Sharpe Ratio Research Articles

Backtest overfitting in the machine learning era: A comparison of out-of-sample testing methods in a synthetic controlled environment

We present a comprehensive framework to assess these methods, considering the unique characteristics of financial data like non-stationarity, autocorrelation, and regime shifts. Through our analysis, we unveil the marked superiority of the Combinatorial Purged (CPCV) method in mitigating overfitting risks, outperforming traditional methods as evidenced by its lower Probability of Backtest Overfitting (PBO) and superior Deflated Sharpe Ratio (DSR) test statistic. Walk-Forward, by contrast, exhibits notable shortcomings in false discovery prevention, characterized by increased temporal variability and weaker stationarity. This contrasts with CPCV’s demonstrable stability and efficiency. We introduce novel variants of CPCV, including Bagged CPCV and Adaptive CPCV, which enhance robustness through ensemble approaches and dynamic adjustments based on market conditions. Our empirical validation using historical SP 500 data confirms these advanced cross-validation methods’ practical applicability and resilience. The analysis also suggests that choosing between Purged K-Fold and K-Fold necessitates caution due to their comparable performance and potential impact on the robustness of training data in out-of-sample testing. Our investigation utilizes a Synthetic Controlled Environment incorporating advanced models like the Heston Stochastic Volatility, Merton Jump Diffusion, and Drift-Burst Hypothesis alongside regime-switching models. This approach provides a nuanced simulation of market conditions, offering new insights into evaluating cross-validation techniques. We also address the computational aspects of these methods, demonstrating that parallelization significantly improves efficiency, making them feasible for large-scale financial datasets. Our study underscores the necessity of specialized validation methods in financial modeling, especially in the face of growing regulatory demands and complex market dynamics.

Dynamically adjusted cross-currency carry strategy

We examine time-varying excess return in a dynamically adjusted international “cross-currency” carry strategy, with a focus on excess returns. Unlike traditional carriers that fund and invest in bonds with the same maturities, the novel dynamic strategy involves funding with a 1-year low-yield treasury bond and investing in 2- to 10-year high-yield bonds in other currencies. An analysis of Sharpe ratios, foreign exchange (FX), and yield excess return shows variations in the joint expectation hypotheses expectations hypothesis of the term structure (EHTS) and the uncovered interest rate parity (UIRP) lead to profits. However, the international strategies perform worse than the domestic carry strategies. Predictive factors, such as the Cochrane–Piazzesi, show limited effectiveness due to FX volatility. Therefore, future studies should examine more predictability factors.

Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

The rapid growth in FinTech is attracting significant investor attention. There are concerns that FinTech stocks may be a source of systemic risk and this has implications for the stability of the financial sector. This study uses a TVP-VAR model to estimate the dynamic connectedness between global FinTech stocks and the stocks of large US banks, regional US banks, US insurance, and global private equity. The findings reveal that FinTech and insurance stocks are net receivers of shocks while large US banks are a major source of shocks. Thus, FinTech stocks are not a source of systemic risk but can be affected by financial contagion and systemic risk originating from large US banks. The highest connectedness was observed at the onset of the COVID-19 lockdowns illustrating the dramatic impact that a health pandemic can have on systemic risk. Systemic risk also increased during the 2023 US bank panic. Connectedness has implications for constructing investment portfolios. The equally weighted, risk parity, and risk parity connectedness portfolios have similar portfolio summary statistics, but lower risk adjusted returns than the maximum Sharpe ratio portfolio which better captures the low correlation between FinTech stocks and traditional financial stocks. These results are robust across various portfolio rebalancing periods (daily, weekly, monthly).

Improved estimation of the correlation matrix using reinforcement learning and text-based networks

We propose a data-driven methodology to shrink the correlation matrix and, hence, the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumptions on the stock returns and can be applied to any covariance matrix target. It focuses on the special case of the global minimum variance portfolio and investigates the economic value of our methodology by utilizing text-based networks (Hoberg and Phillips, 2016). The portfolio selection problem, hence, boils down to determining the optimal shrinkage policy using RL. The empirical analysis utilizes a large universe of stocks covering more than 400 assets and 20 years as a testing period. Overall, the proposed portfolio rule outperforms state-of-the-art shrinkage techniques in terms of out-of-sample volatility, Sharpe ratio, and downside risk net of transaction costs. Our research highlights the effectiveness of our RL-driven approach and underscores the value of alternative data sources in ex-ante forming robust portfolio rules.

