Higher Sharpe Ratios Research Articles

The Use of RONA/WACC as a Proxy for Investment Quality

Proxies for stock investment quality have varied from multi-factor models such as the Piotroski F-Score to simple one-factor return on capital measures. Economic value added, measured as the ratio of return on net assets relative to the weighted average cost of capital, has not been used as a proxy for stock quality. The aim of this study was to demonstrate that economic value added can be used effectively as a proxy for stock quality. Industrial stocks listed on the JSE ALSI between 31 January 2006 and 31 December 2015 comprised the population of this study. Three portfolios (comprising 33% of the population each) were formed monthly and were held for 12 months leading to the creation of 360 portfolios. The portfolios were formed on the basis of the RONA/WACC ratio; stocks with the highest ratios comprised HEV portfolios, stocks with the lowest ratios comprised LEV portfolios and stocks with median ratios comprised MEV portfolios. HEV portfolios earned a mean return of 19.52% relative to 13.09% for MEV portfolios, 16.31% for LEV portfolios and 13.73% for the overall equity market. The maximum cumulative value of R1 invested in HEV portfolios was equal to R5.30 as at the end of the study period. The maximum cumulative value of R1 invested in MEV and LEV portfolios was equal to R4.14 and R4.40 respectively. The maximum corresponding value of R1 invested in the general equity market was, R3.50. The superior returns of HEV portfolios were observed to be more risk efficient relative to both MEV and LEV portfolios and relative to the market portfolio on the basis of higher Sharpe Ratios. The RONA/WACC ratio has thus been shown to have been an effective proxy for stock quality.

Optimized Fundamental Portfolios

We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights.

Optimal ETF Portfolios for Different Objectives and Horizons

This study identifies optimal portfolios for investors with different objectives and horizons, usinginvestable exchange-traded funds of major asset classes. The optimal portfolios offer much higher Sharpe ratios than the market portfolio. Only three funds representing corporate bonds, NASDAQ 100 index, and gold contribute to the optimal portfolios. Since short investment horizons are examined, most of the optimal portfolios invest mainly in corporate bonds. However, the optimal portfolios with market volatility invest mostly in the NASDAQ 100 index. As the only asset with normally distributed returns, gold participates in optimal portfolios for investors concerned about negative skewness and excess kurtosis.

Seeking a Better Portfolio with Industry Recommendations

ABSTRACTThis paper provides novel evidence that investors can build a better performing portfolio by exploiting industry level consensus recommendations. A minimum variance portfolio, combined with consensus recommendations, yields a higher Sharpe ratio and certainty equivalent returns. A minimum variance portfolio with no short-selling constraint consistently outperforms an equally weighted portfolio when exploiting consensus recommendations, which is an innovation compared to the existing literature. Our results suggest that sell-side analysts and brokers provide valuable information in the financial market and we benefit from incorporating the information in the portfolio optimisation.

OUT-OF-SAMPLE STOCK RETURN PREDICTION USING HIGHER-ORDER MOMENTS

We analyze variance, skewness and kurtosis risk premia and their option-implied and realized components as predictors of excess market returns and of the cross-section of stock returns. We find that the variance risk premium is the only moment-based variable to predict S&P 500 index excess returns, with a monthly out-of-sample [Formula: see text] above 6% for the period between 2001 and 2014. Nonetheless, all aggregate moment-based variables are effective in predicting the cross-section of stock returns. Self-financed portfolios long on the stocks least exposed to the aggregate moment-based variable and short on the stocks most exposed to it achieve positive and significant Carhart 4-factor alphas and a considerably higher Sharpe ratio than the S&P 500 index, with positive skewness.

