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We use New York City (NYC) taxi data to identify trips between mutual fund offices and local firm headquarters. NYC funds overweight the stocks of local firms they visit via taxi, and firm visits are associated with superior investment performance. Firm visits are elevated prior to earnings announcements, and mutual fund trades that are associated with firm taxi visits predict earnings surprises. The results are generally stronger when fund and firm executives share educational connections. Additional tests support the conclusion that funds' local bias and investment performance are driven by portfolio managers' efforts and ability to actively gather material information. [ABSTRACT FROM AUTHOR]

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature. [ABSTRACT FROM AUTHOR]

Time series forecasting models are essential decision support tools in real-world domains. Stock market is a remarkably complex domain, due to its quickly evolving temporal nature, as well as the multiple factors having an impact on stock prices. To date, a number of machine learning-based approaches have been proposed in the literature to tackle stock trend prediction. However, they typically tend to analyze a single data source or modality, or consider multiple modalities in isolation and rely on simple combination strategies, with a potential reduction in their modeling power. In this paper, we propose a multimodal deep fusion model to predict stock trends, leveraging daily stock prices, technical indicators, and sentiment in daily news headlines published by media outlets. The proposed architecture leverages a BERT-based model branch fine-tuned on financial news and a long short-term memory (LSTM) branch that captures relevant temporal patterns in multivariate data, including stock prices and technical indicators. Our experiments on 12 different stock datasets with prices and news headlines demonstrate that our proposed model is more effective than popular baseline approaches, both in terms of accuracy and trading performance in a portfolio analysis simulation, highlighting the positive impact of multimodal deep learning for stock trend prediction. [ABSTRACT FROM AUTHOR]

We propose a cointegration-based method for evaluating the long-term independence of sectoral Real Estate Investment Trusts (REITs) within a portfolio. We employ this innovative approach to construct diversified portfolios. Our argument is that sectors contributing less to cointegration provide greater risk reduction benefits to the portfolio. To gauge the level of independence, we use the reciprocal of the aggregate likelihood ratio (RAL) statistics. Based on these statistics, we introduce a novel allocation strategy that assigns a higher weight to a sector with a larger RAL. Using CRSP/Ziman REIT sector data, we demonstrate that this new strategy outperforms traditional methods and the real estate market benchmark. Our paper introduces a fresh framework for REIT portfolio management and provides guidance on sector allocation. [ABSTRACT FROM AUTHOR]

This study evaluates the calendar-anomalies-based active portfolio strategies. It uses daily closing prices of the S&P 500 Total Return Index (TRI) and MSCI All Country World Index (ACWI). The study tests the presence of calendar anomalies like day of the week, date of the month, and month of the year using dummy variable regression analysis, and backtests the performance of portfolio strategies to exploit those anomalies. Although this study does not find the day-of-the-week anomaly in the S&P 500 TRI and MSCI ACWI, it finds the date-of-the-month anomaly in both. Moreover, while it does not find the month-of-the-year anomaly in the S&P 500 TRI, it does find it in the MSCI ACWI. In addition, the negligible difference in the annualized returns for day, date, and month portfolios suggests that calendar-anomalies-based portfolio strategies do not generate superior returns for a particular day, date, or month portfolio. This study concludes that, contrary to popular belief, calendar anomalies do not exist in the US and world market indices. [ABSTRACT FROM AUTHOR]

This paper examines the co‐jump transmission in 20 commodity futures returns in the United States using co‐jump network models. Specifically, it reveals co‐jumping behavior in both static and time‐varying settings, considering the overall commodity markets and various commodity groups separately, which helps us understand the dynamic changes in co‐jump dependencies at the overall and sector levels. The main results reveal that co‐jump heterogeneity exists among commodities but is generally more apparent within each commodity group, and co‐jumps vary over time. Gold exerts the strongest influence, with many commodity futures being influenced by the jumps behavior in gold returns. During the COVID‐19 outbreak and the Russia–Ukraine war, the energy group ranks highest in terms of co‐jump network centrality. Our empirical analysis highlights the portfolio performance and risk reduction, and an additional examination shows that centrality information from the co‐jump network contains a highly and statistically forecasting power for US stock market volatility. [ABSTRACT FROM AUTHOR]

