NEC Research Institute Technical Report #2000-168.
A brief version appears in Science 291: 987-988, February 9 2001 (Letters).

The Power of Play: Efficiency and Forecast Accuracy in Web Market Games

David M. Pennock
NEC Research Institute
4 Independence Way
Princeton, NJ 08540 USA
voice: 609-951-2715
fax: 609-951-2488

Steve Lawrence
NEC Research Institute
4 Independence Way
Princeton, NJ 08540 USA

C. Lee Giles
School of Information Sciences and Technology and
Department of Computer Science and Engineering
Pennsylvania State University
504 Rider Building, 120 South Burrowes Street
University Park, PA 16801 USA

Finn Årup Nielsen1
Informatics and Mathematical Modelling
Technical University of Denmark, Building 321
DK-2800 Lyngby, Denmark


We analyze the efficiency and forecast accuracy of two market games on the World Wide Web: the Hollywood Stock Exchange (HSX) and the Foresight Exchange (FX). We quantify the degree of arbitrage available on HSX, and compare with a real-money market of a similar nature. We show that prices of HSX movie stocks provide good forecasts of actual box office returns, and that prices of HSX securities in Oscar, Emmy, and Grammy award outcomes constitute accurate assessments of the actual likelihoods that nominees will win. Similar investigations reveal that FX securities prices serve as reliable indicators of uncertain future events. We argue that, in certain circumstances, market simulations can furnish some of the same societal benefits as real markets, and can serve as acceptable substitute testbeds for conducting experiments that would otherwise be difficult or impossible.

analysis of artificial markets, World Wide Web market games, market simulations, forecast accuracy, economic efficiency, arbitrage, Hollywood Stock Exchange, Foresight Exchange, utility for intangibles


The core service of a market is to facilitate the exchange of items between individuals. The use of prices for these items, denominated in a common currency (e.g., US dollars), simplifies trading across multiple markets, alleviating the combinatorial nature of direct barter. Prices reflect an agreement between buyers and sellers, and serve as a quantitative measure of the value of the item being exchanged, as compared to other marketable items.

When markets attract broad participation, prices can encode the sum total of a large amount of disparate and distributed information. The prices reflect, in a very real sense, the consensus opinion of a myriad of informed and well-motivated traders. As such, even nonparticipating observers may stand to benefit from the informational value of market signals. As an example, the odds in a horse race, determined solely by market forces at the track, can be viewed as assessments of the likelihoods that the various horses will win. Empirical studies verify that odds on horses do indeed match very closely with their observed frequencies of winning [1,18,19,20,22].

As traditional markets expand onto electronic platforms, and as new electronic marketplaces emerge, price information will be available and accessible in quantities previously unimaginable. Nevertheless, markets will still only cover a miniscule fraction of arenas for which informed forecasts might be valuable or interesting. Many barriers exist for the establishment of new markets, including high costs, government regulation, and the threat of lawsuits. Artificial markets, on the other hand, suffer from no such difficulties. Web market games, in particular, often feature moderate operating costs for setup, maintenance, advertising, searching, and transacting, and benefit from worldwide audience potential. Permission is not required from government authorities or regulatory officials. Lawsuits are much less of a concern. There is little need for carefully crafted disclaimers or facilities for dispute resolution. Users can remain anonymous, and record keeping can be somewhat lax. All of these factors have contributed to a growing prevalence of market games on the web, some enjoying widespread popularity. Of course, artificial markets cannot satisfy societal demand for the exchange of items. However, in this paper we present evidence that some market simulations can function reasonably well in the dual role as aggregators and disseminators of information.

Theories of market equilibrium, including the rational expectations theory of information propagation, usually depend on the assumption that participants maximize expected utility, where utility is derived from consumables or monetary equivalents. Indeed, laboratory economics experiments in which subjects are not ``paid to play'' are often questioned on the grounds of a lack of true incentives. In a game without monetary backing, utility is presumably extracted solely from entertainment value, educational value, bragging rights, and/or other intangible sources. Does market efficiency simply break down under these conditions, or can non-monetary rewards actually drive price coherence, information aggregation, and forecast accuracy? We find evidence that, in some cases, they can and they do. In Section 3 we quantify price coherence on the Hollywood Stock Exchange (HSX). Equivalent portfolios trade at reasonably consistent prices, and, over time, large inefficiencies disappear, as players presumably take advantage. In Section 4 we evaluate the collective competence of traders on HSX and on the Foresight Exchange (FX), by measuring the prescient value of market prices. In doing so, we find that HSX stock prices are reliable indicators of what movies will do well at the box office, that HSX award option prices provide accurate assessments of which nominees will likely win entertainment awards, and that FX prices constitute accurate probabilistic judgments for a variety of uncertain future events.

