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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]

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]

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]

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]

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]

This paper adopts the multi-criterion decision-making model of fuzzy-TODIM and genetic algorithm (GA) for optimal portfolio allocation. We applied Markowitz's portfolio parameters as inputs for the fuzzy TODIM model to rank stocks that are constituents of each index from three different markets. Portfolios are then generated dynamically using three weighting techniques and subject to multi-objective criteria and additional constraints. The results indicate a significant variation in performance metrics between the model-generated portfolios and the market indices. Replication of the procedure produces a similar outcome. Moreover, the out-of-sample tests conducted over 3 years validate the results' robustness, indicating that fuzzy TODIM, combined with GA, can achieve superior performance in dynamic portfolio allocation. [ABSTRACT FROM AUTHOR]

For financial index tracking, it is desirable to build a sparse portfolio of a small number of assets to save transaction costs. For all we know, a majority of the pertinent literatures on sparse index tracking are mainly concentrated on the penalized least squares estimation under the cardinality and no short-selling constraints. Nevertheless, the return series of financial index often exhibit outliers, and thus the above literatures may fail to produce a robust solution for index tracking. In this paper, we indeed to provide a general procedure to build robust portfolio that can undertake stock selection and capital allocation for financial index tracking. To be more realistic, we further take the full investment constraint (or budget constraint) into consideration. Numerical simulations indicate that the proposed method has good resistance to heavy-tailed error and outlier contamination. Finally, the out-of-sample performance of the new portfolios is compared empirically by tracking the SSE 50 index and FTSE China A50 index. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framing approach to investigate the disposition effect by linking it to regret and prospect theory. We build upon the wide framing hypothesis and extend prior research by including metrics of perceived risk and the number of days stocks are at a gain. Using a sample of 24 million trading operations conducted by nearly 4,000 Italian investors from 2010 to 2018, we demonstrate that investment decisions are influenced not only by portfolio aggregate performance but also by asset trading history and perceived volatility. Our results align with several theoretical frameworks that could explain the disposition effect, such as mental accounting, prospect theory and regret aversion. Furthermore, by incorporating both narrow and wide framing factors, along with their interactions, our approach provides a more comprehensive understanding of the disposition effect. • We link the disposition effect to perceived risk and portfolio performance. • We leverage a new dataset of 24 million trading operations. • Disposition effect diminishes as the proportion of gains in the portfolio rises. • The perceived risk of an asset is positively related to the willingness to sell it. • Past performance reduces the asymmetry in the willingness to sell at a loss or gain. [ABSTRACT FROM AUTHOR]

Combining the stock prediction with portfolio optimization can improve the performance of the portfolio construction. In this article, we propose a novel portfolio construction approach by utilizing a two‐stage ensemble model to forecast stock prices and combining the forecasting results with the portfolio optimization. To be specific, there are two phases in the approach: stock prediction and portfolio optimization. The stock prediction has two stages. In the first stage, three neural networks, that is, multilayer perceptron (MLP), gated recurrent unit (GRU), and long short‐term memory (LSTM) are used to integrate the forecasting results of four individual models, that is, LSTM, GRU, deep multilayer perceptron (DMLP), and random forest (RF). In the second stage, the time‐varying weight ordinary least square model (OLS) is utilized to combine the first‐stage forecasting results to obtain the ultimate forecasting results, and then the stocks having a better potential return on investment are chosen. In the portfolio optimization, a diversified mean‐variance with forecasting model named DMVF is proposed, in which an average predictive error term is considered to obtain excess returns, and a 2‐norm cost function is introduced to diversify the portfolio. Using the historical data from the Shanghai stock exchange as the study sample, the results of the experiments indicate the DMVF model with two‐stage ensemble prediction outperforms benchmarks in terms of return and return‐risk characteristics. [ABSTRACT FROM AUTHOR]

