Biased Beliefs,Asset Prices,and Investment: A Structural Approach

We structurally estimate a model in which agents’ information processing biases can cause predictability in firms’ asset returns and investment inefficiencies. We generalize the neoclassical investment model by allowing for two biases—overconfidence and overextrapolation of trends—that distort agents’ expectations of firm productivity. Our model's predictions closely match empirical data on asset pricing and firm behavior. The estimated bias parameters are well identified and exhibit plausible magnitudes. Alternative models without either bias or with efficient investment fail to match observed return predictability and firm behavior. These results suggest that biases affect firm behavior, which in turn affects return anomalies.     

Efficient tests of stock return predictability

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.     

Predictability of stock markets with disequilibrium trading

This paper analyses the predictability of a hypothetical market with freely negotiated prices on which exists a censoring of one-period returns which are in excess of an arbitrary level (‘floor’ and ‘ceiling’). It is shown that the expected value of returns (adjusted for drift) conditional on last period information regarding the censoring are equal to zero (and therefore the market is not predictable in mean) if there is no intertemporal spillover on the market. A simple simulation model is proposed and applied for the analysis of the effects of intertemporal and cross-spillovers resulting from quantity constraints. Statistical predictability tests are proposed, based on the corrected Student-t statistic of a regression of returns of some information concerning the previous censoring. An illustrative empirical analysis of six main time series of returns on the Warsaw Stock Exchange confirms their ex-ante, but not ex-post, predictability.     

Investor Psychology and Security Market Under- and Overreactions

We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations ("momentum"), short-run earnings "drift," but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy.     

Short-term predictability of equity returns along two style dimensions

This study uses daily return data on 20 portfolios split along two dimensions, growth/value and market size, over the period of four decades and employs over 12,000 trading rules to investigate the short-term predictability of portfolio returns. It shows that, historically, portfolios of small stocks and value stocks have been more suitable for active trading strategies since returns on value portfolios exhibit more predictability than returns on growth portfolios and returns on portfolios of large stocks appear to be less predictive than returns on portfolios of small stocks. The predictive ability of trading rules is all but gone during the 2000s. Popularization of exchange-traded funds and the introduction of quote decimalization on the exchanges are the most likely reasons behind the lack of predictability.     

ON THE RATIONALITY OF COMMON STOCK RETURN VOLATILITY

This paper derives the relationship between the population unconditional variance of common stock returns and the variance of expected returns conditional on a well-specified information set. As a consequence, a lower bound is obtained for the variance of common stock returns. The sample counterpart of this bound is then empirically tested against the sample variance of returns. The paper's main conclusion can be stated as follows: the observed volatility of real (inflation-adjusted) common stock returns is not “irrationally” large. The paper admits of this conclusion because the point estimate of the lower-bound variance derived in this model is actually larger than the point estimate of common stock return volatility. However, since these point estimates are found to have a statistically insignificant difference, equality of the two variances cannot be ruled out. Hence, “rationality” of common stock returns—as implied by a utility-based valuation conditional on a specified information set—cannot be rejected.     

Are stock markets really efficient? Evidence of the adaptive market hypothesis

This study examines the adaptive market hypothesis in the S&P500, FTSE100, NIKKEI225 and EURO STOXX 50 by testing for stock return predictability using daily data from January 1990 to May 2014. We apply three bootstrapped versions of the variance ratio test to the raw stock returns and also whiten the returns through an AR-GARCH process to study the nonlinear predictability after accounting for conditional heteroscedasticity through the BDS test. We evaluate the time-varying return predictability by applying these tests to fixed-length moving subsample windows and also examine whether there is a relationship between the level of predictability in stock returns and market conditions. The results show that there are periods of statistically significant return predictability, but also episodes of no statistically significant predictability in stock returns. We also find that certain market conditions are statistically significantly related to predictability in certain markets but each market interacts differently with the different market conditions. Therefore our findings suggest that return predictability in stock markets does vary over time in a manner consistent with the adaptive market hypothesis and that each market adapts differently to certain market conditions. Consequently our findings suggest that investors should view each market independently since different markets experience contrasting levels of predictability, which are related to market conditions.     

ARE LONG-HORIZON STOCK RETURNS PREDICTABLE? A BOOTSTRAP ANALYSIS

This paper examines empirical evidence of predictability of long-horizon real and excess stock returns in the UK using univariate as well as multivariate Variance Ratio tests. In order to estimate the sampling distribution of the test statistics, artificial histories ofstock returns are generated from their empirical distribution using the bootstrap method. This allows the construction of significance levels of the test statistic which are free from distributional assumptions. The empirical results indicate that there is no evidence of mean reversion in stock prices even if a wider information set to forecast stock returns is used and that the significance of historical Variance Ratio statistics has been overstated by previous studies.     

Biased information weight processing in stock markets

The concepts of over- and underreaction are frequently used in behavioral financial research to explain investor behavior and resulting market phenomena. This research often makes arbitrary assumptions about which of the two biases is prevalent in a specific situation although psychological research offers more explicit insights. Investors overreact towards information of low weight and underreact if the information has high weight (high reliability). We propose a model that transfers these experimental findings to a financial market setting. Our time-series and cross-sectional empirical analyses support the hypothesis that investors misperceive information weight, which leads to short-term predictability in returns.     

Stock-specific sentiment and return predictability

This paper quantifies the impact of stock-specific news sentiment on future financial returns. Daily predictive regressions yield significant t-statistics for 7% at most of our sample of more than 1000 large stocks listed in the USA. While a few assets do run through pockets of predictability, the evidence suggests that the feedback effect is stronger in the reverse direction: returns are more likely to drive future sentiment than the other way around.     

