A STUDY OF BUSINESS GAME STOCK PRICE ALGORITHMS Developments in Business Simulation and Experiential Learning, Volume 31, 2004 A STUDY OF BUSINESS GAME STOCK PRICE ALGORITHMS Joseph Wolfe Experiential Adventures LLC Jwolfe8125@aol.com Steven Gold Rochester Institute of Technology scgbbu@rit.edu ABSTRACT A number of studies have examined the algorithms business games use to simulate real-world company functions. This study extends that research tradition into the area of the firm's stock price algorithm while increasing the range of validaties consid- ered. An investigation of the stock price algorithms associated with six computer-based management games revealed diversity in the number and treatment of the variables used to create company stock prices. This diversity created radically different firm stock prices. These valuations also differed under company conditions of economic growth and decline. Most stock price results would meet a face validity test under conditions of im- proving firm performance but most would be challenged under conditions of company decline. INTRODUCTION For a great number of years computer-based game users had little direct knowledge of the quality of the algorithms their games used to model the firm's functions and outputs. Although there were early minor exceptions to this rule, such as the American Management Association's Top Management Deci- sion-Making Simulation (Ricciardi, et. al., 1957) and The Execu- tive Game (Henshaw & Jackson, 1966). In these two cases the authors divulged their algorithms to demonstrate company op- erations could be quantified and thereby promote the agenda that the business decision making process could be made more ra- tional. It was not until the early-1980s, however, that the field began to examine and question the theoretical validity of the algorithms used in their games. Under Goosen's (1981) prodding various studies have subsequently investigated the algorithms game designers have used to model various game-related func- tions. A recent paper by Gold and Pray (2001) summarized AB- SEL's interest in game algorithms. It cited 19 papers dealing with marketing routines, 12 pertaining to finance and account- ing, three associated with product quality, eight dealing with operations management/production and two connected with hu- man resources. To this list of 44 papers there must be added a recent paper by Thavikulwat (2002) on modeling currency ex- change rates as well as his contribution (Thavikulwat, In Press) on game design which is part of Simulation & Gaming's Sympo- sium Issue on business games. Missing from this research array, however, is work on how a company's stock price is created as Goosen, Foote and Terry (1994, 63) have observed “How simu- lation designers model the complex cost of capital issues is a well kept secret.” This is an especially unfortunate state of af- fairs because a firm's stock price has various uses and perform- ance implications within business gaming contexts. Most top management games generate the firm's stock price as an indica- tor of the firm's effectiveness (Keys & Biggs, 1990). Stock prices also indicate a major part of a company’s cost of capital, interact with debt as a part of its financial structure and therefore rates-of-return, and are often used directly or indirectly by in- structors to determine the company's performance for course/player grading purposes. This paper seeks to remedy this situation by examining the stock price routines employed by a sample of computer-based games and will make judgements as to their employment of sound financial markets theory, face va- lidity or "reasonableness" and the presence of any particular re- porting bias under conditions of firm stability, growth and de- cline. LITERATURE REVIEW This review covers two main topics intimately related to simulation design. The first topic deals with the role theory plays in the development of a computer-based game's algorithms. The second topic concerns itself with the amount of fidelity and con- comitant complexity an algorithm must possess within a teach- ing/learning business game if it is to be effective. If a game de- signer wishes to capture the part of the real world being simu- lated, that part of the world must be amenable to descriptive cap- ture and be reproducible in mathematical form. Some real world phenomena, such as a firm’s production function, supply mar- kets to firms in a simulated industry, and pricing effects indus- try-level and firm-level demand have been successfully modeled in various business games. Others, such as morale, dedication and R&D creativity have only been modeled by derived proxies. The easiest algorithm to create is one where a dominant theory explains the phenomena. If there are conflicting theories, or if no actual theory exists the modeler is basically free to create what- ever model meets a face