Sharpe Ratio Research Articles

The Financial Feasibility of Investing in a Sustainability Index in India

ESG is at the center of dialogue for investments, corporate governance and management. It used as an acronym for ‘Environmental, Social and Governance’ factors. The rapid rise of ESG has led to the prominence of socially responsible investing and has created a dual objective for investor’s capital. Researchers have previously studied the volatility of sustainable investments and whether SRI affects portfolio performance. Analyzing the financial feasibility of investing in a sustainability index was done by utilizing the historical data of returns on BSE ESG Index, NIFTY 500 Index and the BSE ESG 100 Index from October 2017 to April 2024. S&P BSE 100 ESG Index was used as a proxy for a sustainability index and the S&P BSE 100 Index was the index used to proxy traditional investments. This study has utilized financial ratios along with EGARCH and Granger causality test to understand the risk-reward characteristics of sustainable investments and compare them with traditional investments to understand whether it is financially viable to be a sustainably responsible investor. T test was conducted to identify whether is a significant difference between the returns of the sustainability index and its parent index. Results suggest that the sustainability index has a lower risk-adjusted return than traditional investments however, there is not a significant difference between the returns of the sustainability index and its parent index. There is a presence of strong persistent volatility in the sustainability index and negative shocks have a greater impact on the volatility of the index. Data also suggests a bi-directional relationship between the S&P 500 ESG Index and India’s sustainability index. The study recommends investors to consider the cost associated with investing in sustainability themed instruments while also taking into considerations the factors which are responsible for volatility of sustainable investments as well as the asymmetrical impact of negative shocks on returns.

Navigating median and extreme volatility in stock markets: Implications for portfolio strategies

This study explores the interdependencies among developed stock markets using the LASSO technique with quantile regression within a network analysis framework. Traditional forecasting methods often fail during volatile market conditions, necessitating innovative approaches that blend interconnectedness and factor modeling. By employing quantile regression, which examines financial assets across various distribution quantiles, this study addresses tail risk, a critical aspect of market behavior during crises. The network analysis framework provides insights into the relationships between stock markets, highlighting how variables interact within a complex system. The study assesses market behaviors at different quantile levels, considering clustering coefficients to analyze cycles, middlemen, ins, and outs. Additionally, it examines the impact of several factors on market interconnectedness, offering insights into the interplay of individual stocks with broader market conditions. Key findings demonstrate that incorporating interconnectedness factors into forecasting models enhances accuracy and informs better decision-making, leading to portfolios that can withstand extreme market conditions and provide superior risk-adjusted returns.

Optimizing stock market volatility predictions based on the SMVF-ANP approach

The stock market is considered one of the most complicated financial systems, comprising several components or inventories, whose prices vary substantially over time. Bursaries include revealing market tendencies over time. All investors in the stock market aim to maximize profits and reduce the associated risks. Stock currency forecasts are a significant financial concern that is being handled even more closely. In recent years, various neural network and hybrid models have surpassed classic linear and non-linear techniques to produce reliable prediction outcomes. This study investigates the efficacy of dynamic and effective stock market forecasting using neural network models. This study examines market transmission mechanisms and assesses the predicted links between multiple financial and economic factors. A stock market volatility and artificial network prediction (SMVF–ANP) approach is presented. The models analyzed included a multi-layer perceptron (MLP), dynamic artificial neural network, and generalized autoregressive conditional heteroscedasticity to extract additional input variables. The results reveal that the trade strategies led by the classification models yield superior risk-adjusted returns compared to the buy-and-hold approach and those led by the neural network and linear regression model-level estimate predictions. The numerical results show that the proposed SMVF-ANP method achieved a high performance ratio of 94.1%, an enhanced prediction ratio of 98.4%, a high stock market volatility rate of 96.7%, a reduced mean square percentage error ratio of 16.3%, a probability rate of 32.7%, an increased efficiency rate of 96.9%, and an accuracy ratio of 97.2% compared to other methods.

