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We construct and analyze the equilibrium of a model of delegated portfolio management in which money managers signal their investment skills via fund transparency. To lower the costs of transparency, high-skill managers rely on their performance to separate from low-skill managers over time. In contrast, medium-skill managers rely on transparency to separate, especially when it is difficult for investors to tell them apart through performance alone. Low-skill managers mimic high-skill managers in opaque funds, hoping to replicate their performance and compensation. The model yields several novel empirical predictions that contrast transparent and opaque funds. 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 investigate how transaction costs change the number of characteristics that are jointly significant for an investor's optimal portfolio and, hence, how they change the dimension of the cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalance different characteristics often cancel out. Thus, transaction costs provide an economic rationale for considering a larger number of characteristics than that in prominent asset-pricing models. 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]

A common practice in the finance literature is to create characteristic portfolios by sorting on characteristics associated with average returns. We show that the resultant portfolios are likely to capture not only the priced risk associated with the characteristic but also unpriced risk. We develop a procedure to remove this unpriced risk using covariance information estimated from past returns. We apply our methodology to the five Fama-French characteristic portfolios. The squared Sharpe ratio of the optimal combination of the resultant characteristic-efficient portfolios is 2.13, compared with 1.17 for the original characteristic portfolios. [ABSTRACT FROM AUTHOR]

The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. [ABSTRACT FROM AUTHOR]

According to our survey about climate risk perceptions, institutional investors believe climate risks have financial implications for their portfolio firms and that these risks, particularly regulatory risks, already have begun to materialize. Many of the investors, especially the long-term, larger, and ESG-oriented ones, consider risk management and engagement, rather than divestment, to be the better approach for addressing climate risks. Although surveyed investors believe that some equity valuations do not fully reflect climate risks, their perceived overvaluations are not large. [ABSTRACT FROM AUTHOR]

"Quantitative Investing: from Theory to Industry" by Lingjie Ma is a comprehensive textbook that covers quantitative investment theory and practice. The author draws on their 15 years of experience in the investment industry and 6 years of teaching to provide explanations on financial markets and econometric modeling. The book is divided into nine chapters, each covering different investment topics, finance theories, portfolio performance, statistical models, and R programming for data analysis. It includes real-world examples, R codes, and references, making it a valuable resource for advanced students and practitioners in finance and investments. [Extracted from the article]

In this article, we propose a new theoretical approach for developing hedging strategies based on swap variance (SwV). SwV is a generalized risk measure equivalent to a polynomial combination of all moments of a return distribution. Using the S&P 500 index and West Texas Intermediate (WTI) crude oil spot and futures price data, as well as simulations by varying the distribution of asset returns, we investigate the dynamic differences between hedge ratios and portfolio performances based on SwV (with high moments) and variance (without high moments). We find that, on average, the minimizing‐SwV hedging suggests more short futures contracts than minimizing‐variance hedging; however, when market conditions deteriorate, the minimizing‐SwV hedging suggests fewer short positions in futures. The superior posthedge performances of the mean‐SwV hedged portfolios over the mean‐variance hedged portfolios in highly volatile or extremely calm markets confirm the efficiency of the mean‐SwV hedging strategy. [ABSTRACT FROM AUTHOR]

This paper analyzes the diversification benefits of adding alternative crypto‐assets in a traditional portfolio, from the perspective of an investor who seeks to achieve multiple objectives. Our analysis is based on daily and weekly return data for eight different assets including Bitcoin, Ethereum, Ripple, for the crypto‐assets and NASDAQ, S&P500, Dow‐Jones, Crude‐Oil, and Gold, for traditional assets. We use both in‐sample and out‐of‐sample estimation procedures to analyze these data sets. We apply the weighted sum of deviations goal programming method to solve a bi‐objective optimization problem where investors optimize simultaneously portfolio risk and return. For a variety of investor characteristics, ranging from risk‐seeking to risk‐averse, we show that augmenting portfolios with alternative crypto‐currencies improves portfolio performance and the efficiency frontier, compared to standard portfolios. This improvement is even more observable for risk‐seeking investors, for both in‐sample and out‐of‐sample estimation procedures. [ABSTRACT FROM AUTHOR]

