Traditional value measures performed poorly over the past three decades. We reevaluate the value strategy using a new measure—the ratio of cash-based operating profitability to price (COP/P)—and find a zero-investment portfolio that buys the highest-COP/P stocks and shorts the lowest-COP/P stocks earns monthly returns of 0.78% on a value-weighted basis and 1.04% on an equal-weighted basis. The COP/P effect holds even for large-capitalization stocks and exists even in the post-1990 period, when book-to-market does not predict returns. The COP/P measure subsumes many widely used value measures and the conservative-minus-aggressive investment factor.

The cash conversion cycle (CCC) refers to the time span between the outlay of cash for purchases to the receipt of cash from sales. It is a widely used metric to gauge the effectiveness of a firm's management and intrinsic need for external financing. This paper shows that a zero-investment portfolio that buys the lowest CCC decile stocks and shorts the highest CCC decile stocks earns 5%–7% alphas per year. The CCC effect is prevalent across industries, remains even for large capitalization stocks, distinct from the known return predictors, and cannot be explained by the financial intermediary leverage risk.

Using data from a Twitter-like investor social platform, we document evidence consistent with self-enhancing transmission bias. We find investors are more likely to post about their better-performing stocks. Their followers are more likely to buy the posted stocks than other non-posted stocks. The effect of the postings on follow-up purchases is consistent with an attention-based interpretation: the postings bring the discussed stocks into the followers’ choice set and increase their purchases. We also find that postings’ effect on follow-up purchases is related to postings’ perceived credibility. The performance-postings relationship is stronger among more volatile stocks and the relationship between postings and follow-up purchases is stronger among stocks with higher recent returns, shedding light on the spread of high-variance and extrapolative strategies. We also document that the social network features influential nodes.

We examine how stakes affect investor behaviors. In our unique setting, investors trade stocks in real accounts using their own money and simultaneously in a simulated setting. Our real-world within-investor estimation shows that investors exhibit stronger biases and perform worse in higher-stakes real accounts than in lower-stakes simulated accounts. Investors exhibit strong biases in both types of accounts, and the biases in both are strongly positively correlated. Such behavioral consistency suggests that low-stakes experiments are informative about real-world behaviors. Using additional account-level datasets, we demonstrate external validity by documenting a stronger (reverse) disposition effect on stocks (funds) with greater portfolio weights.

We explore the psychology of stock price levels and provide evidence that investors suffer from a nominal price illusion in which they overestimate the room to grow for low-priced stocks relative to high-priced stocks. While it has become increasingly clear that nominal price levels influence investor behavior, why prices matter to investors is a question that as of yet has gone unanswered. We find widespread evidence that investors systematically overestimate the skewness of low-priced stocks. In the broad cross-section of stocks, we find that investors substantially overweight the importance of price when forming skewness expectations. Asset pricing implications of our findings are borne out in the options market. A zero-cost option portfolio strategy that exploits investor overestimation of skewness for low-priced stocks generates significant abnormal returns. Finally, investor expectations of future skewness increase drastically on days that a stock undergoes a split to a lower nominal price. Empirically, however, future physical skewness decreases following splits.

We test the hypothesis that, when thinking about allocating money to a stock, investors mentally represent the stock by the distribution of its past returns and then evaluate this distribution in the way described by prospect theory. In a simple model of asset prices in which some investors think in this way, a stock whose past return distribution has a high (low) prospect theory value earns a low (high) subsequent return, on average. We find empirical support for this prediction in the cross-section of stock returns in the U.S. market, and also in a majority of forty-six other national stock markets.

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 examine the trading behavior of Chinese domestic investors after they were given access to the B-share market in 2001. Surprisingly, we find that only 2% of investors began buying B shares. Even among these 2%, investors were less likely to buy B shares if they had more experience in the A-share market, and vice-versa. Thus, prior market experience limits the extent to which investors respond to A/B-share premiums and liquidity and lowers their performance. Our findings cannot be explained by government intervention, investor heterogeneity, foreign currency constraint, A/B-share liquidity or speculation differentials, or information advantage.

We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.