Stock return predictability and model uncertainty

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.     

Contrarian flows,consumption and expected stock returns

We investigate the relation between contrarian flows, consumption growth, and market risk premium. We construct a contrarian flows measure by summing up the capital flows to stocks that go against the total flow of the aggregate market. We show that the contrarian flows are negatively influenced by the same-quarter consumption growth. During bad times, the majority of investors who are affected by the negative shock reduce their equity exposure, and these extra supplies of risky assets are absorbed by contrarian investors who are least affected by the consumption shock. Using quarterly stock market data, we find that the contrarian flows forecast market returns at short-to-intermediate horizons. The predictability stems from the component that is explained by the consumption growth, and therefore the consumption growth contains valuable information about the market risk premium. Moreover, the predictability is stronger for growth stocks than for value stocks, and hence it negatively predicts the value premium. This is because the contrarian flows measure the market risk premium and growth stocks bear more discount rate risk than value stocks. Out-of-sample tests show that the main results are robust to data-snooping bias.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

A multivariate model of strategic asset allocation

We develop an approximate solution method for the optimal consumption and portfolio choice problem of an infinitely long-lived investor with Epstein–Zin utility who faces a set of asset returns described by a vector autoregression in returns and state variables. Empirical estimates in long-run annual and post-war quarterly U.S. data suggest that the predictability of stock returns greatly increases the optimal demand for stocks. The role of nominal bonds in long-term portfolios depends on the importance of real interest rate risk relative to other sources of risk. Long-term inflation-indexed bonds greatly increase the utility of conservative investors.     

Assessing the Economic Significance of Return Predictability: A Research Note

The predictability of stock returns is often assessed using classical statistical significance from predictive regressions. Statistical inference, however, can belie the economic importance with which investors regard various predictors. This paper examines the influence that predictors have on an investor's optimal portfolio allocations. The results show that return predictability is sufficient to induce horizon effects in optimal allocations. After incorporating estimation risk, however, little evidence of predictability remains. We also assess the relative importance of three predictor variables. While dividend yield is the most important predictor, optimal allocations are also sensitive to the term spread and the relative bill rate.     

How do investors react under uncertainty?

It has long been accepted that risk plays an important role in determining valuation where risk reflects that investors are unsure of future returns but are able to express their prior expectations by a probability distribution of these returns. Knight (1921) introduced the concept of uncertainty where investors possess incomplete knowledge about this distribution and so are unable to formulate priors over all possible outcomes. One common approach for making uncertainty tractable is to assume that investors faced with uncertainty will base their decisions on the worst case scenario (i.e. follow maxmin expected utility). As a consequence it is postulated that investors will become more pessimistic as uncertainty increases, upgrading bad news and downgrading good news. Using Australian data, we find evidence that investors react to bad news at times of high market uncertainty but largely ignore good news which is consistent with them taking on a pessimistic bias. However, we also find evidence of the reverse when market uncertainty is low with investors taking on an optimistic stance by ignoring bad news but reacting to good news. We also find that the impact that market uncertainty has on the reaction of investors to new information is modified by the prevailing market sentiment at the time of the announcement. Besides throwing light on the question of how uncertainty impacts on investor behaviour, our findings seriously challenge the common assumption made that investors consistently deal with uncertainty by applying maxmin expected utility.     

Does Academic Research Destroy Stock Return Predictability?

We study the out‐of‐sample and post‐publication return predictability of 97 variables shown to predict cross‐sectional stock returns. Portfolio returns are 26% lower out‐of‐sample and 58% lower post‐publication. The out‐of‐sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication‐informed trading. Post‐publication declines are greater for predictors with higher in‐sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post‐publication increases in correlations with other published‐predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.     

Differences of Opinion and the Cross Section of Stock Returns

We provide evidence that stocks with higher dispersion in analysts' earnings forecasts earn lower future returns than otherwise similar stocks. This effect is most pronounced in small stocks and stocks that have performed poorly over the past year. Interpreting dispersion in analysts' forecasts as a proxy for differences in opinion about a stock, we show that this evidence is consistent with the hypothesis that prices will reflect the optimistic view whenever investors with the lowest valuations do not trade. By contrast, our evidence is inconsistent with a view that dispersion in analysts' forecasts proxies for risk.     

Are extreme returns priced in the stock market? European evidence

This paper revisits some recently found evidence in the literature on the cross-section of stock returns for a carefully constructed dataset of euro area stocks. First, we confirm recent results for US data and find evidence of a negative cross-sectional relation between extreme positive returns and average returns after controlling for characteristics such as momentum, book-to-market, size, liquidity and short term return reversal. We argue that this is the case because these stocks have lottery-like characteristics, which is attractive to certain investors. Also, these stocks tend to be very volatile so that arbitrageurs are discouraged from correcting potential mispricing. As a consequence, these stocks are often overpriced and hence face lower expected returns. Second, when we control for extreme returns, the recently found negative relationship between idiosyncratic risk and future returns is less robust. In our models, after adding maximum returns, the relationship is insignificant and sometimes even positive. We also find that idiosyncratic skewness and coskewness play an important role for asset pricing, as predicted by several theoretical models.     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

Market Segmentation and Cross‐predictability of Returns

We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value‐relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross‐predict each other's returns, (ii) the magnitude of return cross‐predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.     