validity test. Regardless of whether the model relies on a commonly- agreed upon theory, or is one that springs from the designer's imagination, the complexity paradox noted by Cannon (1995) must also be dealt with. As an algorithm becomes more com- 190 mailto:Jwolfe8125@aol.com mailto:scgbbu@rit.edu Developments in Business Simulation and Experiential Learning, Volume 31, 2004 plex, and therefore closer to the real-world complexities being modeled, the algorithm's ability to exemplify clear causal rela- tionships becomes more obscure. When this occurs it becomes more difficult to explain or understand results. Therefore, de- pending also on the level of sophistication possessed by the game learner and instructor, it is possible that simpler and in- complete algorithms are preferred. STOCK PRICE THEORY The finance field has generated many asset valuation meth- ods. This review, nonetheless, will limit itself to the field’s “classic” methods while additionally recognizing the recent trend towards the use of behavioral finance and stock market psychology. A widely accepted stock valuation theory has been summarily expressed by English (2001, 21-22). “The market value of any firm is the sum of its capital and the present value of its future abnormal earnings…Simply put firms with superior investment opportunities, perhaps forged through competitive advantage or gained as a first mover, have higher valuations.” Damodaran (2002) bundles the various approaches to equity valuation into three broad categories, and argues that each of these approaches may generate significantly different results. They are: 1. Discounted cash flow 2. Relative valuation 3. Contingent claim valuation With the discounted cash flow theory cash flows are dis- counted at a risk-adjusted discount rate to arrive at an estimate of value. In this case, the equity value depends on expected future cash flows and the investor’s required return. The growth of fu- ture cash flows depends on the company’s return on investments and growth, which is based on a company’s business opportuni- ties and competitive advantage. With the relative valuation the- ory the value of a firm’s stock price, and therefore its equity, depends on the pricing of comparable firms based on such fun- damental factors as price-to-earnings, price-to-book , or price-to- sales ratios. Other less common relative valuation measures in- clude price-to-cash flows, price-to-dividends, and Tobin’s Q. The third approach, the contingent claim valuation theory, ar- gues the present value of cash flows is dependent on the likeli- hood of a set of events. This approach is similar to determining “option values” in finance. An example is the realization of FDA approval of a drug and the value of that approval. In this case, the value of the firm may be greater or less than the expected cash flows. However, the explanatory power of contingent claim valuation is limited. Small or new firms may have very high option values that are contingent upon the realization of certain events, like a technological breakthrough in product develop- ment. But for large, established firms, which comprise the bulk of the market for commonly traded stocks, option values are generally a relatively small part of the overall portfolio of the firm’s developed products. These three approaches are based on economic values sub- ject to the rule of rationally objective markets. Under this suppo- sition the premise is that all economic returns and risks expecta- tions have been assessed by rational investors. There is ample evidence, however, that this simplifying assumption of rational- ity is does not accurately reflect true market behavior. Thus a newer approach, which has been supported by a growing body of evidence, is based on psychology and is in the realm of behav- ioral finance. According to Fernandez (2002) behavioral finance theory suggests that share prices do not follow any rational valuation rule but rather depend on the state of euphoria or pes- simism existing at the time in the financial markets. This is con- sistent with Schiller’s (1999) hypothesis that the market is based not only on fundamentals factors but that psychological factors, such as Alan Greenspan’s celebrated “irrational exuberance”, may be a major driver of stock market prices. Interestingly for the game modeler, Schiller (1999) has found stock purchaser behavior is truly irrational but conveniently irrational in predict- able ways. It was noted that as stock prices rise, the level of exu- berance increases in a circular and self-feeding fashion due to the previous rise in stock prices. In this case, one can expect that successive share prices will be correlated or will repeat in simi- lar cycles, thus giving credibility to the approach taken by the technical analysts who chart patterns to predict returns. The “speculative bubble” theory supports this point