Sharpe Ratio Research Articles

Optimizing Mutual Fund Selection with Fuzzy Logic: A Case Study of Nasdaq Exchange

This study examines mutual fund performance through advanced methodologies, emphasizing fuzzy logic and the Fuzzy Sharpe Ratio. A thorough literature review highlights recent developments, such as the integration of machine learning techniques with fuzzy logic models. The research utilizes data from the Bombay Stock Exchange spanning January 1, 2024, to October 31, 2024, applying the Fuzzy Risk-Adjusted Return method. Moreover, an analysis of major technology companies reveals Alphabet Inc as the top-ranked firm, demonstrating its robust performance metrics and establishing it as a premier investment choice. Conversely, Tesla Inc ranked lowest, reflecting comparatively weaker performance. These results illustrate the competitive dynamics and varied performance levels among leading tech companies, influenced by market strategy, innovation, and financial stability. This analysis underscores the value of fuzzy logic in enhancing mutual fund evaluations and positions Alphabet Inc as a strong candidate for investor consideration.

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.

A Study on Optimal Equity Portfolio in the Indian Capital Market

This study evaluates optimal equity portfolio construction in the Indian capital market by applying the Sharpe Single Index Model. Data from 20 companies in the NIFTY 50 index are used to analyze risk-return relationships and identify companies for portfolio inclusion. The study concludes that constructing an optimal portfolio with selected stocks, such as Hero MotoCorp and Bharat Petroleum, based on their risk-adjusted returns, offers a strategy for maximizing returns with controlled risk

Enhancing investment strategies through machine learning: A comprehensive analysis across market sectors

Abstract. This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.

An Intertemporal Risk Factor Model

Prominent factor models are based on tradable factors that do not represent theoretically relevant risks. To address this issue, we develop a factor model that captures the risks to long-term investors present in the intertemporal capital asset pricing model (ICAPM). Empirically, we construct intertemporal risk factors as long-short portfolios based on stock exposures to dividend yield and realized variance. These tradable factors mimic news to long-term expected returns and volatility, and they offset part of the marginal utility increase in recessions induced by wealth declines. Our intertemporal factor model estimation implies significant risk prices that are consistent with the ICAPM restrictions under moderate risk aversion. Moreover, our model performs well relative to previous factor models in terms of its tangency Sharpe ratio and its pricing of key test assets, including single stocks, industry portfolios, and portfolios sorted on risk exposures and lagged anomalies. This paper was accepted by Will Cong, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00261 .

Probability weighting and equity premium prediction: Investing with optimism

AbstractEmpirically motivated theoretical models of probability weighting which overweight tail events are finding many applications in finance. However, probability weighting has not yet been applied to equity premium prediction or to constructing optimal market timing investment strategies. We show that a measure of market optimism from a representative agent asset pricing model with probability weighting can be used to construct optimal dynamic investment strategies that outperform the buy‐and‐hold strategy and strategies generated by 17 leading equity premium predictors. We further show that this theory‐based measure of market optimism predicts the equity premium and market Sharpe ratio in‐sample and out‐of‐sample. The predictability is not subsumed by disaster probabilities, market sentiment, or market skewness. Our results indicate that our theory‐based measure provides a distinct channel for predicting aggregate stock returns.

Financial performance of Greenex and carbonex in comparison to BSE Sensex

This study aims to know the financial performance of Greenex and corbonex compared with the BSE Sensex during the post covid and pre-COVID. The study uses risk-adjusted return methods such as the Sharpe ratio, Treynor Ratio, and Jensen's Alpha for methodology. The data is collected from the period 2010-2023 which is divided into three sections i.e., overall period (2010-2023), pre covid (2010-2020), and post covid (2020-2023). The findings of the study are that the performance of the carbonex is good in all three periods when compared with BSE Sensex and this study is useful for portfolio managers, individuals, investors, and also the common public.

Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning

This study presents a new method for optimising high-risk trading (HFT) strategies using deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural network architectures with sophisticated business data processing techniques. The framework employs a combination of convolutional neural networks for manual order analysis, short-term memory networks for time series processing, and a multi-head listening mechanism for body fusion. We formulate the HFT problem based on Markov Decision Processes and use the Proximal Policy Optimization algorithm for training. The model is evaluated using tick-by-tick data from the NASDAQ exchange, including ten liquid stocks in 6 months. The experimental results show the superiority of our method, achieving a Sharpe ratio of 3.42, outperforming the learning model and machine learning based on benchmarks up to 33%. The proposed strategy has demonstrated strong performance across a wide range of regulatory markets and has shown potential for strategic objectives. Sensitivity analysis confirms the model's stability across a range of hyperparameters. Our findings suggest that the DRL-based approach can improve HFT performance and provide better market adaptation and risk management. This research leads to the continuous evolution of algorithmic trading strategies and shows the potential of AI-driven approaches in financial markets.

