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Semantic fingerprinting is a leading AI solution that combines recent developments from cognitive neuroscience and psycholinguistics to analyze text with human- level accuracy. As an efficient method of quantifying text, it has already found its application in finance where the semantic fingerprints of company descriptions have been shown to successfully predict stock return correlations of Dow Jones Industrial Average ( DJIA) constituents. By extension, it has been suggested that diversified portfolios could be constructed to exploit the fundamental (dis)similarity between companies' core activities (measured by the semantic overlap of company descriptions). This paper follows the performance of two portfolios made of the same DJIA constituent companies: the "minimum semantic concentration" portfolio (constructed with text-based portfolio weights) and the traditional "minimum variance" portfolio, over a time span of 16 years including two high volatility events: the 2007 - 2009 financial crisis and the COVID pandemic. The results confirm that textual analysis using semantic fingerprinting is consistently successful in predicting stock return correlations and is valuable as a portfolio selection criterion. However, in times of high market volatility the fundamental information given by the companies' core activities, while still relevant, might carry less weight.
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Orientation: The performance of three different portfolio allocation strategies is assessed in a developed and a developing economy during different economic conditions over a period of seven years. Research purpose: Evaluate the performance of the portfolios – namely, the tangent, minimum-variance, and maximally diversified portfolio – across a developed and a developing economy and investigate the advantages and disadvantages that each portfolio poses in differing economic conditions. Motivation for the study: Understanding the benefits and drawbacks of each of these portfolios in times of crisis and in times of economic expansion could assist asset managers in making effectiveallocation decisions for their portfolios in different economic conditions. Research approach/design and method: Portfolio optimisation under various constraints. Main findings: Tangent portfolios produced superior returns to the other portfolios and the US portfolios consistently outperformed the South African ones. The minimum variance portfolio provided greater returns and downside protection than the maximally diversified portfolio during the COVID-19 market crash for the developed economy, while the opposite was observed for the developing economy. Practical/managerial implications: Practical knowledge of how each of the portfolios perform within different economic climates can assist asset managers to produce positive performance in times of recession and expansion. Contribution/value-add: Information and analysis on each of these portfolio asset allocation strategies during various economic conditions assists asset managers in finding the most effective way to structure their portfolios. Copyright © 2022– All Rights Reserved.
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In this paper, we present a framework for evaluating risk contagion by merging financial networks with machine learning techniques. The framework begins with building a financial network model based on the inter-institutional correlation network, followed by analyzing the structure and overall value changes of the financial network under the stress of a liquidation shock. We then examine the network's evolution over time. We also use three machine learning techniques to assess the abnormal volatility of important financial institutions in the financial network. Finally, we evaluate the spillover effects of risk volatility in financial networks on ESG investments. The findings suggest that the financial network becomes more robust as the connections among financial institutions become more intricate. This leads to an improvement in the ability of the financial network to withstand systemic risk events. Overall, our study provides evidence of the negative impact of risk spillovers in financial networks on ESG investments, highlighting the need for a more sustainable and resilient financial system. This innovative framework combining financial network modeling and machine learning prediction provides a deeper understanding of the evolution of financial networks and a more accurate evaluation of abnormal volatility in financial networks.
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PurposeEven though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.Design/methodology/approachThe authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.FindingsFirst, the authors found a strong evidence of Bitcoin's mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.Originality/valueThe study significantly extends the empirical literature of Bitcoin's portfolio diversification by taking EPU into consideration. To the best of authors' knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin's hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.
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Socially Responsible Investments (SRI) have recently generated much interest among asset owners, managers and academicians. Though the Efficient Market Theory suggests that stock prices fully reflect all available information, few existing studies indicate that Environmental, Social and Governance (ESG) portfolios deliver superior risk-adjusted performance. ESG investing is at a nascent stage in India but is growing rapidly, especially after the COVID-19 pandemic. Asset managers always face the dilemma of choosing between different screening methods, screening intensities and stock weighting schemes to deliver outperformance. Our study attempts to investigate the impact of these portfolio construction criteria on the risk-adjusted performance of ESG portfolios in India. Our results show that there exists a trade-off between superior investment performance and unsystematic risk of ESG portfolios. Investors can benefit from investing in equally-weighted best-in-class portfolios constructed using ESG scores. We highlight the implications of our findings for asset owners, managers, index providers and regulators, and also provide directions for future research in the area of ESG portfolio management. © 2023 John Wiley & Sons Ltd.
