Showing papers on "Sharpe ratio published in 2016"
TL;DR: In this paper, the authors performed an investigation on weekly data of 25 mutual funds for the period of May 16, 2010 to April 28, 2016 and found no market timing skill persistent to the fund managers.
Abstract: This study principally analyzes the fund managers’ ability to outguess the market in Bangladesh. We perform the investigation on weekly data of 25 mutual funds for the period of May 16, 2010 to April 28, 2016. To serve our objective, we tested both selection and market timing skills of the fund managers. We have used six measures; average return, Sharpe ratio, Treynor ratio, Information ratio, Jensen’s alpha and M square; to confirm the selection skill of fund managers and found no selection skill persistent to most of the fund managers (excluding Aims 1st M.F, ICB AMCL 2nd NRB M.F. and 6th ICB M.F.). In addition, the negative values of alpha indicate that fund managers become not only failed to add value to their portfolio, but also pool wrong assets which hurt the return resulting negative profit. On the other hand, we have employed two popular methodologies; Treynor and Mazuy [24] and Henriksson and Merton [10]; to test the market timing skill of fund managers and found no market timing skill persistent to the fund managers. Thus, with a little exception, we can conclude that fund managers have no ability to outguess the market in Bangladesh.
296 citations
TL;DR: In this paper, the authors show that cash-based operating profitability (a measure that excludes accruals) outperforms measures of profitability that include accrual, and that an investor can increase a strategy's Sharpe ratio more by adding just a Cash-Based Operating Profitability Factor (CBOF) to the investment opportunity set than by adding both an accruality factor and a profitability factor.
166 citations
TL;DR: In this paper, the authors show that taking less risk when volatility is high produces large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors.
Abstract: Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in factors' volatilities are not fully offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions and crises yet still earns high average returns. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.
101 citations
TL;DR: This study differs from models in prior studies in two significant ways: (1) it leverages market information contained in high-volume social media data rather than news articles and (2) it does not evaluate sentiment.
93 citations
TL;DR: In this article, a simple modification, sticky wages because of infrequent resetting together with a constant elasticity of substitution (CES) production function leads to both smoother wages and higher equity volatility.
Abstract: In standard production models, wage volatility is far too high, and equity volatility is far too low. A simple modification–sticky wages because of infrequent resetting together with a constant elasticity of substitution (CES) production function leads to both smoother wages and higher equity volatility. Further, the model produces several other hard-to-explain features of financial data: high Sharpe ratios, low and smooth interest rates, time-varying equity volatility and premium, a value premium, and a downward-sloping equity term structure. Procyclical, volatile wages are a hedge for firms in standard models; smoother wages act like operating leverage, making profits and dividends riskier.
91 citations
TL;DR: In this article, a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model was proposed.
81 citations
TL;DR: This paper found that the U.S. and international stock markets enjoy high returns and Sharpe ratios on days surrounding scheduled FOMC meetings, consistent with global investors demanding a premium to bear risks associated with Federal Reserve decisions.
Abstract: Both the U.S. and international stock markets enjoy high returns and Sharpe ratios on days surrounding scheduled FOMC meetings, consistent with global investors demanding a premium to bear risks associated with Federal Reserve decisions. There is no comparable result for other major central banks, whose announcements do not command positive risk premia either globally or, more surprisingly, domestically. Other macroeconomic announcements have impact on local stock markets and, to some extent, even on the U.S. market. The world CAPM explains the cross-section of stock returns during FOMC announcements but not during other central banks' announcements. These findings suggest that the Federal Reserve exerts a unique impact on global equity prices that does not simply stem from the size and importance of the U.S. economy.
66 citations
TL;DR: The solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operating continuously and with minimal human intervention.
