Higher Sharpe Ratios Research Articles

Minimum-variance portfolio optimization: an asset pricing model approach

ABSTRACT This paper employs novel asset pricing model methods to construct the global minimum-variance portfolio G. Out-of-sample analyses of U.S. stock returns show that optimized G portfolios have relatively higher expected returns, lower variance, and higher Sharpe ratios than those based on traditional variance-covariance matrix estimation methods. Robustness checks confirm these findings and show that our G portfolio has lower variance than currently available minimum-variance exchange traded funds (ETFs). We conclude that asset pricing models can be used to build high performing G portfolios. Implications to portfolio management are discussed.

The prescriptive nature of market timing and predictive portfolios

Abstract Accepted by: Konstantinos Nikolopoulos We explore the ability of market timing, model-based forecasts that include incremental information over and above momentum, to create long-short portfolios that consistently provide superior returns to traditional benchmarks, and frame our analysis in the idea of “prediction-led prescription” for forward-looking decision making. Our methodology consists of linear and non-linear forecasts with model selection, that predict the direction of future returns and apply then into the construction of portfolios. Our results strongly recommend that predictive-based portfolio allocation can produce excess returns and higher Sharpe ratios, above and beyond the individual return series that are utilized to create the forecasts and traditional portfolio benchmarks. The use of “predictive methods in portfolio construction reinforces the idea of prediction-led prescription” as portfolios are prescriptions for investment management and have an exclusively forward-looking nature.

EVALUATING STOCK SELECTION IN THE SAAS INDUSTRY: THE EFFECTIVENESS OF THE RULE OF 40

The Rule of 40 is a popular financial guideline used by software-as-a-service (SaaS) industry participants to assess the operational health of the companies. This paper investigates the effectiveness of the Rule of 40 as a stock selection criterion. Our study analyses a sample of 1756 SaaS companies worldwide spanning the period 2003-2022. The findings demonstrate that the Rule of 40 adds value and delivers a moderately high Sharpe ratio as a stock selection tool. A modified rule, the SaaS Investing Rule of 65, is proposed and found to outperform the Rule of 40 in identifying relative winners and losers within the SaaS space. The effectiveness of the rules raises practical implications for investors and analysts. Additionally, we explore the effectiveness of alternative versions of the Rule of 40 using different measures of profitability, as well investigate whether the returns are driven by traditional style factors.

Safe haven properties of industrial stocks against ESG in the United States: Portfolio implication for sustainable investments

This study explores the safe haven role of industry sectors classified by the Global Industry Classification Standards (GICS) for global Environmental, Social, and Governance (ESG) stocks in the United States. Our study applies the novel cross-quantilogram and quantile time-frequency connectedness approach for daily time series data from November 2019 to October 2023. Our findings confirm that industry sectors, mainly financials, industrial, communication, information technology, energy, consumer staples, and consumer discretionary, are the safe haven avenues at down, normal, and up market conditions; however, these safe haven avenues vanish for the long-term investment horizon. We also explore the risk spillover patterns for the total time domain and the decomposed spillover level for the short- and long-term spillovers. Our connectedness finding shows that the net transmitters and net receiver patterns are similar for the extreme markets but dramatically change for the normal markets. Finally, based on the portfolio rebalancing strategy, the returns of our portfolio, comprised of industry sectors and the ESG index, outperform the overall market. Moreover, our portfolio yields lower negative returns and a higher Sharpe ratio when compared with the overall market returns. These findings confirm the benefits of diversification by reducing downside portfolio risk. This study is primarily relevant for socially responsible portfolio and risk management investors.

Pro Trader RL: Reinforcement learning framework for generating trading knowledge by mimicking the decision-making patterns of professional traders

This study proposes a novel reinforcement learning (RL) framework, professional trader RL (Pro Trader RL), which mimics the decision-making patterns and trading philosophy of professional traders in stock trading. By exploiting the characteristics of RL, the framework aims to learn efficient trading strategies while mimicking the trading philosophy and risk management methods of professional traders. The framework takes into account the complex nature of the stock market and presents an integrated approach to RL, from data pre-processing to buying, selling and stop-loss. Pro Trader RL consists of four main modules. Data Preprocessing, Buy Knowledge RL, Sell Knowledge RL and Stop Loss Rule, each of which plays the role of professional traders knowledge. The results of three experiments show that the framework achieves high returns and Sharpe ratio regardless of market conditions and has stable performance with low maximum drawdown (MDD), which is superior to the state-of-the-art research. The proposed framework provides a novel approach to applying RL to the stock market and is expected to be useful and applicable in real-world trading settings.

