Enhancing momentum investment strategy using leverage

Previous studies examine investment strategies based on leverage and momentum; none investigates both variables jointly as an investment strategy. This paper is the first incorporating leverage and momentum together. We show that low past returns (losers) forecast future negative abnormal returns only among stocks with high leverage levels, but not among stocks with low leverage levels. However, high past returns (winners) forecast future positive abnormal returns independently of leverage level. As a result, the negative relation between leverage and future abnormal returns is only observed among loser stocks, and the positive relation between past returns and future abnormal returns is only shown among non‐low leverage stocks. Our results are important in achieving better investment strategies: buying winners' stocks (independently of their level of leverage) and short‐selling losers' stocks with high leverage yield higher abnormal returns than strategies based on only one of these variables. Our two‐dimensional strategy yields risk‐adjusted abnormal returns of 15.66% per annum, whereas the single leverage or momentum strategies yield 7.70% and 7.96% per annum, respectively. The difference is nearly 8% and economically significant. If leverage is considered as proxy for default risk, our results, contrary to previous evidence, show that momentum profits are not exclusive of default stocks, and that momentum returns are not only driven by negative returns yielded by distress stocks.     

Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization

We extract information on relative shopping interest from Google search volume and provide a genuine and economically meaningful approach to directly incorporate these data into a portfolio optimization technique. By generating a firm ranking based on a Google search volume metric, we can predict future sales and thus generate excess returns in a portfolio exercise. The higher the (shopping) search volume for a firm, the higher we rank the company in the optimization process. For a sample of firms in the fashion industry, our results demonstrate that shopping interest exhibits predictive content that can be exploited in a real‐time portfolio strategy yielding robust alphas around 5.5%.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Decision‐Based Forecast Evaluation of UK Interest Rate Predictability

This paper illustrates the importance of density forecasting and forecast evaluation in portfolio decision making. The decision‐making environment is fully described for an investor seeking to optimally allocate her portfolio between long and short Treasury bills, over investment horizons of up to 2 years. We examine the impact of parameter uncertainty and predictability in bond returns on the investor's allocation and we describe how the forecasts are computed and used in this context. Both statistical and decision‐based criteria are used to assess the predictability of returns. Our results show sensitivity to the evaluation criterion used and, in the context of investment decision making under an economic value criterion, we find some potential gain for the investor from assuming predictability. Copyright © 2015 John Wiley & Sons, Ltd.     

Risk factor beta conditional value‐at‐risk

We propose a new approach to the estimation of the portfolio Value‐at‐Risk. Based on the assumption that the same macroeconomic factors affect returns of all assets in a portfolio, this methodology allows the generation of the sequence of hypothetical future equilibrium portfolio returns given the historical values of the underlying macroeconomic factors and the asset betas with respect to these factors. Value‐at‐Risk is then found as an appropriate percentile of the corresponding hypothetical distribution of the portfolio profits and losses. The backtesting results for the six Fama–French benchmark portfolios and the S&P500 index show that this approach yields reasonably accurate estimates of the portfolio Value‐at‐Risk. Copyright © 2008 John Wiley & Sons, Ltd.     

Economic factors and the stock market: a new perspective

It has been widely accepted that many financial and economic variables are non‐linear, and neural networks can model flexible linear or non‐linear relationships among variables. The present paper deals with an important issue: Can the many studies in the finance literature evidencing predictability of stock returns by means of linear regression be improved by a neural network? We show that the predictive accuracy can be improved by a neural network, and the results largely hold out‐of‐sample. Both the neural network and linear forecasts show significant market timing ability. While the switching portfolio based on the linear forecasts outperforms the buy‐and‐hold market portfolio under all three transaction cost scenarios, the switching portfolio based on the neural network forecasts beats the market only if there is no transaction cost. Copyright © 1999 John Wiley & Sons, Ltd.     

Does geographic location matter to stock return predictability?

Building on recent and growing evidence that geographic location influences information diffusion, this paper examines the relation between firm's location and the predictability of stock returns. We hypothesize that returns on a portfolio composed of firms located in central areas are more likely to follow a random walk than returns on a portfolio composed of firms located in remote areas. Using a battery of variance ratio tests, we find strong and robust support for our prediction. In particular, we show that the returns on a portfolio composed of the 500 largest urban firms follow a random walk; however, all variance ratio tests reject the random walk hypothesis for a portfolio that includes the 500 largest rural firms. Our results are robust to alternative definitions of firm's location and portfolio formation.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.     

Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns

This paper evaluates the accuracy of 1‐month‐ahead systematic (beta) risk forecasts in three return measurement settings; monthly, daily and 30 minutes. It was found that the popular Fama–MacBeth beta from 5 years of monthly returns generates the most accurate beta forecast among estimators based on monthly returns. A realized beta estimator from daily returns over the prior year generates the most accurate beta forecast among estimators based on daily returns. A realized beta estimator from 30‐minute returns over the prior 2 months generates the most accurate beta forecast among estimators based on 30‐minute returns. In environments where low‐, medium‐ and high‐frequency returns are accurately available, beta forecasting with low‐frequency returns are the least accurate and beta forecasting with high‐frequency returns are the most accurate. The improvements in precision of the beta forecasts are demonstrated in portfolio optimization for a targeted beta exposure. Copyright © 2016 John Wiley & Sons, Ltd.     

Residual income,non‐earnings information,and information content

We extend Ohlson's (1995) model and examine the relationship between returns and residual income that incorporate analysts' earnings forecasts and other non‐earnings information variables in the balance sheet, namely default probability and agency cost of a debt covenant contract. We further divide the sample based on bankruptcy (agency) costs, earnings components and growth opportunities of a firm to explore how these factors affect the returns–residual income link. We find that the relative predictive ability for contemporaneous stock price by considering other earnings and non‐earnings information is better than that of models without non‐earnings information. If the bankruptcy (agency) cost of a firm is higher, its information role in the firm's equity valuation becomes more important and the accuracy of price prediction is therefore higher. As for non‐earnings information, if bankruptcy (agency) cost is lower, the information role becomes more relevant, and the earnings response coefficient is hence higher. Moreover, the decomposition of unexpected residual income into permanent and transitory components induces more information than that of the unexpected residual income alone. The permanent component has a larger impact than the transitory component in explaining abnormal returns. The market and industry properties and growth opportunity also have incremental explanatory power in valuation. Copyright © 2008 John Wiley & Sons, Ltd.     

Yield curve forecast combinations based on bond portfolio performance

We propose an economically motivated forecast combination strategy in which model weights are related to portfolio returns obtained by a given forecast model. An empirical application based on an optimal mean–variance bond portfolio problem is used to highlight the advantages of the proposed approach with respect to combination methods based on statistical measures of forecast accuracy. We compute average net excess returns, standard deviation, and the Sharpe ratio of bond portfolios obtained with nine alternative yield curve specifications, as well as with 12 different forecast combination strategies. Return‐based forecast combination schemes clearly outperformed approaches based on statistical measures of forecast accuracy in terms of economic criteria. Moreover, return‐based approaches that dynamically select only the model with highest weight each period and discard all other models delivered even better results, evidencing not only the advantages of trimming forecast combinations but also the ability of the proposed approach to detect best‐performing models. To analyze the robustness of our results, different levels of risk aversion and a different dataset are considered.     

Equity return predictability,its determinants,and profitable trading strategies

This paper explains cross‐market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross‐sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the cross‐sectional variation of return predictability, only value of stock traded (a measure of liquidity) is found to have robust explanatory power by Leamer's (1985) EBA. However, Sala‐i‐Martin's (1997) EBA reports that value of stock traded, gross domestic product (GDP) per capita, level of information and communication technology (ICT) development, governance quality, and corruption perception are robust determinants. We further find that a strategy of buying (selling) aggregate market portfolios of the countries with the highest positive (negative) return predictability statistic in the past 24 months generates statistically significant positive returns in the subsequent 3 to 12 months. In the individual country level, a trading rule of buying (selling) the respective country's aggregate market portfolio, when the return predictability statistic turns out positive (negative), outperforms the conventional buy‐and‐hold strategy for many countries.     

Statistical estimation of optimal portfolios for non‐Gaussian dependent returns of assets

This paper discusses the asymptotic efficiency of estimators for optimal portfolios when returns are vector‐valued non‐Gaussian stationary processes. We give the asymptotic distribution of portfolio estimators ? for non‐Gaussian dependent return processes. Next we address the problem of asymptotic efficiency for the class of estimators ?. First, it is shown that there are some cases when the asymptotic variance of ? under non‐Gaussianity can be smaller than that under Gaussianity. The result shows that non‐Gaussianity of the returns does not always affect the efficiency badly. Second, we give a necessary and sufficient condition for ? to be asymptotically efficient when the return process is Gaussian, which shows that ? is not asymptotically efficient generally. From this point of view we propose to use maximum likelihood type estimators for g, which are asymptotically efficient. Furthermore, we investigate the problem of predicting the one‐step‐ahead optimal portfolio return by the estimated portfolio based on ? and examine the mean squares prediction error. Copyright © 2008 John Wiley & Sons, Ltd.     