There is growing evidence that integrating environmental, social and governance (ESG) activities, which include businesses' non-financial information, into investment decisions can increase risk-adjusted returns and contribute to long-term sustainability. This study examined whether portfolios that are compatible not only with financial performance but also with sustainability goals and ethical issues can be created. In addition, this study evaluates ESG and smart beta strategies together in supporting sustainable investment. For this, portfolios were created based on high (low) ESG score, high (low) market value and high (low) return on assets (ROA) in each quarter between January 2021 and September 2023. Then, the excess returns and risk-adjusted (CAPM) returns of these portfolios were examined. Accordingly, it cannot be said that portfolios created based on high ESG scores in Turkiye provide better returns according to risk than low ESG portfolios and smart beta strategies. It can be said that the best investment strategy between the specified periods is investment in high market capitalization or high ROA portfolios. [ABSTRACT FROM AUTHOR]

This study is the first to examine the efficacy of the Cumulative Prospect Theory value of the past return (CPTV) to explain fund flow in emerging markets funds by considering the impact of fund managers’ characteristics and behavioural biases on fund investors’ investment decisions. Besides, this study also investigates the impact of fund investors’ ability to identify fund managers’ skills. The findings suggest that CPTV has a significant and positive association with subsequent fund flow and emerging market funds’ performance. Notably, the study documents a negative relationship between fund flow and fund size and also highlights that newer funds generate greater fund flows. Furthermore, the findings indicate that investors investing in emerging market funds tend to behave differently, allocate money for alpha, and avoid giving distorted income for factor-related returns to fund managers. The results demonstrate that managers’ skill and portfolio concentration decision affects fund flow and CPTV relationships. The findings illustrate that fund managers who concentrate their portfolio and invest in high CPTV stocks tend to generate higher fund flow than their diversified counterparts. [ABSTRACT FROM AUTHOR]

The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial time series data. However, several issues arise with historical financial time series data, making it challenging to train models effectively to achieve the portfolio diversification objective: an insufficient amount of training data and the uncertainty deficiency problem, wherein the uncertainty that existed in the past is not visible in the present. Insufficient datasets, characterized by small data size, result in information asymmetry and compromise portfolio performance. This limitation underscores the importance of adopting a pattern-centric data augmentation approach, capable of unveiling hidden patterns and structures within the financial time series data. To address these challenges, this paper introduces the financial time series decomposition-based variational encoder-decoder (FED) method to augment financial time series data, overcoming the limitations of insufficient training data and providing a more realistic and dynamic simulation of the financial market environment. By decomposing the data into distinct components, such as trend, dispersion, and residual, FED leverages pattern-centric data augmentation within the financial time series data. In the environment generated using the FED method, this paper proposes a two-class portfolio diversification, called FED2Port. It integrates stochastic elements into the reward function, enabling a reinforcement learning algorithm to learn from a comprehensive spectrum of financial market uncertainties. The experimental results demonstrate that the proposed model significantly enhances portfolio performance. [ABSTRACT FROM AUTHOR]

A major challenge in specifying design requirements for policy-based seismic retrofits is ensuring that the desired performance enhancements are achieved across a portfolio of buildings with diverse structural characteristics and hazard. To address this challenge, a modular optimization methodology is developed. A prediction module uses machine learning-based surrogate models as compact statistical links between building structural characteristics and seismic performance outcomes. The optimization module uses regionally targeted objective functions to determine the retrofit enhancements that achieve the most desirable aggregated performance outcome for the portfolio of buildings. Lastly, the evaluation module benchmarks the performance of the optimized retrofit scheme against the existing inventory as well as more conventional retrofit approaches. The ability of the framework to balance the need for effective (improved performance) and efficient (minimizing cost) retrofits is demonstrated by applying it to the inventory of buildings under the purview of the Los Angeles Soft-Story Ordinance. [ABSTRACT FROM AUTHOR]

