Higher Sharpe Ratios Research Articles

Intangible assets and the cross-section of stock returns

We examine whether intangible assets are priced in the cross-section of stock returns. We find that intangible asset intensity has more explanatory power than size, value, profitability, and investment. An intangibles-based long-short factor has a higher Sharpe ratio than these established factors. Adding the intangible factor to the Fama-French five-factor model improves the description of average returns and makes the investment factor redundant. The intangible factor is distinct from traditional growth strategies, provides a hedge to value and quality strategies, and expands investors’ opportunity sets. Intangible intensity as characteristic is more important than as risk factor, consistent with intangibles-based mispricing.

Foreign Currency Forecasting: What Can Stock and Bond Markets Tell Us?

This paper provides a comprehensive statistical and economic evidence on the forecasting power of local-currency equity and bond returns in predicting exchange rate returns. We first construct out-of-sample (OOS) forecasts using various model specifications of equity and bond returns, and assess their statistical accuracy against the naive random walk (RW) model. Next, we test their economic value by designing a trading strategy based on the sign of our OOS forecasts. Using a sample of 28 countries, we find three key results: (1) our in-sample forecasts show that all the slope estimates of our predictive regressions are significant, indicating that the RW model is misspecified; (2) the statistical accuracy criteria suggest that our OOS forecasts outperform the RW model; and (3) our trading strategies, that use stock and bond returns, generate high and significant Sharpe ratios which are uncorrelated with the traditional risk factors and other popular determinants of currency returns.

A Machine Learning Approach for the Short-term Reversal Strategy

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

Uncertainty Network Risk and Currency Returns

We examine the pricing of a horizon specific uncertainty network risk, extracted from option implied variances on exchange rates, in the cross-section of currency returns. Buying currencies that are receivers and selling currencies that are transmitters of short-term shocks exhibits a high Sharpe ratio and yields a significant alpha when controlling for standard dollar, carry trade, volatility, variance risk premium and momentum strategies. This profitability stems primarily from the causal nature of shock propagation and not from contemporaneous dynamics. Shock propagation at longer horizons is priced less, indicating a downward-sloping term structure of uncertainty network risk in currency markets.

Evaluating the Effectiveness of the Momentum Strategy for Responsible Investment in the Russian Stock Market

In modern conditions, the integration of ESG-criteria into investment decisions of asset managers is considered as a key factor in sustainable economic development. We examine the effectiveness of the Momentum-ESG strategy based on the Responsibility and Openness Index in comparison with the Momentum strategy, which is based on the Moscow Exchange Broad Market Index, since December 2011 to December 2020. We propose an algorithm for integrating ESG criteria into momentum strategy. We select "winners" and "losers" stocks based on their monthly return. The Momentum-ESG strategy has a high Sharpe ratio for a time horizon of 12 months, the Momentum strategy - for a time horizon of 6 months. The testing of the Momentum-ESG strategy shows its greater efficiency in terms of the Sharpe ratio compared to the Momentum strategy, which does not take into account the ESG-factors, in the investing period.

CAN THE LEADING US ENERGY STOCK PRICES BE PREDICTED USING THE ICHIMOKU CLOUD?

The aim of this study is to investigate if Ichimoku Cloud can serve as a technical analysis indicator to improve stock price prediction for leading US energy companies. The methodology centers on the application of the Ichimoku Cloud as a trading system. The daily stock prices of the top ten constituents of the S&P Composite 1500 Energy Index - spanning the period from 12th April, 2012 to 31st July, 2019 - were sourced for experimentation. The performance of the Ichimoku Cloud is measured using both the Sharpe and Sortino ratios to adjust for total and downside risks. The analysis is split into pre and post oil crisis to account for the drop in energy stock prices during the July 2014 - December 2015. The model is also benchmarked against the naive buy-and-hold strategy. The capacity of the Ichimoku indicator to provide signals during strengthening trends is analyzed. Despite the drop in energy stock prices, number of trades continued to increase along with profit opportunities. The PSX stock ranked first, with the highest Sharpe ratio, Sortino ratio, and Sharpe per number of trade. As expected, a number of buying signals occurred during strengthening bullish periods. Surprisingly, various sell signals also occurred during similar strengthening bullish trends. Most of the buy and sell signals under the Ichimoku indicator occurred outside of strengthening of bullish or bearish trends. The overall findings suggest that speculators can benefit from the use of the Ichimoku Cloud in analyzing energy stock price movements. In addition, it has the potential to reduce susceptibility to changes in energy prices. Last, the strength of the trend in place needs to be captured as it served as an additional layer of information which can improve the decision making process of the trader.