Stock Price Co-Movement and the Foundations of Pairs Trading

We study the theoretical implications of cointegrated stock prices on the profitability of pairs-trading strategies. If stock returns are fairly weakly correlated across time, cointegration implies very high Sharpe ratios. To the extent that the theoretical Sharpe ratios are “too large,” our results suggest that either i) cointegration does not exist pairwise among stocks, and pairs-trading profits are a result of a weaker or less stable dependency structure among stock pairs, or ii) the serial correlation in stock returns stretches over considerably longer horizons than is usually assumed. Empirically, there is little evidence of cointegration, favoring the first explanation.

Practical Applications of An Index Methodology for Diversifying Business Risk

<h3>Practical Applications Summary</h3> In <b><i>An Index Methodology for Diversifying Business Risk</i></b>, published in <b><i>The Journal of Index Investing</i></b>, <b>Rory Riggs</b> and <b>Jonathan Chandler</b>, both of <b>Syntax, LLC</b>, and <b>Mark T. Finn</b>, of Vantage Consulting Group, introduce a methodology for constructing an equity index in which the weight assigned to each component is determined by reference to the nature of the business risks to which it is exposed. The methodology uses the technique of statistical stratification to maximize the index’s diversification across different categories of business risks. Using the populations of the S&amp;P 500 and the S&amp;P MidCap 400 indexes, the authors compare the performance of stratified indexes created using their methodology to both market-capitalization-weighted and equal-weighted indexes comprising the same components. They find that the stratified indexes delivered higher returns and higher Sharpe ratios than the others. <b>TOPICS:</b>VAR and use of alternative risk measures of trading risk, performance measurement

Factors That Fit the Time Series and Cross-Section of Stock Returns

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly dominates PCA and finds weak factors with high Sharpe-ratios that PCA cannot detect. Studying a large number of characteristic sorted portfolios we find that five latent factors with economic meaning explain well the cross-section and time-series of returns. We show that out-of-sample the maximum Sharpe-ratio of our five factors is more than twice as large as with PCA with significantly smaller pricing errors. Our factors are based on only a subset of the stock characteristics implying that a significant amount of characteristic information is redundant.

Gross profitability and momentum

PurposeThe purpose of this paper is to examine the combined performance of momentum and a gross profitability-based strategy. The motivation stems from the strong performance of momentum on the short side and profitability on the long side, suggesting a potentially superior combined strategy. Gross profitability is also a measure of firm quality, so that another motivation is to contribute to a growing literature on factor-based investing that includes momentum and quality as potential factors.Design/methodology/approachThe empirical approach employed in the paper is standard in the asset pricing literature. The firms are sorted into portfolios based on profitability and momentum, and the combined performance is studied through independent double sorting. Both value-weighted and equally weighted returns are reported in case of key empirical results.FindingsThe combined strategy results in superior performance. Specifically, the strategy produces results 2.75 greater than the momentum strategy, and about four times as high as the profitability strategy. The strategy also has much higher Sharpe ratio that improves further when combined with size and value strategies.Research limitations/implicationsThe research has significant implications for academics and practitioners alike. A new investment strategy that has not been explored in the literature is presented. The superior performance of the strategy presents a challenge for the market efficiency, and would be of interest to academics and practitioners working in the area of investment management.Practical implicationsThere has been a growing interest in multi-factor investing in recent years. The paper documents that superior performance is achieved by combining two of the popular factors, namely profitability and momentum.Originality/valueThe research is the first to study the combined performance of profitability and momentum, and provide evidence on the superiority of the combined strategy.

Risk Prices Vary in the Cross Section

Equal compensation across assets for the same risk exposures is a bedrock of asset pricing theory and empirics. Yet real-world frictions can violate this equality and create high-Sharpe ratio opportunities. We develop new methods for asset pricing with cross-sectional heterogeneity in compensation for risk. We extend k-means clustering to group assets by risk prices and introduce a formal test for whether differences in risk premia across market segments are too large to occur by chance. Using portfolios of US stocks, international stocks, and assets from multiple classes, we find significant evidence of cross-sectional variation in risk prices for all 159 combinations of test assets, factor models, and time periods. Variation in risk prices is as important as variation in risk exposures for explaining the cross-section of expected returns.