Background and Related Work

Efficient Markets

An economist strolling down Wall Street spots a twenty dollar bill lying on sidewalk, but decides not to pick it up. No, this particular economist is not simply too rich to be bothered. Rather, he concludes without checking that the bill is a forgery. ``If it were a real twenty dollar bill,'' he reasons, ``someone else would have picked it up already.'' This widely told joke exemplifies what is called the efficient markets hypothesis. Roughly speaking, the hypothesis claims that there are no free lunches in the financial markets: every potential profit comes with some amount of risk, and prices are inherently unpredictable. If there is a risk-free profit momentarily available, or if prices are predictable, then someone will take advantage of the opportunity almost instantly, exploiting it until it is gone. There are various forms of the efficient markets hypothesis, and at least four different degrees of efficiency to consider:
  1. Internal coherence: prices are self-consistent or arbitrage-free: no trader can make a sure profit without any risk.
  2. Internal unpredictability: future prices are not predictable based on current and past prices. Also called the weak form of the efficient markets hypothesis.
  3. Unpredictability: future prices are not predictable based on any currently available information, including prices, economic variables, fundamental data, etc. Also called the semi-strong form of the efficient markets hypothesis.
  4. Expert-level accuracy: Prices fully reflect all information available to all traders. Informed experts cannot consistently outperform naive traders. In particular, when prices constitute forecasts, market estimates are at least as accurate as expert assessments. Also called the strong form of the efficient markets hypothesis.
Economists have developed a large body of theory to account for all four levels of market efficiency. And empirical evidence verifies--for the most part--that markets are indeed largely absent of arbitrage, are highly unpredictable, and can yield extremely accurate forecasts.

In this paper, we examine artificial markets for the same signs of efficiency, focusing on the first and fourth types: internal coherence and expert-level accuracy. We survey additional background material on these two forms of efficiency in Sections 2.1.1 and 2.1.2, respectively.

Internal Coherence: No-Arbitrage

Almost all economic theories of equilibrium assume, at a minimum, that equivalent portfolios are priced consistently with one another, such that arbitrage opportunities do not exist [2,3,11].2
Many important results of financial economics are based squarely on the hypothesis of no arbitrage, and it serves as one of the most basic unifying principles of the study of financial markets. [21]
For example, if the same company's stock is listed on both the Tokyo and New York stock exchanges, then the two prices should be the same at all times, modulo the exchange rate and transaction costs. If the prices do ever diverge significantly, traders will purchase the stock in the cheaper market and immediately sell it in the more expensive market, thereby driving the two prices back together. As a second example, monetary exchange rates should never be such that a ``round trip'' from one currency through one or more others and back again yields a positive gain.

A third example arises in the context of a securities market. In the parlance of economic theory, a security is defined as a lottery ticket that pays off $1 contingent on the outcome of some uncertain event. For example, the owner of a security ``$1 if and only if it rains tomorrow'' will be paid $1 if it rains tomorrow, and nothing otherwise.3 Now imagine a market of two disjoint and exhaustive securities: ``$1 if and only if it rains tomorrow'' and ``$1 if and only if it does not rain tomorrow''. Owning both securities guarantees the holder a payoff of exactly $1 regardless of whether it rains. Thus the total price to buy both securities should never dip below $1--otherwise, the buyer can obtain a risk-free profit. Similarly, in the absence of arbitrage, the total price to sell both securities can never exceed $1.

We will examine a fourth example in Section 2.2, where no-arbitrage implies that prices of a stock and its corresponding options must conform to a relationship called put-call parity.

Expert-Level Forecast Accuracy: Rational Expectations

The theory of rational expectations (RE) equilibrium accounts for expert-level forecast accuracy in securities markets. RE theory posits that prices are not only coherent, but also reflect the sum total of all information available to all market participants [6,10]. Even when some agents have exclusive access to inside information, prices equilibrate exactly as if everyone had access to all information. The procedural explanation is that prices reveal to the ignorant agents any initially private information; that is, agents learn by observing prices. Several authors show that, if agents begin with identical priors and disparate evidence, repeated observation of some aggregate statistic (e.g., price) will converge to a consensus on posteriors, for various sufficient statistics [7,12,13,14].

Plott et al. [15] investigate, in a laboratory setting, whether parimutuel markets (the type employed at horse races) are able to aggregate information, as postulated by RE theory. In one set of experiments, each subject was given inside knowledge that a subset of horses would definitely not win. Although all subjects were uncertain as to the outcome, their collective information was enough to identify the winning horse with certainty. Information aggregation did occur, and RE-based predictions fit the data well.