Integration of asset preselection with appropriate portfolio optimization techniques can improve the performance of the portfolio optimization models. This paper morphed the potential asset selection and the optimal portfolio construction rather than focusing on one. A large volume of sample data from 25 stocks is used for the experiment from the National Stock Exchange, India, between January 2005 and December 2021. Initially, a 3-step screening approach, an asset selection method is applied to select potential assets. The 3-steps comprise data choice, fundamental screening, and the Long Short Term Memory model anticipating real-time stock prices to shortlist stocks with higher expected returns. The suggested approach is effective in determining the quality of assets. Further, the optimal asset allocation is done by introducing a novel exponentially weighted-mean-variance model. This exponential weighting scheme outperforms the classical Mean-Variance model when applied to the maximum Sharpe ratio model. The proposed model outperforms the five baseline techniques in terms of the Sharpe ratio and average potential returns and risks. Additionally, the proposed model's resilience across diversified time frames is tested through the incorporation of multiple time windows, demonstrating robustness of the performance. [ABSTRACT FROM AUTHOR]

We propose a novel rank-based approach (RBA) that can be applied in portfolio allocation tasks. The proposed approach penalizes downside deviations below zero by using the sign, magnitude, and distributional rank of assets. Performance of RBA is compared with the equally weighted, traditional mean-variance and more recently proposed portfolio strategies including improved estimators for covariance misspecification. Overall, applied various performance metrics, in particular, return-per-unit of risk measures provide convincing evidence of enhanced performance in favour of RBA portfolios. [ABSTRACT FROM AUTHOR]

Pension funds’ investments are increasingly linked to the changes in climate and environmental protection. The integration of environmental, social and governance (ESG) factors into their investment process is still varying in different countries and regions. The limited number of studies on the application of the ESG investment approach by private pension funds in Bulgaria shows that the country lags behind the trends in Europe. Although pension funds do not perceive environmental investments as riskier or less profitable than conventional ones, many of them remain cautious due to the shorterterm financial performance data of green assets. To achieve adequate retirement savings and a high replacement rate saving “more and for longer” is not enough. As far as the topic of portfolio investment performance is on the agenda, one would reasonably ask what the reflection of environmental investments would be on the widely diversified portfolios of pension funds in the country. The present research is dedicated to а mean-variance (MV) portfolio optimization involving a selection of conventional and green assets under different constraints and “shades of green” by using historical data. The empirical results from the portfolio optimizations performed shed light on the questions raised and complement the motivational spectrum “in favour of” or “against” environmental investment. [ABSTRACT FROM AUTHOR]

We examine the effects of stock market access, and in particular trading hours, on retail investment performance. Using discontinuities around time zone borders, we find that plausibly exogenous decreases in waking trading hours are associated with meaningful increases in retail investors' capital gains, as reported on tax returns for the U.S. population. Our results indicate that limiting trading hours curbs active retail trading, leading to improvements in portfolio performance. Our findings identify one negative effect of decreasing barriers to entry for retail investors in trading markets. JEL Classifications: M41; M48; G40; G51. [ABSTRACT FROM AUTHOR]

Motivated by the occurrence of extreme events and nonnormality of returns, we examine the spillovers among the conditional volatility, skewness and (excess) kurtosis of European Union allowances (EUA), Brent oil, natural gas, coal, electricity and clean energy markets. The jointly estimated spillover index in the system of the three higher‐order moments is notably high, exceeding the spillover index estimated for each individual moment separately. This suggests that spillovers across moments in the carbon‐energy system are important for the sake of completeness of the spillover analysis, and should not be ignored. The performance of the portfolio improves after considering higher‐order moments. [ABSTRACT FROM AUTHOR]

Most research in socially responsible investing is about how it influences financial performance. Hardly any study investigates how it affects responsibility. We investigate how different motives of responsible investors affect portfolio design and financial and nonfinancial performance of investments. We study financial, deontological, consequentialist, and expressive motivated responsible investors. The first seek to achieve financial outperformance by relying on environmental, social, and governance (ESG) information. The second avoid investing in controversial issues. The third seek to influence the most sustainable firms. The fourth try to enhance their own social identity by investing responsibly. We find that the specific motivation for socially responsible investing does not significantly affect financial performance. However, the motivation does have a substantial influence on the responsibility score of the portfolio. In particular, deontological portfolios underperform the universe regarding responsibility performance. This might reflect the origin of responsibility scoring where controversial firms usually invest more in responsibility than less controversial ones. The other portfolios all outperform the market. This has important lessons for responsible investment funds: they will need to find out about the motives of their investors to modify portfolio design when investing responsibly. "One size for all" is no option when targeting responsible investors. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