Objective - This study aims to contribute to the field of cryptocurrency portfolio management and rebalancing strategies by empirically investigating the impact of different allocation frequencies and threshold percentages on the risk-adjusted returns of cryptocurrency portfolios. Methodology/Technique - Utilizing a simulation of 10,000 cryptocurrency portfolios comprising seven assets, including Ethereum (ETH), Bitcoin (BTC), Tether (USDT), Litecoin (LTC), Solana (SOL), Dogecoin (DOGE), and Polygon (MATIC), this study examines and compares the effects of different allocation frequencies (daily, weekly, and monthly) in time-based rebalancing and various threshold percentages (5%, 10%, and 15%) in threshold-based strategies on the portfolios' risk-adjusted returns, using the Sharpe ratio. The performance of these strategies is also compared with a passive buy-and-hold strategy. Findings -The research reveals statistically significant differences in the risk-adjusted returns between the buy-andhold strategy and the daily rebalancing and threshold-based strategies with 5% and 10% threshold percentages. The daily rebalancing strategy demonstrates a higher Sharpe ratio, while lower threshold percentages lead to better riskadjusted returns. Novelty - These empirical findings, using a simulation of 10,000 cryptocurrency portfolios, provide valuable insights into optimizing cryptocurrency portfolio performance through rebalancing strategies. Additionally, they highlight the effectiveness of implementing rebalancing techniques in cryptocurrency portfolios, contributing to the understanding of rebalancing optimization in this domain. [ABSTRACT FROM AUTHOR]

In this article, we propose extensions of the general mean-risk portfolio selection models to limit the spillover effect of risk. We use the conditional value at risk (CoVaR) as a measure to identify the spillover risk contribution of securities. The goal is to find a portfolio that minimizes general risk measures while the spillover risk contribution is limited. Based on the frameworks of the general mean-risk models, we additionally guarantee that a portfolio has limited spillover risk contributions. To solve these problems efficiently, we propose a Dantzig-Wolfe decomposition reformulation that yields a strong continuous relaxation bound by introducing a set of feasible investment patterns. We develop an algorithm that incorporates a column generation procedure to reduce the spillover effect of tail risk in subproblems. Finally, we present the performance of the portfolios using real historical data. The results show that our model can provide better portfolios than the general mean-risk models. [ABSTRACT FROM AUTHOR]

The goal of core-satellite investing is to optimally balance the portfolio allocation between a core and satellite investment. This paper provides an explicit solution when the investor's optimality criterion is the third-order and fourth-order expansion of the expected utility function, respectively. Based on a numeric example, we document the sensitivity of the proposed weights to coskewness and cokurtosis components. Finally, we use ETFs to examine the portfolio performance of the coresatellite strategy with higher order moments. We document that integrating the higher order moment in core-satellite investing can improve the financial performance of a portfolio. [ABSTRACT FROM AUTHOR]

A key parameter when investing is Time Horizon. One of the biggest mistakes investors make is not aligning the timeline of their goals with their investment portfolio. In other words, time horizons determine the investment portfolio you should construct. We examine which portfolios are the best for long-term investing, short-term investing, and intraday trading. This study presents a novel approach for portfolio construction based on Network Science. We use daily returns of stocks that compose the Dow Jones Industrial Average (DJIA) for a 25-year period from 1998 to 2022. Stock networks are estimated from (i) Pearson correlation (undirected linear statistical correlations), as well as (ii) Transfer Entropy (directed non-linear causal relationships). Portfolios are constructed in two main ways: (a) only four stocks are selected, depending on their centrality, with Markowitz investing weights, or (b) all stocks are selected with centrality-based investing weights. Portfolio performance is evaluated in terms of the following indicators: return, risk (total and systematic), and risk-adjusted return (Sharpe ratio and Treynor ratio). Results are compared against two benchmarks: the index DJIA, and the Markowitz portfolio based on Modern Portfolio Theory. The key findings are as follows: (1) Peripheral portfolios of low centrality stocks based on Pearson correlation network are the best in the long-term, achieving an extremely high cumulative return of around 3000% as well as high risk-adjusted return; (2) Markowitz portfolio is the safest in the long-term, while on the contrary, central portfolios of high centrality stocks based on Pearson correlation network are the riskiest; (3) In times of crisis, no portfolio is always the best. However, portfolios based on Transfer Entropy network perform better in most of the crises; (4) Portfolios of all stocks selected with centrality-based investing weights outperform in both short-term investing and intraday trading. A stock brokerage company may utilize the above findings of our work to enhance its portfolio management services. [ABSTRACT FROM AUTHOR]