Abstract: GP is applied to learn trading rules that are used to automatically manage a portfolio of stocks.A new Random Sampling method is used to increase the robustness of the strategies evolved.The new Random Sampling method produces strategies able to withstand extreme market environments.The new Random Sampling method produces solutions that perform during out-of-sample testing similarly as during training.The results are based on testing a portfolio of 21 Spanish equities. This paper presents a Robust Genetic Programming approach for discovering profitable trading rules which are used to manage a portfolio of stocks from the Spanish market. The investigated method is used to determine potential buy and sell conditions for stocks, aiming to yield robust solutions able to withstand extreme market conditions, while producing high returns at a minimal risk. One of the biggest challenges GP evolved solutions face is over-fitting. GP trading rules need to have similar performance when tested with new data in order to be deployed in a real situation. We explore a random sampling method (RSFGP) which instead of calculating the fitness over the whole dataset, calculates it on randomly selected segments. This method shows improved robustness and out-of-sample results compared to standard genetic programming (SGP) and a volatility adjusted fitness (VAFGP). Trading strategies (TS) are evolved using financial metrics like the volatility, CAPM alpha and beta, and the Sharpe ratio alongside other Technical Indicators (TI) to find the best investment strategy. These strategies are evaluated using 21 of the most liquid stocks of the Spanish market. The achieved results clearly outperform Buy&Hold, SGP and VAFGP. Additionally, the solutions obtained with the training data during the experiments clearly show during testing robustness to step market declines as seen during the European sovereign debt crisis experienced recently in Spain. In this paper the solutions learned were able to operate for prolonged periods, which demonstrated the validity and robustness of the rules learned, which are able to operate continuously and with minimal human intervention. To sum up, the developed method is able to evolve TSs suitable for all market conditions with promising results, which suggests great potential in the method generalization capabilities. The use of financial metrics alongside popular TI enables the system to increase the stock return while proving resilient through time. The RSFGP system is able to cope with different types of markets achieving a portfolio return of 31.81% for the testing period 2009-2013 in the Spanish market, having the IBEX35 index returned 2.67%.
57 citations
TL;DR: In this paper, the authors used a sample of 32,928 paintings that sold repeatedly between 1960 and 2013 and found an asymmetric V-shaped relation between sale probabilities and returns.
Abstract: This paper shows the importance of correcting for sample selection when investing in illiquid assets that trade endogenously. Using a sample of 32,928 paintings that sold repeatedly between 1960 and 2013, we find an asymmetric V-shaped relation between sale probabilities and returns. Adjusting for the resulting selection bias reduces average annual index returns from 8.7% to 6.3%, lowers Sharpe ratios from 0.27 to 0.11, and materially impacts portfolio allocations. Investing in a broad portfolio of paintings is not attractive, but targeting specific styles or top-selling artists may add value. The methodology naturally extends to other asset classes.
55 citations
TL;DR: This article found important differences in dollar-based and dollar-neutral G10 carry trades and showed that a diversified dollar-carry portfolio has a higher average excess return, a higher Sharpe ratio, minimal skewness, is uncorrelated with standard risk factors, and exhibits no downside risk.
Abstract: We find important differences in dollar-based and dollar-neutral G10 carry trades Dollar-neutral trades have positive average returns, are highly negatively skewed, are correlated with risk factors, and exhibit considerable downside risk In contrast, a diversified dollar-carry portfolio has a higher average excess return, a higher Sharpe ratio, minimal skewness, is uncorrelated with standard risk-factors, and exhibits no downside risk Distributions of drawdowns and maximum losses from daily data indicate a role for time-varying autocorrelation in determining negative skewness at longer horizons
50 citations
Posted Content•
TL;DR: In this article, a robust continuous-time Markowitz portfolio selection problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures, which is solved by a McKean-Vlasov dynamic programming approach, which allows the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures.