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

Jump tail risk exposure and the cross-section of stock returns

We introduce a new jump tail risk measure retrieved from option prices. We examine the cross-sectional pricing of stocks according to their sensitivities to jump tail risk. We find a negative market price of jump tail risk. A high-low portfolio sorted by jump tail risk betas delivers a statistically and economically significant negative premium of -9.95% per year. Risk-adjusted returns are also negative and highly significant. We document that the negative jump tail risk premium is mainly driven by its downside jump tail risk component. On the contrary, the premium of the high-low portfolio sorted by upside jump tail risk betas is insignificant. The negative premium of downside jump tail risk is significant when controlling for various risk factor loadings and firm characteristics, and remains strong for large firms. Our results carry over to a predictive setting, in which we compare subsequent realized returns of the quintile portfolios sorted by downside jump tail risk betas estimated over the previous period.

Great Year Bad Sharpe

Returns distributions are heavy tailed across asset classes. In this note, Smerlak examines the implications of this well-known stylized fact for the joint statistics of performance (absolute return) and Sharpe ratio (risk-adjusted return). Using both synthetic and real data, He shows that, all other things being equal, the investments with the best in-sample performance are never associated with the best in-sample Sharpe ratios (and vice versa). This counter-intuitive effect is unrelated to the risk-return tradeoff familiar from portfolio theory: it is, rather, a consequence of asymptotic correlations between the sample mean and sample standard deviation of heavy-tailed variables. In addition to its large sample noise, this non-monotonic association of the Sharpe ratio with performance puts into question its status as the gold standard metric of investment quality.

Integrating Factor Models Using Bayesian Model Averaging: Evidence from Korea

This study applies the Bayesian model averaging approach to the Korean stock market data to yield an integrated factor model that can address model uncertainty. During the period from 2004 to 2022, we choose 14 factors and 10 macro predictors and generate a total of 10,485,760 candidate factor models by considering all possible combinations of each variable and whether to allow for model mispricing. The integrated model estimates the expected return and covariance matrix by combining the predictions of all the candidate models using Bayesian posterior probabilities as weights. In the Korean stock market, there is no clear winner model with dominant posterior probabilities, implying that model uncertainty is substantial. The optimal portfolio of the integrated model has a higher out-of-sample Sharpe ratio and lower downside risk than benchmark factor models. Model uncertainty has a significant impact on the estimation of the covariance matrix of the integrated model, and model disagreement about expected returns is particularly acute during periods of sharp market declines. In the presence of model uncertainty, Bayesian investors perceive equities to be riskier than historical volatility.

ESG Scores’ Impact on Portfolio Performance, An Evidence from Indonesia

This study is to determine the effect of using the Refinitiv ESG score in stock picking and its impact on portfolio performance using the ESG Score. Methodology for this research using a two-sample t-test of Net Asset Value (NAV) price-return portfolios and the Sharpe ratio as a proxy for risk adjustment return for portfolio evaluation. The results of this study indicate that the use of a Refinitiv rating may serve as a suitable metric for stock selection strategy because companies with higher ESG scores tend to exhibit superior performance. Since ESG ratings in Indonesia are still difficult to obtain, those with access to ESG ratings will have more advantages. Greater return performance is followed by an increased level of risk. Given the assumption of a well-diversified portfolio, it is hypothesized that the increased risk can be attributed to a larger variance coefficient. So that the Sharpe ratio must be used for portfolio evaluation to measure risk-adjusted returns.

Dynamic Black–Litterman Portfolios Incorporating Asymmetric Fractal Uncertainty

This study investigates the profitability of portfolios that integrate asymmetric fractality within the Black–Litterman (BL) framework. It predicts 10-day-ahead exchange-traded fund (ETF) prices using recurrent neural networks (RNNs) based on historical price information and technical indicators; these predictions are utilized as BL views. While constructing the BL portfolio, the Hurst exponent obtained from the asymmetric multifractal detrended fluctuation analysis is employed to determine the uncertainty associated with the views. The Hurst exponent describes the long-range persistence in time-series data, which can also be interpreted as the uncertainty in time-series predictions. Additionally, uncertainty is measured using asymmetric fractality to account for the financial time series’ asymmetric characteristics. Then, backtesting is conducted on portfolios comprising 10 countries’ ETFs, rebalanced on a 10-day basis. While benchmarking to a Markowitz portfolio and the MSCI world index, profitability is assessed using the Sharpe ratio, maximum drawdown, and sub-period analysis. The results reveal that the proposed model enhances the overall portfolio return and demonstrates particularly strong performance during negative trends. Moreover, it identifies ongoing investment opportunities, even in recent periods. These findings underscore the potential of fractality in adjusting uncertainty for diverse portfolio optimization applications.