In earlier work, Plott and Sunder [16,17] conducted laboratory experiments to test the reasonableness of the RE assumption in the context of a securities market. Subjects were initially unaware which of three states would occur. In one study [16], privileged insiders were given categorical knowledge of the underlying state. The RE model's predictions--that equilibrium prices and other economic variables converge as if everyone were aware of the true state--were significantly more accurate than those of other models, including the classical Walrasian hypothesis that agents do not revise their beliefs based on prices. When insiders were given less than certain information, the results were not definitive. In a second study [17], insiders were told only that one of the three states would not occur. The combined knowledge of all subjects was sufficient to logically infer the true state, though no single insider could directly do so. It was found that, in a complete market of three securities, the RE predictions were again the most accurate. In a single security market, the RE equilibrium was not realized. On the other hand, even in this last condition, Forsythe and Lundholm [4], with a similar experimental design, found that RE was verified as long as subjects were sufficiently experienced and knowledgeable.

Beyond the controlled setting of the laboratory, empiricists have analyzed the forecast accuracy of public markets. Perhaps the most direct tests involve horse race betting markets. Several studies demonstrate that odds on horses correlate well with the actual frequencies of victory [1,18,19,20,22]. There does appear to be a small but consistent bias: favorites are underpriced and longshots are overpriced. Weitzman [22] and Ali [1] show how an assumption that the crowd as a whole is risk-seeking can explain this favorite-longshot bias. Beyond horse racing, sports betting markets as a whole provide very accurate forecasts of likely game outcomes.

The Iowa Electronic Market (IEM)4 supports trading in securities tied to the outcome of political and financial events. Their 1988 market, open only to University of Iowa students and employees, offered securities that paid off proportionally to the percentage of votes received by various candidates in that year's US Presidential election. The final prices matched Bush's final percent margin of victory more closely than any of the six major polls [5]. Since opening to the public, subsequent US Presidential election markets have attracted wide participation and following. Other election markets have now opened in Canada5 and Austria.6

The Hollywood Stock Exchange

The Hollywood Stock Exchange (HSX)7 is a popular online market simulation, with approximately 400,000 registered accounts. New accounts begin with H$ two million in ``Hollywood dollars''. Participants can buy and sell movie stocks, star bonds, movie options, and award options. The current top portfolio is worth just over H$1 billion. High ranking portfolios are actually sold at auction on Ebay8 for real money on a regular basis. Based on these sales, the ``exchange rate'' seems to be approximately H$1 million to US$1, with the rate increasing for higher ranked portfolios. HSX is beginning to offer new investment opportunities backed with real money. For example, HSX investors could purchase shares in the movie American Psycho for H$1 million each; these shares paid off about US$1 for every US$5 million of the movie's box office proceeds. HSX cofounder Max Keiser hosts a weekly radio broadcast in Los Angeles, and appears regularly on NBC's Access Hollywood to discuss HSX information. HSX also sponsors a booth at the Sundance Film Festival, and holds an annual Oscar party in Hollywood. Media reports suggest that HSX prices are taken seriously by some Hollywood insiders.

Although the current price of any HSX movie stock is based on the collective whims of HSX traders, the value of the stock is ultimately grounded in the corresponding movie's performance at the box office. Specifically, after the movie has spent four weeks in release, the stock delists and cashes out: shareholders receive H$1 per share for every US$1 million that the movie has grossed up to that point in the US domestic market, as reported by ACNielsen EDI, Inc.9 Traders buy (resp., short sell) stocks that they believe underestimate (overestimate) the movie's eventual performance. The current price, then, is a collective forecast of the movie's four-week box office returns.10

The prices of some stocks adjust after their first weekend in wide, national release. On Friday, trading in the stock is halted; on Sunday, the price adjusts to H$2.9 times the movie's weekend box office numbers (in US$ millions).11 In this case, the stock's price prior to wide release is the HSX traders' forecast of 2.9 times the movie's opening weekend proceeds. The 2.9 factor is meant to project the movie's four week total based on its opening weekend results.

HSX often offers call and put options in nationally released movies. Each option has an associated (constant) strike price $k$, which is a crude estimate of the movie's opening weekend return $r$. On the Friday of opening weekend, options trading is halted; on the following Monday, call options delist and cash out at a price equal to the weekend proceeds (in millions) minus the strike price, or zero if this quantity is negative (i.e., $\max[0, r - k]$). Put options cash out at $\max[0, k - r]$. A high call price (resp., a high put price) suggests that, according to HSX participants, the movie will earn more (less) than the strike price during its opening weekend.