Systematic Currency Volatility Risk Premia

I show that volatility risk of the dollar factor --- an equally weighted basket of developed U.S. dollar exchange rates --- carries a significant risk premium and that it is priced in the cross-section of currency volatility excess returns. The dollar factor volatility risk premium is negative on average with an upward sloping and concave term structure. Consistent with this pattern, I find that dollar factor volatility risk is most significantly priced in the cross-section of volatility excess returns at shorter maturities. A trading strategy that sells (buys) volatility insurance on currencies with high (low) exposure to dollar factor volatility risk delivers high mean excess returns and Sharpe ratios. At shorter maturities, the profitability of this strategy cannot be explained by exposure to traditional currency factors, equity factors, or currency volatility carry factors.

High-Frequency Covariance Matrix Estimation Using Price Durations

We propose a price duration based covariance matrix estimator using high frequency transactions data. The effect of the last-tick time-synchronisation methodology, together with effects of important market microstructure components is analysed through a comprehensive Monte Carlo study. To decrease the number of negative eigenvalues produced by the non positive-semi-definite (psd) covariance matrix, we devise an average covariance estimator by taking an average of a wide range of duration based covariance matrix estimators. Empirically, candidate covariance estimators are implemented on 19 stocks from the DJIA. The duration based covariance estimator is shown to provide comparably accurate estimates with smaller variation compared with competing estimators. An out-of-sample GMV portfolio allocation problem is studied. A simple shrinkage technique is introduced to make the sample matrices psd and well-conditioned. Compared to competing high-frequency covariance matrix estimators, the duration based estimator is shown to give more stable portfolio weights and higher Sharpe ratios while maintaining comparably low portfolio variances.

DCC-GARCH Application in Formulating Dynamic Portfolio between Stocks in the Indonesia Stock Exchange with Gold

T his study tries to form a portfolio by using a method which may accommodate the dynamic of assets correlation and the abnormality of stock return distribution namely DCC-GARCH. Th e objective of th is study is to combine individual stocks with gold, so retail investor can also apply this method. This study us ing data from January 200 9 –December 201 7 period. Samples in this study were nine stocks. The results of this study showed that there were two stocks with higher Sharpe Ratio if combined with gold through dynamic portfolio formation (hedged portfolio) namely BBCA -Gold and SMCB -Gold than unhedged portfolio . And there are three stocks with higher Treynor Ratio if combined with gold through dynamic portfolio formation (hedged portfolio) namely BBCA -Gold , SMCB -Gold and UNTR-Gold than unhedged portfolio . This finding proves that the DCC-GARCH application can improve the risk-adjusted return of these stocks when combined with gold.

Master limited partnerships: Is it a smart investment vehicle?

Master limited partnerships (MLPs) in the energy sector offer positive alpha and a beta lower than one in more recent period of 2001–2016 as oil prices experience a big upward swing. Further analyses confirm a negative relation between MLPs' alphas and their betas, consistent with the pattern documented by Frazzini and Pedersen (2013). MLPs provide no hedge against inflation risk or against a volatile stock market. Simulations show that these MLPs provide investors with higher returns, lower risk, and thus a higher Sharpe ratio than the traditional strategy of buy-and-hold the S&P 500 index fund.

Leverage and Performance Metrics in Asset Pricing

Commonly used performance metrics calculated from tradable long-short strategies constructed using leverage lead to specious conclusions of statistical significance. In particular, even if assets are randomly assigned to the levered strategies' long and short portfolios, too often they generate high Sharpe Ratios and statistically significant CAPM, Carhart, and Fama-French Five Factor alphas. I call this finding the mechanical effect of leverage on performance metrics and suggest various diagnostic tests to control for this effect when testing levered strategies.

