I document a new stylized fact: the higher the degree of institutional ownership (IO) in a portfolio, the more time-varying expected returns rather than changes in expected dividend growth drive changes in its valuation. Empirical evidence suggests that institutions' time-varying sensitivity to...

We find that financial analysts provide more thorough forecasts when firms’ institutional investors are distracted (i.e., when firms are neglected). We establish the causality of the effect by identifying exogenous shocks leading to institutional investor distraction following Kempf, Manconi,...

This paper proposes and tests an investment-flow based explanation for three empirical findings about return predictability -- the persistence of mutual fund performance, the "smart money" effect, and stock price momentum. Motivated by prior studies, I construct a measure of demand shocks to...

Using trader-resolved data, we document lead-lag relationships between groups of investors in the foreign exchange market. Because these relationships are systematic and persistent, order flow is predictable from trader-resolved order flow. We thus propose a generic method to exploit trader...

Measures of a firm's financial strength forecast stock returns. The relation between financial condition and future returns, however, is consistent with two explanations: (1) changes in investors' expectations are impounded gradually over time and, (2) riskier firms - with higher discount rates...

In a true out of sample test we find no evidence that several well-known technical trading strategies predict stock markets over the period of 1987 to 2011. Our test is free of the sample selection bias, data mining, hindsight bias, or any of the other usual biases that may affect results in our...

Many securities markets are organized as double auctions where each incoming limit order --- i.e., an order to buy or sell at a specific price --- is stored in a data structure called the limit order book. A trade happens whenever a market order arrives --- i.e., an order to buy or sell at the...

We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance...

We revisit the apparent historical success of technical trading rules on daily prices of the DJIA index from 1897 to 2011, and use the False Discovery Rate as a new approach to data snooping. The advantage of the FDR over existing methods is that it selects more outperforming rules which allows...