of view in the short-run, but argues that in the long-run share prices will return to their fundamental values. As observed by Fernandez (2002, 52) states: “Bubbles tend to grow during periods of euphoria, when it seems that the market’s only possible trend is upward. However, there comes a day when there are no more trusting investors left and the bubble bursts and vanishes: shares return to their fundamental values”. Yet the supporters of modern portfolio theory question the use of technical or fundamental analysis for predicting stock returns (Malkiel, 1990), and this debate continues today. Modern portfolio theory contends that daily stock price changes are ran- dom and cannot be predicted by past information. The future is unknown and stock prices change very quickly to company dis- closures, public news releases, and other economic events. To shed more light on this debate the performance of professional money managers has been studied for over three decades. The result of these studies, as reported by Gray, et. al., (1999), found money managers have underperformed the market by 2.0%- 3.0% per year. In an average year, only one-third of the equity mutual fund managers beat the market. These results hold no matter the method of equity valuation is used, thus supporting the random walk theory and the efficient market hypothesis. But there has been some success in the use of fundamental analysis. Investment professionals using fundamental analysis have been able to beat randomly selected stocks in the Wall Street Journal's “Investment Dartboard” column. Between 1990 and 1998 the Wall Street Journal tested the performance of investment profes- sionals compared to random dartboard selections. During this time period, the “fundamentalist” investment professionals beat the darts in 60 out of 100 contests. It is also clear from the fi- nance literature that fundamental analysis is more widely sup- ported than technical analysis as a method of equity valuation. THE BASIS OF A VALID LEARNING EXPERIENCE Feinstein and Cannon (2002) have recently reviewed three major constructs bearing on a simulation's validity. Those con- 191 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 METHODOLOGY structs are fidelity, verification and validation. A game's fidelity is the degree the game or experience duplicates the real-world situation it is modeling. Verification deals with the degree the game's structure or its algorithms operate in the manner intended by the game's designer. Validation is concerned with the degree to which the game actually brings about the planned results which are usually changes in the attitudes, knowledge levels or behaviors of its participants. In the context of this paper we are concerned with the fidelity or the amount of verisimilitude a business game's stock price model and its algorithms must pos- sess as it is assumed the algorithms studied have been both al- pha-tested and beta-tested and are therefore bug-free and com- pletely operational. A number of game authors whose games outputted stock prices were contacted and were asked to reveal the routines they used to model company stock prices. The following games be- came this paper's convenience sample: BPG—The Business Policy Game, D.J. Fritzsche, www.eskimo.com/~fritzsch/. CAP—CAPSTONE, Management Simulations, Northfield IL: Management Simulations, Inc. COR—Corporation, J.R. Smith and P.A. Golden, Englewood Cliffs NJ: Prentice Hall. The game designer, when constructing any simulation, must decide what must be modeled, the amount of detail or depth the model must possess and how to cast the modeled phenomena into a mathematically operational form. The ultimate goal for management education and development game is to create a model that is appropriate for the game's teaching or course con- tent purposes keeping in mind the level of sophistication and preparation possessed by the simulation's intended players (Wolfe, 2001). Using teaching effectiveness as the criterion variable perfect fidelity may or may not be necessary or even appropriate as various studies have found higher fidelity levels do not translate into higher educational achievement (Greenlaw, Herron & Rawdon, 1962; Kibbee, 1961), high fidelity actually hinders learning with some groups (Martin & Waag, 1978) and lower fidelity levels can lead to greater educational achievement (Alessi, 1988; Gagne, 1954). GBG—The Global Business Game, J. Wolfe, Mason OH: South- Western College Publishing. MAS—The Management Accounting Simulation, K.R. Goosen, Little Rock AR: Micro Business Publications. MMG—The Multinational Management Game, B.L. Keys and R.A. Wells, Little Rock AR: Micro Business Publica- tions. Each game's stock price routine was captured by reading author- ized versions of the game's computer code and/or simulating the game's routine in spreadsheet form via game author interviews. This research