Differences in Perstistence in Aggregated and Disaggregated UK Stock Returns: A Reconciliation

Studies of the persistence in the returns series of UK stocks, using inter alia variance ratios, have documented clear differences between the relatively low levels of persistence in individual security returns and the relatively high levels of persistence in the returns of portfolios composed of these same securities. In this paper, I reconcile this contrast by showing that portfolio return variance ratios should not be expected to reflect (own) persistence levels in the component security returns, but instead should reflect a 'cross-persistence' between the securities. I calculate synthetic portfolio variance ratios from measures of security return 'cross-persistence' and find that they replicate closely the observed portfolio return variance ratios, which provides empirical support for the theoretical results.     

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.     

Downside risk and asset pricing

We analyze if the value-weighted stock market portfolio is stochastic dominance (SD) efficient relative to benchmark portfolios formed on size, value, and momentum. In the process, we also develop several methodological improvements to the existing tests for SD efficiency. Interestingly, the market portfolio seems third-order SD (TSD) efficient relative to all benchmark sets. By contrast, the market portfolio is inefficient if we replace the TSD criterion with the traditional mean–variance criterion. Combined these results suggest that the mean–variance inefficiency of the market portfolio is caused by the omission of return moments other than variance. Especially downside risk seems to be important for explaining the high average returns of small/value/winner stocks.     

Model Misspecification and Underdiversification

In this paper, we study intertemporal portfolio choice when an investor accounts explicitly for model misspecification. We develop a framework that allows for ambiguity about not just the joint distribution of returns for all stocks in the portfolio, but also for different levels of ambiguity for the marginal distribution of returns for any subset of these stocks. We find that when the overall ambiguity about the joint distribution of returns is high, then small differences in ambiguity for the marginal return distribution will result in a portfolio that is significantly underdiversified relative to the standard mean‐variance portfolio.     

Returns,volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.     

Prediction of return with the minimum variance zero-beta portfolio

This paper tests prediction of returns on stocks using a direct estimate of the minimum variance zero beta portfolio z. The composition of this portfolio is implicit in Black's paper on capital market equilibrium in the absence of riskless borrowing or lending. Portfolios of stocks drawn from the same industries are used to estimate z. The predictions of Black's equilibrium return equation are compared with those of cross-sectional regressions of return on risk.     

Trading strategies based on past returns: evidence from Germany

Among the various strategies studied in this paper, only momentum investing appears to earn persistently nonzero returns: From 1965 to 2014, the classical momentum strategy based on performance over the previous 2–12 months earned an average return of 1.57% per month (excluding microcap stocks and value-weighted returns). In the most recent 10-year period, this return was even larger—2.27%—which is much larger than in the USA. However, profitability net of transaction costs is weak because the strategy involves trading in disproportionately small stocks with high transaction costs, something that is particularly true for the loser portfolio. A strategy that concentrates only on the winner portfolio and thus avoids potential problems associated with (short) selling the costly loser portfolio appears to earn strong and persistently abnormal profits, even after transaction costs.     

Good News for Value Stocks: Further Evidence on Market Efficiency

This article examines the hypothesis that the superior return to so-called value stocks is the result of expectational errors made by investors. We study stock price reactions around earnings announcements for value and glamour stocks over a 5-year period after portfolio formation. The announcement returns suggest that a significant portion of the return difference between value and glamour stocks is attributable to earnings surprises that are systematically more positive for value stocks. The evidence is inconsistent with a risk-based explanation for the return differential.     

Conflict of interest and certification in the U.S. IPO market

We examine the long-term return performance of U.S. IPOs underwritten by relationship banks. We show that, over one- to three-year horizons, IPOs managed by relationship banks experience buy-and-hold benchmark-adjusted returns that are similar to those observed for a matching sample of stocks managed by non-relationship underwriters. This result holds even when the returns' skewness and cross-sectional correlation is accounted for. Further, we examine the calendar-time returns on a portfolio that is long the stocks underwritten by relationship banks and short ex-ante similar stocks taken public by non-relationship institutions. Again, we conclude that the two groups of IPOs yield similar long-run returns. These findings support the certification role of relationship banks and suggest that, in this respect, the effect of the 1999 repeal of Sections 20 and 32 of the Glass–Steagall Act has not been negative.     

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.     

Out-of-Sample Predictability in International Equity Markets: A Model Selection Approach

For 13 major international stock markets, there is much evidence of out-of-sample predictability for growth stocks especially when evaluated with economic criteria, and to a noticeably lesser extent for general stock markets and value stocks. Our results shed light on the recent debate about stock return predictability, using different assets (growth-style indexes), forecasting variables (past returns), forecasting models (nonlinear models), and alternative forecasting evaluation criteria (economic criteria). Our analysis suggests that (growth) stock returns might be predictable.     

