This paper provides an in depth analysis of an investor's reluctance to realize losses and his propensity to realize gains – a behavior known as the disposition effect. Together, sophistication (static differences across investors) and trading experience (evolving behavior of a single investor) eliminate the reluctance to realize losses. However, an asymmetry exists as sophistication and trading experience reduce the propensity to realize gains by 37% (but fail to eliminate this part of the behavior.) Our research design allows us to follow an individual's behavior from the start of his investing life/career. This ability makes it possible to track the evolution of the disposition effect as it is reduced and/or disappears.Our results are robust to alternative explanations including feedback trading, calendar effects, and frequency of observation.

This paper examines the trading behavior of two groups of liquidity providers (specialists and competing market makers) using a six-year panel of NYSE data. Trades of each group are negatively correlated with contemporaneous price changes. To test for return predictability, we sort stocks into quintiles based on each group’s past trades and then form long-short portfolios. Stocks most heavily bought have significantly higher returns than stocks most heavily sold over the two weeks following a sort. Cross-sectional analysis shows smaller, more volatile, less actively traded, and less liquid stocks more often appear in the extreme quintiles. Time series analysis shows the long-short portfolio returns are positively correlated with a market-wide measure of liquidity. A double sort using past trades of specialists and competing market makers produces a long-short portfolio that earns 88 basis points per week (act as complements). Finally, we identify a “chain” of liquidity provision. Designated market makers (NYSE specialists) initially trade against order flows and prices changes. Specialists later mean revert their inventories by trading with competing market makers who appear to spread trades over a number of days. Alternatively, specialists may trade with competing market makers who arrive to market with delay.

We derive closed-form solutions for asset prices and portfolio holdings when agents have asset-specific information and/or information about common components that affect many assets. Our solutions are general, encompass existing information structures, and are used to analyze new structures. A given investor’s portfolio can exhibit highly disperse holdings—e.g., portfolio weights may vary significantly from market capitalization weights. Our model also generates large ranges of asset prices due to information asymmetries. We help explain why US investors (e.g.) may underweight German stocks (e.g.) on average, but overweight a particular German stock relative to its market capitalization weight.

Empirical studies linking liquidity provision to asset prices follow naturally from inventory models. Liquidity suppliers and market markers profit from providing immediacy to less patient investors, but have limited inventory-carrying and risk-bearing capacity. Similarly, limits to arbitrage arguments rely on certain market participants accommodating buying or selling pressure. These liquidity suppliers/arbitrageurs are willing to accommodate trades—and, therefore, hold suboptimal portfolios—only if they are able to buy (sell) at a discount (premium) relative to future prices. Thus, large liquidity-supplier inventories should coincide with large buying or selling pressure, which causes price movements that subsequently reverse themselves. By identifying and studying the inventories of traders who are central to the trading process and whose primary roll is to provide liquidity—New York Stock Exchange (NYSE) market

This paper explores daily international portfolio flows into and out of 44 countries from 1994 through 1998. We find several facts concerning the behavior of flows and their relationship with equity returns. First, we detect regional flow factors that have increased in importance through time. Second, the flows appear to be stationary, but far more persistent than returns. Third, flows are strongly influenced by past returns, a finding consistent with positive feedback trading by international investors. Fourth, inflows have positive forecasting power for future equity returns, and this power is statistically significant in emerging markets. Fifth, the sensitivity of local stock prices to foreign inflows is positive and large. Sixth, prices seem consistent with flow persistence, in that transitory inflows impact future returns negatively.

ABSTRACT The paper tests whether individuals have value‐relevant information about local stocks (where “local” is defined as being headquartered near where an investor lives). Our methodology uses two types of calendar‐time portfolios—one based on holdings and one based on transactions. Portfolios of local holdings do not generate abnormal performance (alphas are zero). When studying transactions, purchases of local stocks significantly underperform sales of local stocks. The underperformance remains when focusing on stocks with potentially high levels of information asymmetries. We conclude that individuals do not help incorporate information into stock prices. Our conclusions directly contradict existing studies.

ABSTRACT This paper analyzes the trading behavior of stock market investors. Purchases and sales are highly correlated when we divide investors geographically. Investors who live near a firm's headquarters react in a similar manner to releases of public information. We are able to make this identification by exploiting a unique feature of individual brokerage accounts in the People's Republic of China. The data allow us to pinpoint an investor's location at the time he or she places a trade. Our results are consistent with a simple, rational expectations model of heterogeneously informed investors.

We test the implications of a multi-asset equilibrium model in which a finite number of risk-averse liquidity providers accommodate non-informational trading imbalances. These imbalances generate predictable reversals in stock returns. An imbalance in one stock also affects the prices of other stocks. The magnitude of the cross-stock price pressure depends on the correlations of the stocks’ underlying cash flows. The model implies that non-informational trading increases the volatility of stock returns. We confirm the model's implications using data from the Taiwan Stock Exchange.

We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model’s parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.

This paper mathematically transforms unobservable rational expectation equilibrium model parameters (information precision and supply uncertainty) into a single variable that is correlated with expected returns and that can be estimated with recently observed data. Our variable can be used to explain the cross section of returns in theoretical, numerical, and empirical analyses. Using Center for Research in Security Prices data, we show that a −1σ to +1σ change in our variable is associated with a 0.31% difference in average returns the following month (equaling 3.78% per annum). The results are statistically significant at the 1% level. Our results remain economically and statistically significant after controlling for stocks' market capitalizations, book-to-market ratios, liquidities, and the probabilities of information-based trading.