We apply a Time‐Varying Parameter Vector Auto Regressive (TVP‐VAR) connectedness approach on global assets to investigate time‐varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID‐19 and the Russia–Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID‐19 and the Russia–Ukraine war and that cumulative returns of portfolios are higher during COVID‐19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts. [ABSTRACT FROM AUTHOR]

The disposition effect for a stock significantly weakens if the portfolio is at a gain, but is large when it is at a loss. We find this portfolio‐driven disposition effect (PDDE) in four independent settings: U.S. and Chinese archival data, as well as U.S. and Chinese experiments. The PDDE is robust to a variety of controls in regression specifications and is not explained by extreme returns, portfolio rebalancing, tax considerations, or investor heterogeneity. Our evidence suggests that investors form mental frames at both the stock and the portfolio levels and that these frames combine to generate the PDDE. [ABSTRACT FROM AUTHOR]

Purpose: Farmland is an important property sector that has attracted the attention of institutional investors globally in recent years. This paper examines the risk-adjusted performance and portfolio diversification benefits of Australian farmland in a portfolio over the eight-year period of Q2:2015–Q2:2023, highlighting the unique property management dimensions to this property sector for its effective role in an institutional investor's property portfolio. Design/methodology/approach: Using the quarterly ANREV Australian farmland index over Q2:2015–Q2:2023, the risk-adjusted performance and portfolio diversification potential of Australian farmland is assessed. Constrained mixed-asset portfolios are used to assess the potential added-value role of Australian farmland in a mixed-asset portfolio. Analyses are also done for the farmland sub-sectors of annual farmland and permanent farmland. Findings: Australian farmland is seen to show strong risk-adjusted performance but at a much higher risk level than that seen for direct property. Diversification benefits from Australian farmland are also evident, with an important role by Australian farmland seen in the mixed-asset portfolio. Specific farmland property management strategies are identified for the effective inclusion of farmland in an institutional investor's property portfolio, including the potential benefits towards net zero carbon strategies. Originality/value: This is the first research that provides an independent empirical examination of the strategic importance of Australian farmland property for institutional investors using the institutional investment-grade ANREV Australian farmland database, from both an investment and environmental perspective. The unique property management implications for Australian farmland property are also highlighted, including the potential role of Australian farmland in net zero carbon strategies by institutional investors. [ABSTRACT FROM AUTHOR]

Each portfolio model has its own advantages. Behavioral portfolio models can depict investor psychology, but it is not easy to obtain the preference parameters. We propose an interaction‐based combined (InCo) portfolio that determines the proportions of assets based on investor preferences. We take the existing models as alternatives and innovatively develop a visualization tool to present the portfolio performance. Hesitant fuzzy set theory is used to describe investor evaluations and to then determine the proportions of alternatives. The out‐of‐sample performance of four strategies is tested on four datasets. The results show that InCo portfolio performs better in various markets. [ABSTRACT FROM AUTHOR]

This article addresses a portfolio selection problem with trading costs on stock market. More precisely, we develop a simple generalized method of moments (GMM)-based test procedure to test the significance of trading costs effect in the economy with a flexible form of transaction costs. We also propose a two-step procedure to test overidentifying restrictions in our GMM estimation. In an empirical analysis, we apply our test procedures to the class of anomalies used in Novy-Marx and Velikov (2016). We show that transaction costs have a significant effect on investors' behavior for many anomalies. In that case, investors significantly improve the out-of-sample performance of their portfolios by accounting for trading costs. [ABSTRACT FROM AUTHOR]

The 50th anniversary issue of the Journal of Portfolio Management reflects on the evolution of portfolio management over the past five decades, acknowledging the challenges and opportunities within the field. The issue explores the interplay between theory and practice, highlighting the limitations of traditional models in today's complex financial markets. Thought leaders propose forward-looking strategies that incorporate behavioral biases, technological disruption, and changing market structures to improve decision-making and portfolio efficiency. The issue emphasizes the need for continual evolution in thinking and practice, underscoring the importance of adapting academic insights to the complexities of real-world markets. [Extracted from the article]