Effective denoising strategies are increasingly important for portfolio investors. Considering that the common ensemble empirical mode decomposition (EEMD)-based stepwise denoising algorithms suffer from white noise interference and ignore the effect of low-frequency redundant noise components on the portfolio, a novel complete EEMD with adaptive noise (CEEMDAN) denoising algorithm is proposed to improve the portfolio performance. Specifically, we apply CEEMDAN to decompose noisy prices into a series of intrinsic mode functions (IMFs). Then, a series of tests based on the correlations between the original noisy prices and the decomposed IMFs are performed to identify which IMFs are noisy modes. If the tests accept the null hypothesis, the IMFs are considered as noisy components. Finally, we use the soft-threshold technique to process the noisy components and sum the non-noisy components to construct the denoised prices. The empirical results show that under the dynamic minimum-CVaR framework, the proposed CEEMDAN denoising algorithm is not affected by white noise and outperforms the EEMD denoising and stepwise denoising algorithms in improving out-of-sample portfolio returns. Overall, the proposed CEEMDAN denoising is the optimal denoising algorithm, which can help investors improve portfolio performance to the greatest extent. [ABSTRACT FROM AUTHOR]

This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk--return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies. [ABSTRACT FROM AUTHOR]

We investigate whether sophisticated volatility estimation improves the out-of-sample performance of mean-variance portfolio strategies relative to the naive 1/N strategy. The portfolio strategies rely solely upon second moments. Using a diverse group of portfolios and econometric models across multiple datasets, most models achieve higher Sharpe ratios and lower portfolio volatility that are statistically and economically significant relative to the naive rule, even after controlling for turnover costs. Our results suggest benefits to employing more sophisticated econometric models than the sample covariance matrix, and that mean-variance strategies often outperform the naive portfolio across multiple datasets and assessment criteria. [ABSTRACT FROM AUTHOR]

In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format. We derive a new f-Beta similar to the Standard Betas and also extended it to previous works in Drawdown Betas. The f-Beta evaluates portfolio performance under an optimally perturbed market probability measure, and this family of Beta metrics gives various degrees of flexibility and interpretability. We conduct numerical experiments using selected stocks against a chosen S&P 500 market index as the optimal portfolio to demonstrate the new perspectives provided by Hellinger-Beta as compared with Standard Beta and Drawdown Betas. In our experiments, the squared Hellinger distance is chosen to be the particular choice of the f-divergence function in the f-divergence induced risk measures and f-Betas. We calculate Hellinger-Beta metrics based on deviation measures and further extend this approach to calculate Hellinger-Betas based on drawdown measures, resulting in another new metric which is termed Hellinger-Drawdown Beta. We compare the resulting Hellinger-Beta values under various choices of the risk aversion parameter to study their sensitivity to increasing stress levels. [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 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]

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]

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]

This study is intended as a note and provides an extension to a much-used and established test for portfolio efficiency, the Gibbons, Ross, and Shanken GRS-Wald test. Tests devised to measure portfolio efficiency are crucial to the theoretical issues related to CAPM (Capital Asset Pricing Model) testing and have applications for the fund manager who seeks to rank portfolio performance. This study looks at the GRS-Wald test for portfolio efficiency and extends it to make it visually more interpretive without any loss of generality in its structure. The geometrically recast statistic draws upon the trigonometric properties of a portfolio in the mean-variance space and a mathematical proof of the equivalence of the two statistics is provided. The GRS-Wald test is a widely used statistic in studies addressing the issue of portfolio efficiency and CAPM deviations. A simulation demonstrates the use of the recast GRS-Wald test in testing for the mean-variance efficiency of a test portfolio. The study also provides a table of the GRS-Wald test, based on a range of mean-variance locations (cosine of portfolio angles) at which the test portfolio and the efficient market portfolio can be placed. [ABSTRACT FROM AUTHOR]

Advanced elastomers are increasingly used in emerging areas, for example, flexible electronics and devices, and these real‐world applications often require elastomers to be stretchable, tough and fire safe. However, to date there are few successes in achieving such a performance portfolio due to their different governing mechanisms. Herein, a stretchable, supertough, and self‐extinguishing polyurethane elastomers by introducing dynamic π–π stacking motifs and phosphorus‐containing moieties are reported. The resultant elastomer shows a large break strain of ≈2260% and a record‐high toughness (ca. 460 MJ m−3), which arises from its dynamic microphase‐separated microstructure resulting in increased entropic elasticity, and strain‐hardening at large strains. The elastomer also exhibits a self‐extinguishing ability thanks to the presence of both phosphorus‐containing units and π–π stacking interactions. Its promising applications as a reliable yet recyclable substrate for strain sensors are demonstrated. The work will help to expedite next‐generation sustainable advanced elastomers for flexible electronics and devices applications. [ABSTRACT FROM AUTHOR]