The payoff structure of HSX movie options is analogous to so-called European options in the financial markets. In an arbitrage-free market, when a movie stock, a call option, and a put option are all available for the same movie, the three prices must conform to a relationship called put-call parity: $s/2.9 = k+c-p$, where $s$, $k$, $c$, and $p$ are the stock, strike, call, and put prices, respectively. Consider the value of the following two portfolios: (1) twenty nine call options plus H$29$k$ in cash, and (2) twenty nine put options plus 10 shares of the stock. After the weekend, portfolio 1 will be worth $29k + 29\max[0, r-k] = \max[29k, 29r]$ while portfolio 2 will be worth $\max[0, k-r] + 10(2.9r) = \max[29r,
29k]$. Since the two portfolios pay off equivalently, their prices at any time before the weekend should be equal; otherwise, a trader can buy the cheaper portfolio and short sell the other, locking in a guaranteed profit.12

Occasionally, HSX offers securities (which they call award options) associated with particular awards ceremonies--for example, the 72nd Annual Academy Awards, or Oscars, sponsored by the Academy of Motion Picture Arts and Sciences. Five options, corresponding to the five award nominees, are available within each award category (for example, Oscar award options were available for each of the eight major Oscar categories of best picture, best actor, best actress, best supporting actor, best supporting actress, best director, best original screenplay, and best adapted screenplay). Within each category, the winning option cashes out at H$25, and the other four cash out at H$0. Before awards are announced, an option's price can be interpreted as its estimated likelihood of winning. For example, when Kevin Spacey's price was twice that of Denzel Washington, the consensus of HSX opinions was that Spacey was roughly twice as likely to win as Washington. By normalizing prices within each category, likelihoods can be converted into probabilities. Notice that, since it is certain that one and only one of the five options within a particular category will pay off H$25, the bundle price of all five options should be worth H$25 at all times. If the combined price ever dips below H$25, a participant could guarantee an arbitrage profit by purchasing all five and waiting (if necessary) until the winner is announced, at which time he or she can pocket the difference. If the combined price ever moves above H$25, participants can guarantee a profit by short selling all five options.

Internal Coherence in Artificial Markets

In a real market, monetary incentives are the driving force behind efficiency and, in particular, internal price coherence. In an artificial market, on the other hand, there are no direct monetary incentives. When an arbitrage loophole presents itself in a simulation, why should anyone care to take advantage of it? Incentives must come from intangible sources (e.g., the desire to perform well in the game) or from indirect sources (e.g., the desire to do well enough to sell one's portfolio on Ebay). Do HSX players have utility for Hollywood dollars and, if so, are their resulting incentives strong enough to maintain internal price consistency in the game?

To analyze these questions, we quantify the degree of coherence in the HSX stock and options markets (Section 3.1), and in the HSX award options market (Section 3.2).

Put-Call Parity

We test how closely HSX stock and options prices conform to put-call parity. We gathered weekend halt prices (i.e., prices after Friday's halt and before the weekend adjust) for 75 movie stocks and their corresponding options appearing on HSX during the period of March 3, 2000 to September 1, 2000. Figure 1 graphs the stock estimate of weekend box office returns ($s/2.9$) versus the options estimate ($k+c-p$). In an arbitrage-free market, the two estimates must be the same. The figure demonstrates a reasonably close adherence to put-call parity at price halt. The correlation between the two estimates is 0.989, the slope of the best-fit line to the data (the solid line in the figure) is 1.04, and the mean difference between estimates is 1.35. Any point in the figure not lying on the line $y=x$ (the dashed line) indicates a potential arbitrage opportunity. Since HSX restricts each player's investment to a maximum of 10,000 shares of any option, exploiting all of these inefficiencies averages about H$13,500 per movie--a relatively small, though non-negligible, amount in terms of the game.

Figure 1: Put-call parity in the HSX stock and options markets at price halt. Points plot $s/2.9$ versus $k+c-p$ for each movie. The dashed line where $s/2.9 = k+c-p$ corresponds to perfect parity; the solid line is the best linear fit.

In an efficient market, prices should adhere to put-call parity at all times, not just at price halt. Moreover, any movements away from parity that do occur should revert rather quickly, as participants take advantage of arbitrage opportunities. We test these hypotheses by measuring the divergence from parity over time, and the likelihood of closure toward parity. For each stock and its corresponding options, we recorded the quantity $k+c-p-s/2.9$ every four hours. This quantity is the divergence from parity, or the available arbitrage per option share, or the price of a portfolio of H$1 cash, one call option, a short position in one put option, and a short position in $1/2.9$ stocks. We sorted the resulting 971 portfolio prices, and grouped them into ten buckets: nine buckets with exactly 100 portfolios each, and the last with the remaining 71. Within each bucket, we computed the fraction of portfolios that were observed at a higher price four hours later, and the fraction of portfolios that were lower in price four hours later. Figure 2 graphs the frequency of upward and downward movements versus the average price. Points marked as diamonds indicate the frequency of an upward shift at the next time step, given the average current price; points marked as circles indicate the frequency of a downward shift at the next time step, given the average current price. The market is not completely free of arbitrage--prices diverge at times from parity by as much as H$6.5. Nevertheless, the market does exhibit signs of internal coherence. When prices are too high, they are much more likely go down at the next time step as, presumably, participants take advantage of arbitrage opportunities. When prices are too low, they are more likely to increase. Large deviations from coherent prices might be partially explained by trading limits of 10,000 option shares per person, so that even astute and ``wealthy'' investors cannot fully exploit arbitrage windows.