Factors display strong cross-sectional momentum that subsumes momentum in industries and other portfolio characteristics. The profits of all these momentum strategies—based on factors, industries, and other characteristics—significantly correlate with each other and therefore likely emanate from the same source. If factors display momentum, so will any set of portfolios with cross-sectional variation in factor loadings. Consistent with factors being at the root of momentum, we find that momentum in industry-neutral factors explains industry momentum, but industry momentum explains none of the factor momentum. Cross-sectional factor momentum concentrates in the first few highest-eigenvalue factors and is distinct from time-series factor momentum. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We propose and test a framework of private information acquisition and decision timing for asset allocators hiring outside investment managers. Using unique data on due diligence interactions between an institutional allocator and 860 hedge fund managers, we find that the production of private information complements public information. The allocator strategically chooses how much proprietary information to collect, reducing due diligence time by 18 months and improving outcomes. Funds selected by the manager outperform those not selected by 9 |$\%$| over 20 months. The outperformance relates to the allocator learning about fund return-to-scale constraints and manager skill before other investors. [ABSTRACT FROM AUTHOR]

How do investors' risk preferences influence the relationships between investors' online channel use intensity and both their trading behaviors and performance? This study answers this important question even as investors are increasingly rely on the Internet for their trading activities. We leverage rare and unique micro-level historical dataset from more than 7,000 investor accounts over a 44-month period between 2010 and 2013 at a large brokerage firm in China. The dataset and analyses enable us to provide new insights into how investors' online channel use intensity and risk preferences jointly influence their trading behaviors and performance, even though some other aspects of financial markets have changed considerably over the years. The findings reveal that although online channel use intensity is associated with increased trading volume, trading frequency, and investment returns, these effects differ across investors with different risk preferences. We find that while online channel use intensity has strong positive effects on transaction frequency for both risk-seeking and risk-averse investors, it has a much lower effect on trading volume for risk-averse investors than for risk-seeking investors. We further find that risk-averse investors with higher online channel use intensity outperform investors with other risk preferences in terms of investment performance. This paper contributes to the emerging literature at the intersection of information systems and behavioral finance by revealing the moderating role of risk preferences in the relationships between investors' online trading channel use intensity and both their trading behaviors and outcomes. We discuss the implications for research and practice. [ABSTRACT FROM AUTHOR]

Adhesives are being used ubiquitously, such as automotive, building, electronics, and beyond. Due to the lack of rational design strategies, they have yet to achieve a performance portfolio: mechanically robust, highly adhesive, fire‐retardant, switchable, and sustainable (e.g., biobased, reusable, biodegradable) to ensure their practical applications. Herein, a fire‐retardant phosphorus‐containing pimaric acid bio‐derivative, AD, as functional segments, is rationally engineered to prepare biobased polyurethane (PU) adhesive that realizes such an integrated performance portfolio. Because of dynamic hydrogen‐bonding and π–π stacking of polar AD, the as‐prepared PU adhesive exhibits an ultrahigh adhesion force of 38.8 N cm–1. As‐prepared adhesive can be readily reused benefiting from its good solubility in ethanol and exhibits temperature‐responsive switchable adhesion without degraded adhesion. Also, the adhesive shows intrinsic fire retardance due to its biphasic modes of action. The labile ester bonds in the structure enable the adhesive to completely degrade in the presence of lipase or dilute acid. Further demonstration of its promising applications as an adhesive for nanocomposite heat dissipators shows superior dissipating efficiencies to commercial heat sinks. This work offers a novel design approach for creating next‐generation sustainable high‐performance adhesives with functional integration and circular life cycles, which are anticipated to find extensive real‐world applications. [ABSTRACT FROM AUTHOR]

I reconstruct the historical performance of individual portfolios owned by Parisian investors during the French Belle-Epoque , which was characterized by a massive concentration of wealth. Using the value of inherited bequests as a proxy for ex ante wealth, I show that wealthier investors not only exhibited greater betas and thus benefited from the bull market, but also captured positive alphas, which translated into greater Sharpe ratios. Their performance was enhanced by diversification tilted toward equity and foreign assets. I identify heterogeneity in returns as a significant driver of the rise of wealth inequalities during the Belle-Epoque. [ABSTRACT FROM AUTHOR]

The sparse portfolio optimization problem aims to select a subset of assets from a given set of investment options in order to maximize portfolio returns while minimizing risk. In this paper, we study a class of portfolio problem with the feasible set being the intersection of a polyhedral set and a sparse set. To solve this constrained problem, one efficient approach is the penalty method. We put the sparse constraint on the objective function to get the penalized formulation. We study the $ \epsilon $-optimality and establish an exact penalty result under some mild conditions. Moreover, we reveal the equivalent form of the sparse constraint can be further expressed as the $ \ell_1 $-projection onto the sparse set. Based on this result, an efficient MM-ADMM algorithm is designed by combining majorization-minimization methodology with alternating direction method of multipliers. Extensive numerical experiments demonstrate the efficiency of our model for producing the higher Sharpe ratio when compared with several existing models on six real-world data sets. [ABSTRACT FROM AUTHOR]