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This research aims to investigate the asset allocation performance of three different optimization methods commonly applied in the literature for a portfolio composed of univariate returns generated from Mixed and Elliptic copulas instead of historical data. As a result, returns of five equities traded at the BIST30 index of the Turkish Stock Market were obtained. Dynamics of the univariate return series are modelled with GARCH processes with Student-t distributed innovations. Following the marginal modelling, a five-dimensional dependence structure between the series is modelled with Elliptical and Mixed copulas. From the fitted Mixed and Elliptical copula functions, daily returns of the equities are simulated which are employed by the specified optimization methods in order to find out methodology specific optimal portfolio allocations. Performance of the constructed optimal portfolios are compared according to varying risk and reward to variability ratios yielding results especially in favor of the Mixed and Student t copulas. The main contribution of this research is to be able to fill the gap in the literature on the out-of-sample portfolio allocation performance of copula functions where there are still fewer papers compared to the dependency modelling or the in-sample portfolio allocation performance of copulas. [ABSTRACT FROM AUTHOR]

The range of non-EPS forecast types provided by individual analysts to I/B/E/S has increased dramatically over time but varies considerably across firms. We propose that in providing a broader range of forecast types, analysts can signal superior research ability and research effort. Consistent with this hypothesis, we document positive associations between the number of forecast types (NFT) an analyst provides and common proxies for research quality, including earnings forecast accuracy, price target accuracy, stock recommendation profitability, market reactions to stock recommendation revisions, and analyst career outcomes. The effects of NFT are incremental to other quality proxies used in the literature and are distinct from the issuance of specific non-EPS forecast types studied previously (e.g., cash flow forecasts). We demonstrate the information value of NFT to investors by examining the out-of-sample performance of portfolios formed conditional on NFT and exploiting revisions in consensus earnings forecasts and individual analysts' stock recommendations. We conclude that the number of forecast types an analyst provides for a firm is a readily available proxy for the quality of her research. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving information technology sectors, agile methodologies have been employed by software development organizations to facilitate the large-scale, efficient, and swift development of digital products. Notably, it is a widely accepted principle that an increase in project delivery predictability results from more effective portfolio management. Despite the abundant resources within software engineering that address project management and agile development performance, the measurement of portfolio delivery performance integrating sustainability principles is under-researched. This paper aims to underline a solution to bridge this gap by proposing a data-driven scoring model explicitly designed for software firms to integrate sustainability metrics into their project and portfolio performance assessment. The model is primarily aimed at monitoring and enhancing delivery performance while also reinforcing the sustainability of the software development lifecycle. A thorough literature review was conducted to discern gaps in existing practices, followed by development of a scoring model melded with delivery and sustainability metrics seamlessly. Validated through a case study, the findings reveal that the model influences the performance and sustainability dynamics within software development entities. The insights gained from this study underscore the pivotal role of a harmonized delivery and sustainability metrics system in enhancing the sustainability and efficiency of software development undertakings. [ABSTRACT FROM AUTHOR]

Purpose: Political risk is an important determinant of portfolio returns. The basic purpose of this study is to revisit the importance of political risk in a constrained portfolio, namely, a Shariah-compliant equity portfolio (SCEP). Furthermore, the performance of such a constrained portfolio is also compared with a conventional portfolio that invests in all stocks. Design/methodology/approach: The portfolios are constructed from stock-level data and invested in 61 international markets. The set of Shariah-compliant stocks is obtained with screening guidelines of Dow Jones Islamic Market indices. The weights of each constituent in both Shariah-compliant and conventional portfolios are driven by its relative exposure to political risk for the period 1996–2018. Findings: Results show that, compared to conventional investors, Shariah-compliant investors gain substantial benefits when the allocation decision is based on political risk. A Shariah-compliant portfolio outperforms its conventional counterpart by 7.98% annually when tilted toward politically stable countries. The economic benefits further increase to 804 basis points when the portfolio allocates more funds to politically unstable countries. The tilted SCEP successfully reduces the downside risk, resulting in improved financial performance stability. Originality/value: To the best of the authors' knowledge, this is the first effort of its nature to highlight the importance of political risk in the context of SCEPs. [ABSTRACT FROM AUTHOR]

The paper analyzes household portfolio dynamics in Europe, focusing on the period from 2010 to 2017 using data from the European Central Bank's Household Finance and Consumption Survey (HFCS). The study examines the evolution of household portfolios after the 2008 financial crisis, with housing assets found to be one of the key drivers. On the aggregate level, the household portfolio remained stable between 2010 and 2017. Housing importance remained crucial during all the time and drove the majority of portfolio performance for households in 40-99 percentiles of the wealth distribution. With the evolution of household portfolios, age cohorts 45-54, 65 and older strengthen their positions by increasing the share of their owned assets in comparison to other age cohorts, which lost part of their shares. I conclude the paper with some stylized facts showing the relationship between home ownership, the share of portfolio in housing assets and wealth inequality between countries. The stylized facts provide evidence that household portfolios significantly concentrated in housing assets during the analyzed decade. Furthermore, the transition of households from renters to home owners contributed significantly to the changes in wealth inequality that occurred after the 2008 financial crisis. [ABSTRACT FROM AUTHOR]

