Gradual Information Diffusion and Asset Price Momentum

Gradual information diffusion model predicts that as private information travels across the population, pricing accuracy would improve and asset prices would exhibit momentum as a result. In laboratory markets I investigate the market’s aggregation capacity in response to varying proportions of informed traders as a consequence of information diffusion. The results demonstrate that pricing errors are high when private information is dispersed and that, as the information spreads, the market gradually revise the errors and manifest momentum. Analysis suggests that aggregation under dispersed information conditions is hampered by three factors: equilibrium multiplicity, slow arrival of myopic traders, and anonymous trading.

Are Under- and Over-reaction the Same Matter? A Price Inertia based Account

Theories on under- and over-reaction in asset prices fall into three types: (1) they are respectively driven by different psychological factors; (2) they are driven by different types of investors; and (3) they reflect un-modeled risk. We design an asset market where information arrives sequentially over time and is revealed asymmetrically to investors. None of the three hypotheses is supported by our data: (1) Investors do not respond differently to public information and private information, and they do not behave in ways that are claimed by multiple psychological models; (2) no groups of investors are identified to drive under- or over-reaction in particular; (3) price deviation from expected payoff cannot be justified by risk metrics. We find that prices react insufficiently to news surprises, possibly because of cautious conservatism on the part of investors and under-reacting drifts outnumber overreacting reversals substantially. Contrary to common beliefs, we find that over-reaction is caused by slow adjustment of prices to surprises, similar to the cause of under-reaction. It is the degree of price inertia that drives the relative frequencies of under- and over-reaction. We propose a simple price inertia theory of under- and over-reaction: when information arrives sequentially over time, the market is characterized by a slow convergence toward intrinsic value; when news surprises are of the same signs, prices falls behind newly updated intrinsic values, manifesting under-reacting drifts; when news surprises change signs, prices again do not adjust quick enough to catch up with the new intrinsic values, manifesting a temporal pattern of overreacting reversals.Experimental finance, under-reaction, overreaction, behavior, price inertia, risk aversion

Affecting Policy by Manipulating Prediction Markets: Experimental Evidence

Documented results indicate prediction markets effectively aggregate information and form accurate predictions. This has led to a proliferation of markets predicting everything from the results of elections to a company’s sales to movie box office receipts. Recent research suggests prediction markets are robust to manipulation attacks and resulting market outcomes improve forecast accuracy. However, we present evidence from the lab indicating that well funded, single minded manipulators can in fact destroy a prediction market’s ability to aggregate information. Our results clearly indicate that the usefulness of prediction markets as inputs to decision making may be limited.Information Aggregation, Prediction Markets, Manipulation

Durability, Re-trading and Market Performance

Key differential structural characteristics of environments studied in previous market experiments have documented large divergences in their observed performance, particularly discrepancies in their convergence to expected equilibrium outcomes. We investigate why this should be so. The type of competitive equilibrium where a market clears at a particular price as initiated by Arrow and Debreu (1954) has long been studied in the laboratory. We refer to these experiments as Supply and Demand (SD) experiments. SD experiments are highly reduced in form: items are not re-tradable, buyers and sellers are specialized in these roles, and no second commodity, cash, is used as a medium of exchange, although cash enters as a numeraire qua reward incentive for subjects. Markets with these features that are repeated over time converge rapidly to the predicted equilibrium under a regime of strict private dispersed information on individual values that define the equilibrium predictions. In contrast, consider asset markets, in which shares can be freely re-traded against cash within and across periods, shares have well-defined common values based on common public information on expected cash “dividend” yields, and individuals are not specialized as buyers or sellers. These markets produce price bubbles that converge only with experience across repeat sessions. The prospect of re-trade, and perhaps the lack of buyer/seller specialization, results in market behavior that contrasts sharply with the perishable goods that characterize the SD experiments. Building on this background analysis we report new experiments that combine features of both environments and initiate an investigation of how commodity durability that constrains re-trading characteristics affect the observed variation in market performance.