Mean-Variance (MV) is a method that collects several assets using appropriate weight intending to maximize profits and to reduce risk. Stock market conditions are very volatile, mean variance method does not reach stock market fluctuation well because MV method is only limited to one time period. This study proposes a mean variance complex-based approach that transforms real returns into complex returns by using Hilbert transform to construct an optimal mean-variance portfolio based on complex returns and then find its dynamic asset allocation. The results show that with the same risk tolerance, the mean variance complex-based approach outperforms MV method in profits, losses, and portfolio performance tests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When a benchmark model is inefficient, including test assets in addition to the benchmark portfolios can improve the performance of the optimal portfolio. In reality, the efficiency of a benchmark model relative to the test assets is ex ante unknown; moreover, the optimal portfolio is constructed based on estimated parameters. Therefore, whether and how to include the test assets becomes a critical question faced by real world investors. For such a setting, we propose a combining portfolio strategy, optimally balancing the value of including test assets and the effect of estimation errors. The proposed combining strategy can work together with some existing estimation risk reduction strategies. In both empirical data sets and simulations, we show that our proposed combining strategy performs well. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.01767. [ABSTRACT FROM AUTHOR]

The Journal of Portfolio Management has published a special issue that includes in-depth interviews with four chief investment officers (CIOs) and five theme-oriented articles on critical investment topics. The interviews provide insights into the decision-making processes and strategies of influential asset management companies. The CIOs emphasize the importance of long-term strategies, technology, risk management, client expectations, and leadership styles. The theme-oriented articles cover topics such as fixed-income investing, portfolio diversification in the post-COVID era, leadership in investment management, and the complexities of integrating environmental, social, and governance (ESG) factors into investment portfolios. The articles offer valuable insights into the evolving investment landscape and provide practical knowledge for market professionals. [Extracted from the article]

In the face of changing investment intentions, the integration of environmental, social and governance (ESG) considerations into portfolio optimization is becoming the norm. Indeed, the purely financial dimension of investments is beginning to give way to the sustainable or responsible dimension which aims to generate long-term financial returns. Such an investment strategy has the constraint of investing only in companies which, according to the rating agencies, best meet these three criteria. To study the impact of similar investment strategies on portfolio performance, we compared the performance of 3 portfolios constructed with different ESG preferences. Built on the basis of the Best-in-class approach, the first is composed of the highest rated assets (AAA and AA) while the second has average ratings (A and BBB) and the third of the worst ESG ratings (BB, B and CCC). The results obtained in a robust mean-LPM theoretical framework, we managed to show that a preference for stocks with average ESG ratings in the composition of a portfolio to have the best risk-return trade-off. [ABSTRACT FROM AUTHOR]

This work aims to address the optimal allocation instability problem of Markowitz's modern portfolio theory in high dimensionality. We propose a combined strategy that considers covariance matrix estimators from Random Matrix Theory (RMT) and the machine learning allocation methodology known as Nested Clustered Optimization (NCO). The latter methodology is modified and reformulated in terms of the spectral clustering algorithm and Minimum Spanning Tree (MST) to solve internal problems inherent in the original proposal. Markowitz's classical mean-variance allocation and the modified NCO machine learning approach are tested on the financial instruments that compose the S&P 500 index listed on the New York Stock Exchange (NYSE) from 2012 to 2021. The modified NCO algorithm achieves stable allocations by incorporating RMT covariance estimators. A particular combination of covariance estimators, known as two-step estimators involving a hierarchical clustering technique, achieves the best portfolio performance. Surprisingly, a common pattern suggests that the improvement in performance is agnostic to the allocation strategy and, in fact, relies heavily on the covariance estimator selection. Our results suggest that the amount of leverage can be drastically reduced in investment strategies through RMT inference and statistical learning techniques. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to construct a portfolio based on risk-adjusted performance and distribution-based returns and determine the efficiency using the Data Envelopment Analysis (DEA) approach. In this study, the role of return distribution in the efficiency of risky assets is also examined to form a diversified portfolio consisting of assets with varying degrees of performance. Methodology: In this study, the diversified portfolio performance of 28 firms during 1398-1402, based on the riskadjusted value and conditional risk-adjusted value obtained from the probability distributions of returns, was compared with the minimum-variance Markowitz portfolio performance in terms of the Sharpe ratio. After estimating the maximum likelihood parameters of the model, the risk values for each stock were calculated based on the empirical return distribution, the Cauchy distribution, and the normal distribution. These risk values were then used in the data envelopment analysis to calculate the efficiency scores of each company. Findings: The diversified portfolio with stock performance degrees outperforms the minimum-variance Markowitz portfolio in terms of risk-adjusted and conditional risk-adjusted values. The probability distribution of returns leads to different results in calculating stock risk-adjusted value/conditional value, with the empirical return distribution and normal distribution providing a more desirable performance (in terms of the Sharpe ratio) compared to the Cauchy distribution and sample ratios. Originality/Value: In the literature, an efficient portfolio is usually formed by calculating asset weights in the stock basket so that the Sharpe ratio reaches its maximum value. In the current study, this hypothesis is challenged in favor of the proposed method, which estimates portfolio weights based on the efficiency of risky assets. [ABSTRACT FROM AUTHOR]