Bond risk premia and realized jump risk

We find that augmenting a regression of excess bond returns on the term structure of forward rates with an estimate of the mean realized jump size almost doubles the R² of the forecasting regression. The return predictability from augmenting with the jump mean easily dominates that offered by augmenting with options-implied volatility and realized volatility from high-frequency data. In out-of-sample forecasting exercises, inclusion of the jump mean can reduce the root mean square prediction error by up to 40%. The incremental return predictability captured by the realized jump mean largely accounts for the countercyclical movements in bond risk premia. This result is consistent with the setting of an incomplete market in which the conditional distribution of excess bond returns is affected by a jump risk factor that does not lie in the span of the term structure of yields.     

Robust inference in single firm/single event analyses

Single firm/single event (SFSE) studies are relevant in corporate finance. Since inference on abnormal returns in this context necessarily relies on the time series variance of these abnormal returns, the implied problem of heteroscedasticity is obvious, although hard to solve. We analyze robust inference in an SFSE setting using Monte Carlo and resampling experiments. Estimation is biased when the calibration and event period occur in different volatility regimes. We develop a unique specification test for these structural breaks. The most robust inference is obtained by using intraday data and a multiplicative component GARCH estimator.     

Return reversals in the bond market: Evidence and causes

The finance literature has shown that equity returns are predictable using past returns. This study extends that literature by examining bond return predictability. Using returns constructed from dealer bid prices, we find short- to intermediate-term reversals in investment grade corporate bond returns. These reversals are larger in the first half of the sample period and consistent with the predictions of dealer inventory cost models. This supports Jegadeesh and Titman’s [J. Financ. Intermed. 4 (1995) 116] assertion that daily, weekly, and monthly reversals in equity returns come from dealer inventory considerations, not behavioral biases. Finally, unlike equity returns, we find no evidence of momentum in bond returns.     

Why inexperienced investors do not learn: They do not know their past portfolio performance

Recently, researchers have gone a step further from just documenting biases of individual investors. More and more studies analyze how experience affects decisions and whether biases are eliminated by trading experience and learning. A necessary condition to learn is that investors actually know what happened in the past and that the views of the past are not biased. We contribute to the above mentioned literature by showing why learning and experience go hand in hand. Inexperienced investors are not able to give a reasonable self-assessment of their own past realized stock portfolio performance which impedes investors' learning ability. Based on the answers of 215 online broker investors to an Internet questionnaire, we analyze whether investors are able to correctly estimate their own realized stock portfolio performance. We show that investors are hardly able to give a correct estimate of their own past realized stock portfolio performance and that experienced investors are better able to do so. In general, we can conclude that we find evidence that investor experience lessens the simple mathematical error of estimating portfolio returns, but seems not to influence their “behavioral” mistakes pertaining to how good (in absolute sense or relative to other investors) they are.     

Momentum is really short-term momentum

We demonstrate the estimation biases that arise when stock returns from 12 month prior and 2 month prior are included within intermediate and recent past momentum profits. These biases lead to an overestimation of intermediate past momentum but an underestimation of recent past momentum in the US market. There is no significant difference between the predictability of stock performance in the intermediate past and the recent past once we exclude these two months from the construction of momentum strategies in the US and each of the 26 major international markets.     

Asymmetric Information and the Predictability of Real Estate Returns

This article examines the relation between systematic price changes and the heterogeneity of investors information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which information asymmetry alters the dynamic relation between returns and trading volume. We employ a filter-rule methodology to determine predictability in returns and augment the return-based conditioning set with trading volume. The additional conditioning information is necessary since the model is underspecified when predictability is based on returns alone. Our results provide new insight into the coexistence of informational and noninformational exchange in the speculative markets for real estate assets. Specifically, we find that the predictability of real estate returns is generally more indicative of portfolio rebalancing effects than an adverse-selection problem. These results are unique in addressing the time-variation in information asymmetry.     

A MULTIFACTOR ANALYSIS OF COUNTRY FUND RETURNS

Previous closed‐end country fund research concludes that returns behave more like the U.S. market than like their target markets. We argue this finding may be biased by model misspecification and inappropriate estimation techniques. We propose a single‐equation model containing five hypothesized factors of fund returns. We estimate this model for nineteen pooled seasoned funds using a time‐series cross‐section regression that corrects for two types of autocorrelation. We show that returns are strongly related to target markets. Returns are also related to changes in discounts, exchange rates, and other countries' markets, but are only weakly related to the U.S. market. JEL classification: G10, G12     

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.     

Machine learning for US cross-industry return predictability under information uncertainty

This paper investigates the association between industry information uncertainty and cross-industry return predictability using machine learning in a general predictive regression framework. We show that controlling for post-selection inference and performing multiple tests improves the in-sample predictive performance of cross-industry return predictability in industries characterized by high uncertainty. Ordinary least squares post-least absolute shrinkage and selection operator models incorporating lagged industry information uncertainty for the financial and commodity industries are critical to improving prediction performance. Furthermore, in-sample industry return forecasts establish heterogeneous predictability over US industries, in which excess returns are more predictable in sectors with medium or low uncertainty.     

TIME-VARYING FACTORS AND CROSS-AUTOCORRELATIONS IN SHORT-HORIZON STOCK RETURNS

In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggests that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have a strong causal effect on returns on small stocks; (ii) trading volume is positively related to own- and cross-autocorrelations in weekly portfolio returns; and (iii) significant cross-autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.