The Active Versus Passive Investing Debate: Evidence From the 2018-2023 Market Cycle

The paper investigates the research question: Which investment management style, active or passive, produced better risk-adjusted performance from January 1, 2018, to December 31, 2023 (Prondzinski, 2010)? The comprehensive time period was further subdivided into two periods: January 2020 – May 2023, the Pandemic period, and March 2022 – December 2023, the interest rate hiking period without any interest rate cuts. The study tested twenty-seven hypotheses derived from this research question for the specified periods addressed. The study, consisting of 27 statistical tests, found that on a risk-adjusted basis, the Sharpe ratios of active indices (proxies for active management) significantly exceeded the passive indices (proxies for passive management) in nine of the periods tested) (Prondzinski, 2010).

The Size Effect Anomaly in the Indian Stock Market

The size effect in stock market implies that small firm stocks generate higher risk-adjusted returns from that of large firm stocks. We did not find a size effect in the Indian stock market for the period of October 2005 to September 2023. We also examined the prevalence of size effect separately by calendar months. Small firm stocks generated significantly higher returns from than of large firms in April and December. Alternately, we did not find a size effect during January, November-December and March to May. We conclude only April and December months’ exhibit a size effect in the Indian stock market.

Performance Analysis of Selected Mutual Fund Schemes: A Case Study

This study aims to evaluate the performance of selected mutual fund schemes offered by Aditya Birla Sun Life Mutual Fund. By leveraging secondary data from April 2022 to March 2023, this research analyzes the Net Asset Value (NAV) and corresponding returns of five mutual fund schemes. Utilizing statistical tools and the Sharpe Index Model, the study assesses risk-adjusted returns to rank the schemes and provide insights into their relative performance. The findings serve as a valuable resource for individual investors, organizations, and regulators, facilitating informed investment decisions and highlighting market trends. The research underscores the significance of mutual funds as a viable investment vehicle for diverse portfolios and examines the efficacy of specific schemes in achieving their investment objectives.

Effect of Macroeconomic Factors on Mutual Funds Risk and Return: An Empirical Study from Bangladesh

This study aims to identify the influence of macroeconomic factors on the risk and return of mutual funds in Bangladesh. Monthly closing price data of 27 mutual funds from Bangladesh are collected from December 2015 to December 2022. Additionally, monthly data of 6 macroeconomic variables, i.e., deposit rate, export, import payments, remittance, broad money (M2) and GDP growth rate, are gathered for this study. This study utilized standard deviation and beta as risk measures, and the Sharpe and Treynor ratios are applied as risk-adjusted return (RAR) measures. All the risk and risk-adjusted return measures are computed using 12-months rolling window method. The random effect model of panel data analysis is applied to find the influence of macroeconomic variables on the risk and return of mutual funds. Overall findings indicate that macroeconomic factors significantly influence mutual fund risk exposure. On the other hand, risk-adjusted return (RAR) is also significantly influenced by the macroeconomic variables. This empirical evidence helps practitioners and institutional investors in their decision-making in the situation of market asymmetric. Keywords: Risk-Adjusted Return, Bangladesh, Mutual Fund, Standard Deviation, Beta, Sharpe Ratio, Treynor Ratio

Is there a diversification paradox in real estate investment funds' value?

PurposeThis paper studies diversification and value in the investment portfolios of (non-listed) Real Estate Investment Funds (REIFs) exploring how the value of diversification is captured by the market and by investors (beyond reported valuations).Design/methodology/approachWe apply the Herfindahl-Hirschman Index (HHI) to study the level of concentration versus diversification in the investment portfolios of REIFs (both in terms of segment and geographical diversification). We use a dataset from INREV with data from 62 investment portfolios, with an average of 86 REIFs per portfolio for the period of 2008–2020 (to study segment diversification). We use a second dataset from INREV with data from 30 investment portfolios with an average of 79 REIFs per portfolio for the period of 2005–2020 (to study geographical diversification). We employ a cluster analysis approach to identify common features among the investment funds.FindingsWe conclude that (segment diversified) portfolios with higher degrees of leverage exhibit higher income yields, albeit diversification is captured indirectly through asset choices – more diversified portfolios tend to exhibit a stronger risk and return relationship. Also, geographical diversification creates value (more significantly by for the correct combination of countries carefully choosing what different geographies to group in the diversified portfolio).Research limitations/implicationsOne limitation of our study is that our portfolios are funds of funds, since the available data could not reach the asset detail, but we believe this does not compromise our results.Practical implicationsDiversification leads to higher risk-adjusted returns which suggests that properties may be undervalued (market value) in the framework of the Gordon Model, contrary to expectations (regarding investment value).Originality/valueInvestors capture the value of diversification differently, suggesting a gap between market value and investment value that can be explored.

