Dividend Dynamics,Learning, and Expected Stock Index Returns

We present a latent variable model of dividends that predicts, out‐of‐sample, 39.5% to 41.3% of the variation in annual dividend growth rates between 1975 and 2016. Further, when learning about dividend dynamics is incorporated into a long‐run risks model, the model predicts, out‐of‐sample, 25.3% to 27.1% of the variation in annual stock index returns over the same time horizon, with learning contributing approximately half of the predictability in returns. These findings support the view that investors' aversion to long‐run risks and their learning about these risks are important in determining stock index prices and expected returns.     

Accounting‐Based Probabilistic Prediction of ROE,the Residual Income Valuation Model and the Assessment of Mispricing in the Swedish Stock Market

Using Swedish stock market data, this study investigates whether an investment strategy based on publicly available accounting information can generate abnormal investment returns. The strategy involves two steps. First, an accounting‐based probabilistic prediction model of changes in the medium‐term book return on owners' equity (ROE) is estimated. Second, market expectations of changes in medium‐term ROE are assessed through observed stock prices and the residual income valuation model. Stock market positions over 36‐month holding periods are taken when the accounting‐based predictions of ROE and the market expectations differ. Over the period 1983–2003, the investment strategy generated values of Jensen's alpha corresponding to an average monthly excess return for a hedge position of up to 0.8% for a sample of manufacturing companies. In the main this hedge return was caused by strong positive returns to the long positions, and additional analyses show that the returns appear to have been affected by a positive market sentiment bias (i.e., positive ROE surprises being associated with stronger price reactions than negative ROE surprises) making out‐of‐sample inferences somewhat dubious. Furthermore, most of the investment returns accrued over holding periods up to around 1995, with no indications of market mispricing over the last third (1995–2003) of the investment period. The empirical results are consistent with market investors having become more sophisticated in their use of publicly available accounting information over time.     

Infrequent Rebalancing,Return Autocorrelation,and Seasonality

A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross‐section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross‐sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross‐section of stock returns, which can help explain available empirical evidence.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach

We incorporate regime shifts in the mean of price‐dividend ratios into the present value model of van Binsbergen and Koijen (2010) who propose a latent variable approach to modeling expected returns and dividend growth rates. We find that accounting for regime shifts results in much lower persistence of expected returns and higher volatility of expected returns, and thus higher in‐sample predictability, when compared to the results from the van Binsbergen and Koijen (2010) model. We also show that the main source of the increase in the mean of price‐dividend ratios in the mid‐1990s is a decrease in the mean of expected returns.     

Long‐Term Return Reversals: Overreaction or Taxes?

Long‐term reversals in U.S. stock returns are better explained as the rational reactions of investors to locked‐in capital gains than an irrational overreaction to news. Predictors of returns based on the overreaction hypothesis have no power, while those that measure locked‐in capital gains do, completely subsuming past returns measures that are traditionally used to predict long‐term returns. In data from Hong Kong, where investment income is not taxed, reversals are nonexistent, and returns are not forecastable either by traditional measures or by measures based on the capital gains lock‐in hypothesis that successfully predict U.S. returns.     

Idiosyncratic Volatility Covariance and Expected Stock Returns

Given that the idiosyncratic volatility (IDVOL) of individual stocks co‐varies, we develop a model to determine how aggregate idiosyncratic volatility (AIV) may affect the volatility of a portfolio with a finite number of stocks. In portfolio and cross‐sectional tests, we find that stocks whose returns are more correlated with AIV innovations have lower returns than those that are less correlated with AIV innovations. These results are robust to controlling for the stock's own IDVOL and market volatility. We conclude that risk‐averse investors pay a premium for stocks that pay well when AIV is high, consistent with our model.     