Investment Horizon Related Momentum in Volatility Scaled Momentum Strategies

Recent studies have shown that the Sharpe ratio and alpha for the momentum strategy can be increased, by scaling the momentum strategy, by the inverse of its historical volatility. However, we find that the higher Sharpe ratio and the alphas of the volatility scaled momentum strategy, in comparison to traditional momentum strategy, depends on the length of the investment horizon. Such that, the longer the investment horizon the better is the performance of the volatility scaled momentum strategy. It is further shown that the higher returns associated with volatility scaled strategies for different horizons have no risk-based explanation and instead linked with the variance of long side of scaled strategies. The main underpinning of these findings is the persistence in the volatility of momentum strategy and its negative predictive relationship with momentum returns that only exist when volatility of momentum strategy is higher.

How Much Information Do We Need to Time the Market?

In this article, the authors present an analytical explanation for why it can be difficult to devise a successful market timing strategy. The authors derive formulas to estimate the minimum required information coefficient for a timing strategy to outperform a buy-and-hold market benchmark, both with and without an alpha target. They show that markets with high Sharpe ratios and those that have low volatility are by nature hard to time. They also show that having high market exposure in a market timing strategy is generally beneficial; however, there can be a critical point beyond which additional market exposure makes timing more difficult. The authors extend the model to cover practical considerations such as transaction costs, skewness and fat tails, and market timing with two correlated assets. Finally, they present a case study to illustrate how investors could apply their framework to choose the optimal market exposure in a market timing strategy using the S&P 500.

Could Omega Ratio Perform Better than Sharpe Ratio?

In this paper, we will investigate whether there is any Sharpe ratio rule or Omega ratio rule that can be used to show that one asset outperforms another asset if it has a higher Sharpe ratio and/or Omega ratio. We find that Sharpe ratio rule could not detect preference of both risk averters and risk seekers in some strong dominance cases. We set up the Omega ratio rule and find that the Omega ratio rule is better than the mean-variance rule because the former could the former can detect the first order stochastic dominated asset but the latter cannot. We also show the superiority of the Omega ratio rule over any Sharpe ratio rule by using hedging funds data and discuss the advantage of using the Omega ratio rule to stochastic dominance rule.

Conditional Dynamics and the Multi-Horizon Risk-Return Trade-Off

We propose testing asset-pricing models using multi-horizon returns (MHR). MHR serve as powerful source of conditional information that is economically important and not data-mined. We apply MHR-based testing to linear factor models. These models seek to construct the unconditionally mean-variance efficient portfolio. We reject all state-of-the-art models that imply high maximum Sharpe ratios in a single-horizon setting. Thus, the models do a poor job in accounting for the risk-return trade-off at longer horizons. Across the different models, the mean absolute pricing errors associated with MHR are positively related to the magnitude of maximal Sharpe ratio in the single-horizon setting. Model misspecification manifests itself in strong intertemporal dynamics of the factor loadings in the SDF representation. We suggest that misspecification of the dynamics of loadings arises from the common approach towards factor construction via portfolio sorts.

Covering the world: global evidence on covered calls

Typical covered call strategies may be decomposed, using a risk and performance attribution methodology, into three components: equity exposure, short volatility exposure and equity timing. This paper applies that attribution methodology to covered calls on eleven global indexes. We find that the relative risk and return contributions of the three components are remarkably consistent across our cross-section of indexes. Across the board, the covered call’s equity exposure is responsible for most of the strategy’s risk and return, while the short volatility exposure has the highest Sharpe ratio of the strategy’s components. The returns from the equity timing exposure are statistically insignificant in all eleven indexes, yet this exposure contributes a relatively large amount of the strategy’s risk. These results provide further evidence that managing equity exposure in covered calls provides superior risk-adjusted returns. Further, a globally diversified portfolio of risk-managed covered calls may be viewed as a defensive alternative to global equity, providing similar returns with lower volatility and lower drawdowns.