Jump robust two time scale covariance estimation and realized volatility budgets

We estimate the daily integrated variance and covariance of stock returns using high-frequency data in the presence of jumps, market microstructure noise and non-synchronous trading. For this we propose jump robust two time scale (co)variance estimators and verify their reduced bias and mean square error in simulation studies. We use these estimators to construct the ex-post portfolio realized volatility (RV) budget, determining each portfolio component’s contribution to the RV of the portfolio return. These RV budgets provide insight into the risk concentration of a portfolio. Furthermore, the RV budgets can be directly used in a portfolio strategy, called the equal-risk-contribution allocation strategy. This yields both a higher average return and lower standard deviation out-of-sample than the equal-weight portfolio for the stocks in the Dow Jones Industrial Average over the period October 2007–May 2009.     

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the Extreme Risk Index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study seems to be the first application of extreme value techniques in portfolio management on a large scale. The primary aim of our investigation is the potential of ERI in practice. The performance of this strategy is benchmarked against the minimum variance portfolio and the equally weighted portfolio. These fundamental strategies are important benchmarks for large-scale applications. Our comparison includes annualized portfolio returns, maximal drawdowns, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator. Our results show that the ERI strategy significantly outperforms both the minimum-variance portfolio and the equally weighted portfolio on assets with heavy tails.     

An empirical examination of jump risk in asset pricing and volatility forecasting in China's equity and bond markets

Understanding jump risk is important in risk management and option pricing. This study examines the characteristics of jump risk and the volatility forecasting power of the jump component in a panel of high-frequency intraday stock returns and four index returns from Shanghai Stock Exchange. Across portfolio indexes, jump returns on average account for 45% to 64% of total returns when jumps occur. Market systematic jump risk is an important pricing factor for daily returns. The average jump beta is 62% of the average continuous beta for individual stocks. However, the contribution of jump risk to total risk is limited, indicating that statistically significant jumps in the stochastic process of asset price are rare events but have tremendous impacts on the prices of common stocks in China. We further document that accounting for jump components improves the performance of volatility forecasting for some equity and bond portfolios in China, which is confirmed by in-the-sample and out-of-sample forecasting performance analysis.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

Do Mutual Funds Profit from the Accruals Anomaly?

Using data on both fund stockholdings and fund returns, we examine whether actively managed equity mutual funds trade on and profit from the accruals anomaly. We find that few, if any, mutual funds trade on the anomaly. The top 10% of mutual funds that have the highest portfolio weights in low‐accruals stocks have a greater, but still relatively small, exposure to low‐accruals stocks. Nonetheless, these funds make significant profit net of actual transaction costs, exhibiting an average Fama‐French three‐factor alpha of 2.83% per year. We also find that these funds are smaller, less diversified, and exhibit higher fund return volatility and higher fund flow volatility.     

Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both returns and bid-ask spreads from intraday data, we incorporate the measurement of commonalities in liquidity and comovements of stocks and bid-ask spreads into the forecasting of three types of liquidity-adjusted intraday Value-at-Risk (L-IVaR). In a preliminary analysis, we document strong extreme comovements in liquidity and strong tail dependence between bid-ask spreads and log returns across the firms in our sample thus motivating our use of a vine copula model. Furthermore, the backtesting results for the L-IVaR of a portfolio consisting of five stocks listed on the NASDAQ show that the proposed models perform well in forecasting liquidity-adjusted intraday portfolio profits and losses.     

The Effect of Market Regimes on Style Allocation

We analyse time-varying risk premia and the implications for portfolio choice. Using Markov Chain Monte Carlo (MCMC) methods, we estimate a multivariate regime-switching model for the Carhart (1997) four-factor model. We find two clearly separable regimes with different mean returns, volatilities, and correlations. In the High-Variance Regime, only value stocks deliver a good performance, whereas in the Low-Variance Regime, the market portfolio and momentum stocks promise high returns. Regime-switching induces investors to change their portfolio style over time depending on the investment horizon, the risk aversion, and the prevailing regime. Value investing seems to be a rational strategy in the High-Variance Regime, momentum investing in the Low-Variance Regime. An empirical out-of-sample backtest indicates that this switching strategy can be profitable, but the overall forecasting ability for the regime-switching model seems to be weak compared to the iid model.     

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.     

The selection of multinational equity portfolios: forecasting models and estimation risk

The problem considered is the selection of a portfolio of international assets, particularly the forecasting of the inputs to a selection algorithm. Four models of the asset return generating process are considered, two of which ignore the international nature of the universe of assets, two which exploit it in different ways. Several estimation methods are considered for each component: expected return, variance and covariance of returns. The combinations of model and estimation method are first evaluated in terms of their forecasting performance for the components mentioned for the individual assets. The universe used is the components of the Financial Times Eurotrack 100 Index. Significant differences were found between the forecasting accuracy of the methods considered for each component. In the final stage of the analysis, a comparison of the returns on portfolios chosen using each combination showed a significant difference. The analysis suggests that the choice of estimation method is more critical than the choice of pricing model.