Adaptive Risk Parity Strategies for Managing Portfolio Risk in Volatile Markets

This paper presents a novel model for Adaptive Risk Parity Portfolio Construction, designed to dynamically adjust portfolio weights based on changing market conditions and economic regimes. Traditional portfolio strategies, such as Modern Portfolio Theory (MPT), often rely on static assumptions that can lead to suboptimal performance in volatile markets. In contrast, the adaptive risk parity approach aims to equalize risk contributions across asset classes while responding to fluctuations in economic indicators, such as GDP growth and inflation rates. By employing techniques such as Markov switching models and machine learning for regime identification, the proposed model allows for real-time adjustments to asset allocations. The back-testing results demonstrate that the adaptive risk parity portfolio outperforms static risk parity and traditional mean-variance optimized portfolios in terms of risk-adjusted returns and drawdown management. This research contributes to the field of portfolio management by providing a framework that enhances resilience and adaptability in investment strategies.

Optimizing Mutual Fund Selection with Fuzzy Sharpe Ratio: An Empirical Study of Indian Markets

Optimizing Mutual Fund Selection with Fuzzy Sharpe Ratio: An Empirical Study of Indian Markets

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

Connectedness of global energy markets with global agricultural, natural resources, and socially responsible investments using extended GFI

PurposeThe study aims to investigate the risk transmission from COVID-19 to global agriculture, energy, natural resources/mining and environmentally/socially responsible investments. Additionally, it explores the connectedness of global energy indices with global agriculture, natural resources/mining and environmentally/socially responsible investments. The study develops a new COVID-19-based Global Fear Index (GFI) to achieve the objectives, thus contributing to the prevailing literature.Design/methodology/approachThe data of Global indices are selected from January 2020 to December 2021. The study uses multivariate BEKK-GARCH and TVP-VAR models to explore COVID-19 risk transmission and connectedness between global indices.FindingsSignificant shock and volatility transmissions from COVID-19 to all global indices are observed. Results show that global agriculture, natural resource/mining markets and environmentally and socially responsible investments are safe havens during COVID-19. Furthermore, these global investment choices are barely connected with global energy indices.Practical implicationsPortfolio managers and investors should invest in global indices to gauge the risk-adjusted return during the pandemic and upcoming health-related risks. Investors in energy sectors are advised to diversify the risk by adding safe-haven assets to their portfolios.Social implicationsThe findings shed light on the importance of environmentally and socially responsible investments as a separate asset class where ecologically friendly and socially sentimental investors could invest in diversifying the risk of their portfolios.Originality/valueThe paper offers valuable insights to policymakers and investors regarding asset pricing, risk management and financial market stability during pandemic-type emergencies.

Integrating corporate social responsibility with financial outcomes: stock performance of firms hiring US veterans during COVID-19

PurposeWe propose an alternative rationale for why some firms employ veterans, driven not solely by benevolence but also by the prospect of enhanced outcomes. Financially, hiring veterans could correlate with improved stock market performance for the hiring company while aligning with corporate social responsibility (CSR) initiatives. Our study centers on the stock market performance of companies hiring veterans. It aims to underscore a lesser-known facet of the veteran employment discourse and its connection to the hiring firm's financial performance.Design/methodology/approachThis paper evaluates the stock market performance of three VETS portfolios (made of companies that hire veterans) compared to the benchmark SPDR S&P 500 ETF. Using a modular approach, we create three VETS passive indices: VETSEW (equal-weighted index), VETSPW (price-weighted index) and VETSVW (value-weighted index). The study analyzes the annual returns, portfolio allocations, risk-adjusted performance metrics and style analysis of the portfolios from January 1, 2020, to December 31, 2022.FindingsThe findings indicate that all three VETS portfolios outperformed the benchmark, with higher ending balances and superior risk-adjusted ratios such as the Sharpe and Sortino ratios. Notably, the portfolios demonstrated resilience during challenging periods, including the COVID-19 pandemic, subsequent recovery and an inflationary period.Research limitations/implicationsLimitations include the paper's focus solely on stock returns, suggesting a need for broader financial and management ratios. Moreover, a deeper exploration into how veterans contribute during turbulent times is suggested for further investigation. Although the study touches upon the financial performance of veteran-focused companies during challenging economic times, it does not extensively delve into the specific ways in which veterans add value under such circumstances, presenting an opportunity for further exploration.Practical implicationsFirms that employ veterans amid the COVID-19 pandemic demonstrate favorable risk-adjusted returns, underscoring the potential of veterans as valuable crisis-time assets. Our research further underscores the correlation between veteran hiring and enhanced financial prowess. These insights carry significant policy implications, including CSR initiatives for hiring veterans, skill translation and training and collaboration with veteran organizations.Social implicationsThe paper's findings suggest significant implications: (1) Policymakers could incentivize firms to hire veterans through tax benefits or grants, leveraging their skills for organizational resilience. (2) Collaborative efforts between policymakers and firms can promote responsible hiring, boosting a company's reputation through diversity and inclusion, positively impacting society. (3) Support for skill translation from military to civilian jobs is crucial. Programs certifying skills and tailored education aid veterans' successful transition into the workforce. (4) Collaborations between policymakers, veteran organizations and private sector entities can create networks, job placements and support systems for veterans' employment.Originality/valueNumerous prior studies within the domain of corporate social responsibility have predominantly neglected the contributions veterans offer to businesses and the underlying reasons behind firms' decisions to employ them. Our research uniquely concentrates on the stock market performance of companies that choose to hire veterans.