Abstract: This paper studies a robust continuous-time Markowitz portfolio selection pro\-blem where the model uncertainty carries on the covariance matrix of multiple risky assets. This problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dynamic programming approach, which allows us to characterize the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures. We provide explicit solutions for the optimal robust portfolio strategies and illustrate our results in the case of uncertain volatilities and ambiguous correlation between two risky assets. We then derive the robust efficient frontier in closed-form, and obtain a lower bound for the Sharpe ratio of any robust efficient portfolio strategy. Finally, we compare the performance of Sharpe ratios for a robust investor and for an investor with a misspecified model. MSC Classification: 91G10, 91G80, 60H30
TL;DR: This article found that exchange rate movements are in fact unrelated to differentials in country-level equity returns, and that a trading strategy that invests in countries with the highest expected equity returns and shorts those with the lowest generates substantial returns and Sharpe ratios.
Abstract: The sign of the correlation between equity returns and exchange rate returns can be positive or negative in theory. Using data for a broad set of forty-two countries, we find that exchange rate movements are in fact unrelated to differentials in country-level equity returns. Consequently, a trading strategy that invests in countries with the highest expected equity returns and shorts those with the lowest generates substantial returns and Sharpe ratios. These returns partially reflect compensation for global equity volatility risk, but significant excess returns remain after controlling for exposure to standard risk factors.
TL;DR: In this paper, a genetic algorithm (GA) was used to select an optimal combination of technical indicators, fundamental indicators and volatility indicators for improving out-of-sample trading performance.
Abstract: Recurrent reinforcement learning (RRL) has been found to be a successful machine learning technique for building financial trading systems. In this paper, we use a genetic algorithm (GA) to improve the trading results of a RRL-type equity trading system. The proposed trading system takes the advantage of GA's capability to select an optimal combination of technical indicators, fundamental indicators and volatility indicators for improving out-of-sample trading performance. In our experiment, we use the daily data of 180 S&P stocks (from the period January 2009 to April 2014) to examine the profitability and the stability of the proposed GA-RRL trading system. We find that, after feeding the indicators selected by the GA into the RRL trading system, the out-of-sample trading performance improves as the number of companies with a significantly positive Sharpe ratio increases.
TL;DR: In this article, the authors show that technical indicators deliver stable economic value in predicting the US equity premium over the out-of-sample period from 1966 to 2014, and translate the predictive power of technical indicators into a standard investment strategy.
TL;DR: In this paper, a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model was proposed.
Abstract: In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. During the recent financial crisis, the trend factor earns 0.75% per month, while the market loses −2.03% per month, the short-term reversal factor loses −0.82%, the momentum factor loses −3.88%, and the long-term reversal factor barely gains 0.03%. The performance of the trend factor is robust to alternative formations and to a variety of control variables. From an asset pricing perspective, it also performs well in explaining cross-section stock returns.
TL;DR: In this article, a Bayesian-averaging portfolio choice strategy with excellent out-of-sample performance was proposed, which takes into account model uncertainty, parameter uncertainty, and non-stationarity.
Abstract: We propose a Bayesian-averaging portfolio choice strategy with excellent out-of-sample performance. Every period a new model is born that assumes means and covariances are constant over time. Each period we estimate model parameters, update model probabilities, and compute robust portfolio choices by taking into account model uncertainty, parameter uncertainty, and non-stationarity. The portfolio choices achieve higher out-of-sample Sharpe ratios and certainty equivalents than rolling window schemes, the 1/N approach, and other leading strategies do on a majority of 24 datasets.
TL;DR: This paper showed that introducing time-varying skewness in the distribution of ex-protected growth prospects in an otherwise standard endowment economy can up to double the model implied equity Sharpe ratios, and produce a substantial amount of fluctuation in equity risk premia.
Abstract: We show that introducing time-varying skewness in the distribution of ex- pected growth prospects in an otherwise standard endowment economy can up to double the model implied equity Sharpe ratios, and produce a substantial amount of fluctuation in equity risk premia. Looking at the Livingston Survey, we docu- ment that the first and third cross-sectional moments of the distribution of GDP growth rates made by professional forecasters can predict equity excess returns, a finding which is consistent with our consumption based asset pricing model.
