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Financial markets are still exposed to various crises that increase stock price fluctuations and make predictions extremely difficult (Nguyen et al., 2024). Although there are many diversification methods for building investment portfolios, there has been no agreement on the best of them. This research aims to identify the most effective strategy for constructing an optimal investment portfolio by comparing the mean-variance (MV) model and risk-parity (RP) contribution strategies during the uncertain market period 2018–2022. The study used a quantitative and an optimization method following Ofikwu (2019) based on three critical criteria: 1) low asset correlation, 2) the highest Sharpe ratio, and 3) a mixed strategy for a sample of firms listed in the Amman Stock Exchange (ASE). The results show that the MV model has the highest Sharpe ratio (15.06 percent and 11.84 percent) when applied to the Sharpe and mixed strategies respectively. In comparison, using the low correlation strategy the RP model gains the highest Sharpe ratio (14.92 percent). During COVID-19, all portfolios had a higher positive return and lower total risk than the market portfolio. Both models are valid to be used during uncertain conditions. It highlights the effectiveness of strategies in navigating challenging market conditions and offers practical guidance for investors in uncertain times for asset allocation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p / n → c ∈ (0 , 1). A good performance of the theoretical findings is documented in the simulation study. In an empirical study, we apply the theoretical results to real data of the stocks included in the S&P 500 index. [ABSTRACT FROM AUTHOR]

Asset allocation and investment times are two correlated aspects of investments; however, most research on investment decisions focuses on asset allocation with investment times fixed. This study aims to provide methods to optimize both asset allocation and liquidation time. We use value at risk (VaR), the most widely used risk indicator in the financial industry, to measure the risk of an investment, and construct an investment decision model for risk-oriented investors, which is continuous-time VaR minimization. We adopt a statistical method to estimate VaR that avoids assumptions that are difficult to verify; the corresponding continuous-time VaR minimization model becomes unsolvable with conventional optimization methods. We first suggest an approach to solve the model by solving a sequence of discrete-time VaR minimization models. This approach leads to the optimal solution under some conditions but yields an approximation to the optimal in general. Solving more discrete-time VaR minimization models produces a better result but has an additional computational burden. To improve the efficiency of the optimality-seeking process, we propose a novel strategy to reduce computing load significantly. We examine the reliability of the strategy by conducting computation experiments with actual data from a stock market. [ABSTRACT FROM AUTHOR]

This study aims to improve asset allocation by combining traditional finance theory and behavioral finance. The authors construct efficient portfolios using modern portfolio theory (MPT) and identify an appropriate portfolio for a particular investor through a risk assessment score (RAS). The identified portfolio is not only optimal but also suitable based on the investor's risk profile. An RAS questionnaire is designed to produce an additive score in the range of 0 to 100 (i.e., a percentage) that maps linearly to the range of the tangency slope. Their framework is highly flexible and cost effective in terms of both capital market assumptions and the delineation of investor preferences. To emphasize the flexibility of the process, they provide an example questionnaire for retail investors in India, as well as an illustration of a three-asset-class efficient frontier. They also briefly discuss the ability of mapping responses to qualitative inquiries to the tangency slope in the allocation decisions of institutional investors (e.g., defined-benefit pension funds). [ABSTRACT FROM AUTHOR]

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

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

We propose an industry volatility spillover index and find that this new predictor has an impressive in-sample and out-of-sample predictability of aggregate stock returns. A one-standard-deviation increase in this index leads to a 4.14% increase in the expected excess return over the next month. Furthermore, a mean-variance investor can realize sizable economic gains by using this spillover index in his/her asset allocation. The forecasting power of the spillover index remains significant after controlling for popular economic variables and newly proposed predictors. Due to the absence of learning, the spillover index shows increasing predictive ability over the recent period. The economic origins of the spillover index's success stem from both the cash flow and discount rate channels, as well as significant association with investor sentiment and tail risk. The return predictability of this index is pervasive across characteristic-sorted portfolios and is particularly strong for large-cap or difficult-to-value stocks. [ABSTRACT FROM AUTHOR]