Constructing stock portfolios by sorting on ESG ratings: Does the rating provider matter?

An interesting and well documented observation is that the same company receives heterogeneous ESG ratings from different rating providers. Consequently, an important question is whether sorting stocks on ESG ratings of different rating providers results in portfolios that are similar or substantially different regarding their constituents, performance, and risk. We employ the ratings of five rating providers on stocks listed in North America, Europe, Asia-Pacific (excluding Japan), and Japan for the period from 2014 until the end of 2019 and analyze whether the corresponding quintile stock portfolios actually have similar or different portfolio constituents as well as Sharpe ratios, alphas, and idiosyncratic risk. Our analysis indicates that the portfolios considerably differ regarding their constituents. In contrast, and most importantly, Sharpe ratios, alphas, and idiosyncratic risk of corresponding portfolios are not significantly different.

Performance of Commodity Futures-Based Dynamic Portfolios

This paper analyzes the return performance of various commodity futures-based dynamic portfolios over the period from 31 January 1986 to 31 July 2023. By constructing 30 distinct portfolios categorized by style and performance, we assess their potential for enhancing the performance of traditional portfolios consisting of equity and bonds. We find that most commodity portfolios do not offer statistically significant returns, either in terms of average or risk-adjusted returns. Only the portfolios in the basis category and portfolios in the term structure category exhibit significantly positive risk-adjusted returns, indicating their potential value for portfolio enhancement. The performance of these portfolios is not different in pre-financialization and financialization periods.

Projected gradient descent method for cardinality-constrained portfolio optimization

Cardinality-constrained portfolio optimization aims at determining the investment weights on given assets using the historical data. This problem typically requires three constraints, namely, capital budget, long–only, and sparsity. The sparsity restraint allows investment managers to select a small number of stocks from the given assets. Most existing approaches exploit the penalty technique to handle the sparsity constraint. Therefore, they require tweaking the associated regularization parameter to obtain the desired cardinality level, which is time-consuming. This paper formulates the sparse portfolio design as a cardinality-constrained nonconvex optimization problem, where the sparsity constraint is modeled as a bounded ℓ0-norm. The projected gradient descent (PGD) method is then utilized to deal with the resultant problem. Different from existing algorithms, the suggested approach, called ℓ0-PGD, can explicitly control the cardinality level. In addition, its convergence is established. Specifically, the ℓ0-PGD guarantees that the objective function value converges, and the variable sequences converges to a local minimum. To remedy the weaknesses of gradient descent, the momentum technique is exploited to enhance the performance of the ℓ0-PGD, yielding ℓ0-PMGD. Numerical results on four real-world datasets, viz. NASDAQ 100, S&P 500, Russell 1000, and Russell 2000 exhibit the superiority of the ℓ0-PGD and ℓ0-PMGD over existing algorithms in terms of mean return and Sharpe ratio.