The performance of a financial portfolio depends on the output of two tasks: first, a forecasting process, where quantities of interest for the investors, such as the rate of return and risk for each stock, are predicted into the future, and second, an optimization process, where those individual stocks are formed into the portfolio optimizing the combined risk and reward features. However, in very large dimensions, when the number of stocks is high, those two quantitative problems often become intractable because of a loss in precision. This paper introduces a forecasting and portfolio formation strategy in multiple periods based on the splitting of the multivariate forecasting model into multiple bivariate forecasting models and updating investment weights at each period based on the predicted target quantities for the returns and the covariances. The methodology proposed is suitable for a very large portfolio of assets. The experimental results are based on a sample of one thousand stocks from the Chinese stock market. For such a large sample, the forecast and optimization process is executed speedily. The investment strategies are benchmarked with the equally weighted portfolio. In the long run, they offer a better investment performance in terms of a higher rate of return or lower risk, compared with this portfolio, demonstrating the applicability and economic value of the proposed methodology in practice. [ABSTRACT FROM AUTHOR]

Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance the two objectives. Erwin and Engelbrecht proposed a set-based approach to single-objective portfolio optimization, namely, set-based particle swarm optimization (SBPSO). SBPSO selects a sub-set of assets that form a search space for a secondary optimization task to optimize the asset weights. The authors found that SBPSO was able to identify good solutions to portfolio optimization problems and noted the benefits of redefining the portfolio optimization problem as a set-based problem. This paper proposes the first multi-objective optimization (MOO) approach to SBPSO, and its performance is investigated for multi-objective portfolio optimization. Alongside this investigation, the performance of multi-guide particle swarm optimization (MGPSO) for multi-objective portfolio optimization is evaluated and the performance of SBPSO for portfolio optimization is compared against multi-objective algorithms. It is shown that SBPSO is as competitive as multi-objective algorithms, albeit with multiple runs. The proposed multi-objective SBPSO, i.e., multi-guide set-based particle swarm optimization (MGSBPSO), performs similarly to other multi-objective algorithms while obtaining a more diverse set of optimal solutions. [ABSTRACT FROM AUTHOR]

GARCH-type models have been widely used for forecasting crude oil price volatility, but often ignore the structural changes of time series, which may lead to spurious volatility persistence. Therefore, this paper focuses on the smooth and sharp structural changes in crude oil price volatility, i.e., smooth shift and regime switching, respectively, and investigates which structural change based GARCH models have better performance for forecasting crude oil price volatility. The empirical results indicate that, first, the flexible Fourier form (FFF) GARCH-type models considering smooth shift can accurately model structural changes and yield superior fitting and forecasting performance to traditional GARCH-type models. Second, the Markov regime switching (MRS) GARCH model incorporating regime switching exhibits superior fitting performance compared to the single-regime GARCH-type models, but it does not necessarily beat the counterparts for forecasting. Finally, the FFF-GARCH-type models outperform MRS-GARCH for forecasting crude oil price volatility and portfolio performance. [ABSTRACT FROM AUTHOR]

We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA). For a given list of assets, the portfolio optimization problem is formulated as quadratic binary optimization constrained on the number of assets contained in the portfolio. QAOA has been suggested as a possible candidate for solving this problem (and similar combinatorial optimization problems) more efficiently than classical computers in the case of a sufficiently large number of assets. However, the practical implementation of this algorithm requires a careful consideration of several technical issues, not all of which are discussed in the present literature. The present article intends to fill this gap and thereby provides the reader with a useful guide for applying QAOA to the portfolio optimization problem (and similar problems). In particular, we will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. Viewing at the application of QAOA on error-prone NISQ hardware, we also analyse the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware). Finally, we define a criterion for distinguishing between 'easy' and 'hard' instances of the portfolio optimization problem. [ABSTRACT FROM AUTHOR]