Firm efficiency and stock returns: Australian evidence

We employ a stochastic frontier approach to estimate firm efficiency - the efficiency with which a firm converts its inputs into output. We find a negative relation between firm efficiency and the cross-section of stock returns (‘firm efficiency effect’) in the Australian stock market. The firm efficiency effect is robust after controlling for other firm characteristics and is more pronounced in stocks with high limits-to-arbitrage. However, we find no evidence that firm efficiency is a priced factor in the cross-section. Our findings suggest that investors misprice firm efficiency and arbitrage costs perpetuate its return predictability.     

Left-tail momentum: Underreaction to bad news,costly arbitrage and equity returns

This paper documents a significantly negative cross-sectional relation between left-tail risk and future returns on individual stocks trading in the US and international countries. We provide a behavioral explanation to this anomaly based on the idea that investors underestimate the persistence in left-tail risk and overprice stocks with large recent losses. Thus, low returns in the left-tail of the distribution persist into the future causing left-tail return momentum. We find that the left-tail risk anomaly is stronger for stocks that are more likely to be held by retail investors, that receive less investor attention, and that are costlier to arbitrage.     

The value premium,aggregate risk innovations,and average stock returns

We test whether innovations in aggregate risk, interpolated from a vector autoregressive system that contains the Chen et al. (1986) five factors as in Petkova (2006), are common factors in cross-sectional stock returns. We provide direct evidence that innovation in industrial production growth, a classical business-cycle variable that summarizes the state of the economy, is associated with the cross-sectional return predictability of individual stocks. We conclude that the role of innovation in aggregate risk is not random, and furthermore that it provides guidance concerning an important source of nonfinancial market-based risk in asset returns.     

Sequential Learning,Predictability, and Optimal Portfolio Returns

This paper finds statistically and economically significant out‐of‐sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time‐varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.     

The price of sin: The effects of social norms on markets

We provide evidence for the effects of social norms on markets by studying “sin” stocks—publicly traded companies involved in producing alcohol, tobacco, and gaming. We hypothesize that there is a societal norm against funding operations that promote vice and that some investors, particularly institutions subject to norms, pay a financial cost in abstaining from these stocks. Consistent with this hypothesis, we find that sin stocks are less held by norm-constrained institutions such as pension plans as compared to mutual or hedge funds that are natural arbitrageurs, and they receive less coverage from analysts than do stocks of otherwise comparable characteristics. Sin stocks also have higher expected returns than otherwise comparable stocks, consistent with them being neglected by norm-constrained investors and facing greater litigation risk heightened by social norms. Evidence from corporate financing decisions and the performance of sin stocks outside the US also suggest that norms affect stock prices and returns.     

Momentum Strategies

We examine whether the predictability of future returns from past returns is due to the market's underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Market risk, size, and book–to–market effects do not explain the drifts. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.     

Predictability of Stock Returns: Robustness and Economic Significance

This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s, but increased to a level where, net of transaction costs, it could have been exploited by investors in the volatile markets of the 1970s.     

Option trading: Information or differences of opinion?

This paper investigates the motive of option trading. We show that option trading is mostly driven by differences of opinion, a finding different from the current literature that attempts to attribute option trading to information asymmetry. Our conclusion is based on three pieces of empirical evidence. First, option trading around earnings announcements is speculative in nature and mostly dominated by small, retail investors. Second, around earnings announcements, the pre-announcement abnormal turnovers of options seem to predict the post-announcement abnormal stock returns. However, once we control for the pre-announcement stock returns, the predictability completely disappears, implying that option traders simply take cues from the stock market and turn around to speculate in the options market. Third, cross-section and time-series regressions reveal that option trading is also significantly explained by differences of opinion. While informed trading is present in stocks, it is not detected in options.     

Consumption growth predictability and asset prices

We derive and test a consumption-based intertemporal asset pricing model in which an asset earns a risk premium if it performs poorly when expected future consumption growth deteriorates. The predictability of consumption growth combined with the recursive preference delivers news about future consumption growth an additional risk factor, in addition to news about current consumption growth. We model the consumption growth dynamics using a vector autoregressive (VAR) structure with a set of instrumental variables commonly used for forecasting future economic growth. Our VAR estimation provides strong empirical support for future consumption growth predictability. The cross-sectional test shows that the model explains reasonably well the dispersion in average excess returns of 25 portfolios sorted on size and book-to-market, as well as 25 portfolios sorted on size and long-term return reversal. Growth stocks and long-term winners underperform value stocks and long-term losers, respectively, because growth stocks and long-term winners hedge adverse changes in the future consumption growth opportunities.