Parsing the Performance of Stock Market Indices Amidst COVID-19 and Russia-Ukraine War: A Comparative Study of DSEX, Nifty-50, and KSE-100

The primary objective of this investigation is to assess the performance of the benchmark indices in the stock markets of Bangladesh, India, and Pakistan, denoted by DSEX (Dhaka Stock Exchange Broad Index), Nifty-50 (National Stock Exchange Fifty), and KSE-100 (Karachi Stock Exchange 100 Index), respectively. Data is meticulously gathered across distinct periods, namely the pre-COVID, the COVID-19, and the concurrent COVID-19 era along with the Russia-Ukraine War period, and amid the consequent global ongoing financial crises. The study encompasses information spanning from July 2, 2013 to May 9, 2023, employing descriptive statistics, where both mean returns and risk-adjusted returns were calculated consecutively. The evaluation of diversification among the indices during the specified periods is conducted through descriptive statistics and Ordinary Least Squares (OLS) estimation. The findings of this study indicate that all the indices exhibited negative mean returns during the COVID-19 phase. Furthermore, both mean returns and risk-adjusted returns of the indices witnessed substantial increments amid the inflationary environment concurrent with the COVID-19 and Russia-Ukraine War periods. OLS estimation results suggest an absence of a discernible diversification relationship among the indices. In summary, our results underscore the dynamic nature of index prices across diverse temporal and global crisis scenarios. These implications offer valuable insights for investors and stockholders, enabling them to discern hedging opportunities and identify diversifiable indices within the global stock markets across disparate periods and crises.

Financial viability decision rules in residual valuation method of property development appraisal: case study of London, England

Purpose An appraisal is normally conducted to determine financial viability of property development projects for several purposes. The residual valuation method is normally used to appraise such projects and the purpose of the paper is to examine its financial viability decision rules (FVDRs) used by practitioners. Design/methodology/approach The qualitative research approach was adopted based on the case study strategy of enquiry where 48 development appraisal reports from 37 Royal Institution of Chartered Surveyors registered firms in London were accessed from the internet and critically reviewed. Findings Site-specific and area-wide development appraisals for planning purposes dominated the reports. Five FVDRs were identified. A development project is financially viable if: (i) computed residual profit expressed as a percentage return is equal to or greater than a determined market benchmark risk-adjusted return; (ii) computed residual profit expressed as a percentage return is positive; (iii) calculated residual land value is greater than open market land value or benchmark land value; (iv) computed residual land value is positive; and (v) there is a surplus when appraisal cost variables including land costs plus allowance for developer’s profit are deducted from gross development value. In some reports, it was discovered some appraisal cost variables were excluded whilst others were inappropriately treated. Practical implications The first and third FVDRs are reasonable whilst the remaining are fraught with problems and using them can make development projects that are financially unviable to be viable. Also, excluding relevant cost variables and treating some inappropriately understate the appraisal cost component resulting in incorrect financial viability outcomes. These can lead to wrong recommendations about financial viability being proffered that negatively affect the practitioners’ clientele. The dominance of development appraisals for planning purposes shows the important role development appraisals continue to play in the English planning system. Originality/value To the best of the author’s knowledge, it is the first time FVDRs in development appraisals have been systematically investigated in England with resultant new empirical findings and arguments.

Implicit Sharpness-Aware Minimization for Domain Generalization

Domain generalization (DG) aims to learn knowledge from multiple related domains to achieve a robust generalization performance in unseen target domains, which is an effective approach to mitigate domain shift in remote sensing image classification. Although the sharpness-aware minimization (SAM) method enhances DG capability and improves remote sensing image classification performance by promoting the convergence of the loss minimum to a flatter loss surface, the perturbation loss (maximum loss within the neighborhood of a local minimum) of SAM fails to accurately measure the true sharpness of the loss landscape. Furthermore, its variants often overlook gradient conflicts, thereby limiting further improvement in DG performance. In this paper, we introduce implicit sharpness-aware minimization (ISAM), a novel method that addresses the deficiencies of SAM and mitigates gradient conflicts. Specifically, we demonstrate that the discrepancy in training loss during gradient ascent or descent serves as an equivalent measure of the dominant eigenvalue of the Hessian matrix. This discrepancy provides a reliable measure for sharpness. ISAM effectively reduces sharpness and mitigates potential conflicts between gradients by implicitly minimizing the discrepancy between training losses while ensuring a sufficiently low minimum through minimizing perturbation loss. Extensive experiments and analyses demonstrate that ISAM significantly enhances the model’s generalization ability on remote sensing and DG datasets, outperforming existing state-of-the-art methods.