Figure 2: Arbitrage closure toward put-call parity in the HSX stock and options markets. Each diamond point (resp., circle point) displays the fraction of portfolios that move up (down) in price after four hours, versus the average current price.

Award Options

In this section, we analyze price coherence in the HSX award options market. In an efficient market, the combined price for a bundle of options in the same award category would remain at H$25 at all times. We examine the actual prices of options in the Oscar and Emmy awards markets for deviations from H$25, and for the likelihood of closure toward H$25. We recorded the sum of the five options within each category every four hours from March 3 to September 1, 2000. We sorted the resulting 1895 bundle prices, and merged them into ten buckets: nine buckets with exactly 200 bundles each, and the last with the remaining 95. Within each bucket, we computed the fraction of bundles that were observed at a higher price four hours later, and the fraction of bundles that were lower in price four hours later. Figure 3 graphs the frequency of upward and downward movements versus the average price. Prices diverge at times from H$25 by close to 40%, yet a clear trend is evident whereby highly underpriced and overpriced bundles are likely to revert toward the coherent price. Notice that the crossover point, where up and down swings are equally likely, occurs above H$25. We postulate that this is because participants generally prefer to buy rather than to sell short.

Figure 3: Arbitrage closure in the HSX Oscar and Emmy options markets. Each diamond point (resp., circle point) displays the fraction of bundles that move up (down) in price after four hours, versus the average current price.

Figure 4: Arbitrage closure in the IEM NY Senate market.

For comparison, we conducted a similar arbitrage analysis using data from the Iowa Electronic Market (IEM), a real-money exchange offering securities in political events. We obtained daily closing prices for the NY Senate market, still in progress as of this writing. Participants can buy or sell shares of six securities: ``US$1 if and only if Hillary Clinton wins the election'', ``US$1 if and only if another Democrat wins'', ``US$1 if and only if Rick Lazio wins'', ``US$1 if and only if Rudy Giuliani wins'', ``US$1 if and only if another Republican wins'', and ``US$1 if and only if any other candidate wins''. In order for the market to be arbitrage free, the sum of the bid prices of the six securities cannot exceed US$1, and the sum of the ask prices cannot fall below US$1. We did not have access to bid and ask prices, so we performed the calculations using the last transaction prices; this provides an upper bound on the amount of arbitrage in the market. We sorted bundles by price and grouped them into buckets. We computed the fraction of bundles in each bucket that were up the next day, and the fraction that were down. Figure 4 displays the results. Maximum deviations from US$1 were approximately 6%. Low prices were very likely to move upward the next day, high prices very likely to turn back downward. Interestingly, the crossover point is still above US$1, suggesting that perhaps even in IEM there is a preference for buying over selling. There are also effective trading limits in IEM, since participants can join the market with a maximum US$500 investment. Comparing Figures 3 and 4, it is clear that IEM prices are more coherent than HSX prices, as one might expect, given that the former is grounded in real money while the latter is not. Nevertheless, the general shape of the two plots are similar.

Forecast Accuracy in Artificial Markets

Internal price coherence is one, fairly minimal, standard of market efficiency. Stronger forms of efficiency imply market competence as well and coherence: prices actually reflect an aggregation of information distributed among the participants, and market forecasts are as accurate as expert assessments. While coherence in artificial markets is of academic interest, competence in artificial markets promises real societal benefits in the form of cheap and reliable forecasts. We now examine whether this stronger notion of efficiency can hold in market games, by assessing the forecast accuracy of the HSX stock and options markets (Section 4.1), the HSX award options market (Section 4.2), and the Foresight Exchange market (Section 4.3).

Box office forecasts: HSX movie stocks and options

Before a movie stock on HSX adjusts, its price constitutes an estimate of 2.9 times the movie's opening weekend proceeds. We gathered the halt prices $s_h$ (Friday morning's prices) and adjust prices $s_a$ (2.9 times the actual return) for movies opening during the period March 3, 2000 to September 1, 2000. We also collected the published forecasts of Brandon Gray at Box Office Mojo.13 We quantify and compare HSX predictions to Box Office Mojo predictions for 50 movies appearing on both sources. Figure 5 plots the actual box office return $s_a/2.9$ versus the HSX estimate $s_h/2.9$ for each movie. The correlation between actual and estimate is 0.940. The slope of the best-fit line to the data (the solid line in the figure) is 1.16, the mean absolute error is 3.57, and the mean percent error is 31.5%.

Figure 5: Accuracy of HSX movie stock forecasts for opening weekend box office returns. The dashed line corresponds to ideal accuracy; the solid line is the best linear fit.