step generated Exhibit 1 which lists the variables or identities used by the sampled games. Given the variables employed by each game, and the finan- cial results required to operationalize the variables used by the games, sets of universal balance sheets and income statements were generated to cover three test conditions. One set covered each firm's stock price behavior for one period of play. This set was a "static" or instantaneous stock price test. A second set used the results of a firm experiencing improving economic re- sults over a four-period time span. This set, labeled "Positive", was a dynamic test to determine the degree to which each game's stock price algorithm reflected positive results, and thus which of the games demonstrated stronger upward biases. The third set of economic results, labeled "Negative", employed a firm that was experiencing increasingly unfavorable economic outcomes. This set was used to determine the degree each game's stock price algorithm reflected the firm's deteriorating performance. The profit trends for the firms that obtained positive and nega- tive results over the four periods are presented in Exhibit 2. In bringing this to the instructional level the instructor's task, when players go through the experiential learning process created by the business game, is to act as an aid or coach to the players. In doing so questions inevitably arise as to how some- thing in the game works. It is at that point learning can occur as the instructor must first understand the knowledge level the player brings to the situation and then fill the gap between the player's knowledge level and that which is portrayed by the game's model (Bransford, Brown & Cocking, 2000; Wellman, 1990). As the model's fidelity level, and its associated complex- ity increases, it naturally becomes increasingly more difficult for the instructor to explain how the phenomena operates. This can make it very hard to draw out any lesson(s) that can be learned. In this instance we are dealing with what we term is a game's explanatory validity, which is the ease with which various phe- nomena can be explained or rationalized to players. As an im- mediate aid to obtaining explanatory validity the routine being modeled should operate in a fashion that is readily apparent, or if not immediately apparent, ultimately grasped through greater exposure to the routine's operations or coaching by the instruc- tor. In the context of this paper as applied to a firm's stock price, players expect stock prices to increase when company economic performance improves. If one firm outperforms another firm the better-performing firm should obtain the better stock price. If such relationships do not eventually occur the game's face valid- ity can suffer unless there is some alternate explanation for the lack of expected results. RESULTS A review of the variables used by the games to create their stock prices was previously presented in Exhibit 1. There was universal agreement on the need to use the firm's current profits, number of outstanding shares and owner's equity as part of any algorithm that created a company's stock price. Almost all games used various devices or "traps" to insure that stock prices would not fall too low, or that company solvency was assured. These traps took to form of not allowing players to declare liquidating dividends, which would artificially inflate the firm's stock price, and not allowing them pay out dividends that would be financed through emergency funds and one which would be otherwise financially unsound. Another common trap kept stock prices 192 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 from falling below a certain level. This type of safeguard, while not recognizing the firm's true market value under cases of poor economic performances, allows a firm to remain "in play" in the game's equity market thus permitting them to remain in the game's competition although at a very low performance level. COR used the greatest number of such traps while also reward- ing certain strategic moves with stock price increases. Overall a rather short-term view was taken in determining stock prices. This was evidenced through the use of determining the current period's stock price based on the EXHIBIT 1 Stock Price Variables Employed Game Variable BPG CAP COR GBG MAS MMG Percent Pervious Period Stock Price - - X X - X 50.0 Short-Term Price Smoothing - - X - - - 16.7 Current Period Profit X X X X X X 100.0 Previous Period Profit - - - X - - 16.7 Near-Term Period Profit X - - - X - 33.3 Outstanding Shares X X X X X X 100.0 Short-Term EPS Trend - - - X X - 33.3 Short-Term Per/Share Dividend X X X X - X 83.3 Long-Term Per/Share Dividend - X - - - - 16.7 Retained Earnings - - X X - X 50.0 Owner's Equity X X X X X X 100.0 Cash Account X - - X - - 16.7 Liquidity -- X - X - - 33.3 Emergency Loan -- X - - - - 16.7 Credit