Short-term contrarian in the carbon emission market

We consider the short-term contrarian effects in daily data from carbon emission markets. Technical analysis is conducted with three contrarian trading rules on four carbon emission markets of China. Various individual and joint test statistics are developed for testing the contrarian profits from individual trading path as well as a group of trading paths. Empirical evidence shows that the contrarian profits are statistically and economically significant across the technical rules. Particularly, the contrarian moving average group trading generates average returns of 3.8 to 13.8 % per month across the different carbon markets during the period of 2013–2023 and the corresponding Sharpe ratios range from 0.9 to 2.01, while the Sharpe ratios from the buy-and-hold strategy are all below 0.12. Further tests support that the contrarian profits are not eliminated by transaction costs, and not dominated by extreme returns. We also emphasize on the explanations of the contrarian profit. Among the considered variables, the market volatility is the most significant explanatory variable for contrarian profits across four markets, whereas the market illiquidity and non-trading breaks are also significant in certain markets. The finding supports the explanation of the behavioural bias of overreaction for the short-term contrarian effect. Other explanations, including short-selling constraint, clearing stress, price limits, government interventions, etc. are also discussed in the paper.

Lottery stocks in Brazil: investigating risk premium and investor behavior

PurposeThis study investigates the paradox of lotteries in financial markets, challenging traditional utility models predicated on rational behavior amid uncertainty. It explores why investors are drawn to lotteries despite the potential trade-off between risk-adjusted returns and sporadically substantial gains.Design/methodology/approachEmploying a multifaceted approach, the study first scrutinizes diverse theories elucidating the perplexing behavior of lottery investors. Subsequently, it assesses the premium attached to lottery stock shares in the Brazilian financial market using distinct methodologies, thereby offering a comprehensive analysis of this phenomenon. Finally, the study estimates the risk premium associated with the lottery stocks applying an extended Fama–French multifactor model and searching for evidence of overlap with other risk-based anomalies.FindingsThis research unveils theories underpinning seemingly irrational investor behavior vis-à-vis lotteries, revealing the motivations propelling investors to willingly exchange risk-adjusted returns for the allure of substantial but infrequent gains. Empirical evidence delineates the extent of the premium paid for lottery stocks in the Brazilian market.Originality/valueThe study’s novelty lies in its amalgamation of theoretical exploration, empirical analysis and the application of the Fama–French factor model to gauge the risk premium associated with lottery-related behavior. Furthermore, its investigation of lottery stocks within the Brazilian market introduces a distinctive dimension, elucidating market dynamics and investor behaviors unique to the region.

Integrating LSTM Networks with Mean-Variance Optimization for Enhanced Portfolio Construction: An Empirical Study on UK Stock Market

Portfolio optimization has long been a central theme in finance. With ongoing advancements in machine learning, there is a significant opportunity to integrate predictive methods into portfolio optimization. This paper proposes leveraging Long Short-Term Memory (LSTM) networks alongside the established Mean-Variance (MV) optimization framework to construct optimal portfolios. These portfolios aim to help financial investors effectively manage and mitigate risk while maximizing returns. The study meticulously screened the leading stocks of 12 prominent UK companies listed on the London Stock Exchange (LSE), known for their influence and visibility. Initially, the study applies the LSTM networks to predict stock price volatility and integrates these predictions into the MV model to allocate portfolio weights effectively. To underscore the superiority of the proposed approach, the study compares cumulative returns from portfolios optimized for maximum and minimum variance Sharpe ratios with real data against the FTSE100 index over the same period. The results demonstrate that the portfolio optimized with the maximum Sharpe ratio significantly outperforms both the conservative minimum variance portfolio and the FTSE100 index. This empirical evidence underscores the practical value of integrating advanced machine learning techniques with established financial theory, enabling more innovative and efficient investment strategies to meet the evolving needs of financial investors.

Quantile connectedness among fintech, carbon future, and energy markets: Implications for hedging and investment strategies

This study proposes a novel framework to explore the quantile connectedness among fintech, carbon future, and energy markets with implications for hedging and investment strategies. We aim to construct portfolio strategies for extreme market conditions based on the results of quantile connectedness to address the complex price fluctuations in the energy markets. Using the daily data from January 4, 2017, to March 6, 2023, we find that (i) carbon future and crude oil are the net receivers, while fintech and clean energy are the net contributors. However, the net spillover direction of fintech, carbon futures, and energy markets change frequently under extreme market conditions; (ii) the COVID-19 pandemic and Russia-Ukraine conflict significantly enhance connectedness across all quantile levels; (iii) the sample period is dominated by the lower quantile connectedness, indicating that investors react more strongly to bad news. Furthermore, the result of portfolios shows that, compared to normal market conditions, the dynamic investment weights of minimum connectedness (MCOP) exhibit greater volatility under extreme market conditions. This highlights the differences in portfolio adjustments across different market conditions. In addition, the result shows that the minimum connectedness and minimum correlation portfolio have a greater cumulative return than the minimum variance portfolio. The analysis based on the Sharpe ratio shows that in extreme market conditions, carbon futures and fintech can serve as effective hedging tools for the energy markets. Robustness tests further confirmed our conclusions. Our results have positive implications for decision-makers and investors about risk management and diversified portfolio selection.