Accounting Standards and Financial Market Stability: An Experimental Examination

We examine the effect on asset mispricing of different accounting methods in an experimental asset market characterized by bubbles and crashes. In particular, we study three alternative asset value reporting treatments: (1) Fair Value (also known as Mark-to-Market – M2M), (2) Historical Cost (HC) and (3) Marked to Fundamental Value (M2F). In addition, each of these treatments is replicated in two different financial leverage conditions. In the first condition (No Loan) traders must purchase assets from their available cash balances without the option of borrowing. In the second condition, (Loan), traders are given the option of taking out loans based on their balance sheet to finance asset purchases. In the No Loan condition, we find that reporting accounting values alone to subjects in a balance sheet format does not have a significant effect on mispricing for any of our alternative accounting method treatments. In the Loan conditions, however, the M2F and M2M accounting methods exacerbate asset mispricing, yet the two differ in leverage dynamics. M2F markets are completely immune to defaults, while M2M markets experience the most frequent as well as most severe defaults

Gradual Information Diffusion and Asset Price Momentum

Gradual information diffusion model predicts that as private information travels across the population, pricing accuracy would improve and asset prices would exhibit momentum as a result. In laboratory markets I investigate the market’s aggregation capacity in response to varying proportions of informed traders as a consequence of information diffusion. The results demonstrate that pricing errors are high when private information is dispersed and that, as the information spreads, the market gradually revise the errors and manifest momentum. Analysis suggests that aggregation under dispersed information conditions is hampered by three factors: equilibrium multiplicity, slow arrival of myopic traders, and anonymous trading

Are Under- and Over-Reaction the Same Matter? A Price Inertia Based Account

Theories on under- and over-reaction in asset prices fall into three types: (1) they are respectively driven by different psychological factors; (2) they are driven by different types of investors; and (3) they reflect un-modeled risk. We design an asset market where information arrives sequentially over time and is revealed asymmetrically to investors. None of the three hypotheses is supported by our data: (1) Investors do not respond differently to public information and private information, and they do not behave in ways that are claimed by multiple psychological models; (2) no groups of investors are identified to drive under- or over-reaction in particular; (3) price deviation from expected payoff cannot be justified by risk metrics. We find that prices react insufficiently to news surprises, possibly because of cautious conservatism on the part of investors and under-reacting drifts outnumber overreacting reversals substantially. Contrary to common beliefs, we find that over-reaction is caused by slow adjustment of prices to surprises, similar to the cause of under-reaction. It is the degree of price inertia that drives the relative frequencies of under- and over-reaction. We propose a simple price inertia theory of under- and over-reaction: when information arrives sequentially over time, the market is characterized by a slow convergence toward intrinsic value; when news surprises are of the same signs, prices falls behind newly updated intrinsic values, manifesting under-reacting drifts; when news surprises change signs, prices again do not adjust quick enough to catch up with the new intrinsic values, manifesting a temporal pattern of overreacting reversals

Affecting Policy by Manipulating Prediction Markets: Experimental Evidence 1

Documented results indicate prediction markets effectively aggregate information and form accurate predictions. This has led to a proliferation of markets predicting everything from the results of elections to a company’s sales to movie box office receipts. Recent research suggests prediction markets are robust to manipulation attacks and resulting market outcomes improve forecast accuracy. However, we present evidence from the lab indicating that well funded, single minded manipulators can in fact destroy a prediction market’s ability to aggregate information. Our results clearly indicate that the usefulness of prediction markets as inputs to decision making may be limited

Bubbling with Excitement: An Experiment

In an experimental setting, we study the role of emotions in markets. Our experimental market is modeled on those of Smith, Suchanek, and Williams (1988) and Caginalp, Porter, and Smith (2001). Participants take part in a laboratory market in which they trade a risky asset over a computer network. Prior to trading, they watch short videos that are exciting and upbeat—chase scenes; neutral—segments from a historical documentary; fearful—scenes from a horror movie; or sad—scenes from a drama. Larger asset pricing bubbles develop in experimental markets run subsequent to the exciting videos relative to the other three conditions. The differences in the magnitude and amplitude of the bubbles are both economic and statistically significant. A follow-up study indicates that the phenomenon may be explained by excited people’s greater inclination to extrapolate past positive market trends into future asset prices