seriously as something to be concerned about. They may occur.

An early version of that innovation module is found in Carlsson et al. (1997). We plan tentatively to introduce a venture capital application in that module that endogenizes the selection of winning projects from R&D investments by modelling the industrial competence of venture capitalists as they learn from investment experience. Below we discuss the role of insiders in MOSES as they inform the market through, and learn from their trades.

Technical details are presented in Taymaz (1999).

Note already here that this selection becomes theoretically irrelevant if we reduce the state space by assumption to the narrow fully transparent one of the WAD model. Market selection draws transactions costs and innovative behavior therefore cannot occur in the WAD model.

For practical data availability reasons the statistical resolution of the MOSES model defines the firm or the division of a firm as the minimum, autonomous market agent. Carleson (1991) has however taken measurement down to a finer level of resolution within the so defined MOSES agent, and demonstrated quantitatively how the large part of aggregate productivity change has been intermediated by reorganizing production structures.

There is a fine distinction here. Industrial policy in the sense of targeting successful macro outcomes by way of interfering with the Schumpeterian Creative Destruction process of Table 1 is theoretically impossible and bound to fail (Eliasson, 2000b). It requires a one-shot successful policy outcome, which we have failed repeatedly to achieve by designed policy experiments on the MOSES model. Some long term improvement can be achieved after a series of policy experiments. To the extent the MOSES model is regarded as a realistic representation of the real economy such experiments can then be regarded as a learning experience for real policy.

This may be because few of these models¸ if any, are really agent based, featuring both heterogenous agents and Stockholm School expectations plan outcome feed backs, as well as agent selection and economy wide market self-organization, which together give the MOSES model unique and empirically recognizable properties. See further below.

Note that our concept of a business or investment opportunities space has little to do with similar concepts appearing in neoclassical literature, which always imply a space with almost fully known opportunities. Our point is to assume the opportunities space to be sufficiently large and complex to make it mostly unknown to each and all actors. Since opportunities depend on how each actor reacts to the decisions of all heterogeneous and differently sized actors in the market there is no theoretical chance for even a reasonably informed market except by awkward assumption. We claim that ours is an empirically reasonable assumption. Smith and Watts (1992), on the other hand, define the opportunities set to be captured by the ratio of the market to the booked value of the firm. S &W hence assume that there exists a marginal group of actors in the market that is fully informed about the size and the economic value of all opportunities. This is a typical assumption of static equilibrium neoclassical economics that the MOSES model has long departed from.

Since this is a theoretical paper, we want to be strict on this point. The state spaces of the EOE and of the MOSES model are not infinite. At each point in time the state space is sufficiently large, but still bounded from above, to keep all agents, including central government, ignorant of all that can be known about its content, except for a fraction that differs between agents. To prevent the unrealistic consequence that with time everything will eventually be learned about we impose an Information Paradox (Eliasson, 1990b; Eliasson, 2001) stating that the opportunities space expands from (costly) exploration of agents, and faster the more intense that exploration in that firms, being challenged, learn and discover new combinations (innovation). We furthermore make the empirical assumption that the opportunities space thereby expands faster than all exploring agents can learn about its expanding content. Therefore, we acknowledge the empirical possibility (the paradox) that the markets are getting increasingly ignorant of what can in principle be known. In MOSES analysis we frequently refer to this as the Särimner Proposition, from then pig in the Viking Sagas that was eaten for supper in Valhalla by the constantly brawling Vikings, but came back next morning, to be eaten again next evening (Eliasson, 1987; Eliasson, 1992a). We depart from the Viking analogy in that the opportunities space grows from being explored. The game is positive.

To discount a future flow of profits requires a known future discounting rate, which is assumed in the WAD model. We rather think of it as an ex ante concept, and that trades take place in the corresponding expected present values, that therefore both differ between agents and frequently turn out mistaken ex post. This is where Stockholm School economics enter (Eliasson, 1993; Eliasson, 2000a).

Original ambitions were much higher, but modelling more complex matching on trading floors placed impossible demands on available computing capacity. Since the markets we are studying are largely made up of computerized transactions this problem illustrates the importance of C&C technology for the existence of these markets and suggests that the random pairing device may be an empirically acceptable simplification to begin with.

For Model Of the Swedish Economic System.

In principle households also face convex utility sets, even though they are assumed to be atomic in size and all identical (“representartive”) satisfying the conditions for a Stone type aggregate consumption system with a Friedman (1957) type permanent income consumption target and a Modigliani and Brumberg (1955) life cycle saving proposition, all modified onto a non linear format with saving as a separate future consumption category. For more details, see Eliasson (1985).

Through a separate survey to C&C firms, which made it possible to separate out that new sub industry from the investment goods sub industry and the private services sector in the input output table, a major statistical job, in fact.

As noted in footnote 1.

Firms may make strategic decisions on whether to opt boldly for high risk ventures with a low probability of a very high return, and vice versa for a high probability of an acceptable return or any strategy in between. In fact, it would even be possible to assign predictable such strategies to the real firms in the model, or the firms could be asked to characterize themselves before a simulation experiment (see Eliasson, 1985). For practical reasons this mechanism has been turned off in the experiments reported on in this paper.

Excess profitability so defined is called ꜫ in (3a) in Eliasson (1991a).