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Purpose: This research aims to examine the impact of the COVID-19 pandemic on the performance of stock portfolios in Indonesia, particularly the sharia stock portfolios and common stocks in response to information sentiment due to the COVID-19 pandemic on the Indonesian stock exchange. Theoretical framework: Research by Bash & Alsaifi (2019) and Shanaev & Ghimire (2019) examined the relationship between stock returns through the stock index on political activity or its connection with environmental exploitation (Alsaifi et al., 2020;Guo et al., 2020). The relationship between stock price changes with sports (Buhagiar et al., 2018) and disasters (Kowalewski & Śpiewanowski, 2020). Design/methodology/approach: The data used in this study were 564 stocks, consisting of 356 Islamic stocks and 208 common shares provided by the Osiris database. The model testing used panel data regression with a time lag approach, where information on the number of positive cases, deaths, and recoveries due to COVID-19 each week affected the performance of the stock portfolio on that day. Findings: The testing of the performance of sharia and conventional stock portfolios showed that there is no significant difference in response to the COVID-19 pandemic information, but statistical statistics on Islamic stocks have a tendency to be more resistant to pressure from selling by investors in the Indonesian capital market compared to common stock portfolios, this is proven by the sharia stock correlation coefficient shown to be positive, on the other hand, the stock regression coefficient shows the opposite direction. Research, Practical & Social implications: This research is expected to be a recommendation material that has benefits for the development of Islamic economics, primarily can be implemented by sharia-based banks in Indonesia and other countries. Originality/value: This research focuses on studying the performance of stock portfolios in Indonesia. This research responds to the impact of the Covid-19 pandemic on the performance of stock portfolios, which is focused on Islamic stock portfolios. © 2022 AOS-Estratagia and Inovacao. All rights reserved.
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PurposeThis paper aims to empirically examine the performance of the high-ESG (environment, social and governance) portfolio vis-a-vis the low-ESG portfolio at the Indian stock market before and during the Covid19 pandemic. Design/methodology/approachThe absolute rate of return and several risk-adjusted performance measures, for instance, Sharpe ratio, Modigliani-Modigliani measure, Treynor ratio, Jensen's alpha, information ratio, Fama's decomposition measure and Fama and French's three-factor model, have been used in this study along with the t-test. FindingsAll three indices (CARBONEX, GREENEX and BSE 500) had better returns during Covid19 period as compared to the pre-Covid19 period. However, these returns were not statistically significant. During Covid19, the risk of the indices also rose, but they provided better returns for the additional risk taken. Finally, it is concluded that the performance of high-ESG and low-ESG stock portfolios did not differ significantly in both periods. Practical implicationsThe study is relevant to individual and institutional investors, financial advisors, portfolio managers, corporations, policymakers, market regulators and society at large. Social implicationsThis study emphasized the need to expand the role of ESG investment in India for the benefit of people, communities and society as a whole. Originality/valueThis research is the first of its kind, to the best of the authors' knowledge, that compares the performance of a high-ESG portfolio with a low-ESG portfolio both before and during the Covid19, particularly in the Indian context.
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Under stress events, most of the asset prices tend to be positively correlated breaking the diversification benefits. In this study, we explore the performance of different assets particularly during stress events (the 2008 crisis and COVID-19 crisis) which can come to the rescue of portfolio managers as hedging strategies. Further, the analysis evaluates the performance of different combinations of portfolios with and without including volatility assets. Empirical results indicate that with only an allocation of 5% of the portfolio to volatility asset class, investors with different risk appetites were able to achieve 10% expected returns with reduced uncertainty.
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This article investigates the connection between US logistics companies’ commitment to environmental, social and fair governance (ESG) strategy and their performance on the US stock market during the 2007–2022 period. The research considers historical data analysis, CAPM and a comparison of optimised portfolios. According to the results of the analyses, ‘green’ logistics stocks are less volatile, and hence less risky, and more profitable compared to ‘non-green’ logistics stocks. The Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact on stock volatility, in terms of the US stock market. Optimised during the time of the Ukrainian crisis, green logistics portfolios were shown to have higher returns, but also risks and Sharpe ratios, than ‘non-green’ ones. The results confirm there to be a connection between companies’ commitment to ESG strategy and enhanced stock performance, which contributes to the importance of the ESG agenda.
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This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.
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Orientation: Environmental, social and governance (ESG) factors have evolved from peripheral significance (2000s) to a leading factor (2022) for many corporates. Most are now assigned ESG grades;which are increasingly scrutinised by investors. Research purpose: An ideal milieu might involve rewards for responsible firms and penalties for culprits, but in a profit-driven world, this is not always true. Investors demand profitability so some trade-off is required. Motivation for the study: Recent work to measure and optimise portfolio performance while observing corporate conscientiousness is promising: return/risk profiles comparable to those attained by unconstrained portfolios appear possible. Research approach/design and method: Portfolio optimisation using Lagrangian calculus. As ESG scores worsen, portfolio performance should be adversely affected, and we then apply – for the first time – these portfolio optimising developments to emerging market corporates. Main findings: ESG grades have improved over time, with both a statistically significant risk reduction and an increase in returns (the reverse for deteriorating ESG grades). As volatility increases, optimal ESG grades increase slowly as associated Sharpe ratios decrease. This could be due to an option-like reliance of inherent value upon underlying volatility. Practical/managerial implications: With better knowledge of trends, asset managers who take ESG metrics into account can confidently assert that ESG compliant portfolios can generate healthy risk adjusted returns (Sharpe ratios) and that these values are improving over time. Contribution/value-add: ESG compliant portfolios have become viable investments while adhering to sensible, responsible investment principles. ESG scores are improving globally, albeit at different rates.