Portfolio management based on a reinforcement learning framework

AbstractPortfolio management is crucial for investors. We propose a dynamic portfolio management framework based on reinforcement learning using the proximal policy optimization algorithm. The two‐part framework includes a feature extraction network and a full connected network. First, the majority of the previous research on portfolio management based on reinforcement learning has been dedicated to discrete action spaces. We propose a potential solution to the problem of a continuous action space with a constraint (i.e., the sum of the portfolio weights is equal to 1). Second, we explore different feature extraction networks (i.e., convolutional neural network [CNN], long short‐term memory [LSTM] network, and convolutional LSTM network) combined with our system, and we conduct extensive experiments on the six kinds of assets, including 16 features. The empirical results show that the CNN performs best in the test set. Last, we discuss the effect of the trading frequency on our trading system and find that the monthly trading frequency has a higher Sharpe ratio in the test set than other trading frequencies.

Research on cryptocurrency portfolio based on Markowitz model

In recent years, the booming growth of the cryptocurrency market has garnered significant attention from a diverse range of investors with the overall market capitalization experiencing continuous expansion. This surge in popularity, however, is accompanied by challenges arising from the market's inherent high volatility and complexity. The increasing diversity of available assets presents investors with a complex landscape to navigate. The quest to build an optimal investment portfolio has become a focal point, particularly as market participants seek strategies that not only capitalize on the market's potential for substantial returns but also effectively manage the inherent risks. In this paper, nine cryptocurrencies have been chosen to construct the portfolios, which include BTC, Ether, Tether, Binance, ripple, USDC, ADA, DOGE and TRX. The data selected comprises the daily price of the above assets from November 1, 2020, to October 9, 2023. This paper presents a comprehensive framework for constructing and optimizing cryptocurrency investment portfolios, by employing the Markowitz model and Sharpe ratio analysis. We examined these nine selected digital currencies to determine the optimal investment allocation. Our findings suggest that the highest sharpe ratio is achieved when each investment cryptocurrencies (according to the order above) allocates 0, 0.003, 0.762, 0.038, 0.005, 0.172, 0, 0.014 and 0.007, respectively, to each cryptocurrency. And the lower risk portfolio is obtained when each cryptocurrencies accounted for 2.740e-20, 6.315e-20, 0.5, 0, 0.001, 0.499, 0, 1.419e-04 and 4.210e-20, respectively. This study contributes to the sustainable development of the cryptocurrency market by providing valuable insights for financial institutions, policymakers, and other stakeholders. However, limitations of this research come from the small number of selected digital currencies and the exclusion of risk-free assets such as government bonds. Future research should consider expanding the sample size and incorporating risk-free rates to enhance the applicability and reliability of investment strategies. In conclusion, this research offers investors a practical framework to navigate the complexities of the cryptocurrency market and optimize their investment decisions for long-term value appreciation.

Broadband Internet and the Stock Market Investments of Individual Investors

ABSTRACTWe study the effects of broadband internet use on the investment decisions of individual investors. A public program in Norway provides plausibly exogenous variation in internet use. Our instrumental variables estimates show that internet use causes a substantial increase in stock market participation, driven primarily by increased fund ownership. Existing investors tilt their portfolios toward funds, thereby obtaining more diversified portfolios and higher Sharpe ratios, and do not increase their trading activity in stocks. Overall, access to high‐speed internet spurs a “democratization of finance,” with individuals making investment decisions that are more in line with the advice from portfolio theory.