In view of increasing competition and stricter lending criteria within the corporate banking space, the banking salesforce is expected to aggressively market services and solutions while being mindfully consistent with its risk management policies. Hence, absorbing cues from the market allows them to keep themselves abreast with current developments that might affect their customers' portfolio performance. Drawing on the sense-making theory, this study investigates the moderation and joint moderation effects of adhocracy (or innovative) culture and work experience on the link between information acquisition and job performance of B2B salespeople within the banking industry. We established two-way and three-way interaction models on a set of data involving 539 B2B salespeople from 18 Malaysian banks. The results indicate that (i) adhocracy culture significantly moderates the effects of information acquisition on job performance, and (ii) the moderating effect is contingent upon the employees' work experience. Theoretically, our findings suggest boundary conditions for market-oriented behavior effectiveness that are conditioned upon the varying levels of work experience and adhocracy culture. In practise, managers should consider instilling inventive and heuristic thinking among the salespeople to help them gather market intelligence and customer knowledge more effectively and reduce their cognitive load in the face of information overload and rapidly evolving technologies. [ABSTRACT FROM AUTHOR]

The objectives of this study are to analyze the presence of environmental values in ESG indexes based on the positive value relationship between energy and the environment found in existing studies and to identify the characteristics of environmental, social, and governance (ESG) investments by examining the performance of ESG investments compared to market portfolios, as well as to consider the implications of ESG investment in clean energy policy based on the results of the empirical analysis of this study. This paper contributes threefold. First, using a supply and demand-based pricing model, we propose a new model of the expected return, risk, and performance ratio of the ESG premiums defined by the log price differences between ESG indexes and a market portfolio to analyze how ESG investments perform. Second, based on the empirical findings of this study that the correlation between ESG indexes and energy prices is a decreasing function of the latter in contrast to other environmental assets, this paper suggests that ESG indexes cannot adequately demonstrate environmental values but reflect social and governance values, i.e., (E)SG, in the complementary perspective. Third, using the empirical findings of this study that the information ratio of the ESG premium tends to be positive but declines and approaches zero over time, this paper indicates that although ESG investments can be significant compared to market index investments, the significance is limited because ESG investment performance converges with market portfolio performance. In conclusion, two points should be noted about ESG investing in clean energy policy: given the dilution of environmental values in ESG investing, a clear distinction should be made between ESG investing and renewable energy investing; even if investments in renewable energy projects are treated as ESG investments, reflecting current market trends, we should be prepared for the fact that the superior investment performance due to social and governance values, rather than environmental values, will not last long. [ABSTRACT FROM AUTHOR]

In this study, we examine the risk characteristics of the FAANG stocks by constructing a minimum variance portfolio (MVFAANG). Google and Apple are found to have the highest portfolio weights. These FAANG stocks showed a remarkable growth in value till 2022, which increased even more rapidly during the pandemic. However, since 2022 these stocks experienced a precipitous decline in value. We further explore into the risk dynamics of MVFAANG by decomposing its total volatility in permanent and transitory components. Our results show that the portfolio’s volatility entirely consists of a considerably high permanent volatility. Our findings also indicate that the value-at-risk of MVFAANG is also substantially high, reaching 25% during the pandemic. Finally, using GSADF model we identify multiple structural breaks and explosiveness in MVFAANG. Overall results indicate that MVFAANG possesses substantial risk, which calls for cautious consideration for the investors. [ABSTRACT FROM AUTHOR]

Dangers of Ignoring Market Friction When Leveraging Financial Portfolios Portfolio leveraging is a standard industry practice to target higher fund returns, for example, in risk parity asset allocation. However, the existing models of optimal bet sizing fail to integrate analytically the impact on leveraged portfolio selection resulting from market liquidity issues. The paper "Optimal Leveraged Portfolio Selection Under Quasi-Elastic Market Impact" considers a market in which both temporary and permanent impact on trading prices are present with the former impact being sufficiently large relative to the latter. Our analytical conclusions, supported by a case study that uses even relatively more liquid U.S. exchange-traded fund assets, demonstrate that fund managers are ill advised to ignore market friction when leveraging to achieve target higher returns. Not only risk-adjusted returns significantly deteriorate, but also those losses become steeper when setting higher targets requiring increased levels of leverage. Moreover, leverage-constrained and less risk-averse investors ignoring liquidity costs ex ante face the most losses in expected utility ex post. We study optimal portfolio choice under leveraging to improve portfolio performance when trade execution faces market impact. We consider a quasi-elastic market with continuous trading in which temporary liquidity costs are sufficiently large relative to permanent impact. The resulting convex optimization model is used to show analytically that an unlevered portfolio maximizing the Sharpe ratio is no longer a tangency portfolio, and increasing the portfolio target mean leads to severely undermining the risk-adjusted returns and requiring increased portfolio leverage. This paper develops theoretical properties underlying the relationships among target mean, leverage, and Sharpe ratio in optimal portfolio selection under market impact. The Sharpe-leverage efficient frontiers under market impact are consistently dominated when setting higher return targets. Moreover, leverage-constrained and less risk-averse investors ignoring liquidity costs ex ante suffer the most losses in expected utility. Detailed computational analyses are provided using real-world data to support and highlight our analytical findings. Funding: The research of the corresponding author, J. Chen, was partially supported by the National Natural Science Foundation of China [Grants 72171012 and 11801023]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2462. [ABSTRACT FROM AUTHOR]