Summary: Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross‐section of state‐specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared with several benchmark models including univariate Markov switching models. [ABSTRACT FROM AUTHOR]

The 1/N investment strategy, characterized by equally allocating wealth among available investment options, has garnered significant scholarly attention. Simultaneously, risk assessment and management play a critical role in financial decision-making, leading to the development of a diverse array of risk measure models. This paper aims to synthesize these two strategies and propose a novel approach for constructing a naive diversification strategy by incorporating commonly employed risk measures in financial analysis. The research involves an in-depth exploration of various risk measures utilized by financial professionals to enhance effective risk management. These measures include Mean-Variance (MV), Mean Absolute Deviation (MAD), Semi Standard Deviation (MSV), Value at Risk (VaR), Conditional Value at Risk (CVaR), Entropic Value at Risk (EVaR), Drawdown at Risk (DaR) of uncompounded cumulative returns, Conditional Drawdown at Risk (CDaR) of uncompounded cumulative returns, and Entropic Drawdown at Risk (EDaR) of uncompounded cumulative returns. To validate the efficacy of the proposed model, a real-world empirical case study utilizing the annual financial statements of the NASDAQ100 (NDX) database is conducted. The empirical findings from this study carry practical implications for investors and risk managers engaged in portfolio management. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid meta-heuristic algorithm called CEBWO (cross-entropy method and beluga whale optimization) is presented to solve the mean-CVaR portfolio optimization problem based on jump-diffusion processes. The proposed CEBWO algorithm combines the advantages of the cross-entropy method and beluga whale optimization algorithm with the help of co-evolution technology to enhance the performance of portfolio selection. The method is evaluated on 29 unconstrained benchmark functions from CEC 2017, where its performance is compared against several state-of-the-art algorithms. The results demonstrate the superiority of the hybrid method in terms of solution quality and convergence speed. Finally, Monte Carlo simulation is employed to generate scenario paths based on the jump-diffusion model. Empirical results further confirm the effectiveness of the hybrid meta-heuristic algorithm for mean-CVaR portfolio selection, highlighting its potential for real-world applications. [ABSTRACT FROM AUTHOR]

We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that the Sharpe ratio algorithm lacks monotonicity. Since the version of "omega" in the literature uses weights that distort the actual gain–pain ratio faced by investors, we propose new weights. We use data from 30 stocks using the past 474 months (39+ years) of monthly closing prices, ending in May 2024. Our buy-sell recommendations also use newer "pandemic-proof" out-of-sample portfolio performance comparisons from the R package 'generalCorr'. We report twelve sets of ranks for both out-of- and in-sample versions of the six algorithms. Averaging the twelve sets yields the top and bottom k stocks. For example, k = 2 suggests buying Visa Inc. and Johnson & Johnson while selling Coca-Cola and Procter & Gamble. [ABSTRACT FROM AUTHOR]

The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets. [ABSTRACT FROM AUTHOR]

The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences. [ABSTRACT FROM AUTHOR]