Posted Content•
TL;DR: In this paper, a generalized exponential moving average (EMA) model with time-varying expected return in financial markets is proposed, which effectively applies a particle lter (PF) to sequential estimation of states and parameters in a state space framework, and three types of anomaly detec- tors are implemented easily in the PF algorithm to be used for investment decision.
Abstract: This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in nancial markets. In par- ticular, we effectively apply a particle lter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detec- tors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum- variance and risk parity portfolios. Our dataset is monthly total returns of global nancial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.
TL;DR: In this article, the authors adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
Abstract: Combining long-only-constrained factor sub-portfolios is generally not a mean-variance-efficient way to capture expected factor returns. For example, a combination of four fully invested factor sub-portfolios — low beta, small size, value, and momentum — captures less than half (e.g., 40%) of the potential improvement over the market portfolio’s Sharpe ratio. In contrast, a long-only portfolio of individual securities, using the same risk model and return forecasts, captures most (e.g., 80%) of the potential improvement. We adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
Posted Content•
TL;DR: In this article, the authors developed simple and intuitive formulas of the squared Sharpe ratio that investors should expect from estimated efficient portfolios, which can be used to assess the value of efficient portfolios as investment vehicles, given the investment environment.
Abstract: Investors often adopt mean-variance efficient portfolios for achieving superior risk-adjusted returns. However, such portfolios are sensitive to estimation errors, which affect portfolio performance. To understand the impact of estimation errors, I develop simple and intuitive formulas of the squared Sharpe ratio that investors should expect from estimated efficient portfolios. The new formulas show that the expected squared Sharpe ratio is a function of the length of the available data, the number of assets and the maximum attainable Sharpe ratio. My results enable the portfolio manager to assess the value of efficient portfolios as investment vehicles, given the investment environment.
TL;DR: In this article, the authors adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
Abstract: Combining long-only-constrained factor subportfolios is generally not a mean–variance-efficient way to capture expected factor returns. For example, a combination of four fully invested factor subportfolios—low beta, small size, value, and momentum—captures less than half (e.g., 40%) of the potential improvement over the market portfolio’s Sharpe ratio. In contrast, a long-only portfolio of individual securities, using the same risk model and return forecasts, captures most (e.g., 80%) of the potential improvement. We adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
TL;DR: A generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets, and 3 types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision are proposed.
Abstract: This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme are superior to the one based on the standard EMAs and well-known traditional strategies such as equally-weighted, minimum-variance and risk parity portfolios.Our dataset is monthly total returns of global financial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.
TL;DR: This paper investigated the biases in the back-tested performance of alternative beta strategies using a sample of 215 commercially promoted trading strategies across five asset classes and established a link between performance deterioration and strategy complexity, with the realized reduction in live vs. backtested Sharpe ratios of the most complex strategies exceeding those of the simplest ones by over 30 percentage points.
Abstract: We investigate the biases in the backtested performance of “alternative beta” strategies using a sample of 215 commercially promoted trading strategies across five asset classes. Our results lend support to the cautions in recent literature regarding backtest overfitting and lack of robustness in trading strategy performance during the ”live” period (out of sample). We report a median 73% deterioration in Sharpe ratios between backtested and live performance periods for the strategies in our sample. We establish a link between performance deterioration and strategy complexity, with the realized reduction in live vs. backtested Sharpe ratios of the most complex strategies exceeding those of the simplest ones by over 30 percentage points. The robustness of strategy exposure to risk factors varies between asset classes and strategies, and appears reasonable in equity volatility and FX carry strategies, but quite weak in the equity value strategy in particular.
TL;DR: This article found that human perception contradicts the market efficiency assertions that high expected returns are accompanied by high risk and that past returns are not correlated with future returns and found that the last month realized returns are positively correlated with next month perceived returns and that they are negatively correlated with perceived risk.
Abstract: We find that human perception contradicts the market efficiency assertions that high expected returns are accompanied by high risk and that past returns are not correlated with future returns. A survey of investors reveals that the last month realized returns are positively correlated with next month perceived returns and that they are negatively correlated with perceived risk. Neither expected return nor perceived risk captures the entire effect. Thus, in the human mind the “perceived Sharpe ratio” is positively correlated with short-term past returns. The effect does not depend on gender, education, income, and portfolio value, but it is more profound among older investors.