Labor‐Technology Substitution: Implications for Asset Pricing

This paper studies the asset pricing implications of a firm's opportunities to replace routine‐task labor with automation. I develop a model in which firms optimally undertake such replacement when their productivity is low. Hence, firms with routine‐task labor maintain a replacement option that hedges their value against unfavorable macroeconomic shocks and lowers their expected returns. Using establishment‐level occupational data, I construct a measure of firms' share of routine‐task labor. Compared to their industry peers, firms with a higher share of routine‐task labor (i) invest more in machines and reduce more routine‐task labor during economic downturns, and (ii) have lower expected stock returns.     

Inflation and Asset Returns in a Monetary Economy

Postwar U.S. data are characterized by negative correlations between real equity returns and inflation and by positive correlations between real equity returns and money growth. These patterns are closely matched quantitatively by an equilibrium monetary asset pricing model. The model also implies negative correlations between expected asset returns and expected inflation, and it predicts that the inflation-asset return correlation will be more strongly negative when inflation is generated by fluctuations in real economic activity than when it is generated by monetary fluctuations.     

Broker recommendations and Australian small‐cap equity fund management

This study examines whether the abnormal performance of active Australian small‐cap equity fund managers is associated with broker recommendations. Our evidence supports the investment value of broker recommendations, showing significant abnormal returns (ARs) both pre‐ and post‐broker recommendations. We find that when a factor‐mimicking portfolio based on broker recommendations is added to a Carhart (1997) model, annual alphas are reduced by 48 basis points. Using transaction‐level data, buy trades following broker recommendations earn significant cumulative ARs of 1.56 per cent after 60 days. Overall, we find that broker recommendations account for an economically significant component of alphas.     

A Consumption-Based Explanation of Expected Stock Returns

When utility is nonseparable in nondurable and durable consumption and the elasticity of substitution between the two consumption goods is sufficiently high, marginal utility rises when durable consumption falls. The model explains both the cross‐sectional variation in expected stock returns and the time variation in the equity premium. Small stocks and value stocks deliver relatively low returns during recessions, when durable consumption falls, which explains their high average returns relative to big stocks and growth stocks. Stock returns are unexpectedly low at business cycle troughs, when durable consumption falls sharply, which explains the countercyclical variation in the equity premium.     

Contrarian Profits and the Overreaction Hypothesis: the Case of the Athens Stock Exchange

This paper investigates the existence of contrarian profits and their sources for the Athens Stock Exchange (ASE). The empirical analysis decomposes contrarian profits to sources due to common factor reactions, overreaction to firm‐specific information, and profits not related to the previous two terms, as suggested by Jegadeesh and Titman (1995). Furthermore, in view of recent evidence that common stock returns are related to firm characteristics such as size and book‐to‐market equity, the paper decomposes contrarian profits to sources due to factors derived from the Fama and French (1993, 1996) three‐factor model. For the empirical testing, size‐sorted sub‐samples that are rebalanced annually are employed, and in addition, adjustments for thin and infrequent trading are made to the data. The results indicate that serial correlation is present in equity returns and that it leads to significant short‐run contrarian profits that persist even after we adjust for market frictions. Consistent with findings for the US market, contrarian profits decline as one moves from small stocks to large stocks, but only when market frictions are considered. Furthermore, the contribution to contrarian profits due to the overreaction to the firm‐specific component appears larger than the underreaction to the common factors.     

Profitability,Value, and Stock Returns in Production‐Based Asset Pricing without Frictions

In a production‐based asset pricing model without adjustment costs and with decreasing returns to scale following Brock (1982), stock returns at the firm level are determined by profitability, the book‐to‐market ratio, and the change in future profitability prospects. Although firms with low book‐to‐market ratios are normally more profitable and profitable firms are predicted to have higher returns, the stylized fact that book‐to‐market ratios positively forecast returns still holds theoretically, but with specific predicted exceptions. These implications are confirmed empirically.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