This article introduces the time-zero direct alpha approach for estimating the outperformance of a private market investment portfolio relative to benchmark(s). To the authors' knowledge, this is the first published method for private markets to remove the impact of investment timing and accommodate multiple underlying investments with distinct benchmarks in a rigorous manner, without relying on unreliable heuristics. As demonstrated in the article, these problems of investment timing and unobservable subportfolio weights over time can add meaningful noise to estimates of relative performance. This method builds upon the commonly used direct alpha measure for comparing private market returns to public benchmarks. The authors believe that time-zero direct alpha can give private market analysts valuable information for manager selection, portfolio construction, and liquidity planning. [ABSTRACT FROM AUTHOR]

The author presents a framework for specifying and comparing different versions of the calculation of the ex-post "contribution to a return" financial performance measure for each of three levels of application. Employing this framework, the author analyzes a conceptually complete set of versions within each level in order to determine which version best coherently captures the intuitive intent in applying the measure at that level. Specifically, the three levels that the author will explicate are the ex-post contribution of 1) the return of a component of a portfolio on a day to the portfolio's total return for that day, 2) the total return of a portfolio on a day to a portfolio's total return for a multi-day period, and 3) the return of a component of a portfolio on a day to a portfolio's total return for a multi-day period. [ABSTRACT FROM AUTHOR]

The authors first characterize a variety of systematic strategies in terms of timing and sizing skills based on their frequencies and magnitudes of gains and losses over time and then move on to analyze how these characteristics differ over macroeconomic regimes, such as inflationary and recessionary periods. The results are based on new methodologies for significance testing of gain- and loss-based performance measures and complement previous results based on analysis of the Sharpe ratio of these strategies. The empirical results have implications for outcome-orientated portfolio construction as well as strategic and tactical asset allocation, because strategies that have desirable properties--as well as strategies with problematic performance--are identified under each regime. This approach also allows for identification and attribution of the inherent sources of these differences. [ABSTRACT FROM AUTHOR]

In this article, the author explains the methods for altering critical facets of the traditional performance measurement framework to handle the unique attributes and challenges associated with alternative investments. The practices described in this article are already used in the alternative investment industry. Due to the expansion of traditional asset manager product lines to alternatives and the expanded use of vehicles enabling wealth managers and individual investors access to private markets, many analysts are interested in a primer on enhancing their performance measurement process. [ABSTRACT FROM AUTHOR]

Tracking a portfolio's emissions profile over time is a key requirement for any type of climate-aware investment strategy. The challenge in tracking those profiles is that climate metrics are influenced by not only the emissions of companies in the portfolio but also portfolio managers' decisions, as well as other financial variables such as weights in the portfolio or companies' enterprise values. In this article, the authors develop an attribution framework that allows investors to disentangle these effects. They focus on financed emissions, which aggregate greenhouse gas emissions "owned" by a portfolio's holdings, and financed-emissions intensity, which adjusts financed emissions by dividing it by portfolio value. Their approach is to first calculate contributions by looking at changes in a specific input variable while keeping all other input variables constant. Next, they account for effects of simultaneous changes. The results are organized in an attribution tree that allows for a systematic drill-down into the different effects. [ABSTRACT FROM AUTHOR]

In this article, the author develops a parsimonious attribution framework for evaluating the environmental, social, and governance (ESG) performance of a portfolio. The attribution model decomposes portfolio ESG performance into three principal components: a value effect, a weighting effect, and an interaction effect. The author illustrates his approach using the equity portfolios of US public pension funds over time and finds that US public pensions' positive ESG performance over the past decade is mainly due to their underlying holdings boosting their ESG scores over this period. By contrast, pension portfolio weight changes in high and low ESG-scoring firms over this period contributed negatively to their ESG performance, both in absolute terms and relative to the market portfolio. Furthermore, public pensions' portfolio weighting behavior (the weighting effect) explains most of the variation in their ESG performance. The findings suggest that the proposed ESG attribution framework can help meet the demand for transparency regarding the ESG performance of investment assets. [ABSTRACT FROM AUTHOR]