We propose a novel portfolio evaluation method, a distance-based approach, which directly evaluates the portfolio composition rather than portfolio returns. In this approach, we consider a portfolio as an estimator for an in-sample tangency portfolio, which we define as the optimal reference portfolio. We then evaluate the portfolio by computing its vector distance to the optimal reference portfolio. In search of the proper distance-based performance measure, we choose four representative vector distances and compare their suitability as a new portfolio performance measure. Through extensive statistical analysis, we find that the Euclidean distance is the most proper distance-based performance measure of the four representative vector distances. We further verify that a portfolio with a large Euclidean distance is not desirable because not only does it provide a low utility implied by the first four moments of portfolio returns, but also it is not likely to maintain its long-term performance. Hence, the Euclidean distance can complement the return-based performance measures by confirming the reliability of a portfolio in its investment performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • A strong and room-temperature self-healing DCNC/50PEDA vitrimer is created; • The room-temperature self-healing efficiency of DCNC/50PEDA reaches 96.0%; • The tensile strength of DCNC/50PEDA is up to 36.0 MPa; • DCNC/50PEDA shows desired closed-loop recyclability; • DCNC/50PEDA features exceptional adhesion performances and fire retardancy. Epoxy vitrimers represent a new class of high-performance sustainable resins because of their desired mechanical and thermally malleable properties. Unfortunately, existing epoxy vitrimers cannot self-heal at room temperature (R.T.) due to the trade-off between mechanical robustness, recyclability, and the 'frozen' state of vitrimer networks at R.T. Herein, a high-performance hyperbranched epoxy vitrimer (DCNC/50PEDA) via curing bis(2,3-epoxypropyl) cyclohex-4-ene-1,2-dicarboxylate (DCNC) with 50 wt% of a phosphorus/silicon-containing polyethyleneimine (PEDA) at R.T., and the key to this design lies in rationally integrating complementary dynamic non-covalent hydrogen-bonding and π-π stacking and covalent β-hydroxy ester bonds into the high-mobility branched units of the DCNC/50PEDA network. This design endows the vitrimer with a room-temperature self-healing efficiency up to 96.0%, high mechanical strength reaching 36.0 MPa, and desired closed-loop recyclability. Moreover, its strong adhesion to a variety of substrates and exceptional fire retardancy, e.g., a limiting oxygen index of 39.0% and a desired UL-94 V-0 rating, make it an outstanding fire-retardant coating for flammable substrates, such as wood. Such a performance portfolio enables DCNC/50PEDA to outperform existing self-healing polymer and vitrimers counterparts. This work establishes a promising complementary dynamic design protocol for creating self-healing, strong, recyclable, and fire-safe polymers by integrating dynamic non-covalent interactions and covalent bonds, which hold great real-world applications in industries, such as bulk materials, coatings, and adhesives. [ABSTRACT FROM AUTHOR]

We study a class of optimal portfolio problems with relative performance and constant relative risk aversion (CRRA) risk preferences in a partially observable financial market. The price of a stock is described by a factor model and agents (investors or fund managers) make use of stock price data to estimate the factor process. All agents have CRRA (power or logarithmic) utility functions and they all want to maximize their utilities of above-average wealth and average wealth is the geometric mean of the overall wealth. We use a competition weight to control the tradeoff between absolute and relative performance. We consider both an n -agent game and the corresponding mean field game (MFG). For the finite population game, we give a linear feedback Nash equilibrium. For the MFG, we obtain a linear feedback mean field equilibrium (MFE). Our results show that the limiting strategy for the finite population game can be derived as equilibrium of suitable MFG for CRRA case in a partially observable financial market. • We build two novel models, the n-agent game model and the mean field game model, for competitive optimal portfolio problems with partial information. • We clear up two kinds of optimization problems with partial information by virtue of state decoupling and measure change. • We gain the linear feedback Nash equilibria for the n-agent game and the mean field game. [ABSTRACT FROM AUTHOR]