ASSET ALLOCATION WITH ASSET-CLASS-BASED AND FACTOR-BASED RISK PARITY APPROACHES

The asset allocation strategy is important to manage assets effectively. In recent years, the risk parity strategy has become attractive to academics and practitioners. The risk parity strategy determines the allocation for asset classes in order to equalize their contributions to overall portfolio risk. Roncalli and Weisang (2016) propose the use of “risk factors” instead of asset classes. This approach achieves the portfolio diversification based on the decomposition of portfolio risk into risk factor contribution. The factor-based risk parity approach can diversify across the true sources of risk whereas the asset-class-based approach may lead to solutions with hidden risk concentration. However, it has some shortcomings. In our paper, we propose a methodology of constructing the well-balanced portfolio by the mixture of asset-class-based and factor-based risk parity approaches. We also propose the method of determining the weight of two approaches using the diversification index. We can construct the portfolio dynamically controlled with the weight which is adjusted in response to market environment. We examine the characteristics of the model through the numerical tests with seven global financial indices and three factors. We find it gives the well-balanced portfolio between asset and factor diversifications. We also implement the backtest from 2005 to 2018, and the performances are measured on a USD basis. We find our method decreases standard deviation of return and downside risk, and it has a higher Sharpe ratio than other portfolio strategies. These results show our new method has practical advantages.

The Value of Using Predictive Information Optimally

For mean-variance investors, using predictive information unconditionally optimally produces better portfolios than using predictive information conditionally optimally. The latter is more usually done in practice. Empirically, the unconditionally optimal portfolios have higher Sharpe ratios and certainty equivalents than the conditionally optimal portfolios. They also have lower turnover, leverage, losses and draw-downs. Moreover, measures of the whole distribution tend to prefer the unconditionally optimal portfolios, especially once transaction costs are accounted for. With transaction costs, the unconditionally optimal portfolios often second-order stochastically dominate the conditionally optimal portfolios. The unconditionally optimal portfolios are also preferred in terms of Sharpe ratio, certainty equivalent, costs, losses, draw-downs and stochastic dominance to mean-variance optimal portfolios that do not use predictive information. However, whether unconditionally optimal portfolios are preferred to minimum variance or 1/N portfolios depends on the asset universe.

Asset selection based on high frequency Sharpe ratio

In portfolio choice problems, the classical Mean–Variance model in Markowitz (1952) relies heavily on the covariance structure among assets. As the number and types of assets increase rapidly, traditional methods to estimate the covariance matrix and its inverse suffer from the common issues in high or ultra-high dimensional analysis. To avoid the issue of estimating the covariance matrix with high or ultra-high dimensional data, we propose a fast procedure to reduce dimension based on a new risk/return measure constructed from intra-day high frequency data and select assets via Dependent Sure Explained Variability and Independence Screening (D-SEVIS). While most feature screening methods assume i.i.d. samples, by nature of our data, we make contribution to studying D-SEVIS for samples with serial correlation, specifically, for the stationary α-mixing processes. Under α-mixing condition, we prove that D-SEVIS satisfies sure screening property and ranking consistency property. More importantly, with the assets selected through D-SEVIS, we will build a portfolio that earns more excess return compared with several existing portfolio allocation methods. We illustrate this advantage of our asset selection method with the real data from the stock market.

Predictive Equity Analytics: Portfolio Crisis Proofing and Superior Returns

Investor’s returns are enhanced by predictive equity analytics ability to avoid drawdowns and capture gains in bull or bear markets. Predictive equity analytics enables optimal stock selection, timing and bid price with accuracy and reliability. Portfolio value behavior over time is positive and generally a non-decreasing step function. Next trade day predictive equity filtering significantly reduces left tail risk enabling positive returns with high probability. Hedging (crisis proofing) of equity portfolios is a natural artifact of predictive equity analytics with no additional cost and no additional financial instruments required. Portfolios are dynamically constructed using dynamic diversification with higher Sharpe ratios and maximal investment efficiency. Predictive equity analytics enables mean-variance return dominance.

Establishing the risk denominator in a Sharpe ratio framework for share selection from a momentum investment strategy approach

Background: Based on the static mean-variance portfolio optimisation theory, investors will choose the portfolio with the highest Sharpe ratio to achieve a higher expected utility. However, the traditional Sharpe ratio only accounts for the first two moments of return distributions, which can lead to false portfolio performance diagnostics with the presence of asymmetric, highly skewed returns. Aim: With many criticising the standard deviation's applicability and with no consensus on the ascendency of which other risk denominator to consult, this study contributes to the literature by validating the importance of consulting value-at-risk as the more commendable risk denominators for the Johannesburg Stock Exchange. Method: These results were derived from a novel index approach that produces a comprehensive risk-adjusted performance evaluation score. Results: Of the 24 Sharpe ratio variations under evaluation, this study identified the value-at-risk Sharpe ratio as the better variation, which led to more profi table share selections for long-only portfolios from a one-year and five-year momentum investment strategy perspective. However, the attributes of adjusting for skewness and kurtosis exhibited more promise from a three-year momentum investment strategy perspective. Conclusion: The results highlighted the ability to outperform the market, which further emphasised the importance of active portfolio management. However, the results also conrfimed that active and more passive equity portfolio managers will have to consult different Sharpe ratio variations to enhance the ability to outperform the market and a buy-and-hold strategy.