A Stock Market Decision-Making Framework Based on CMR-DQN

In the dynamic and uncertain stock market, precise forecasting and decision-making are crucial for profitability. Traditional deep neural networks (DNN) often struggle with capturing long-term dependencies and multi-scale features in complex financial time series data. To address these challenges, we introduce CMR-DQN, an innovative framework that integrates discrete wavelet transform (DWT) for multi-scale data analysis, temporal convolutional network (TCN) for extracting deep temporal features, and a GRU–LSTM–Attention mechanism to enhance the model’s focus and memory. Additionally, CMR-DQN employs the Rainbow DQN reinforcement learning strategy to learn optimal trading strategies in a simulated environment. CMR-DQN significantly improved the total return rate on six selected stocks, with increases ranging from 20.37% to 55.32%. It also demonstrated substantial improvements over the baseline model in terms of Sharpe ratio and maximum drawdown, indicating increased excess returns per unit of total risk and reduced investment risk. These results underscore the efficiency and effectiveness of CMR-DQN in handling multi-scale time series data and optimizing stock market decisions.

A three-level nested portfolio optimization model with position allocation

Existing portfolio optimization models cannot well capture the real position allocation requirement, leading to limited impact in practice. To overcome this challenge, we propose a three-level nested portfolio optimization model with position allocation. Within this model, the inner and middle levels collaboratively determine the optimal portfolio, while the outer level focuses on optimizing the holding proportion of each stock in the optimal portfolio. Compared with existing models with portfolio weights, the proposed model imposes the position allocation constraint that precisely characterizes the limitations on holding each stock. This constraint is crucial for investors to obey securities trading regulations involving position limitations and to mitigate the potential impact of market risks. To address the nonlinear and nonconvex nature of the novel model, we develop an intelligent optimization algorithm by effectively hybridizing the support vector regression and the enhanced grey wolf optimizer. We comprehensively evaluate its performance using eight metrics, including accumulative return, annual return, Sharpe ratio, maximum drawdown, absolute and relative win ratios, predictive precision and accuracy. The experimental results indicate that (i) the proposed model can achieve more excess returns than those stock selection models not considering position allocation, especially for the large-cap stocks; (ii) compared with other state-of-the-art meta-heuristics, the enhanced grey wolf optimizer can yield better portfolio in conjunction with the support vector regression; (iii) in the context of the Chinese A-share stock market, specific financial indicators such as return on equity, inventory turnover rate, net income growth rate, and debt-to-equity ratio should be given greater consideration compared to other financial metrics.

GDP-linked bonds as a new asset class

Using stochastic spanning tests without any distributional assumptions on returns, we show that the two classes of GDP-linked bonds, floaters and linkers, are not spanned by a broad benchmark set of stocks, bonds, and cash for a wide range of design specifications. Thus, they provide a new asset class with significant diversification benefits for investors, with proportional investments to these novel instruments estimated in the double digits and an increase in Sharpe ratios by up to 0.37 over the benchmark. The benefits depend on the market risk premium, but they persist for a wide range of premia estimates from existing literature and are robust to a randomized test. Using the generalized method of moments regressions, we document the finance and macro determinants of GDP-linked bond returns.

Dynamic mean-variance portfolio selection under factor models

Utilizing insights from financial literature and empirical financial data, we introduce a comprehensive system of factor models designed to capture both return and risk dynamics. Our focus extends to addressing the multi-period mean-variance portfolio selection challenge within the framework of these proposed factor models. Through rigorous analysis, we formulate a semi-analytical optimal portfolio policy, characterized by a linear relationship with the current wealth level. The coefficients of this optimal policy are intricately linked to a specific stochastic process known as the future investment opportunity (FIO), reflecting the investor's anticipation of future investment prospects. Furthermore, empirical examination within the U.S. market context underscores the efficacy of our approach. By incorporating the factor models for return and risk, our optimal portfolio policy exhibits superior out-of-sample Sharpe ratio compared to benchmark policies.