Modelling Time‐Variation in the Stock Return‐Dividend Yield Predictive Equation

Using data for forty markets, this paper examines the nature and possible causes of time‐variation within the stock return‐dividend yield predictive regression. The results in this paper show that there is significant time‐variation in the predictive equation for returns and that such variation is linked to economic and market factors. Furthermore, the strength and nature of those links are themselves time‐varying. The inclusion of this time‐variation in the predictive equation increases the predictive power compared to the standard constant parameter predictive model. Evidence is also reported for time‐varying dividend growth predictability. Long‐horizon predictability is also examined with evidence reported that the nature of the factors affecting time‐varying predictability changes with horizon. The results here, while directly contributing to the returns predictability debate, in particular regarding its existence and source, may also inform the discussion that links time‐varying expected returns (and risk premium) to economic factors.     

The Really Long-Run Performance of Initial Public Offerings: The Pre-Nasdaq Evidence

Financial economists have intensely debated the performance of IPOs using data after the formation of Nasdaq. This paper sheds light on this controversy by undertaking a large, out‐of‐sample study: We examine the performance for five years after listing of 3,661 U.S. IPOs from 1935 to 1972. The sample displays some underperformance when event‐time buy‐and‐hold abnormal returns are used. The underperformance disappears, however, when cumulative abnormal returns are utilized. A calendar‐time analysis shows that over the entire period, IPOs return as much as the market. The intercepts in CAPM and Fama–French regressions are insignificantly different from zero, suggesting no abnormal performance.     

Value versus Growth: Time‐Varying Expected Stock Returns

Is the value premium predictable? We study time variations of the expected value premium using a two‐state Markov switching model. We find that when conditional volatilities are high, the expected excess returns of value stocks are more sensitive to aggregate economic conditions than the expected excess returns of growth stocks. As a result, the expected value premium is time varying. It spikes upward in the high volatility state, only to decline more gradually in the subsequent periods. However, out‐of‐sample predictability of the value premium is close to nonexistent.     

CAPM and Option Pricing With Elliptically Contoured Distributions

This article offers an alternative proof of the capital asset pricing model (CAPM) when asset returns follow a multivariate elliptical distribution. Empirical studies continue to demonstrate the inappropriateness of the normality assumption for modeling asset returns. The class of elliptically contoured distributions, which includes the more familiar Normal distribution, provides flexibility in modeling the thickness of tails associated with the possibility that asset returns take extreme values with nonnegligible probabilities. As summarized in this article, this class preserves several properties of the Normal distribution. Within this framework, we prove a new version of Stein's lemma for this class of distributions and use this result to derive the CAPM when returns are elliptical. Furthermore, using the probability distortion function approach based on the dual utility theory of choice under uncertainty, we also derive an explicit form solution to call option prices when the underlying is log‐elliptically distributed. The Black–Scholes call option price is a special case of this general result when the underlying is log‐normally distributed.     

Estimation of expected return: CAPM vs. Fama and French

Most practitioners favour a one-factor model (CAPM) when estimating expected return for an individual stock. For estimation of portfolio returns, academics recommend the Fama and French three-factor model. The main objective of this paper is to compare the performance of these two models for individual stocks. First, estimates for individual stock returns based on CAPM are obtained using different time frames, data frequencies, and indexes. It is found that 5 years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provide the best estimate. However, performance of the model is very poor; it explains on average 3% of differences in returns. Then, estimates for individual stock returns are obtained based on the Fama and French model using 5 years of monthly data. This model, however, does not do much better; independent of the index used, it explains on average 5% of differences in returns. These results therefore bring into question the use of either model for estimation of individual expected stock returns.     

Multimarket Trading and Liquidity: Theory and Evidence

We develop a new model of multimarket trading to explain the differences in the foreign share of trading volume of internationally cross‐listed stocks. The model predicts that the trading volume of a cross‐listed stock is proportionally higher on the exchange in which the cross‐listed asset returns have greater correlation with returns of other assets traded on that market. We find robust empirical support for this prediction using stock return and volume data on 251 non‐U.S. stocks cross‐listed on major U.S. exchanges.