Rating -- - - X - - 16.7 Stock Market Index X - - X - - 33.3 Seasonal Index X - - - - - 16.7 Consumer Price Index X - - - - - 16.7 Exchange Rate - - - - - X 16.7 Debt/Equity (Leverage) - - - - X X 33.3 Cost of Capital - - - - X - 16.7 Market Research - - X - - - 16.7 Lost Sales - - X - - - 16.7 Liquidating Dividend Trap - - 1 1 - - 33.3 Excessive Dividend Trap - 1 2 - - - 66.7 Depressed Stock Price 1 1 2 1 - 1 100.0 Strategic Factor Recognition - - 2 - - - 33.3 Total 10 9 16 14 7 9 39.5 EXHIBIT 2 Company Profits by Period Period Economic Performance Trend 1 2 3 4 Percent Change Positive $411,822 $480,580 $496,811 $530,419 +28.8 Negative -$1,340,663 -$1,557,669 -$1,912,286 -$1,990,954 -48.5 previous period's price, near-term profit and the previous pe- riod's earnings/share and any dividends declared based on earn- ings/share. If the number of algorithmic traps are eliminated from the analysis GBG used the greatest number of variables in its stock price routine with MAS using the fewest. The effects of the diversity in the variables used to generate each game's stock price is witnessed in Exhibits 3-5. Exhibit 3 presents a one-time, static analysis of stock price results. Even though common operational results, income statements and bal- ance sheets were employed and were the materiel from which all stock prices were derived, the results were not uniform. Thus a COR firm's valuation resulted in a $12.99 stock price while the same firm in CAP was worth $3.54 a share. A GBG firm was worth about the average of the two at $8.01. In this analysis, BPG and MAS were closely valued, ranging from $5.49 to $5.81. The stock price generated by MMG's algorithm was much lower, with a value of only $3.54, while COR's was much higher with a price of $12.99. COR was the highest because it assumed a much higher standardized price-to-earnings ratio than the BPG model. The MAS model does not standardize for price-to- 193 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 194 earnings ratios but adjusts for expected increases in future earn- ings. The MMG model generates a very low stock price because it does not consider either the firm's price-to-earnings ratio or expected increases in future earnings. EXHIBIT 3 Static One-Period Stock Prices Game Stock Price BPG $ 1.54 CAP $ 3.54 COR $12.99 GBG $ 8.01 MAS $ 0.05 MMG $ 0.08 Exhibits 4-5 display the stock prices generated by each game under the dynamic conditions of relative company success or failure over four later periods of play. This analysis indicates that both upward and downward stock price biases exist between the games surveyed. In Exhibit 4 when positive results were ob- tained COR's stock price increased 11.8% from $52.14 to $58.29 per share although they flattened between Periods 3 and 4 de- spite an increased economic performance. CAP's stock price increased from $4.04 to $4.75 per share or a 17.6% increase. Respective stock price increases of 44.3% and 19.3% were gen- erated by BPG and MMG. On the other hand, the GBG firm's stock illogically fell 4.0%. Such was also the case for MAS whose stock price fell 35.5%. For those games whose stock prices increased the increase can be attributed to the weights put on earnings growth in their algorithms and, in MMG's case, the lower cost of capital outlays. In that game the cost of capital depends on the debt-to-equity ratio which declines when earn- ings increase. The decline in the MAS's stock price, despite the favorable economic results its firms were obtaining, can be at- tributed to the decrease in the growth-rate of its earnings. Al- though the firm's earnings were increasing, the growth rate was decreasing thus the lower earnings growth rate decreased the expected value of future earnings. Similarly, the decline in GBG’s stock price may be attributed to the fact the algorithm uses the “change” in earnings per share, as well as the “change” in other economic performance factors, to determine the firm’s stock price. In the first period the change in earnings per share is Exhibit 4 Stock Prices Associated with Positive and Negative Economic Results Positive Economic Results $0.00 $10.00 $20.00 $30.00 $40.00 $50.00 $60.00 $70.00 1 2 3 4 Period St oc k Pr ic e CAP COR GBG BPG MAS MMG Negative Economic Results $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 1 2 3 4 Period St oc k Pr ic e CAP COR GBG BPG MAS MMG Developments in Business Simulation and Experiential Learning, Volume 31, 2004 DISCUSSION very large and then declines. The large initial “change” in earn- ings may be viewed as causing an optimistically large increase in stock price, which is ultimately corrected when the change in earnings declines, despite the fact that total earnings are increas- ing. The algorithmic feature of using changes or growth rates in earnings could be found only in the MAS and GBG models. The GBG algorithm also diminished the firm’s stock