Whether ARIMA-based Portfolio Can Beat the Market Index

Portfolio optimization is a long-term topic in the financial field, which can maximize returns while minimizing risks. It is widely used in production and daily life, such as stock investment, production optimization, and engineering models. This article selects data from 10 stocks, namely KO, PG, PEP, CL, MDLZ, STZ, PM, KMB, GIS, and K, from January 24, 2024, to June 14, 2024. Firstly, this article uses the ARIMA model to learn the first 70% of the data and predict the last 30% of the data, and then uses portfolio strategy to evaluate the results. This article obtained the maximum Sharpe ratio model on the Monte Carlo effective boundary. By combining the maximum Sharpe ratio model with the SP 500 index within the same time frame, this article concludes that the market performs better. This article provides different portfolio allocation strategies for risk averse investors seeking stable positive returns in turbulent markets.

Desirability of Sukuk as a New Investment Avenue: An Exploratory Study in Bangladesh

Sukuk can be a worthwhile device for meeting demand of those willing to invest in Shariah-compliant avenues of investment, which has been largely catered by Islamic banking products in Bangladesh. Using survey data collected from existing market investors, this study aims to explore the factors contributing to their choice of investment in sukuk. The risk-return preference of the investors is crucial for structuring the sukuk for issuance. Major factors contributing to the investment decision of the sukuk of surveyed participants also include confirmation of the use of proceed of the project in a Shariah-compliant way, risk-adjusted return, timely payment of the expected cash flows, tradability in the secondary market, awareness regarding the expertise, and independence of the Shariah advisors in the Shariah supervisory board (SSB). To facilitate the new instrument and utilize it for the development of the country, especially for the infrastructural development and green projects, government and other policymakers can contemplate offering government guarantee as well as tax exemptions demanded by the respondents of this study. The respondents expressed aversion to longer-term investment opportunities; rather sukuk maturity of up to five years was found desirable. Apprehension for upholding a distinct special purpose vehicle (SPV) and trustee for sukuk was revealed in the study. Although sufficient care was taken in selecting the sample, the limitation of the study is that it may not be deemed conclusive due to the lower sample size. A larger scale study may be conducted to reckon the insight of more potential investors in the market.

Differences of Performance Measurement in Mutual Funds and Their Impact on Investment Decisions

This study discusses the performance of mutual funds in the capital market, divided into conventional and sharia mutual funds. The population of this research is all mutual funds, both conventional and sharia, which are traded on the Bibit application platform. Total of 77 conventional mutual funds and 31 sharia mutual funds. Based on sampling, 17 conventional mutual funds and 9 sharia mutual funds were obtained. The research observation period spans from 2021 to 2023. This research uses a quantitative approach with the Risk-Adjusted Return method, consisting of the Sharpe, Treynor, and Jensen methods. Then it will analyze the differences in performance between conventional and sharia mutual funds. The Mann-Whitney U Test is the analysis technique employed using SPSS 25. The results showed that there was no difference in performance between conventional mutual funds and sharia mutual funds based on the Shape, Treynor, and Jensen methods.

Trading Momentum in the U.S. Crude Oil Futures Market

This paper investigates if the Rate of Change (ROC), as a popular measure of momentum, can serve as a reliable technical analysis indicator to improve stock price prediction for U.S. West Texas Intermediate (WTI) crude oil futures market. The methodology centers on the application of the ROC and/or Moving Average (MA) price crossover/cross under strategies as a trading system. End of month futures prices of West Texas Intermediate (WTI) crude oil prices are collected for the period 28th May 2004 – 30th April 2024. The performance of the trading system is measured using both the Sharpe and Sortino ratios, thereby adjusting for total and downside risks. The model is also benchmarked against the naïve buy-and-hold strategy. Overall findings suggest a ROC based on 14-month periods outperform other momentum-based indicators, including when combined with price-crossover moving average strategies, and the naïve buy-and-hold strategy. After adjusting for the negative returns, the downside risk or semi-deviation amounted to 8.5%, and a Sortino value of 4.58. The Sortino value is however biased due to the 295% return witnessed in 2009. Findings have some vital policy implications for regulatory bodies and traders in the WTI crude oil energy futures market.