Practical Applications of Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

<h3>Practical Applications</h3> In <b><i>Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power</i></b>, published in the Summer 2020 issue of <b><i>The Journal of Alternative Investments</i>, Takashi Kanamura</b> of <b>Kyoto University</b> helps meet the need for new tools to manage the risks involved with generating renewable energy. The economics of hedging the risks associated with the production of renewable energy are fundamentally different from those associated with traditional energy products. The author makes a unique contribution to the literature by providing a framework for pricing solar energy options. Conventional means of option pricing rely on the notion of a replicating portfolio. Yet, in the case of renewable energy, where prices for the underlying asset do not exist, and the market is incomplete, traditional pricing techniques are not possible. The author instead applies a method of establishing “good-deal bounds” (Cochrane 2000). This approach rules out excessively good prices that can result in arbitrage opportunities or unsustainably high Sharpe ratios. His empirical estimates further illustrate that there is a basis risk when using weather derivatives as a proxy for a solar energy option. <b>TOPICS:</b>ESG investing, emerging, options, performance measurement

Price Index Insurances in the Agriculture Markets

In this paper, we introduce price index insurances on agricultural goods. Seemingly similar to derivatives, there are significant differences between price index insurances and derivatives. First, unlike derivatives, there are no entrance barriers for purchasing insurances, making them the risk management tools that are accessible to almost all farmers. Second, since insurances are issued at a certain number for any individual farm, unlike futures, for example, they cannot be used for speculation and are used solely for hedging price risk. Third, unlike forwards, they are heavily regulated and do not default and cause counterparty risk. Besides all differences (or benefits), such products have just recently been introduced in the agricultural insurance market. In this paper, we investigate if there could have been a financially viable market where these products are traded. More precisely, we investigate if an insurance company can design a portfolio of optimal contracts that gives a higher Sharpe ratio than the financial market index prices (in our paper FTSE 100 and other three major indexes). To reach the paper's objective we take three steps, by considering theoretical, practical and corporation standpoints. In the first step, we will see how an optimal contract would look like from the demand side in a theoretical setup and we obtain the optimal contract from the farmers' standpoint. In the second step, by adopting a more practical approach, by meeting the Key Performance Indicators (KPI) requirements set by the market participants (both demand and supply side), we find the optimal policy specifications from the first step, in the market equilibrium. This step also helps to find some unobservable market parameters like volatility. Finally, by adopting a corporation standpoint we encounter our model to the UK farm index prices and find an optimal portfolio of the products on products from 10 commodities. We find out that investing in such a business is financially viable, as the optimal insurance portfolio produces a Sharpe ratio that outperforms FTSE 100 and other major market indexes.

Price Index Insurances in the Agriculture Markets

In this article, we introduce price index insurances on agricultural goods. Although these seem similar to derivatives, there are significant differences between price index insurances and derivatives. First, unlike derivatives, there are no entrance barriers for purchasing insurances, making them the risk management tools that are accessible to almost all farmers. Second, since insurances are issued in a certain number for any individual farm, unlike futures, for example, they cannot be used for speculation and are used solely for hedging price risk. Third, unlike forwards, they are heavily regulated and do not default and cause counterparty risk. In addition to all differences (or benefits), such products have just recently been introduced in the agricultural insurance market. In this article, we investigate if there could have been a financially viable market where these products are traded. More precisely, we investigate whether an insurance company can design a portfolio of optimal contracts that gives a higher Sharpe ratio than the financial market index prices (here, FTSE 100 and other three major indexes). To reach the article’s objective, we take three steps, in considering theoretical, practical, and corporation standpoints. In the first step, we show what an optimal contract would look like from the demand side in a theoretical setup and we obtain the optimal contract from the farmers' standpoint. In the second step, by adopting a more practical approach in meeting the Key Performance Indicators requirements set by the market participants (both demand and supply side), we find the optimal policies specifications from the first step, in the market equilibrium. This step also helps to determine some unobservable market parameters like volatility. Finally, by adopting a corporation standpoint we bring our model to the U.K. farm index prices and find an optimal portfolio of the products on products from 10 commodities. We demonstrate that investing in such a business is financially viable, as the optimal insurance portfolio produces a Sharpe ratio that outperforms the FTSE 100 and other major market indexes.