LQ45 Stock Portfolio Optimization by considering Return Predictions using the Holt Winter Method

A portfolio represents a selection of assets owned by individuals or groups with the goal of generating profit. Stocks are among the various forms of investments available. Investors typically consider two key factors: expected returns and associated risks. Portfolio optimization endeavors to maximize returns while minimizing risks. With the progression of time, portfolio optimization strategies increasingly incorporate return predictions using machine learning techniques. In this study, the Holt-Winter method is employed to forecast stock prices and returns, known for its accuracy in estimating seasonal time series data. Previous research primarily utilized the Mean- Variance method for portfolio optimization, but the outcomes were deemed unsatisfactory. Therefore, this study adopts a novel approach by integrating the Mean-Variance model with forecasting. Performance testing of the optimal portfolio emphasizes sensitivity, aiming for substantial average returns, minimal standard deviation, and a high Sharpe ratio. Comparative analysis with the LQ45 index portfolio reveals that the sensitivity-driven portfolio exhibits superior performance, yielding higher values in terms of average returns, standard deviation, and Sharpe ratio.

The risk-adjusted performance of convertible venture contracts

We obtain additional insights on the risk-adjusted performance of convertible equity contracts widely used in the U.S., the diversity of venture capitalists (VCs) and their risk-return positioning. Our analysis uses a double-hazard agency framework in which VCs maximize the expected utility of their return from the convertible equity contract and the resulting Sharpe ratio incorporates both risks related to venture failure and valuation at exit. Theoretical results show that the convertible contract exhibits greater alignment with important venture characteristics relative to the pure-equity contract and provides a larger equity share to the entrepreneur as failure risk, VC productivity and development horizon increase, and when growth prospects and entrepreneur's productivity decrease. As failure risk goes to zero, it converges to the pure-equity contract. Our results further show that VCs with heterogeneous funding capacities and productivities can coexist in the venture finance market as they experience similar risk-adjusted returns. VCs with greater funding capacity can generate a competitive advantage by investing at later stages, which results in a higher Sharpe ratio and expected return from the convertible investment.

Connectedness between healthcare cryptocurrencies and major asset classes: Implications for hedging and investments strategies

This paper studies dynamic connectedness between four prominent healthcare cryptocurrencies, namely MediBloc, MediShares, Medicalchain, and Dentacoin, and bond, equity, and commodity markets along with USD and Bitcoin. The daily data span from February 2018 to April 2023. We applied the quantile VAR method and the wavelet quantile correlation approach to measure the connectedness. The quantile VAR method reveals a strengthening interrelation among the assets during COVID-19 and Russia-Ukraine military conflict, while the wavelet quantile correlation highlights the existing diversification opportunities. To test portfolio performance, we resort to minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio techniques. The minimum variance portfolio is best performing portfolio with highest Sharpe ratio. In addition, considering the minimum connectedness and minimum correlation portfolios, the exposure to healthcare cryptocurrencies also improves portfolio diversification. Based on the hedging effectiveness, we show that the investment in the healthcare cryptocurrencies reduces the volatility for all the selected portfolios though currently in a limited degree. However, the investors are advised to regularly monitor their asset allocation as, with the passage of time, the strength of hedging attributes may change. Our study provides important implications for policymakers and the portfolio managers.

Global uncertainty and the spillover of tail risk between green and Islamic markets: A time-frequency domain approach with portfolio implications

This study first calculates the left tail risks in the renewable energy, green, sustainable, Islamic equity and Islamic bond markets using CAViaR models. Then, applying TVP-VAR based connectedness method it estimates various measures of dynamic connectedness and spillover of the tail risks at short, medium, and long term. Furthermore, through wavelet coherence analysis it examines how various global uncertainties impact such connectedness in time and frequency domain. Lastly, it estimates hedging effectiveness, optimal portfolio weights and Sharpe Ratios to provide practical implications for the market participants. The results show that the tail risk connectedness and spillover are mainly driven by the short-term dynamics emphasizing the importance of short-lived noises in the transmission of down-side risks. Usually, the connectedness of the tail risks is higher during the period of COVID-19 and Russia-Ukraine war. Though the tail risks of Islamic bond and developing Islamic equity markets are mostly isolated in the full sample showing hedging potential of these markets, the network analysis shows their increased connectedness with other markets during the period of COVID-19 and Russia-Ukraine war. In general, mature market indices such as Dow Jones Sustainable World and MSCI global environment indices, Dow Jones Islamic World, US and UK market indices are consistently net contributors/transmitters of tail risk shocks while Islamic bond and developing Islamic equity market indices are net receivers of shocks when they are connected to the network. Two uncertainty indices, that follow volatility related to gold and US dollar, demonstrate hedging potential and predictive power on the connectedness. Dow Jones Islamic World market index usually carries significant weights in optimal portfolio allocations while Islamic bonds from GCC countries have higher Sharpe Ratios indicating their promise as instruments for risk-adjusted profit.