We hypothesise that societal norms cause institutional investors to discriminate against polluters. Consistent with this hypothesis, we document relatively lower institutional ownership for the most polluting firms in the United States. Institutional discrimination against polluters increases over 1987–2018, in line with increasing environmental awareness. Institutions are primarily averse to investments in dioxin emitters. Institutions that prioritize portfolio performance are less sensitive to societal norms against polluters, as are institutions with shorter investment horizons and more aggressive trading strategies. Lastly, we test whether investor discrimination affects polluter equity valuations and creates systematic trading opportunities, but find no convincing evidence of a polluter return premium. [ABSTRACT FROM AUTHOR]

This study investigates the anchoring bias and disposition effect in investor trading decisions under different market volatility conditions (stable and volatile markets) and examines their impact on portfolio performance. Employing a quasi-experimental design, participants engage in interactive trading with four securities—two with potential negative returns and two with positive returns—within a simulated asset market. The findings reveal the presence of both the disposition effect and the anchoring bias among individual investors in India. Notably, market volatility influences these behavioral biases, with the disposition effect more pronounced in volatile markets, while the anchoring bias is significant in stable markets. Furthermore, investors exhibiting the disposition effect tend to have lower portfolio performance, while those influenced by the anchoring bias achieve relatively better results. These insights can aid individual investors in recognizing their behavioral biases and making informed trading decisions to enhance portfolio performance. Additionally, this study presents valuable suggestions to financial institutions and regulatory government agencies engaged in similar experiments, with the goal of improving financial decision-making and investment behavior. [ABSTRACT FROM AUTHOR]

This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg's 12-month target price consensus, which serves as a signal to buy or sell assets. Using a sample of 50 European stocks over a 19-year period (from 1 April 2004 to 31 March 2023), we compare the performance of target-price-based portfolios to traditional alternatives, such as a naïve homogeneous portfolio and the Eurostoxx 50 index, as well as to passive portfolios based on average recommendations. We also look into the mean-variance efficiency of these portfolios and find that all exhibit similar levels of efficiency, which are well below the performance of the theoretical tangent portfolios. Our results indicate that target-price-based portfolios show performance very close to that of the naïve homogeneous portfolio. Even the passive "average" target price portfolios, which require previous knowledge of targets for the entire investment period, are unable to outperform the naïve portfolio. Our main findings are based on a 15-year investment horizon but are robust when considering smaller maturities and out-of-sample data. We also investigate the impact of rebalancing on portfolio performance and find that it does pay off in the long run (over an 8-year investment period), but the frequency of rebalancing matters. Rebalancing only once a year is as detrimental to performance as not rebalancing at all. However, it is unclear whether the transaction costs associated with frequent rebalancing would offset any relative outperformance. Overall, our study contributes to the literature on portfolio management and market efficiency by demonstrating the potential benefits and limitations of using target price recommendations to construct portfolios, highlighting the importance of carefully considering rebalancing strategies to achieve optimal performance. [ABSTRACT FROM AUTHOR]

In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the stock index associated with the market factor and, second, the realized covariance matrix calculated from high-frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to 10 top stocks composing the exchange traded fund linked with the investment return of the S&P 500 index and the model is shown to have a stable advantage in portfolio performance. [ABSTRACT FROM AUTHOR]

Using a proposed quantile‐based hedging analysis framework, this paper analyzes the hedging properties and portfolio performance of 15 commodities futures in China, especially under extreme market conditions. Quantile‐on‐quantile regression results show that the dependency structures of most spot and futures prices exhibit an almost similar shape with positive coefficients, which indicates that most futures products are good hedges and safe havens for the corresponding spot prices. Quantile hedge ratios vary with the quantiles, displaying a U‐shape, so the hedge ratios of most commodities should increase moderately in both bearish and bullish markets. As for portfolio performance, metal commodities perform best; agricultural commodities second best, and energy commodities relatively poorly. Out‐of‐sample quantile hedging results are consistent with in‐sample results. Therefore, participating in the futures market can effectively manage risk. The premise is that investors pay attention to market dynamics and adjust their portfolios promptly. [ABSTRACT FROM AUTHOR]

Purpose: Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches. Design/methodology/approach: We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill. Findings: We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used. Originality/value: This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology. [ABSTRACT FROM AUTHOR]

