We use neural data collected from an experimental asset market to measure regret preferences while subjects trade stocks. When subjects observe a positive return for a stock they chose not to purchase, a regret signal is observed in an area of the brain that is commonly active during reward processing. Subjects are unwilling to repurchase stocks that have recently increased in price, even though this is suboptimal in our experiment. The strength of stock repurchasing mistakes is correlated with the neural measures of regret. Subjects with high rates of repurchasing mistakes also exhibit large disposition effects.

When investors sell one asset and quickly buy another (“reinvestment days”), their trades suggest the original mental account is not closed, but is instead rolled into the new asset. Retail investors trading on their own accounts display a rolled disposition effect, selling the new position when its value exceeds the initial investment in the original position. On reinvestment days, these investors display no disposition effect (consistent with no disutility from realizing a loss) and make better selling decisions. Using a laboratory experiment, we show that reinvestment causally reduces the disposition effect and improves trading.

ABSTRACT We experimentally study the transmission of subjective expectations into actions. Subjects in our experiment report valuations that are far too insensitive to their expectations, relative to the prediction from a frictionless model. We propose that the insensitivity is driven by a noisy cognitive process that prevents subjects from precisely computing asset valuations. The empirical link between subjective expectations and actions becomes stronger as subjective expectations approach rational expectations. Our results highlight the importance of incorporating weak transmission into belief‐based asset pricing models. Finally, we discuss how cognitive noise can provide a microfoundation for inelastic demand in the stock market.

When investors sell one asset and quickly buy another (“reinvestment days”), their trades suggest the original mental account is not closed, but is instead rolled into the new asset. Retail investors trading on their own accounts display a rolled disposition effect, selling the new position when its value exceeds the initial investment in the original position. On reinvestment days, these investors display no disposition effect (consistent with no disutility from realizing a loss) and make better selling decisions. Using a laboratory experiment, we show that reinvestment causally reduces the disposition effect and improves trading. Received April 10, 2016; editorial decision January 28, 2017 by Editor Andrew Karolyi.

We use neural data collected from an experimental asset market to measure regret preferences while subjects trade stocks. When subjects observe a positive return for a stock they chose not to purchase, a regret signal is observed in an area of the brain that is commonly active during reward processing. Subjects are unwilling to repurchase stocks that have recently increased in price, even though this is suboptimal in our experiment. The strength of stock repurchasing mistakes is correlated with the neural measures of regret. Subjects with high rates of repurchasing mistakes also exhibit large disposition effects.

Two theoretical literatures, one using Bayesian Nash equilibrium (BNE), and the other using noisy rational expectations equilibrium (NREE), both provide a foundation for understanding how private information is impounded into asset prices, yet some of their predictions are conflicting. Here, we compare for the first time, the two theories using data from carefully controlled laboratory asset markets. In the dynamics, we find strong evidence for BNE theory, although final prices support predictions of the NREE theory. Finally, we document that price volatility increases when information is being impounded in prices.

We experimentally test a theory of risky choice in which the perception of a lottery payoff is noisy due to information processing constraints in the brain. We model perception using the principle of efficient coding, which implies that perception is most accurate for those payoffs that occur most frequently. Across two preregistered laboratory experiments, we manipulate the distribution from which payoffs in the choice set are drawn. In our first experiment, we find that risk taking is more sensitive to payoffs that are presented more frequently. In a follow-up task, we incentivize subjects to classify which of two symbolic numbers is larger. Subjects exhibit higher accuracy and faster response times for numbers they have observed more frequently. In our second experiment, we manipulate the payoff distribution so that efficient coding modulates the strength of valuation biases. As we experimentally increase the frequency of large payoffs, we find that subjects perceive the upside of a risky lottery more accurately and take greater risk. Together, our experimental results suggest that risk taking depends systematically on the payoff distribution to which the decision maker’s perceptual system has recently adapted. More broadly, our findings highlight the importance of imprecise and efficient coding in economic decision making.

ABSTRACT We test whether the display of information causally affects investor behavior in a high‐stakes trading environment. Using investor‐level brokerage data from China and a natural experiment, we estimate the impact of a shock that increased the salience of a stock's purchase price but did not change the investor's information set. We employ a difference‐in‐differences approach and find that the salience shock causally increased the disposition effect by 17%. We use microdata to document substantial heterogeneity across investors in the treatment effect. A previously documented trading pattern, the “rank effect,” explains heterogeneity in the change in the disposition effect.

We use measures of neural activity provided by functional magnetic resonance imaging (fMRI) to test the "realization utility" theory of investor behavior, which posits that people derive utility directly from the act of realizing gains and losses. Subjects traded stocks in an experimental market while we measured their brain activity. We find that all subjects exhibit a strong disposition effect in their trading, even though it is suboptimal. Consistent with the realization utility explanation for this behavior, we find that activity in the ventromedial prefrontal cortex, an area known to encode the value of options during choices, correlates with the capital gains of potential trades; that the neural measures of realization utility correlate across subjects with their individual tendency to exhibit a disposition effect; and that activity in the ventral striatum, an area known to encode information about changes in the present value of experienced utility, exhibits a positive response when subjects realize capital gains. These results provide support for the realization utility model and, more generally, demonstrate how neural data can be helpful in testing models of investor behavior.