Comparative Analysis of Private and Public Mutual Funds: A Performance Evaluation

Abstract: The mutual fund industry is a popular investment avenue that offers investors the opportunity to invest in a diversified portfolio of securities. The industry has witnessed significant growth in recent years, with investors seeking to maximize their returns and reduce risk. This paper presents a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study aims to evaluate the risk-adjusted returns, expense ratios, fund size, asset allocation, and other relevant factors of both private and public mutual funds to determine which type of fund offers better performance. To conduct the analysis, a sample of four private and four public mutual funds was randomly selected, and their performance was evaluated using various performance metrics such as Sharpe ratio, Treynor ratio, and Jensen's alpha. The data was analyzed using statistical tools such as U test, t-test, and regression analysis. The study found that private sector mutual funds performed comparatively better than public sector mutual funds in terms of risk-adjusted returns, expense ratios, and asset allocation. Private sector mutual funds had higher Sharpe ratios and Treynor ratios compared to public sector mutual funds, indicating that they generated higher returns per unit of risk taken. Private sector mutual funds also had lower expense ratios, indicating that they charged lower fees to investors. In terms of asset allocation, private sector mutual funds had a higher allocation towards equity securities compared to public sector mutual funds, which had a higher allocation towards debt securities. The study also identified significant differences in the investment strategies, portfolio management, and investor base of private and public mutual funds. Private sector mutual funds had a more aggressive investment strategy, with a higher allocation towards equity securities, whereas public sector mutual funds had a more conservative investment strategy, with a higher allocation towards debt securities. Private sector mutual funds were also found to be more actively managed compared to public sector mutual funds. Private sector mutual funds had a higher percentage of assets under management from retail investors, while public sector mutual funds had a higher percentage of assets under management from institutional investors. The study provides valuable insights to investors, fund managers, and policymakers regarding the performance of private and public mutual funds. The findings suggest that investors may benefit from investing in private sector mutual funds due to their higher risk-adjusted returns, lower expense ratios, and higher allocation towards equity securities. Fund managers may also benefit from the study by gaining insights into the investment strategies and portfolio management practices of private and public mutual funds. Policymakers may use the study to design policies that promote the growth and development of the mutual fund industry. Overall, this study contributes to the existing literature on mutual funds by providing a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study adds to our understanding of the factors that affect the performance of mutual funds and provides insights into the investment strategies and portfolio management practices of private and public mutual funds.

Index Model with Modern Portfolio Theory and its Application to Find Optimal Portfolio in the American Stock Market

A crucial framework in financial science, the Index Model and Modern portfolio theory provide investors with a scientifically sound method of asset allocation. This study applies solver tables for data manipulation and computing to an empirical analysis of the theory in the setting of the American stock market. Based on predetermined criteria, 10 well-known stocks from various industries were chosen, IM will use these optimization inputs to discover the regions of acceptable portfolios for the various five situations of the extra limitations after calculating the necessary optimization inputs for the entire Index Model according to the month and everyday observations. The definition of the efficient frontier and identification of the portfolio with the highest Sharpe ratio and lowest variance under five distinct restrictions were made possible thanks to the help of the Solver table. The results of the empirical investigation support the relevance of the index model and portfolio theory in the American financial market by demonstrating its applicability and usefulness for global investors. According to the results, investors are encouraged to diversify their portfolios in accordance with the efficient frontier's rules in order to maximize profits while minimizing risks.

US efficient factors in a Bayesian model scan framework

PurposeThe author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al. (2020), and Chib et al.(2022).Design/methodology/approachEhsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.FindingsThe author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data.Originality/valueThe author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.