This paper adopts the fractal analysis approach, specifically a Hurst exponent index in portfolio optimization at the Damascus Securities Exchange (DSE). We construct three portfolios based on the Hurst index from stocks listed on the DSE during the period between 2019 and 2021 and find that these portfolios outperform the market portfolio in terms of returns, Sharpe ratio, Treynor ratio, and alpha. In addition, selecting stocks with high Hurst coefficients further enhances the performance of the portfolio. Importantly, even in out-of-sample tests, the three fractal portfolios continue to outperform the market portfolio. Furthermore, we find that fractal portfolios outperform portfolios formed using the momentum and size strategies demonstrating the superiority of the fractal analysis approach. We conclude that the incorporation of fractal analysis into the portfolio optimization problem allows the creation of a more efficient portfolio. Hence, we recommend that investors consider the Hurst exponent index in their portfolio optimization for better investment decisions. [ABSTRACT FROM AUTHOR]

We re-examine performance persistence amongst UK mutual funds. Specifically, we investigate performance persistence amongst small portfolios of past high-performing funds. In contrast to the more common analysis of decile portfolios of funds, we focus on persistence in the more extreme positive tail of the cross section of fund performance. This paper contributes to the smaller literature on UK rather than US mutual fund performance. We investigate fund persistence based on practitioner index models as well as academic factor models, focusing on small portfolios of funds using inference based on nonparametric persistence test statistics as well as conventional t tests. We provide strong evidence of positive persistence amongst small-size portfolios of (past) high-performing funds that is robust to alternative formation and holding periods and alternative performance models. We also document some sensitivity in inferences on positive persistence when using nonparametric versus conventional tests. [ABSTRACT FROM AUTHOR]

This study examines the aftermarket performance of high-green and low-green IPO and how green IPOs can optimize portfolio allocation. We assume the higher level of greenness increases investors' participation in IPOs. To this end, we develop the utility function and determine that investors prefer to participate in new issues when firms account for greenness measures. This study proposes the global aspects of green measure: the desired level of greenness a firm maintains. We find that IPOs in our sample are far below the global standards of greenness. This evidence suggests they must adopt the necessary actions to make the environment green. Another significant contribution of this study is to measure the performance of high and low-green IPOs in short- and long-run horizons. This study reveals that high-green IPOs are less underpriced. This study estimates the effect of greenness on initial returns and finds an inverse relationship suggesting that high-green IPOs are less underpriced due to lower risk associated with new issues. In terms of measuring longer-term performance, this study determines that high-green IPOs underperform less than low-green IPOs. [ABSTRACT FROM AUTHOR]

This article contributes to the longstanding debate about the relative merits of building multifactor portfolios using a bottom-up approach, informed by factor-based expected returns, and a top-down approach that allocates across factor portfolios. Its main contribution is to prove formally that the solution of the mean-variance optimization solved by a stock picker who uses factors to select securities and that of a mean-variance-efficient allocation across factors are in fact largely equivalent. This finding is corroborated empirically and holds under stringent investment constraints. Moreover, while demonstrating this equivalence, an alternative methodology emerges that makes the best of both approaches. [ABSTRACT FROM AUTHOR]

The Sharpe Ratio, serves as a crucial tool in assessing the relationship between return and risk. This study proposes a critical analysis of the Sharpe Ratio, addressing not only its practical applicability but also its involvement in the decision-making process for investors and portfolio managers. The aim of this research is to uncover the depth of interpretation of the Ratio, surpassing its simple use as an efficiency indicator. A detailed evaluation of the factors influencing the outcome of this ratio is proposed, along with an analysis of its impact on investment decisions. The primary objective of the study is to investigate thoroughly the components of the Sharpe Ratio and how they can provide a more nuanced perspective on performance. This critical analysis makes significant contributions to understanding and applying the Sharpe Ratio in the complex context of financial markets, offering crucial insights for those involved in the investment decision-making process. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies. Design/methodology/approach: The authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution. Findings: The authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies. Originality/value: This paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies. [ABSTRACT FROM AUTHOR]