In this era of high inflation and low interest rates, the public often invests in financial products to increase their passive income and thus increase their savings. Current data show that the public still considers stocks as investment targets. However, because investment is risky and each person's investment personality (risk tolerance) is different, some prefer to take risks to obtain the maximum return, and some are afraid of risks and avoid them to obtain stable returns; nevertheless, the recommendations of current research give little consideration to the risk characteristics of the investment target and the personal characteristics of the investor. However, investment is a trade-off between risk and reward, and the risk acceptance associated with an investment depends on the investor's ability to accept risk. Therefore, in the context of recommending, a consideration of investors' personal characteristics brings recommendations more in line with users' expectations. This study proposes a method to manage investment portfolios based on investors' risk personalities. It is mainly used to classify stocks according to beta indicators (select stocks according to investors' personalities), and financial indicators and an autoencoder are used to score stocks; moreover, technical indicators, covariance matrices, deep reinforcement learning-advantage actor critic (AZC), and proximal policy optimization (PPO) are used for asset allocation with the aim of developing investment portfolios with good performance that are optimally suitable for different types of investors. In the training results, the cumulative return rate obtained by the A2C model is as high as 49.61% for conservative investments, 82.04% for stable investments, and 99.69% for active investments. The cumulative return obtained by the PPO model is as high as 39.92% for conservative investments, 89.89% for stable investments, and 85.61% for active investments. [ABSTRACT FROM AUTHOR]

Using SASB's materiality framework, prior research finds alpha for the portfolio of firms with improving ratings on material ESG issues. We replicate this finding and provide a fundamentals-based perspective on why the materiality portfolio outperforms. Our basic premise is that changes in material ESG issues reflect fundamental firm characteristics. More financially established firms—firms with larger size, lower growth, and higher profitability relative to their sector—are more likely to not only create material strengths but also resolve material weaknesses in their ESG scoring. This fundamental link dictates that one should comprehensively control for fundamental determinants of stock returns before attributing portfolio outperformance to improving material ESG scores. Indeed, we find that the materiality portfolio does not generate alpha after we account for its exposure to profitability and growth factors. Our evidence underscores the issue of correlated omitted fundamental factors in the debate of ESG alpha. Data Availability: Data are available from the sources cited in the text. JEL Classifications: G11; G12; G14; M14; M41; Q51. [ABSTRACT FROM AUTHOR]

In this paper, we develop the theory of functional generation of portfolios in an equity market with changing dimension. By introducing dimensional jumps in the market, as well as jumps in stock capitalization between the dimensional jumps, we construct different types of self‐financing stock portfolios (additive, multiplicative, and rank‐based) in a very general setting. Our study explains how a dimensional change caused by a listing or delisting event of a stock, and unexpected shocks in the market, affect portfolio return. We also provide empirical analyses of some classical portfolios, quantifying the impact of dimensional change in portfolio performance relative to the market. [ABSTRACT FROM AUTHOR]

This study aims to explore the potential impact of the anticipated Georgian Real Estate Investment Trusts (REITs) on the performance of investment portfolios. Due to the absence of Georgian REITs, a simulated financial asset representing a Georgian Residential REIT was engineered. The study employed correlation analysis, portfolio analysis, Sharpe Ratio evaluation, and Efficient Frontier analysis. Portfolios in the study included various assets such as Georgian Commercial Banks USD Certificates of Deposits, Georgian Treasury Bonds, NASDAQ Index, and the Georgian Pension Fund Global Equity Portfolio. Monthly data from 2021-2023 were used to engineer Georgian REIT and estimate expected returns, volatility, Sharpe ratios, and correlation coefficients. The study concludes that incorporating a Georgian REIT into diversified investment portfolios can significantly enhance their attractiveness by improving the riskreturn trade-off. The findings highlight the necessity for Georgian regulators and market participants to consider the introduction of REITs as a viable investment vehicle. Additionally, the research contributes to the limited scholarly literature on REITs' impact in emerging markets, offering a framework for future studies in similar contexts. [ABSTRACT FROM AUTHOR]

The core objective of financial management is to maximize the wealth of shareholders. The dividend relevance and irrelevance and its role in firm's value have always been a conflicting debate in the area of corporate finance while investors always look for securities of the highest expected return at a given level of risk. The objective of the current study is to empirically investigate the riskadjusted performance of 12 portfolios of dividend exposure and no-dividend exposure according to size and market-to-book value. The data of companies listed in PSX was utilised from 2014 to 2022. The portfolio performance was analyzed with the Sharpe Ratio, Jensen alpha and Treynor ratio with the Python pandas library. The findings of the study reported that absolute risk is lower for dividend-paying stocks with CV(5.6%) than non-dividend-paying stocks with CV(6.45%), further, the relative risk measure have a significant value of ß (0.9) for non-dividend-paying portfolios in comparison to blend portfolio ß(0.7) and dividend-paying portfolio ß(0.8) highlighting that lowest systematic risk for blend portfolio of stocks. The originality of the studies is to explore the volatility of portfolios in the perspective of the emerging economy of Pakistan. The findings are of great importance to investors, and fund managers, to efficiently allocate funds for optimal returns. [ABSTRACT FROM AUTHOR]