The HSX options market provides an alternative forecast for opening weekend returns. The quantity $k+c_h-p_h$, where $c_h$ and $p_h$ are the call and put halt prices, should coincide with $s_h/2.9$, due to put call parity. When actual returns are plotted against this options estimate, the correlation is 0.931, the best-fit line's slope is 1.12, the mean absolute error is 3.38, and the mean percent error is 47.0%.

Notice that for both stock and options estimates, there is a slight bias to underprice the best-performing movies and overprice the worst-performing movies. This may be explainable as a manifestation of risk-seeking behavior among HSX participants: traders prefer potential ``sleepers'' with a low probability of a very large payoff, rather than known quantities with a high probability of a moderate payoff. Since payoffs are not in real money, and since motivations may be to sell out on Ebay, or to get onto the leader board quickly, one might expect to see risk-seeking behavior.

Figure 6 displays actual returns versus Box Office Mojo forecasts.14 The correlation is 0.945, the best-fit line's slope is 1.10, the mean absolute error is 3.31, and the mean percent error is 27.5%. Box Office Mojo performed only 4% better than HSX stocks in terms of mean percent error. The correlation in errors between HSX estimates and Box Office Mojo estimates is 0.818, suggesting that the two estimates may result from overlapping sources of evidence. In fact, it is possible that Box Office Mojo observes HSX prices, and/or that some HSX traders read Box Office Mojo forecasts.

Figure 6: Accuracy of Box Office Mojo forecasts for opening weekend returns. The dashed line corresponds to ideal accuracy; the solid line is the best linear fit.

After a movie stock adjusts (or if it does not adjust), its price is a forecast of the movie's four week total box office return $r_4$. We gathered the delist prices $r_4$ and the prices three weeks before delist $s_3$ for 109 movies between March 3, 2000 to September 1, 2000. Figure 7 graphs $r_4$ versus $s_3$ for each movie. The correlation is 0.978, the best-fit line's slope is 1.04, and the mean error is 4.01.

Figure 7: Accuracy of HSX movie stock forecasts for four week total box office returns. The dashed line corresponds to ideal accuracy; the solid line is the best linear fit.

Probabilistic forecasts: HSX Award Options

In the HSX Oscar options market, as it turns out, each nominee with the highest final price in its category did indeed win an Oscar. The Wall Street Journal, amid controversy, published a poll of actual Academy voters days before the Oscar awards ceremony; their report correctly forecasted only seven out of eight winners.

Beyond predicting the most likely winner, we investigate how accurately HSX award option prices reflect all likelihoods of winning. For example, if prices are accurate, then among all options with a normalized price of H$0.1, about one in ten should end up winning. Our accuracy analysis is similar to that conducted for horse races [1,18,19,20,22] and other sports betting markets involving real money. We collected prices of award options associated with the Oscars, Grammies, and Emmies, for a total of 135 options. Grammy options (nine categories) and Emmy options (ten categories) functioned exactly as Oscar options, though winning Grammy options paid out H$42 instead of H$25.

Figure 8: Accuracy of the HSX award options market. Points display observed frequency versus average normalized price for buckets of similarly-priced options. The dashed line where frequency equals price corresponds to ideal accuracy.

Prices were recorded just before the markets closed, and before winners were announced. We sorted the options by price, and grouped them into six buckets. We placed the same number of options (16) in every bucket, under the constraint that every bucket include at least one winning option. We computed the average normalized price of options within each bucket, and the observed frequency within each bucket, or the number of winning options divided by the number of options. Figure 8 plots each bucket's observed frequency versus its average normalized price. If we model options as independent Bernoulli trials, then, in the limit as the number of options goes to infinity, completely accurate prices would imply that bucket points fall on the line $y=x$, where observed frequency equals price. Error bars display 95% confidence intervals under the independent Bernoulli trials assumption. Specifically, the lower error bound is the 0.025 quantile of a Beta distribution corresponding to the observed number of successes (wins) and trials in the bucket, and the upper error bound is the 0.975 quantile. The Beta distribution is the correct posterior distribution over frequency, assuming a uniform prior.15 The length of an error bar decreases as the number of options in the bucket increases. The independence assumption is an idealization, since options within a single award category are actually mutually exclusive. The closeness of fit to the line $y=x$ can be considered a measure of the accuracy of HSX prices. There is some indication of a favorite-longshot bias, although more data is needed to verify. As in horse racing, this bias is potentially explainable by assuming risk-seeking behavior among the participants.