Model uncertainty,thick modelling and the predictability of stock returns

Recent financial research has provided evidence on the predictability of asset returns. In this paper we consider the results contained in Pesaran and Timmerman (1995), which provided evidence on predictability of excess returns in the US stock market over the sample 1959–1992. We show that the extension of the sample to the nineties weakens considerably the statistical and economic significance of the predictability of stock returns based on earlier data. We propose an extension of their framework, based on the explicit consideration of model uncertainty under rich parameterizations for the predictive models. We propose a novel methodology to deal with model uncertainty based on ‘thick’ modelling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy systematically outperform thin modelling. Copyright © 2005 John Wiley & Sons, Ltd.     

WTI crude oil option implied VaR and CVaR: An empirical application

Using option market data we derive naturally forward‐looking, nonparametric and model‐free risk estimates, three desired characteristics hardly obtainable using historical returns. The option‐implied measures are only based on the first derivative of the option price with respect to the strike price, bypassing the difficult task of estimating the tail of the return distribution. We estimate and backtest the 1%, 2.5%, and 5% WTI crude oil futures option‐implied value at risk and conditional value at risk for the turbulent years 2011–2016 and for both tails of the distribution. Compared with risk estimations based on the filtered historical simulation methodology, our results show that the option‐implied risk metrics are valid alternatives to the statistically based historical models.     

Strategic asset allocation by mixing shrinkage,vine copula and market equilibrium

We propose a new portfolio optimization method combining the merits of the shrinkage estimation, vine copula structure, and Black–Litterman model. It is useful for many investors to satisfy simultaneously the three investment objectives: estimation sensitivity, asymmetric risks appreciation, and portfolio stability. A typical investor with such objectives is a sovereign wealth fund (SWF). We use China's SWF as an example to empirically test the method based on a 15‐asset strategic asset allocation problem. Robustness tests using subsamples not only show the method's overall effectiveness but also manifest that the function of each component is as expected.     

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

This study is the first to examine the impacts of overnight and intraday oil futures cross-market information on predicting the US stock market volatility the high-frequency data. In-sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out-of-sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.     

Incorporating higher moments into value‐at‐risk forecasting

Value‐at‐risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram‐Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time‐varying higher‐moments models. Copyright © 2009 John Wiley & Sons, Ltd.     

Sentiment indices and their forecasting ability

The success of any timing strategy depends on the accuracy of market forecasts. In this paper, we test five indices to forecast the 1‐month‐ahead performance of the S&P 500 Index. These indices reflect investor sentiment, current business conditions, economic policy uncertainty, and market dislocation information. Each model is used in a logistic regression analysis to predict the 1‐month‐ahead market direction, and the forecasts are used to adjust the portfolio's beta. Beta optimization refers to a strategy designed to create a portfolio beta of 1.0 when the market is expected to go up, and a beta of ?1.0 when a bear market is expected. Successful application of this strategy generates returns that are consistent with a call option or an option straddle position; that is, positive returns are generated in both up and down markets. Analysis reveals that the models' forecasts have discriminatory power in identifying substantial market movements, particularly during the bursting of the tech bubble and the financial crisis. Four of the five forecast models tested outperform the benchmark index.     

Cross‐correlations and predictability of stock returns

Studies have shown that small stock returns can be partially predicted by the past returns of large stocks (cross‐correlations), while a larger body of literature has shown that macroeconomic variables can predict future stock returns. This paper assesses the marginal contribution of cross‐correlations after controlling for predictability inherent in lagged macroeconomic variables. Macroeconomic forecasting models generate trading rule profits of up to 0·431% per month, while the inclusion of cross‐correlations increases returns to 0·516% per month. Such results suggest that cross‐correlations may serve as a proxy for omitted macroeconomic variables in studies of stock market predictability. Macroeconomic variables are more important than cross‐correlations in forecasting small stock returns and encompassing tests suggest that the small marginal contribution of cross‐correlations is not statistically significant. Copyright © 2000 John Wiley & Sons, Ltd.