Industry return prediction via interpretable deep learning

We apply an interpretable machine learning model, the LassoNet, to forecast and trade U.S. industry portfolio returns. The model combines a regularization mechanism with a neural network architecture. A cooperative game-theoretic algorithm is also applied to interpret our findings. The latter hierarchizes the covariates based on their contribution to the overall model performance. Our findings reveal that the LassoNet outperforms various linear and nonlinear benchmarks concerning out-of-sample forecasting accuracy and provides economically meaningful and profitable predictions. Valuation ratios are the most crucial covariates, followed by individual and cross-industry lagged returns. The constructed industry ETF portfolios attain positive Sharpe ratios and positive and statistically significant alphas, surviving even transaction costs.

Risk-Based Asset Allocation in Factor Investing: Exploring the Inverse Factor Volatility Strategy

This study evaluates the effectiveness of the Inverse Factor Volatility strategy within the context of factor investing, comparing its performance to the conventional Risk Parity strategy. Using quantitative techniques, including portfolio construction and performance metrics analysis, this research employs data from five individual equities spanning the years 2000 to 2022. The methodology involves constructing portfolios based on Inverse Factor Volatility and Risk Parity principles and analyzing performance metrics, including mean returns, risk-adjusted returns, and drawdowns. The findings indicate that, compared to Risk Parity, Inverse Factor Volatility offers superior drawdowns, risk-adjusted returns, and mean returns. These results suggest that Inverse Factor Volatility may be a more effective strategy for portfolio management and could represent an advancement over traditional factor investing methods. The conclusions of this study hold significant implications for portfolio managers seeking to optimize their investment strategies.

ANALYSIS OF OPTIMAL PORTFOLIO FORMATION ON IDX30 INDEXED STOCK WITH THE MEAN ABSOLUTE DEVIATION METHOD

In investing in stocks, an investor must be able to form a stock portfolio to obtain optimal results. Factor analysis is one way to select stocks to form a portfolio. Factor analysis with Principal Component Analysis (PCA) extraction is used to summarize many variables into new smaller factors by producing the same information. The new factor formed is called a portfolio. This study aims to form an optimal portfolio using the Mean Absolute Deviation (MAD) method, which is an alternative to Markowitz optimization, and assess the stock portfolio's performance using the Sharpe index. This research uses IDX30-indexed stocks because the stocks in this index have high market capitalization and liquidity. The data used in this study are daily close stock price data on the IDX30 index from September 20, 2022, to September 20, 2023. The data used is secondary data obtained from the official website https://finance.yahoo.com/. From the analysis, three stock portfolios were obtained. With MAD optimization, the investment weight of each stock is obtained namely, in the first portfolio, the investment weight of AMRT shares is 21.95%, BBCA shares are 30%, BBNI shares are 18.05%, and BBRI shares are 30%. In the second portfolio, the investment weight of AKRA shares is 34.03%, BRPT shares are 40%, and MEDC shares are 25.97%. In the third portfolio, the investment weight of BMRI shares is 50%, and INDF shares are 50%. By measuring the performance of the Sharpe index, the optimal portfolio is obtained in the second portfolio with an expected return portfolio of 0.155% and a portfolio risk of 1.927%.

BRIDGING TRADITION AND INNOVATION: A LITERATURE REVIEW ON PORTFOLIO OPTIMIZATION

Portfolio optimization plays a crucial role in investment decision-making by balancing risk and return objectives. With the aim of improving portfolio performance, while enhancing risk management, this literature review explores traditional and artificial intelligence-powered approaches for portfolio optimization. From the traditional methods of portfolio optimization, methods such as random matrix theory, shrinkage estimators, correlation asymmetries and partial correlation networks are presented. While, from the artificial intelligence realm, techniques such as machine learning efficient frontiers, performance-based regularization, neural network predictors and deep learning models for direct optimization of portfolio Sharpe ratio are highlighted. Intertwining the traditional methods, with artificial intelligence techniques, this review highlights relevant portfolio optimization research useful for academics and practitioners alike.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.