Aurora Borealis
1 May 2014 CFA Institute Journal Review

Biased Beliefs, Asset Prices, and Investment: A Structural Approach (Digest Summary)

  1. Mathias Moersch

The authors incorporate two behavioral biases—overconfidence and overextrapolation of trends—into a neoclassical investment model. These biases distort expectations of firm productivity and lead to asset return predictability and investment inefficiencies. Because the predictions of the authors’ model closely match empirical data, they conclude that these biases affect firm behavior, which in turn, causes return anomalies.

What’s Inside?

The authors fill an existing gap in the literature on asset return predictability and return anomalies by providing a structural approach to mispricing, one that formally links behavioral biases to asset returns.

A neoclassical investment model is augmented to include two behavioral biases of investors: overconfidence and overextrapolation. The model features managers who choose firm investment based on expected productivity, investors who estimate firm productivity based on realized profits, and a soft information signal that summarizes relevant intangible information. Overconfident agents believe the precision of the soft signal to be higher than it actually is, whereas overextrapolating agents assume the persistence of firm productivity is higher than it actually is.

The authors use their model to develop empirical estimates and find that the model’s predictions closely match observed return anomalies. They show that the two behavioral biases contribute to observed return predictability.

How Is This Research Useful to Practitioners?

The authors’ structural approach has four major advantages. First, it allows for a formal econometric analysis of the various theories of biases grounded in a theoretical model. Second, it allows for the estimation and identification of a wide range of different behavioral biases. Although the authors consider two possible biases in detail, the approach can be extended to cover other biases as well. Third, it enables the authors to assess whether the estimated magnitudes of the biases are plausible. Fourth, the model can be compared with other specifications without behavioral biases to assess whether behavioral biases are important.

Although they find that the two behavioral biases are important in matching investment data, the authors warn against simply interpreting the parameter estimates as being constant over time. If investors and managers use historical data to learn from their mistakes, it is reasonable to assume that future patterns of asset return predictability will be different from historical ones. Furthermore, as the structure of the economy changes, behavioral biases may lose importance or disappear.

How Did the Authors Conduct This Research?

The authors first develop a dynamic model of firm investment and asset prices. Managers make investment decisions and investors make pricing decisions based on their information about firm-specific data. Investors cannot observe firm productivity directly and must instead rely on an estimate. The two investor biases the authors explore—overconfidence and overextrapolation of trends—generate endogenous relationships among the model parameters: firm investment, profitability, valuation, and asset returns.

Empirical estimates confirm the validity of the authors’ structural approach. A comparison of the biases implied by return anomalies with biases extracted from surveys reveals that implied overconfidence is large but implied overextrapolation is small. To test the robustness of the results—and especially the relevance of behavioral biases and inefficient investment—the authors estimate alternative model specifications. Because these specifications fail to match return anomalies and firm behavior, the authors conclude that (1) understanding firm behavior requires a model of the biases and (2) comprehending return anomalies necessitates a model of firm behavior. They propose their model as a benchmark for work on mispricing and asset return predictability that seeks to identify the relative importance of risk premiums and behavioral biases.

Abstractor’s Viewpoint

The authors successfully tie behavioral biases to return anomalies and describe the mechanism that links the biases to financial markets. As they rightly stress, it is unlikely that this approach can be exploited directly to uncover future mispricing in asset markets. Instead, the model provides a structured framework for thinking about the importance of behavioral biases as well as plausible avenues for transmission of biases to asset prices.

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