TL;DR: In this paper, the authors derived closed-form expressions for risk measures based on partial moments by assuming the Gram-Charlier (GC) density for stock returns, where the lower partial moments (LPM) can be expressed as linear functions on both skewness and excess kurtosis.
Abstract: We derive closed-form expressions for risk measures based on partial moments by assuming the Gram-Charlier (GC) density for stock returns. As a result, the lower partial moments (LPM) can be expressed as linear functions on both skewness and excess kurtosis. Under this framework, we study the behaviour of portfolio rankings with performance measures based on partial moments, that is, both Farinelli-Tibiletti and Kappa ratios. Contrary to previous results, significant differences are found in ranking portfolios between the Sharpe ratio and the FT family. We also obtain closed-form expressions for LPMs under the semi non-parametric (SNP) distribution which allows higher flexibility (in terms of third- and fourth- order moments) than the GC distribution.
TL;DR: An empirical application based on a large panel of Brazilian interest rate future contracts with different maturities shows that combined forecasts consistently outperform individual models in several instances, specially when economic criteria are taken into account.
TL;DR: In this paper, the authors proposed a new approach to dynamic asset allocation based on detection of change points without fitting a model with a fixed number of regimes to the data, without estimating any parameters and without assuming a specific distribution of the data.
Abstract: The purpose of dynamic asset allocation (DAA) is to overcome the challenge that changing market conditions present to traditional strategic asset allocation by adjusting portfolio weights to take advantage of favorable conditions and reduce potential drawdowns. This article proposes a new approach to DAA that is based on detection of change points without fitting a model with a fixed number of regimes to the data, without estimating any parameters and without assuming a specific distribution of the data. It is examined whether DAA is most profitable when based on changes in the Chicago Board Options Exchange Volatility Index or change points detected in daily returns of the S&P 500 index. In an asset universe consisting of the S&P 500 index and cash, it is shown that a dynamic strategy based on detected change points significantly improves the Sharpe ratio and reduces the drawdown risk when compared with a static, fixed-weight benchmark.
Journal Article•
TL;DR: In this article, the authors adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
Abstract: Combining long-only-constrained factor subportfolios is generally not a mean?variance-efficient way to capture expected factor returns. For example, a combination of four fully invested factor subportfolios?low beta, small size, value, and momentum?captures less than half (e.g., 40%) of the potential improvement over the market portfolio?s Sharpe ratio. In contrast, a long-only portfolio of individual securities, using the same risk model and return forecasts, captures most (e.g., 80%) of the potential improvement. We adapt traditional portfolio theory to more recently popularized factor-based investing and simulate optimal combinations of factor and security portfolios, using the largest 1,000 common stocks in the US equity market from 1968 to 2015.
TL;DR: In this article, the authors developed simple and intuitive formulas of the squared Sharpe ratio that investors should expect from estimated efficient portfolios, which can be used to assess the value of efficient portfolios as investment vehicles, given the investment environment.
TL;DR: In this paper, the authors show that the martingale component in the long-term factorization of the stochastic discount factor due to Alvarez and Jermann (2005) and Hansen and Scheinkman (2009) is highly volatile, produces a downward-sloping term structure of bond Sharpe ratios, and implies that the long bond is far from growth optimality.
Abstract: We show that the martingale component in the long-term factorization of the stochastic discount factor due to Alvarez and Jermann (2005) and Hansen and Scheinkman (2009) is highly volatile, produces a downward-sloping term structure of bond Sharpe ratios, and implies that the long bond is far from growth optimality. In contrast, the long forward probabilities forecast an upward sloping term structure of bond Sharpe ratios that starts from zero for short-term bonds and implies that the long bond is growth optimal. Thus, transition independence and degeneracy of the martingale component are implausible assumptions in the bond market.