This paper aims to examine the publication metrics of literature related to the influential aspects of ESG (environmental, social, and governance), SRI (socially responsible investing), ethical, and impact investing on the portfolio and financial performance literature. It also seeks to identify major patterns and core themes in this topic and draw lessons from the past literature for future directions. Data from the SCOPUS database were used in this study. The 'biblioshiny' R package, also known as 'bibliometrix 3.0', was employed to conduct bibliometric analysis, utilising mapping and clustering techniques on 260 articles, in order to distil the comprehensive knowledge and identify emerging trends in ESG, SRI, ethical, and impact investing. The thematic map classified the ESG, SRI, ethical, impact investing and performance relationship themes into four categories of themes: niche themes (SRI, engagement and ESG), motor themes (corporate financial performance, corporate social performance, ESG, ESG factors, sustainability, performance, integrated reporting, gender diversity, and board size), emerging or declining themes (social responsibility, environmental performance, socially responsible investment, ethical investment, and SRI), and basic or transversal themes (financial performance, corporate social performance, ESG performance, environmental, social, and governance). Socially responsible investing, engagement, and ESG imply a position between niche themes and a highly developed topic/emerging or a decreasing theme, while the impact of COVID-19 on sustainability and financial performance implies a position between a highly developed topic/emerging or decreasing theme and a basic theme. The findings contribute to the enhanced understanding of ESG, SRI, ethical, impact investing and performance, which are crucial for an efficient capital market in promoting sustainability and sustainable development. The study offers vital practical implications and future research directions. [ABSTRACT FROM AUTHOR]

The widespread reliance on gross government debt and deficit/surplus as indicators of fiscal performance has too often neglected the role played by other important parts of a government's balance sheet including their portfolios of financial assets. Partial measures of net worth leaves an important gap in the empirical literature on financial performance and fiscal transparency. The purpose of this paper is to examine the role played by equity investments and their performance in secondary markets. The results suggest that the performance of government equity portfolios correlates strongly with fiscal transparency to the extent that fully transparent governments are expected to generate around 7 percent higher returns than fully opaque governments. We also find strong evidence of governments willingness to inject liquidity, via investing in equities, into other sectors of the economy during periods of financial crisis confirming governments' role as an 'investor of last resort'. [ABSTRACT FROM AUTHOR]

Sürdürülebilir finans çevresel ve sosyal yatırım anlayışla gelecek nesillere daha yaşanabilir dünya bırakmak için ekonomik aktivitenin çevreye ve topluma duyarlı üretim anlayışının gelişmesine katkı sağlamayı amaçlamaktadır. Yatırımcıların sosyal sorumlu yatırım anlayışıyla yatırım yapma düşüncesini sürdürülebilir temalı fonlarla geleneksel temalı fonların performanslarının karşılaştırılmasının önemini arttırmaktadır. Bu çalışmada Türkiye’de sürdürülebilir temalı fonların geleneksel fonlarla ve endekslerle performansları karşılaştırmalı olarak analiz edilmiştir. Sürdürülebilir fonların pazar endeksi olarak BİST Sürdürülebilir endeksi (XUSRD); geleneksel fonların pazar endeksi olarak ise BİST100 endeksi (XU100) seçilmiştir. Türkiye’de sürdürülebilir fonların tarihinin çok eski olmamasından dolayı veri dönemi olarak 2019-2022 yılları günlük verileri analiz edilmiştir. Çalışmada ilk olarak fonların performansları yıllık getiri, standart hata, Sharpe oranı, Jensen alfası ve Treyner ölçütü ile karşılaştırılmıştır. Çalışmanın devamında XU100 ve XUSRD pazar portföyü olarak seçilerek Sermaye Varlıkları Fiyatlama Modeli (SVFM) ile portföylerin getirileri ve sistemik riskleri elde edilmiştir. Çalışmadan elde edilen sonuçlara göre sürdürülebilir temalı fonların performanslarının geleneksel ikamelerine ve pazar endekslerine kıyasla daha iyi olduğu gözlemlenmiştir.

Portfolio optimization relies in three main elements: the utility function, the use of multivariate dependence measure between assets and the returns of the assets. The first problem is well known in finance since one can use several different types of portfolio utility functions depending on her objective function and constraints. The two remaining problems are relevant topics of research: the measure and forecast of the dependence between assets and the forecast of the returns of assets. In this paper, we present a new method with artificial intelligence to estimate the variance and covariance matrices of financial time series data accounting for small sample size, large number of assets and presences of outliers. We compare results using different approaches, e.g., Minimum covariance determinant (MCD), Minimum Volume Ellipsoid (MVE), Minimum Regularized Covariance Determinant (MRCD) and Orthogonalized Gnanadesikan-Kettenring (OGK). The proposed approach is robust under several financial stylized facts and presents good performance with respect to the cumulative returns in the US markets (NASDAQ Stock Exchange) for 95 assets during 2015 to the beginning of 2019. These study contributes to fill in the literature gap in covariance estimation for small sample size, large number of assets and presences of outliers and contributes to investors to making better portfolio management. [ABSTRACT FROM AUTHOR]

We seek the best skewness models for portfolio choice decisions. To this end, we compare the predictive ability and portfolio performance of several prominent skewness models in a sample of 10 international equity market indices. Overall, models that employ information from the option markets outperform models that only rely on stock returns. We propose an option‐based skewness estimator that accounts for the skewness risk premium. This estimator offers the most informative forecasts of future skewness, the lowest prediction errors, and the best portfolio performance in most of our tests. [ABSTRACT FROM AUTHOR]