Our article contains three critical elements. First, by way of motivation, we consider active optimal mean-variance portfolios within the confines of a three-risky-asset model (based on common equity, corporate bonds, and long-dated Treasury securities) and a short-term, riskless security. Within that context, our article models and numerically demonstrates the impact of the magnitude and sign of the interest-rate term premium in optimal asset allocation. Then, based on interest-rate survey data, we demonstrate the occurrence of a negative term premium in the Treasury market, and proceed to permit such an occurrence within a model whereby asset returns covary with a market portfolio. Finally, turning to empirics, we document the relationship between the sign of the term premium and the sign of the covariance between Treasury and equity returns. Recognizing the deficiency in using historical data as inputs to active optimal mean-variance portfolios, we seek to obtain forward-looking inputs to render our model prospective (rather than merely retrospective): we design and implement tests for obtaining forward-looking measures of covariation. [ABSTRACT FROM AUTHOR]

Using monthly data from 1997 to 2023, we construct mean-variance optimized portfolios of common university endowment asset classes, including domestic equity, international equity, global bonds, hedge funds, private equity, real estate, and natural resources. We find substantial variation in optimal allocations to these asset classes across subperiods. Some asset classes are substantially more persistent in receiving allocations than others, while some asset classes rarely receive sizable allocations at all. Our results highlight the relevance of asset allocation in portfolio performance and may inform future decisions by institutional investors and endowment portfolio managers. [ABSTRACT FROM AUTHOR]

We use the daily data from 1 October 2010 to 1 March 2021 on the stock prices of several global, regional, and country-specific ESG indices of the Dow Jones Sustainability Index family (DJSI) and MSCI family to analyse the relative performance of sustainable indices (ESG) and corresponding conventional benchmarks. In terms of classic risk and return characteristics and modern portfolio metrics, we report mixed evidence with some sustainability indices marginally outperforming the traditional benchmarks and others staying at parity. Regionally, the evidence is heterogeneous; the Pan Arab, the Middle East (including Israel), the United States, Emerging Markets, and Europe indices reported a slightly superior performance and those belonging to Asia Pacific, Emerging Africa, and Latin America marginally underperformed compared to their conventional counterparts. The findings of this study imply that sustainable investments appear to be an encouraging investment option, although their progress has not been substantial. For an appreciable outperformance of sustainable investments, a more conducive regulatory framework should be established, including robust incentivizing policies concerning tax rebates or low capital costs. [ABSTRACT FROM AUTHOR]

Portfolio selection often involves a large number of potential constituent assets, although relevant historical data samples tend to be relatively small, termed the high-dimensional small-sample (HDSS) problem. In addition to parameter estimation issues, mean–variance (MV) optimal portfolios also tend to be highly leveraged and fragmented. This study develops cardinality control to generate sparse MV portfolios, while portfolio short exposure is controlled directly via a leverage constraint. Resulting computational difficulties are circumvented by asset position control using absolute and Euclidean norm parametrization within constraints of an MV optimization. Data-driven calibration of the norm parameters utilizing a cross-validation scheme optimizes the out-of-sample test performance of the optimal portfolios while satisfying portfolio sparsity and leverage restrictions probabilistically. An empirical study with large asset sets examines various policy implications on optimal portfolio choice and compared with the standard MV model. Not only are the out-of-sample efficient frontiers of the proposed approach superior, but the optimal portfolios also satisfy the prescribed sparsity and leverage conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective - The integration of environmental, social, and governance (ESG) criteria into investment portfolios has emerged as a critical field of study, underscoring the interconnectedness between financial markets and global sustainability objectives. Methodology/Technique - This systematic literature review analyzes 157 academic documents, focusing on ESG portfolio optimization methodologies and identifying emerging trends. Key methods reviewed include genetic algorithms, dynamic optimization models, multi-objective optimization frameworks, and machine learning techniques. Findings - Despite considerable advancements, gaps remain, such as the need for broader application across diverse markets and asset classes, improved risk-return assessments, and standardized ESG data reporting. Future research should also investigate the role of central banks and regulators in fostering sustainable finance. Novelty - By addressing these gaps, stakeholders can better align investment practices with sustainability goals, contributing to a more resilient and inclusive global economy. [ABSTRACT FROM AUTHOR]