Beyond REITs, Investing in Real Estate Funds

This study examines the performance of U.S. real estate funds of both exchange-traded funds (ETFs) and mutual funds, including passive ETFs, active ETFs, retail-class mutual funds, institutional-class mutual funds, and index mutual funds. The active ETF portfolio stands out with the highest returns and Sharpe ratio and consistently higher alphas in the factor models. It exhibits more prominent performance in downtrend markets and in recent years, whereas its peer retail-class mutual fund portfolio has consistently shown inferior performance. The passive ETF and the index mutual fund portfolios have never surpassed the performance of the active ETF portfolio. Neither choosing the cheap index strategy nor paying for high-cost mutual fund managers proves to be the optimal option for investors. The outperforming portfolio is found to be active ETFs, which sheds light on the continuing surge in their fund flows.

Physical climate change risk and ESG green premium

ABSTRACT We build an asset pricing model that incorporates climate change risk and firms’ ESG engagement to analyse their interactive effects on equity returns. Our model shows that a firm’s ESG engagement has three channels for influencing stock returns: its direct impact on resisting climate change risk, indirect reduction in systematic risk and attraction of green preference, and the empirical evidence shows that both risk–return relation and ESG engagement are priced. We also point out an inevitably negative and convex relation between the market Sharp Ratio and stocks market beta in an economy with climate change risk.

Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization

The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio.

Further Research in the Chinese Mid-cap Stock Market by Using the Markowitz Model, Capital Asset Pricing Model, and Fama-French Model

This article aims to identify the optimal portfolio of 10 mid-cap stocks in the Chinese stock market. Markowitz model is used to analyze the data from January 1, 2024, to July 1, 2024, including 117 trading days in total, and give some advice on portfolio selection. Markowitz’s model uses “Solver” in Excel to generate different portfolios and gets the optimal portfolio when the Sharpe ratio becomes the largest. In the Capital Asset Pricing Model (CAPM) and the Fama-French model, they can judge whether the stock is worth investing by the result given by linear regression in Excel. It turns out that the optimal portfolio of Markowitz has a Sharpe ratio of 2.14. Although the CAPM and Fama-French Model don’t give an exact percentage of each stock, they can take more conditions and factors into consideration to give a better judgment of the amount of the profit of each certain stock. The research fulfills the blank of using these models in the Chinese mid-cap stock market finding some supporting ideas from CAPM and Fama-French Model.

METODE MEAN-SEMIVARIANCE DALAM PEMBENTUKAN PORTOFOLIO REKSA DANA SAHAM TERBAIK BAROMETER BAREKSA

The most popular investment product today is mutual funds. One of the factors in selecting mutual funds is the low investment risk. This risk can be minimized again by combining several assets and then compiling them into a portfolio. The portfolio formation method used in this study is the Mean-Semivariance method and the Sharpe Index to calculate portfolio performance. The data used is the daily net asset value per unit of Mandiri Investa Atraktif Syariah, Rencana Cerdas, Manulife Saham SMC Plus and BNP Paribas Solaris for the period January 3 2022 to March 9 2023, Jakarta Composite Index (IHSG) data as a benchmark and BI-7 Day Reverse Repo Rate as a riskfree interest rate. The minimum risk of the portfolio will be achieved by compiling a portfolio of two mutual funds, namely Mandiri Investa Atraktif Syariah with a weight of 92,50% and the Smart Plan with a weight of 7,50% with an expected return rate of 0,00019 and a risk of 0,000000663. The Sharpe Index is 0.08971 indicating that the portfolio formed has good performance and can be used as a choice of investment assets by investors.

Momentum Effect of Stocks Take the Stock Returns of 50 Firms in the U.S. as an Example

Abstract: This paper randomly selects 50 SP500 US CRSP companies to study the existence of momentum effects in these 50 companies from January 2010 to December 2020. The article uses the CAPM model and the Fama-French model to classify the 50 companies into 10 different portfolios according to the magnitude of the momentum effect and uses the Fama-Macbeth model combined with the momentum factor MOM to investigate whether there is a momentum effect for the 50 companies. The study reveals a momentum effect, evidenced by higher momentum portfolios generally demonstrating better risk-adjusted returns. The momentum effect is only significant when verified by the CAPM model. However, here is an opposite conclusion or it is not significant when verified by the Fama-French three-factor model and the Fama-Macbeth model. It may be because the sample is too small and selected randomly, which is a part of the paper that needs to be improved in the future.