We discuss a continuous-time portfolio optimization problem to beat a stochastic benchmark. The model considers non-linear stochastic differential equations (SDEs) to model the dynamics of assets and economic factors. Unlike existing literature on risk-sensitive criteria, the proposed framework allows the model to capture the non-linearity in assets and factors dynamics. This article contributes to the two essential aspects of the problem; first, the existence and uniqueness of optimal investment strategy, which we prove using stochastic control theory and shows optimal strategies remain unchanged for the finite and infinite time horizon problems. Second, we use forecasting to compare investment performance under different economic factors. We analyze returns for the proposed model for three years in two important financial markets; S&P 100 and Dow 30. Risk versus return analysis indicates the importance of choosing relevant economic factors and their model for better portfolio performance. The results suggest an excellent bet to select the non-linear model over the linear one. • This article examines a benchmarked asset management problem using a non-linear framework, assuming that the residual of economic factors and asset returns are uncorrelated. • The study demonstrates that the optimal strategies remain invariant, regardless of whether they are applied to risk-sensitive problems over finite or infinite time horizons. • The work also evaluates the performance of optimal investment strategies under various economic factors through forecasting. • The empirical analysis covers three years and includes two major financial markets: the S&P 100 and the Dow 30. • The risk-versus-return analysis emphasizes the critical importance of selecting relevant economic factors and their corresponding models. [ABSTRACT FROM AUTHOR]

Portfolio management (PM) is a popular financial process that concerns the occasional reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of maximizing profitability conditioned to a certain level of risk. Given the inherent dynamicity of stock exchanges and development for long-term performance, reinforcement learning (RL) has become a dominating solution for solving the problem of portfolio management in an automated and efficient manner. Nevertheless, the present RL-based PM methods just take into account the variations in prices of portfolio assets and the implications of price variations, while overlooking the significant relationships among different assets in the market, which are extremely valuable for managerial decisions. To close this gap, this paper introduces a novel deep model that combines two subnetworks; one to learn a temporal representation of historical prices using a refined temporal learner, while the other learns the relationships between different stocks in the market using a relation graph learner (RGL). Then, the above learners are integrated into the curriculum RL scheme for formulating the PM as a curriculum Markov Decision Process, in which an adaptive curriculum policy is presented to enable the agent to adaptively minimize risk value and maximize cumulative return. Proof-of-concept experiments are performed on data from three public stock indices (namely S&P500, NYSE, and NASDAQ), and the results demonstrate the efficiency of the proposed framework in improving the portfolio management performance over the competing RL solutions. [ABSTRACT FROM AUTHOR]

The significance of ESG aspects is steadily increasing in directing investment decisions. Achieving financial returns and coordinating investments with more societal objectives requires incorporating ESG factors into portfolio management techniques, but current investor preferences and market developments require ESG-integrated approaches. This study presents a novelty MS-ESGI model that enables dynamic portfolio adjustment through the integration of PSO-GBM predictions for stock prices with ESG criteria model deployed by Amazon sage maker, as well as continuous risk-return optimization through the use of extended MVO model-I and RPO model-II with ESG investing. According to the research findings, the recommended models perform better than other models in terms of volatility, Sharpe ratio, AUC, MAE, RMSE, accuracy, return, and ESG score. With an accuracy of 96%, an RMSE of 0.109, an AUC of 0.9637, and an MAE of 0.0805, the PSO-GBM + MVO model does exceptionally well in the prediction model comparison. PSO-GBM + MVO obtains a return of 39.69%, volatility of 14.23%, Sharpe ratio of 2.8651, and an ESG score of 95 in terms of portfolio performance. It outperformed other models in terms of performance, financial incentives, and forecast accuracy. [ABSTRACT FROM AUTHOR]

Choosing the greatest investment possibilities to maximize the value of one’s stock investment has always been one of the primary concerns of investors. However, issues like human rights, and business ethics, are at the forefront of the public and political conversation in today’s world of globalization and interdependence. Many financial experts, policymakers, and economic and social researchers view Islamic finance as a potential solution to prevent future crises. Consequently, there is a need for new models to facilitate this investment approach. In this study, we propose a new hybrid approach to portfolio selection which combines multiple methodologies like investor topology, cluster analysis, preference modeling and fuzzy multi-objective programming strategy. The proposed model is applied on 9 Tunisian mutual funds including conventional and ethical/Islamic funds in Tunisian financial market. The findings reveal that higher expected losses and greater proportions of ethical or Islamic stocks in a portfolio translate into better portfolio performance as measured by the Sharpe ratio. These findings confirm our hypothesis that ethical investments should be taken into account when developing investment strategies for conventional investors. [ABSTRACT FROM AUTHOR]