market value because in this test case dividends were not increased when prof- its and retained earnings similarly increased. Exhibit 5 Indexed Stock Prices Associated with Positive and Negative Economic Results Indexed Positive Results 0.500 0.700 0.900 1.100 1.300 1.500 1 2 3 4 Period In de x CAP COR GBG BPG MAS MMG Indexed Negative Results 0.000 0.200 0.400 0.600 0.800 1.000 1 2 3 4 Period In de x CAP COR GBG BPG MAS MMG Because of the lack of a predictive theory regarding a firm's stock price, the valuation of any company is not a precise sci- ence. As noted by Damodaran (2002, 2) “The models that we use in valuation may be quantitative, but the inputs leave plenty of room for subjective judgments. [Therefore] it is unrealistic to expect complete accuracy.” The lack of that science is readily apparent in both the elements chosen for inclusion in each game's stock price algorithm, and the results produced by those algorithms. In most cases stock prices logically rose although the magnitude of the increases was sometimes minimized while at other times it was exaggerated. Under the conditions of a firm's deteriorating performance, the use of equity "safety nets" be- came readily apparent. While it is possible under real game- playing conditions, due to initially high stock prices a "dis- tressed" company would never approach the game's stock price floor, the presence of this floor gives that firm artificial support when the simulated stock market actually wants to put the firm into insolvency. In this case, or one where a firm's comparative stock price increase has been minimized compared to the results earned by another firm in the industry, the game's explanatory validity can be seriously threatened. When negative results were experienced in the games sur- veyed, the presence of low stock price traps became readily ap- parent. For COR the stock price decline could never fall below $3.31. In CAP the floor was $1.00 while in MAS the bottom was $0.00 thus eliminating future stock sales as a funding source when this price was reached. BPG employed its trap during the third period of play at $0.10 per share. Stock prices fell logically given the falling earnings involved for GBG and MMG with respective decreases of 28.6% and 65.7%. Because game players pay attention to how their stock prices "trend" over time, any upward or downward biases in- serted into this trending by the game's designer may have strong motivational implications. As presented in Exhibit 5 the biases possessed by the games can be more-readily seen when their values are indexed at a common base period. Under the condi- tions of improving economic performance both COR, MMG, BPG and CAP generated increasing stock prices although COR trailed off during the fourth period despite continued profit in- creases. Both MMG and GBG again generated falling stock prices under the same financial results, owing in part to the de- cline in the growth rate of earnings. GBG’s fall in stock price values are also due to a number of algorithmic factors not related to profits, including: the firm’s comparative performance within its own industry, as well as in this case, being punished by the market by the firm's unwillingness to issue dividends even though it possessed high liquidity and sizeable retained earnings. The inclusion of the role of subjective stock price valuation in the design of a stock price algorithm recognizing and/or re- warding a company's strategic intent (Hamel & Prahalad, 1989), or the suspected outcomes associated with a particular decision, can be found in one of the games surveyed. In COR $1.00 is automatically added to a firm's stock price each time a Venture is purchased and $2.00 is added each time a strategic business unit (SBU) is purchased. While it could be inferred from these decisions that good use will be made of the purchases, it can only be assumed under COR's stock price "reward" system these purchases will be well managed and will result in greater profits. But such may not be the case. Moreover, if the players of this game learn their stock prices can be manipulated by engaging in 195 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 English, J.R. 2001. Applied equity analysis– Stock valuation tech- niques for Wall Street professionals. McGraw-Hill, New York. Venture and SBU purchases they will do so without actually creating shareholder value through the economic use of those purchased assets. Feinstein, A.H., et al. 2002. Constructs of simulation evaluation. Simulation & gaming. 