When low beats high: Riding the sales seasonality premium

This paper examines whether predictable seasonal patterns in firm fundamentals generate time variation in stock returns. Our findings indicate that stock returns are counterseasonal. Specifically, a long-short strategy of buying low-sales season stocks and shorting high-sales season stocks produces an annual alpha of 8.4% (14.5% over the last decade). This seasonal effect has a relatively high Sharpe ratio and occurs independently of previously documented seasonal anomalies. We analyze several possible hypotheses for this phenomenon.

Meteor showers and global asset allocation

Cross-market linkages allow transmission of shocks among markets. Previous measures of such spillovers are based on broader stock market indexes, which cannot identify the industries that are the principal drivers of spillovers and the industries that are most exposed to the spillovers. Using investable equity indexes, we show that basic materials, financials, industrials, technologies, and telecommunication equity sectors were the primary exporters of volatility from the U.S. and that the magnitude of the spillovers increased primarily during and post-2008 financial crisis. There is evidence that Canada was most vulnerable to spillovers, while China’s exposure was the lowest among the countries in the sample. Based on the minimum variance portfolio optimization, we find that investing in foreign industries with low exposure to spillovers from the U.S. generates high Sharpe ratios for U.S. portfolio managers, especially during the financial crisis.

PORTFOLIO OPTIMIZATION WITH MEAN-VARIANCE &amp; MEAN-CVAR: EVIDENCE FROM PAKISTAN STOCK MARKET

The stock market in any country plays a significant role in economic development through saving mobilization and providing diversification opportunities. The investors and wealth managers always looked for those securities, investment to which leads towards maximum returns. The Markowitz portfolio optimization model is based on risk and reward trade off where the returns are measured with Mean and Variance is used as proxy of risk. The framework is criticized due to its normality of returns assumption and in case of non-elliptical distribution the optimization results may lead towards suboptimal portfolios. In emerging markets, returns are attributed to high volatility, low liquidity and increased market concentration. Such volatile phenomena signify the presence of downside risk. The expected shortfall or CVaR has reported ability to measure loss beyond VaR and regarded as coherent risk measure. This study investigated the impact on asset pricing of PSX stocks with Mean Variance and Mean-CVaR model by analyzing KSE100 index stocks for the time period 2009-2018. The empirical findings of the in sample performance of Mean-CVaR optimization for non-normal stocks indicated higher Sharpe ratio while for normal stocks return data portfolio selection and optimization with MV model SR is higher. The results of this study are useful for the investors and fund managers to understand PSX behavior and in selection of optimal portfolios.

Is conflicted investment advice better than no advice?

The benefit of investment advice depends on the quality of advice and the investor's counterfactual portfolio. We use changes in the Oregon University System Optional Retirement Plan to highlight the impact of plan design on the counterfactual portfolios of advice seekers. When brokers are available and target date funds (TDFs) are not, brokers help participants with high predicted demand for advice bear market risk, but they recommend higher-commission options. When brokers are removed and TDFs are added, new high-predicted-demand participants primarily invest in TDFs, which offer similar market risk but higher Sharpe ratios than the broker-advised portfolios within our sample.

Time-Series Efficient Factors

We show that factors in prominent asset pricing models are unconditionally minimum-variance inefficient. Factor returns are positively autocorrelated while risk, conditional on past returns, is constant. We derive a transformation that turns an autocorrelated factor into a factor. Time-series efficient factors earn 40% higher Sharpe ratios than the original factors and contain all the information found in the original factors. Momentum strategies profit from the inefficiencies in factor returns. An asset pricing model with time-series efficient factors, such as an efficient Fama-French five-factor model, prices momentum. Our results impose novel restrictions on theoretical models of asset returns.

Practical Applications of Business Cycle–Related Timing of Alternative Risk Premia Strategies

Practical Applications In Business Cycle–Related Timing of Alternative Risk Premia Strategies, from the Spring 2020 issue of The Journal of Alternative Investments, Bernd Scherer and Matthias Apel relate how they developed a business cycle model to explore its predictive power in a tactical allocation framework. They created an aggregate of economic indicators that identifies economic regimes in order to determine the sensitivities of a host of risk premia strategies across various business cycle phases. Many of these sensitivities, measured as conditional Sharpe ratios, are statistically different from their unconditional, overall risk-adjusted returns. During each regime, a long–short tactical overlay strategy takes a long position in the four alternative risk premia strategies with the highest Sharpe ratios and shorts the four with the lowest. The authors show that the overlay strategy improves the performance of a risk-parity portfolio and provide evidence that the observed return patterns are not a beta-driven result. While their primary interest lies within a universe of investable alternative risk premia strategies, cross-validation tests also applied the model within three additional investment universes: global asset classes, smart betas, and Fama–French risk factors.