Search

Search results

    Bordalo, Pedro, Nicola Gennaioli, Rafael LaPorta, and Andrei Shleifer. Forthcoming. “Diagnostic Expectations and Stock Returns.” Journal of Finance. Abstract

    We revisit La Porta’s (1996) finding that returns on stocks with the most optimistic analyst long term earnings growth forecasts are substantially lower than those for stocks with the most pessimistic forecasts.  We document that this finding still holds, and present several further facts about the joint dynamics of fundamentals, expectations, and returns for these portfolios.  We explain these facts using a new model of belief formation based on a portable formalization of the representativeness heuristic. In this model, analysts forecast future fundamentals from the history of earnings growth, but they over-react to news by exaggerating the probability of states that have become objectively more likely. Intuitively, fast earnings growth predicts future Googles but not as many as analysts believe. We test predictions that distinguish this mechanism from both Bayesian learning and adaptive expectations, and find supportive evidence. A calibration of the model offers a satisfactory account of the key patterns in fundamentals, expectations, and returns.

    Barberis, Nicholas, Robin Greenwood, Lawrence Jin, and Andrei Shleifer. 2018. “Extrapolation and Bubbles.” Journal of Financial Economics 129 (2): 203-227.
    Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer. Working Paper. “Memory, Attention, and Choice”. Abstract

    Building on the textbook description of associative memory (Kahana 2012), we present a model of choice in which options cue recall of similar past experiences. Recall shapes valuation and choice in two ways. First, recalled experiences form a norm, which serves as an initial anchor for valuation. Second, salient quality and price surprises relative to the norm lead to large adjustments in valuation. The model provides a unified account of many well documented choice puzzles including experience effects, projection and attribution biases, background contrast effects, and context- dependent willingness to pay. The results suggest that well-established psychological processes – memory-based norms and attention to surprising features – are key to understanding decision-making.

    Greenwood, Robin, Andrei Shleifer, and Yang You. 2019. “Bubbles for Fama.” Journal of Financial Economics 131 (1): 20-43. Abstract

    We evaluate Eugene Fama’s claim that stock prices do not exhibit price bubbles. Based on US industry returns 1926-2014 and international sector returns 1985-2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in US and international samples.