Stock markets are flooded with various uncertainties such as stock market scenarios, especially the input parameters of stock portfolio models. It is hard for stock market investments to jointly deal with them. Although robust portfolio is the prevailing portfolio policy handling with uncertainties, it always focuses on the worst-case scenario of all the possible realizations of uncertain input parameters, resulting in the optimal portfolios considerably conservative as well as the portfolio performances very inferior. To this end, based on Markowitz's mean–variance (MV) model, this paper builds the new robust portfolio model (RMV-best) from the innovative perspective of best-case scenario, exactly contrary to the model based on the worst-case scenario (RMV-worst). Furthermore, stock market scenarios which always shift uncertainly show the periodic characteristics, and can be divided into three movement statuses: the bull, the bear and the steady. To verify the consistency of RMV-best, RMV-worst and MV portfolio policies over the different motion periods and identify the differences of these portfolio policies at various movement statuses, the mix data including two motion periods and three industry sectors is employed. Eventually, empirical results indicate that RMV-best is always the most favorable portfolio policy at the bull and the bear market, while MV can produce more profitable portfolios at the steady market over two motion periods. However, the drawbacks of RMV-worst are also confirmed in this study. [ABSTRACT FROM AUTHOR]

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Portfolio optimization is a pertinent topic of significant importance in the financial literature. During the portfolio construction, an investor confronts two important steps: portfolio selection and portfolio allocation. This article seeks to investigate portfolio optimization based on the Minimum Spanning Tree (MST) method applied on the Moroccan All Shares Index (MASI) historical stock log returns covering the period from 2 January 2013 to 27 October 2022 allowing us to build two portfolios: MST-Portfolio and MST-Portfolio 2. Portfolio selection was carried out for MST-Portfolio and MST-Portfolio 2, respectively, based on 63 stocks or using the Degree Centrality (DC) measure and portfolio allocation for both portfolios was carried through the use of the Inverse Degree Centrality Portfolio (IDCP). The obtained portfolios were compared with the Minimum Variance Portfolio (MV Portfolio) and Equal Weighting Portfolio (EW Portfolio) using centrality measures, diversification, and backtesting. According to the used indicators analysis, MST-Portfolio and MST-Portfolio 2 are the most well-performed and robust portfolios showing a good performance during the studied period, even during the COVID-19 crisis, and ensuring a good level of diversification. The findings demonstrate that both suggested methods can enhance portfolio performance, evidence that can help investors or active managers when optimizing their portfolios. [ABSTRACT FROM AUTHOR]

By considering the stock market's fuzzy uncertainty and investors' psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur's work (2013) which did not study the different risk measures, and an extension of Chen et al.'s work (2018) which only considered risk-neutral attitude. [ABSTRACT FROM AUTHOR]

• German brokerage introduces a flat-fee alongside a commission-based scheme. • Comparison of switchers to the flat-fee to matched commission-based clients. • Adopters seek and follow (unchanged) financial advice more. • Adopters increase portfolio values and trade more. • Adopters improve portfolio efficiency through higher fund shares. Using a field study at a German brokerage, we investigate advised individual investors' behavior and outcomes after self-selecting into a flat-fee scheme (percentage of portfolio value) for mutual funds. In a difference-in-differences setting, we compare 699 switchers to propensity-score-matched advisory clients who remained in the commission-based scheme. Switchers increase their portfolio values, improve portfolio diversification, and increase their portfolio performance. They also demand more financial advice and follow more advisor recommendations. We argue that switchers attribute a higher quality to the unchanged advisory services. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the performance of low-volatility portfolio strategies representing risk aversion after the 2008 global financial crisis. Five investment portfolios were built by taking into consideration the weight distribution criteria defined by the inverse of the standard deviation of assets, the natural logarithm and exponential of these values, as well as the minimum variance and tangent portfolios, based on the S&P 500 futures index, dollar futures index, US government long-term bond (10-year Treasury Bond) and gold futures. The design of the strategies used both twelve- and thirty-month rolling windows for the standard deviation and conditional volatility estimates. Mean return of portfolio, risk through standard deviation, Sharpe index, and risk-adjusted return were calculated for evaluation purposes. Results have evidenced that, together, risk-based portfolios using 12-month rolling window or conditional volatility were superior to the tangent portfolio, as well as that the minimum variance portfolio was competitive to other alternatives. The main contribution of the current study lies in the fact that risk aversion was relevant to portfolios' performance in the post-crisis period. [ABSTRACT FROM AUTHOR]