Stock Price Booms and Expected Capital Gains

Abstract

Following the recent boom and bust cycles in a number of asset markets around the globe, there exists renewed interest in understanding better the forces contributing

Can Learning Explain Boom-Bust Cycles In Asset Prices? An Application to the US Housing Boom

“…t . The choice of perceived DGP follows a number of papers in the asset pricing learning literature, including Adam, Kuang, and Marcet (2011) and Adam, Beutel, and Marcet (2014). Optimal updating of (11) implies patterns of forecast errors in prices consistent the evidence discussed in section 2.1.…”

“…This work is extended by Asset pricing models with internally rational learning have achieved some success in explaining the observed volatility and persistence in asset prices (most notably stock prices and house prices) over the business cycle, however this research has thus far failed to provide a convincing explanation of price booms. Adam, Beutel, and Marcet (2014) propose a learning framework in which boundedly rational agents believe stock price growth to be governed by a simple linear hidden Markov model. A similar model is examined by Adam and Marcet (2010) with agents that are assumed to be uninformed about the evolution of stock returns instead of price growth.…”

“…Adam, Kuang, and Marcet (2011) implement a learning model, similar in spirit to that found in Adam, Beutel, and Marcet (2014), to try to explain joint house price-currentaccount movements in the G7 over the 2000s. The authors nd that reductions in interest rates in the early 2000s can generate substantial 7 Marshall and Shea (2013) provide an example of a learning model in housing where households are assumed to be uninformed about the evolution of their own consumption.…”

Diagnostic Expectations and Stock Returns

“…Our paper follows extensive research on overreaction and volatility that begins with Shiller (1981), De Thaler (1985, 1987), Cutler, Poterba, and Summers (1990, 1991), and De Long et al (1990a, 1990b. This work often uses mechanical rules for belief updating such as adaptive expectations or adaptive learning (e.g., Barsky and De Long (1993), Barberis et al (2015), Adam, Marcet, and Beutel (2017)). 1 Barberis, Shleifer, and Vishny (1998) is closest in spirit, though not in formulation, to our current work, since it is also motivated by representativeness.…”

Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?

“…Studies by Fisher and Statman (2002), Vissing-Jorgenson (2004), Amromin and Sharpe (2014), Frydman and Stillwagon (2018), and Da, Huang, and Jin (2020) all …nd evidence of extrapolative or procyclical expected returns among stock investors. Greenwood and Shleifer (2014) and Adam, Marcet, and Beutel (2017) show that measures of investor optimism about future stock returns are strongly correlated with past stock returns and the price-dividend ratio. 5 Interestingly, even though a higher price-dividend ratio in the data empirically predicts lower realized stock returns (Cochrane 2008), the survey evidence shows that investors fail to take this relationship into account; instead they continue to forecast high future returns on stocks following a sustained run-up in the price-dividend ratio.…”