33: 425-40. While the discernment and reward of a firm's strategic intent has been incorporated into COR's stock price algorithm, another important subjective element that influences a firm's stock price is missing in all the games surveyed. The role of irrational opti- mism and pessimism is lacking and is an element worthy of in- clusion. The MAS game, which attempts to scientifically model stock price behavior based on rational fundamentals, was the most volatile performing routine surveyed with questionable explanatory validity in its outcomes. This game, along with all the others could greatly benefit by modeling the market psychol- ogy associated with stock price Bull/Bear behavior. Fernandez, P., 2002. Valuation methods and shareholder value crea- tion. Academic Press, San Diego. Gagne, R.M. 1954. Training devices and simulators: Some research issues. American psychologist. 9: 95-107. Gold, S.C., et al. 2001. Historical review of algorithm development for computerized business simulations. Simulation & gaming. 32: 66-84. Goosen, K.R. 1981. A generalized algorithm for designing and de- veloping business simulations. In W.D. Biggs and D.J. Fritzsche (eds.), Developments in business simulation & expe- riential exercises. p. 41-7. Illinois State University, Normal. Goosen, K. R. 1994. Increasing the effectiveness of performance evaluation through the design and development of realistic fi- nance algorithms. Developments in business simulation and experiential learning. 21: 63-69. CONCLUSION The finance field has unfortunately provided business game designers with a wide array of divergent and controversial theo- ries upon which to base the stock price algorithms they employ in their games. This gives game designers, for better and for worse, personal discretion regarding the equity valuation models they create, and accounts for the significant differences produced by the simulation models studied. Although a common set of financial results served as the data base for examining six busi- ness game stock price results, large valuation differences oc- curred for both one period, and for longer-term results under the conditions of improving or deteriorating firm performance. In most cases, stock prices possessed explanatory validity under improving company performance. Such was not the case at the extreme when a firm produced declining results. Gray, G., et al. 1999. The streetsmart guide to valuing a stock. McGraw-Hill, New York. Greenlaw, P.S., Herron, L.W., and Rawdon, R.H. 1962. Business simulation in industrial and university education. Prentice Hall, New Jersey. Hamel, G., and Prahalad, C.K. 1989. Strategic intent. Harvard Busi- ness Review. 67: 63-76. Henshaw, R.C., and Jackson, J.R. 1966. The executive game. Rich- ard D. Irwin, Illinois. Henshaw, R.C., and Jackson, J.R. 1990. The executive game. Irwin, Illinois. Keys, J. B., and Biggs, W. D. 1990. A review of business games. In J. W. Gentry (ed.), Guide to business gaming and experiential learning. p. 48-73. Nichols/GP New Jersey. It is suggested future stock price algorithms attempt to model the subjectivity associated with stock purchase behavior. This subjectivity should involve both evaluations of the firm's strategic intentions based on its major moves, as well as an in- dustry's and economy's "boom/bust" features and investor expec- tations with respect to earnings, risks, and dividends. It is also suggested, as a way to bring about higher levels of explanatory validity to their games, game authors create descriptive sections in their Game Administrator Manuals that explain of how vari- ous game routines operate. This can be done through either flow charts, program code (Henshaw & Jackson, 1990) or prose nar- ratives (Cotter & Fritzsche, 1995; Wolfe, 2003). Kibbee, J.M. 1961. Model building for management games. In A. Newgarden (ed.), Simulation and gaming: A symposium. p. 8- 15. American Management Association, New York. Martin, E.L., and Waag, W.L. 1978. Contributions of platform mo- tion to simulator training effectiveness: Study I—Basic contact. Brooks Air Force Base, Air Force Human Resources Labora- tory, Texas. Miller, M.H., and Modigliani 1961. Dividend policy, growth and the valuation of shares. Journal of business. 31: 411-33. Ricciardi, F.M., Malcolm, D.G., Bellmon, R., Clark, C., Kibbee, J.M., and Rawdon, R.H. 1957. Top management decision- making simulation: The AMA approach. E. Marting (ed.). American Management Association, New York. REFERENCES Schiller, R. 1999. Irrational Exuberance. Princeton University Press, New Jersey. Alessi, S.M. 1988. Fidelity in the design of instructional simula- tions. Journal of computer based instruction. 15: 40-7. Thavikulwat, P. 2002. Model for currency exchange rates and its validation. Simulation & gaming. 33: 5-27. Bransford, , J.D., Brown, A.L., and Cocking, R.R. (eds.) 2000. How people learn: Brain, mind, experience, and school.. National Academy Press, Washington DC. Thavikulwat, P. (In Press). Architecture of computerized business gaming simulations. Simulation & gaming. Wellman, H.M. 1990. The child's theory of mind. MIT Press, Mas- sachusetts. Cannon, H.M. 1995. Dealing with the complexity paradox in busi- ness simulation games. Developments in business simulation and experiential learning. 