We compare HSX prices of Oscar options to reported likelihood assessments from five columnists at the Hollywood Stock Brokerage and Resource (HSBR),16 a fansite of HSX. We use the logarithmic scoring rule to rate the market and the columnists. The logarithmic score is a proper scoring rule [23], and is an accepted method of evaluating probability assessors. When experts are rewarded according to a proper score, they can maximize their expected return by reporting their probabilities truthfully. Additionally, more accurate experts can expect to earn a higher average score than less competent experts. Scores are computed separately within each award category, then averaged. Index the five nominees in a category $i=1,2,\ldots,5$. Let $w_i=1$ if and only if the $i$th nominee wins, and $w_i=0$ otherwise. let $p_1,p_2, \ldots,
p_5$ be the market's or columnist's reported probabilities for the five nominees. Then the assessor's score for the current category is $\ln\left(\sum_{i=1}^5 w_i p_i\right)$. Expert assessments were reported on February 18, 2000. Table 1 gives the average scores for the HSX market, the five columnists, and the consensus of the columnists. Higher scores are better, with 0 the maximum and negative infinity the minimum. Only one of the five experts scored appreciably better than the market on February 18. HSX's score increased almost continuously from the market's open on February 15 to the market's close on March 26. By February 19, the market's score had surpassed all of the scores for all five experts and for their consensus.

Table 1: Evaluation of HSX Oscar forecasts and HSBR columnists' forecasts, according to average logarithmic score. Higher (less negative) scores are better.
assessor score
Feb 18 HSX prices -1.08
Feb 19 HSX prices -0.854
Tom -1.08
Jen -1.25
John -1.22
Fielding -1.04
DPRoberts -0.874
columnist consensus -1.05

Foresight Exchange

Hanson [8,9] proposes what he calls an Idea Futures market, where participants trade in securities that pay off contingent on future developments in science, technology, or other arenas of public interest. For example, a security might pay off US$1 if and only if a cure for cancer is discovered. He argues that the reward structure of such a market encourages honest revelation of opinions among scientists, and provides more accurate probability assessments for use by funding agencies, public policy leaders, the media, and other interested parties. The concept is operational as a web game called the Foresight Exchange (FX).17 There are currently on the order of 3000 registered participants and 200 active claims. Players start with an initial amount of ``FX bucks'' and receive an allowance every week, up to a certain maximum. Participants can buy and sell existing claims, or submit their own claims. Each claim is assigned a judge to arbitrate ambiguous wording, and to ultimately determine whether the claim is true or not on the judgment date. Claims range from technical (e.g., FX$1 if and only if an algorithm for three satisfiability is developed with a particular runtime complexity by the year 2020) to sociopolitical (e.g., FX$1 if and only if Japan possesses nuclear missiles by 2020) to irreverent (e.g, FX$1 if and only if Madonna names her first child Jesus). The developers of the site intend for the prices of these claims to be interpreted as assessments of the probabilities of the various events.

Figure 9: Accuracy of the Foresight Exchange market. Prices are 30 days before claim expiration. Points display observed frequency versus average price. The dashed line corresponds to ideal accuracy.

To determine how accurate these assessment are, we collected historical price information for all retired (completed) claims as of September 8, 2000. Of these, we retained only the 172 that were binary (i.e., paid off if and only if some true-or-false event occurred). We recorded the price of each claim 30 days before it expired. A total of 161 claims were active for at least 30 days, and thus qualified for this data set. We sorted the claims by their 30-day-before-expiration price, grouped them into six buckets of constant size 17 (under the constraint that every bucket contain at least one winning claim), and computed the average price and observed frequency for each bucket. Figure 9 graphs the results. Error bars show 95% confidence intervals based on the assumption that claims are independent Bernoulli trials with a uniform prior over frequency.


The World Wide Web fosters large-scale group activities of all sorts, from competing in games, to trading in markets, to competing in market trading games. But beyond their entertainment value, are there any societal benefits to artificial markets? Despite their lack of grounding in tangible assets, both the Hollywood Stock Exchange (HSX) and the Foresight Exchange (FX) show signs of efficiency, manifested as price coherence and forecast accuracy. In absolute terms, HSX provides informative box office forecasts, while both HSX and FX provide prescient likelihood assessments of uncertain events. In direct and limited comparisons with expert judges, both types of HSX forecasts perform competitively. Relative to a real-money market, arbitrage closure on HSX appears qualitatively similar, though quantitatively much weaker.

What are the implications of these results? For one, interested parties can mine existing market simulations for information, with some reassurance as to accuracy. Alternatively, they can open new artificial markets, with relatively few impediments, as a mechanism for gathering information in areas of personal concern or interest. Economics researchers may also open market games in order to carry out experiments that would otherwise be too costly or too difficult. The analyses bear upon the psychology of incentives for intangibles, and begin to investigate what is needed, at a minimum, in order for beneficial economic properties to emerge from group interactions.


We thank William Walsh, Michael Wellman, Yan Chen, and Chris Meek for advice, insightful comments, and pointers to related work. Thanks to Eric Glover for research and programming assistance. Thanks to James Pancoast and ``Jimmy Impossible'' from the Hollywood Stock Brokerage and Resource (, a fansite of HSX, and to Ken Kittlitz from the Foresight Exchange.