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Purpose>This study aims to investigate the diversification benefits attached to the crypto portfolios when combined with stocks, Forex instruments and commodity assets.Design/methodology/approach>Markowitz diversification techniques have been used to analyze the risk-return tradeoffs of the individual portfolios. Daily prices on cryptocurrencies and the selected asset classes, cover the period before and during the pandemic COVID-19. The portfolio risk of the portfolios was calculated by identical techniques and analyzed with equal criteria.Findings>The results with 270 trails indicate that stocks on average reduce the portfolio risk of crypto portfolios by 36% followed by fiat currency with 30.9% and commodities by 20.8%. Average daily returns stand in line with the standard portfolio theories where riskier portfolios offer higher returns and the other way around.Originality/value>The authors contribute to the current literature by investigating the portfolio risk attached to the crypto portfolios when stocks, commodities and Forex instruments were added separately. To this end, results inform not only retail investors but also portfolio managers on the asset classes that generate better optimization for crypto portfolios.
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Over the last few decades, growing attention to the topic of social responsibility has affected financial markets and institutional authorities. Indeed, recent environmental, social, and financial crises have inevitably led regulators and investors to take into account the sustainable investing issue;however, the question of how Environmental, Social, and Governance (ESG) criteria impact financial portfolio performances is still open. In this work, we examine a multi-objective optimization model for portfolio selection, where we add to the classical Mean-Variance analysis a third non-financial goal represented by the ESG scores. The resulting optimization problem, formulated as a convex quadratic programming, consists of minimizing the portfolio variance with parametric lower bounds on the levels of the portfolio expected return and ESG. We provide here an extensive empirical analysis on five datasets involving real-world capital market indexes from major stock markets. Our empirical findings typically reveal the presence of two behavioral patterns for the 16 Mean-Variance-ESG portfolios analyzed. Indeed, over the last fifteen years we can distinguish two non-overlapping time windows on which the inclusion of portfolio ESG targets leads to different regimes in terms of portfolio profitability. Furthermore, on the most recent time window, we observe that, for the US markets, imposing a high ESG target tends to select portfolios that show better financial performances than other strategies, whereas for the European markets the ESG constraint does not seem to improve the portfolio profitability.
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PurposeThe purpose of this study is to investigate safe-haven properties of environmental, social and governance (ESG) stocks in global and emerging ESG stock markets during the times of COVID-19 so that portfolio managers and equity market investors could decide to use ESG stocks in their portfolio hedging strategies during times of health and market crisis similar to COVID-19 pandemic.Design/methodology/approachThe study uses a wavelet coherence framework on four major ESG stock indices from global and emerging stock markets, and two proxies of COVID-19 fear over the period from 5 February 2020 to 18 March 2021.FindingsThe results of the study show a positive co-movement of the global COVID-19 fear index (GFI) with ESG stock indices on the frequency band of 32 to 64 days, which confirms hedging and safe-haven properties of ESG stocks using the health fear proxy of COVID-19. However, the relationship between all indices and GFI is mixed and inconclusive on a frequency of 0–8 days. Further, the findings do not support the safe-haven characteristics of ESG indices using the market fear proxy (IDEMV index) of COVID-19. The robustness analysis using the CBOE VIX as a proxy of market fear supports that ESG indices do not possess safe-haven properties. The results of the study conclude that the safe-haven properties of ESG indices during the ongoing COVID-19 pandemic is contingent upon the proxy of COVID-19 fear.Practical implicationsThe findings have important implications for the equity investors and assetty managers to improve their portfolio performance by including ESG stocks in their portfolio choice during the COVID-19 pandemic and similar health crisis. However, their investment decisions could be affected by the choice of COVID-19 proxy.Originality/valueThe authors believe in the originality of the paper due to following reasons. First, to the best of the knowledge, this is the first study investigating the safe-haven properties of ESG stocks. Second, the authors use both health fear (GFI) and market fear (IDEMV index) proxies of COVID-19 to compare whether safe-haven properties are characterized by health fear or market fear due to COVID-19. Finally, the authors use the wavelet coherency framework, which not only takes both time and frequency dimensions of the data into account but also remains unaffected by data stationarity and size issues.
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The study reveals that the COVID-19 crisis has had a strong but one-off negative impact on the hedge fund industry. It also shows that during the new coronavirus pandemic, the main components of the hedge fund industry achieved only partially their main investment goal, i.e. they as a whole provided a hedge of the investment risk but did not produce higher than the market return in the conditions of a growing capital market. In this situation, due to the relatively stable М&A market, the Event-Driven Risk Arbitrage strategy was undoubtedly most successful, followed by the Emerging Markets, the Global Macro and the Long/Short Equity strategies. The worst performance was reported for the Fixed Income Arbitrage strategy due to the currently overvalued bond markets and to the expectations for higher inflation rates in the countries with developed capital markets. © 2022, Bulgarska Akademiya na Naukite. All rights reserved.