223: 96-102. Wolfe, J. 2001. Who needs validity? 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South-Western, Ohio. 196 Table of Contents Volume 31, 2004 Controlling the Complexity and Orenting Target Groups by a Modular, Server-Based Business Game System Learning Network Demonstration: Delivering Business Education in a Distance Learning Environment Economic Evolution, Human Capital Investment, and Adult Distributed Electronic Learning: A Literature Review Designing a Globalization Simulation to Teach Corporate Social Responsibility Developing and Teaching an Online / In-Class Hybrid: A Demonstration A Model for Evaluating Online Instruction An Evaluation of a Distributed Learning Course: A Students'-Eye Perspective Blended Learning Strategy Improved Business Writing Skills How to Receive and Process Attachemnts while Greatly Reducing the Risk of Viruses and Trojans Introducing Online Components to a Class: How to Increase teh Likelihood of Success Teaching Strategic Communications Online: Using Learning Outcomes to Develop a Case-Based Course Implementing Distance Approaches to Education: A Panel Discussion for ABSEL: Las Vegas, 2004 MANDI: Learning Management Through Field Sales Experience An International Capital budgeting Experiential Exercise A Primer To Combating Terrorism: Playing It Safe While On Overseas Assignment (An Experiential Exercise) Integrating The Business Curriculum With A Comprehensive Case Study: A Prototype The Case Brief: A Model For Case Analysis, Writing And Discussion Technology Infused Pedagogy And Delivery – A Sure Bet? A Proposal For Panel Discussion Absel Conference 2004 Research Strategy And The Bkl: Getting The Most From The Absel Archives Simple But Effective: Rediscovering The Class Discussion Needle And Thread: An Activity For Examining Various Management Behaviors A Customized Excel Data Analysis System For Use In Undergraduate Marketing Research Team Leader Selection - Does It Matter? The Power Of Perspective: Reframing Your Framing Skills For Innovative Instruction In Leadership And Influence Exercise: How Should Merit Raises Be Allocated? An Online Situation For Problem-Based Learning In A Junior-Level Management Course The Eden Alternative As A Roadway For Change: A Service Learning Quality Improvement Project Avoiding Catastrophe: The Role Of Individual Accountability In Team Effectiveness Omega Systems: A Change Management Exercise The Risks And Rewards Of Providing Students A Structured Cheating Opportunity Experimentation With Assessment Techniques: A Proposal For Panel Discussion Using A 2 - Page Case To Introduce Concepts Of Business Strategy Interactive Session The Integration Of Appreciative Inquiry And Experiential Learning For Peak Performance Appreciative Inquiry Case Story: New York City Leadership Challenge Individual Achievement Versus Team Performance: An Empirical Study With Business Games Some Strategists Don't Learn Or Can't Learn Computer Simulation: A Design Architectonic On The Value Of Bugs In Simulation Environments Online Sales Forecasting With The Multiple Regression Analysis Data Matrices Package Simulation Exercises And Problem Based Learning: Is There A Fit? A Study Of Business Game Stock Price Algorithms Assessing Individual Performance In A Total Enterprise Simulation Information Use In A Business Game Determining The Value Of A Firm Unsorting Algorithms For An Ordered List And Its Application To Business Simulations Teaching Public Finance Management Through Simulation Antecedents Of Game Performance Student Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Implementation And Impacts Of The Balanced Scorecard: An Experiment With Business Games Impact: Shocking The Legacy Mindset Implementation Of The Eepad Framework Of Business Processes In An Accounting Information Systems Course Are Business Games Really Delivering What Students Are Led To Believe?? Reporting Lessons Learned: What Gets Reported; Who Gains Value Teacher Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Student Reactions To The Use Of A Computer-Based Simulation As An Integrating Mechanism For A Mba Curriculum A Cognitive Investigation Of The Internal Validity Of A Management Strategy Simulation Game The Casino Challenge: Making Simulation Delivery A Safe Bet! Accounting For Company Reputation: Variations On The Gold Standard Foreign Currency Hedging: A Simulation The Influence Of Variables Easily Controlled By The Instructor/Administrator On Simulation Outcomes: In Particular, The Variable, Reflection. Absel Awareness Among Business School Faculty Validating Business Simulations: Does High Market Share Lead To High Profitability? Simulation Debriefing Procedures Coaching And Business Simulations: A Formula For Success? A Seminal Inventory Of Basic Research Using Business Simulation Games The Influence Of Scorecard Evaluation On Decisions And Outcomes