Mukhtar M. Ali.
Probability and utility estimates for racetrack bettors.
Journal of Political Economy, 85(4):803-816, 1977.

Kenneth J. Arrow.
The role of securities in the optimal allocation of risk-bearing.
Review of Economic Studies, 31(2):91-96, 1964.

Jacques H. Dreze.
Market allocation under uncertainty.
In Essays on Economic Decisions under Uncertainty, pages 119-143. Cambridge University Press, 1987.

Robert Forsythe and Russell Lundholm.
Information aggregation in an experimental market.
Econometrica, 58(2):309-347, 1990.

Robert Forsythe, Forrest Nelson, George R. Neumann, and Jack Wright.
Anatomy of an experimental political stock market.
American Economic Review, 82(5):1142-1161, 1992.

Sanford J. Grossman.
An introduction to the theory of rational expectations under asymmetric information.
Review of Economic Studies, 48(4):541-559, 1981.

Robin Hanson.
Consensus by identifying extremists.
Theory and Decision, 44(3):293-301, 1998.

Robin Hanson.
Decision markets.
IEEE Intelligent Systems, 14(3):16-19, 1999.

Robin D. Hanson.
Could gambling save science? Encouraging an honest consensus.
Social Epistemology, 9(1):3-33, 1995.

Robert E. Lucas.
Expectations and the neutrality of money.
Journal of Economic Theory, 4(2):103-24, 1972.

Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green.
Microeconomic Theory.
Oxford University Press, New York, 1995.

Richard D. McKelvey and Talbot Page.
Common knowledge, consensus, and aggregate information.
Econometrica, 54(1):109-127, 1986.

Richard D. McKelvey and Talbot Page.
Public and private information: An experimental study of information pooling.
Econometrica, 58(6):1321-1339, 1990.

Lars Tyge Nielsen, Adam Brandenburger, John Geanakoplos, Richard McKelvey, and Talbot Page.
Common knowledge of an aggregate of expectations.
Econometrica, 58(5):1235-1239, 1990.

C. R. Plott, J. Wit, and W. C. Yang.
Parimutuel betting markets as information aggregation devices: experimental results.
Technical Report Social Science Working Paper 986, California Institute of Technology, April 1997.

Charles R. Plott and Shyam Sunder.
Efficiency of experimental security markets with insider information: An application of rational-expectations models.
Journal of Political Economy, 90(4):663-98, 1982.

Charles R. Plott and Shyam Sunder.
Rational expectations and the aggregation of diverse information in laboratory security markets.
Econometrica, 56(5):1085-1118, 1988.

Richard N. Rosett.
Gambling and rationality.
Journal of Political Economy, 73(6):595-607, 1965.

Wayne W. Snyder.
Horse racing: testing the efficient markets model.
Journal of Finance, 33(4):1109-1118, 1978.

Richard H. Thaler and William T. Ziemba.
Anomalies: Parimutuel betting markets: Racetracks and lotteries.
Journal of Economic Perspectives, 2(2):161-174, 1988.

Hal R. Varian.
The arbitrage principle in financial economics.
J. Economic Perspectives, 1(2):55-72, 1987.

Martin Weitzman.
Utility analysis and group behavior: An empirical study.
Journal of Political Economy, 73(1):18-26, 1965.

Robert L. Winkler and Allan H. Murphy.
Good probability assessors.
J. Applied Meteorology, 7:751-758, 1968.

About this document ...

The Power of Play: Efficiency and Forecast Accuracy in Web Market Games

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... Nielsen1
This work conducted while visiting the NEC Research Institute.
Pareto efficiency, a common and mild assumption, implies no-arbitrage.
... otherwise.3
Insurance contracts, futures, options, derivatives, and even stocks can be modeled as portfolios of such atomic securities.
... (IEM)4
... Canada5
... Austria.6
... (HSX)7
... Ebay8
... Inc.9
... returns.10
Although cash holdings do accrue interest on HSX, all analyses in this paper ignore any time value of Hollywood dollars.
... millions).11
Movies released on holiday weekends, and movies with substantial box office receipts prior to wide release, may adjust differently.
... profit.12
In practice, the hedge is not quite so perfect: movie options cash out based on the final box office numbers reported on Monday, while movie stocks adjust according to the box office estimates reported on Sunday, which are projections based on Friday and Saturday returns only.
... Mojo.13
... forecasts.14
Actual returns reported on Box Office Mojo occasionally differed slightly from those reported on HSX. We measured each forecaster against its own reported returns.
... prior.15
Note that the expectation of the Beta distribution, $s+1/n+2$, does not coincide precisely with the observed frequency, $s/n$, where $s$ is the number of successes and $n$ the number of trials. However, as $n$ grows, the two measures converge.
... (HSBR),16
... (FX).17

David Pennock 2001-02-17