This R&D module of Ballot and Taymaz (1998), which has not been used in the experiments of this paper, endogenizes the best practice technologies associated with new investments previously being exogenous.

For a methodological discussion of generalizing from micro to macro, using the MOSES model, see Eliasson (2000c).

For that reason, we prefer to refer to sub industry aggregates as markets rather than sectors.

Computable general equilibrium models, even though frequently used for such calculations, are not recommendable. They are static and as a rule sector models and hence lack the market dynamics that is our concern.

The rest of this section draws directly on Eliasson (1988), Eliasson (1998c); Eliasson (2000b).

This aspect is being addressed in a currently ongoing study , that has later been published as Ballot et al. (2006).

For preliminary work on introducing insiders in the MOSES model, see Trofimov (1995).

See Eliasson (1985) and proof on page 238.

Data on ԑ = R – r is collected annually in the Planning Survey of the Federation of Swedish Industries that is the basic statistical source of micro data for the MOSES model. See for instance Albrecht et al. (1992).

Defined as the expected or actual variance in the prices of securities.

All markets experience a similar pattern of global price fluctuations in the experiments of this study, the only difference being the timing of fluctuations. The trends change relative market prices as in the past period for which this model version has been calibrated and are then exrapolated. The implications of such exogenous global scenarios that are exogenously entered are discussed in Eliasson (1983) where the global long term balanced relation between productivities of best practice technologies, product prices and the international interest rate are specified. Experiments have been run on a model version calibrated as described in Taymaz (1991b).

Exogenous in earlier versions of the model but endogenized on a firm basis in the simulation experiments in this paper as introduced with the new firm innovation module of Carlsson et al. (1997) and Ballot and Taymaz (1998).

The first 6 years are used to generate the data needed for the stock market

Note (when comparing E1001 > E1000 > E1010) that with stock market valuation of shares, but stock market financing no longer allowed, long term growth is lowered. This negative effect is further reinforced if interest rate adjustments are speeded up. This unexpected growth performance is explained by an increased rate of firm exits that is not compensated by an increased rate of entry of firms with more productive new technologies.

If a bad market evaluation of a firm’s shares does not influence its individual borrowing rate macro performance may improve. This means that the bank has not been well informed, and has taken on more risk. Cf E1100 > E1200 and E0110 > E2211, but E1001 > E0110

E1001 > E1101, E1011 > E1111 and E1010 > E1110. Furthermore E1101 ≈ E1201, E1111≈ E1211 and E1110 ≈E1210

Note that for the time being we only have call options in the model.

These are called Type II economic mistakes in Eliasson and Eliasson (1996), who expect Type II mistakes to be quite frequent. Competence bloc theory has been developed to explain the efficiency of market selection of investment projects. The ambition is to introduce competence blocs in the Moses economy.

Currently not part of the standard calibrated model.

Eliasson, 1990a suggests that the information embodied in insider trading may be too economically valuable to be lost by outlawing insider trading. A better way would be to enforce the use of modern C&C technology to immediately let the market know about an insider trade. John and Mishra (1990) use a general equilibrium model to demonstrate how a new equilibrium can be reached with insiders informing the market (“signalling”) through trades.

The markets of the MOSES model are assumed to perfectly trade product quality against product volume. Hence INVEFF and MTEC will reflect product quality improvements. For the outcomes of this insider experiment this is not important, but in other experiments it is. See discussion of product market arbitrage in Eliasson (1976b).

i.e. the α and β in Equations 4 and 5 in Eliasson (1991a). The insider then computes the consequent increase in Rⁿ in (3B), and finally inserts Rⁿ in equation (1) in Section 4.3.

We did not expect this result, and therefore did not prepare for a second round of experiments to check for reasons. Because of the extraordinary demands on computer capacity that were temporarily overcome, this has not yet been possible.

Mahathir has recently also added e-business to his list of complaints (Financial Times, April 22. 2000:3).

Economy wide instability caused by a large inflexible public sector in combination with speeded up market resource allocations has consistently shown up in Moses experiments (see for instance Eliasson, 1986a; Eliasson, 1986b; Eliasson and Taymaz, 1992).

Paper prepared for the project at KTH: The Macroeconomic Effects of Computer and Communications Technology, funded by the Swedish Transport and Communications Research Board (Kommunikations- forskningsberedningen). Published as Stockholm: KTH TRITA-IEO-R 1999-09.

See Eliasson (1977); Eliasson (1985); Eliasson (1991a).

“Short run” refers to expectations for the next quarter.

The number of models that can be used for forecasting in unlimited. In our experiments, we used only two simple forecasting models. See also Antonov and Trofimov (1993).

In the model, we also introduce a small random variation (at most 10% of the expected price) for expectations to account for various types of information the agents may get.

On a model version calibrated as described in Taymaz (1991a).

A study from the project: The Macroeconomic Effects of Computer and Communications Technology,, funded by the Swedish Transport and Communications Research Board in the TRITA series of the department of Industrial Economics at the Royal Institute of Technology (KTH), Stockholm. Because of the extreme demands on computer capacity all simulation experiments technically prepared in this paper and coded in the model have not yet been conducted, notably those on insiders and venture capital. Since the plan is to complete these experiments later when computer. We find that this theoretical presentation is interesting on its own merits because it illustrates the potential for general economic analysis offered by mathematical simulation or microsimulation methods.

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