Investor Psychology and Security Market Under- and Overreactions

The Conditional CAPM Does Not Explain Asset-Pricing Anomalies

I describe asset price dynamics caused by the slow movement of investment capital to trading opportunities. The pattern of price responses to supply or demand shocks typically involves a sharp reaction to the shock and a subsequent and more extended reversal. The amplitude of the immediate price impact and the pattern of the subsequent recovery can reflect institutional impediments to immediate trade, such as search costs for trading counterparties or time to raise capital by intermediaries. I discuss special impediments to capital formation during the recent financial crisis that caused asset price distortions, which subsided afterward. After presenting examples of price reactions to supply shocks in normal market settings, I offer a simple illustrative model of price dynamics associated with slow-moving capital due to the presence of inattentive investors. I ADDRESS THE IMPLICATIONS for asset price dynamics of the apparent slow movement of investment capital to trading opportunities. The arrival of new capital to an investment opportunity can be delayed by fractions of a second in some markets, for example an electronic limit-order-book market for equities, or by months in other markets, such as that for catastrophe risk insurance. Accordingly, prices respond to supply or demand shocks with a sharp reaction because of the relatively small subset of capital (and thus risk-bearing capacity) that is immediately available to absorb a shock on short notice. Such a price impact is substantially reversed over time as additional capital becomes available. The amplitude of the immediate price impact and the pattern of the subsequent recovery can reflect many sorts of attention costs to trade as well as institutional impediments to capital movement, such as search costs for trading

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Presidential Address: Asset Price Dynamics with Slow-Moving Capital

Agreeing to disagree.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

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Information Immobility and the Home Bias Puzzle

The empirical evidence on investor disagreement and trading volume is difficult to reconcile in standard rational expectations models. We develop a dynamic model in which investors disagree about the interpretations of public information. We obtain a closed-form linear equilibrium that allows us to study what restrictions on the disagreement process yield empirically observed volume and return dynamics. We show that when investors have infrequent but major disagreements, there is positive autocorrelation in volume and positive correlation between volume and price volatility. We also derive novel empirical predictions that relate the degree and frequency of disagreement to volume and volatility dynamics.

Disagreement and Learning: Dynamic Patterns of Trade

I develop a dynamic model that nests the rational expectations (RE) and differences of opinion (DO) approaches to study how investors use prices to update their valuations. I show that when investors condition on prices (RE), investor disagreement is related positively to expected returns, return volatility and market beta, but negatively to return autocorrelation. When investors do not use prices (DO), these relationships are reversed. I test these predictions on the cross-section of stocks using analyst forecast dispersion and volume as proxies for disagreement, and find empirical evidence that is consistent with investors using prices on average.

Learning from Prices and the Dispersion in Beliefs

The empirical evidence on investor disagreement and trading volume is difficult to reconcile in standard rational expectations models. We develop a dynamic model in which investors disagree about the interpretation of public information. We obtain a closed-form linear equilibrium that allows us to study which restrictions on the disagreement process yield empirically observed volume and return dynamics. We show that when investors have infrequent but major disagreements, there is positive autocorrelation in volume and positive correlation between volume and volatility. We also derive novel empirical predictions that relate the degree and frequency of disagreement to volume and volatility dynamics.

I study whether investors use an asset’s price to update their beliefs about its payos. I show that in a dynamic economy, the relationship between the dispersion in investor beliefs and returnvolume characteristics can be used to empirically answer this question. When investors have rational expectations (RE) and condition on the price, I show that belief dispersion is positively related to expected returns, return volatility, and correlation between volume and absolute returns, but negatively related to return autocorrelation. When investors do not use the price, as in a dierence of opinions (DO) model, these relationships are reversed. I test these predictions using the crosssection of returns, volume and analyst forecast dispersion over dierent horizons. The results are consistent with the RE model at quarterly and longer horizons, but reject both models at the monthly horizon. I show that a hybrid model with some DO investors and some RE investors can match the short horizon evidence. The empirical results are consistent with the idea that while some investors do not use the price in the short run, they increase their conditioning on the price over time.

Motivated by the insight of Keynes (1936) on the importance of higher order beliefs in financial markets, we examine the role of such beliefs in generating drift in asset prices. We show that in a dynamic setting, a higher order difference of opinions is necessary for heterogeneous beliefs to generate price drift. Such drift does not arise in standard difference of opinion models, since investors' beliefs are assumed to be common knowledge. Our results stand in contrast to Allen, Morris and Shin (2006) and others, as we argue that in rational expectation equilibria, heterogeneous beliefs do not lead to price drift.

Snehal Banerjee

Papers

Disagreement and Learning: Dynamic Patterns of Trade

Learning from Prices and the Dispersion in Beliefs

Disagreement and Learning: Dynamic Patterns of Trade

Learning from Prices and the Dispersion in Beliefs

Price Drift as an Outcome of Differences in Higher-Order Beliefs