One of the basic preconditions of establishing dynamic economic development is founding a stable and contemporary banking system. Namely, the commercial banks are the backbones of the financial system of each country, especially in developing countries such as North Macedonia where the capital market is in initial phase of development. Consequently the banks' loans are the main source for financing the businesses which in turn contributes to the economic development of the countries. Loans play dominant role in banks' activities and they are related with credit risk. Therefore, the banks usually want it to be a performing one, since its nonpayment leads to incidence of huge loss. Consequently, the quality of credit portfolio determines the performance of the banks and it is a crucial factor affecting the profitability of individual banks and the entire banking sector. In the relevant literature, one of the most commonly used proxies of banks' credit portfolio quality is non-performing loans ratio (NPLs). Regarding this, the main objective of the paper is to investigate the relationship between the non-performing loans and banks' profitability in the Macedonian Banking System. [ABSTRACT FROM AUTHOR]

In this study, 5 year (2016-2020) financial performance rankings of companies traded in the Textile, Leather Index in Borsa Istanbul (BIST) are evaluated. The weights of the performance evaluation criteria are obtained with the Fuzzy PIPRECIA method, and the financial performance rankings are obtained with the MARCOS method. 2 portfolios are created for each year and are analyzed with 5 performance criteria. The results reveal that the high performance portfolio A, created according to the MARCOS rankings outperforms portfolio B from 2017 to 2019. When only portfolio returns are considered, portfolio A has a higher return from 2016 to 2020. [ABSTRACT FROM AUTHOR]

Purpose – This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach. Design/methodology/approach – The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of timeseries momentum portfolios. Findings – The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments. Practical implications – The study’s outcomes offer valuable insights for professional investors, capital market regulators and asset management companies. Originality/value – This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach. [ABSTRACT FROM AUTHOR]

In the Indian context, we have applied the Sharpe Single Index model with Elton's portfolio optimization framework and Markowitz Portfolio Theory framework for optimizing intra and inter‐sectoral equity portfolios. Literature provide less evidence on the inter and intra‐sector relationships to which we contribute. We compute 11 sector‐specific optimal portfolios and one inter‐sector optimal portfolio using the NSE sectoral indices like automobiles, banking, financial services, FMCG, IT, media, metals, pharmaceuticals, private banks, PSU banks, realty, and broad‐market inter‐sector index Nifty50. We use the month‐end adjusted closing returns data comprising 15,834 observations from 191 scrips during 2013 to 2019. We create in‐sample optimal portfolios and out‐sample mimic portfolios under the Sharpe and Markowitz models for comparison and validation with optimized weights. Our results show that inter‐sector portfolio performs better than some of the intra‐sector portfolios. Also, the intra‐sector portfolios like realty, metals, information technology, and media generated higher returns with relatively moderate or less risk than their sectoral peers and benchmark indices. We found that Sharpe's model performs better than Markowitz's model. We validated our findings with statistical testing. Thereby, we documented the applicability of the Sharpe Single Index for portfolio optimization in the Indian context. Our research has several implications. Fund managers can make use of our optimal portfolio performance matrix to recommend portfolios to their clients. With our optimized portfolio weights, investors can make sector‐level or inter‐sector investments. Regulators can get a synoptic view of the performance of industry‐level portfolios. Researchers can extend the literature by improvising our portfolio optimization procedure. [ABSTRACT FROM AUTHOR]

This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies' moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks' moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors' knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks' moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading. [ABSTRACT FROM AUTHOR]

We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences, and constraints beyond simple expected returns, within a flexible, forward-looking, and non-linear framework. Our empirical analysis illustrates that predicting the optimal weights directly instead of the traditional two-step approach leads to more stable portfolios with statistically better risk-adjusted performance measures. [ABSTRACT FROM AUTHOR]