Portfolio Optimization Based on Markowitz Investment Theory and Monte Carlo Simulation

In the current global economic recovery, the market still has a certain degree of volatility, in the case of volatility or bad market how to go to the portfolio and optimize it is a very critical task. In this paper, based on Markowitz’s investment theory, Monte Carlo algorithm is applied to achieve portfolio optimization, which is verified by the dataset of five A-share data selected as a representative dataset in the period of global epidemic. For the model and the algorithm, good results are achieved for the objective market, and the market conditions are successfully represented; for the portfolio optimization, the high-risk and highreturn portfolio with the highest Sharpe ratio is finally found, and compared with the portfolio with minimized risk, and the portfolio with the highest Sharpe ratio is concluded as the optimal portfolio for this experiment, which is the optimal portfolio for this experiment, and should not be pursued for low risk but will be the best portfolio for this experiment in the turbulent and downturn market. In a volatile and depressed market, it should not be pursued for low risk but will result in a loss of return, and it should be maximized with a certain degree of volatility.

Enhancing portfolio optimization: A comparative analysis of the mean-variance Markowitz model and risk-parity contribution strategies

Financial markets are still exposed to various crises that increase stock price fluctuations and make predictions extremely difficult (Nguyen et al., 2024). Although there are many diversification methods for building investment portfolios, there has been no agreement on the best of them. This research aims to identify the most effective strategy for constructing an optimal investment portfolio by comparing the mean-variance (MV) model and risk-parity (RP) contribution strategies during the uncertain market period 2018–2022. The study used a quantitative and an optimization method following Ofikwu (2019) based on three critical criteria: 1) low asset correlation, 2) the highest Sharpe ratio, and 3) a mixed strategy for a sample of firms listed in the Amman Stock Exchange (ASE). The results show that the MV model has the highest Sharpe ratio (15.06 percent and 11.84 percent) when applied to the Sharpe and mixed strategies respectively. In comparison, using the low correlation strategy the RP model gains the highest Sharpe ratio (14.92 percent). During COVID-19, all portfolios had a higher positive return and lower total risk than the market portfolio. Both models are valid to be used during uncertain conditions. It highlights the effectiveness of strategies in navigating challenging market conditions and offers practical guidance for investors in uncertain times for asset allocation.

Portfolio Optimization Based on Six Chinese Stocks

China's capital markets started late and there are still many problems with securities trading, adding to the risks for investors. Establishing a portfolio in the A-share market and reducing non-systemic risks through diversification of investment not only reduces the volatility faced by investors, but also stimulates the enthusiasm of investors and enhances the liquidity of the capital market. Therefore, six representative stocks of Yahoo Finance are selected in this paper, and the adjusted closing price of stocks is collected. The mean-variance model is used to find the optimal weight ratio of these six stock portfolios through training sets and test sets. The actual returns are compared with the market to evaluate the outcome of the portfolio. The model results demonstrate that the Sharpe ratio of the portfolio with the highest Sharpe ratio is 68.70%, and the weight of Wuliangye is the highest at 73.55%, while Shenzhen Fountain is only 0.16%. The volatility of the least volatile portfolio is 26.48%, of which YUNNAN BAIYAO has the highest weight (37.78%), and Hithink RoyalFlush has the lowest weight (0.73%). The minimum volatility portfolio yields less than the maximum Sharpe ratio portfolio, but both outperform the market. After excluding the smallest stocks in the portfolio, the portfolio remains stable. The results displays that the mean-variance model can play an effective role in constructing the portfolio of A-share market.

Return-enhancing strategies with international ETFs


 
 
 We show that the average return over the four-day period surrounding the turn of the month is significantly positive in eight out of the nine international exchange-traded funds (ETFs). The strategy of buying-and-holding an ETF during turn-of-the-month (TOM) period and switching to holding T-bills during non-TOM period produces significantly positive monthly average returns. This ETF- T-bills switching strategy also has the lowest risk and highest Sharpe ratio and Sortino ratio than the traditional strategy of buying-and-holding either an index fund or an ETF. Investors pursuing this switching strategy generate a terminal value twice larger than the next best strategy of buying-and- holding an ETF.
 
 

Do commodity factors work as inflation hedges and safe havens?

This study investigates whether commodity futures factor portfolios work as hedges and safe havens against inflation shocks. We observe that momentum, basis momentum, and a combination of factor portfolios act as strong hedges against core inflation shocks, suggesting that holding the factor portfolios generates not only higher Sharpe ratios but also strong hedge effects against inflation. Moreover, the momentum, basis momentum, and value portfolios have weak safe haven properties against inflation shocks. In addition, our empirical results suggest that hedge effects for commodity future portfolios are stronger during the pre-financialization period.