We examine the international impact of recent financial crises on contagion dynamics within international equity portfolios. First, we highlight the importance of macroeconomics for portfolio weighting for each region, and then we examine contagion via a structural regime-switching model and a contagion test. We also examine sources of contagion using regime variables, crisis events, and macroeconomic variables. In particular, we study the Argentine debt crisis, the US financial crisis, and the EU sovereign debt crisis. The macroeconomic variables include changes in market capitalization, trade integration, GDP growth, inflation rate, and interest rate. We also employ two classifications, one relating to the portfolio weighting scheme and another one that considers implied global and regional betas. The empirical findings reveal the existence of financial contagion for all the crises that we investigate. Both methods produce similar results. Stronger contagion is evident for global rather than regional betas. Europe is the region with the highest level of contagion and the one mostly affected by the crises. As far as macroeconomic variables are concerned, they are very important in two ways. They statistically significantly explain contagion, while they also reveal contagion under various portfolio weighting schemes. Both methods suggest that the Argentinian crisis mainly contributes to contagion. The research implications suggest that asset allocation and portfolio management should consider both the global and the regional aspects of contagion as differences can occur. [ABSTRACT FROM AUTHOR]

Allocation of investment assets is key in attaining a sustainable retirement portfolio. In this research article, the authors analyzed the most recent research publications in the area related to asset allocation for retirement and identified those which have the highest impact. The authors' research was conducted using the bibliometric analysis technique of research articles collected from the Scopus database. Most of the research articles were published in reputed journals in the United States, United Kingdom, Australia, and Germany. It was also observed that most of the highly cited research articles in the research area of asset allocation for retirement are focused on financial literacy, increase in retirement age, aging, and pension reforms. The authors' findings identified six research themes in asset allocation for retirement such as 1) asset allocation for retirement planning, 2) methods to increase efficiency, 3) investment preferences for retirement savings 4) financial literacy and retirement planning, 5) reforms on retirement savings, and 6) annuities for retirement income. Furthermore, nineteen future research directions are also provided. In conclusion, the authors aim to assist future researchers in identifying highly cited articles, key authors, contributing countries and research themes in asset allocation for retirement. Overall, the analysis provides comprehensive information in addressing research questions in the field of asset allocation for retirement. [ABSTRACT FROM AUTHOR]

We study how to best combine the sample mean-variance portfolio with the naive equally weighted portfolio to optimize out-of-sample performance. We show that the seemingly natural convexity constraint—the two combination coefficients must sum to one—is undesirable because it severely constrains the allocation to the risk-free asset relative to the unconstrained portfolio combination. However, we demonstrate that relaxing the convexity constraint inflates estimation errors in combination coefficients, which we alleviate using a shrinkage estimator of the unconstrained combination scheme. Empirically, the constrained combination outperforms the unconstrained one in a range of generally small degrees of risk aversion, but severely deteriorates otherwise. In contrast, the shrinkage unconstrained combination enjoys the best of both strategies and performs consistently well for all levels of risk aversion. [ABSTRACT FROM AUTHOR]

This article, titled "A Comment on: 'Presidential Address: Economics and Measurement: New Measures to Model Decision Making' by Ingvild Almås, Orazio Attanasio, and Pamela Jervis," discusses the use of economic theory to develop new measurement tools and how the resulting data can be used to further develop economic theory. The author agrees with the address and focuses on the study of how economic agents perceive the economic environment. The article provides a concrete example of learning about a household's coefficient of relative risk aversion and discusses different approaches to measuring this parameter. It concludes by highlighting the importance of understanding how economic agents perceive their environment in order to make the best use of measurement tools. [Extracted from the article]

Digital financial inclusion has become an important way to reduce poverty and prevent poverty return; however, few studies examine the relationship between digital financial inclusion measurement with poverty return governance. Based on data from the 2017 China Household Financial Survey, we construct a digital financial inclusion indicator for micro‐households, and explore its impact on the risk of households returning to poverty and its mechanisms. Our findings suggest that digital financial inclusion can reduce the risk of Chinese families returning to poverty, and that it has heterogeneous effects on families and regions with different characteristics. The main function is to improve household income level by promoting entrepreneurship and employment, and to improve risk resistance by enhancing household financial market participation and household asset allocation. Further analysis shows that digital financial inclusion has structural effects, nonlinear effects, and substitution effects with private lending in poverty governance. This paper has implications for understanding and improving the poverty governance effectiveness of digital financial inclusion. [ABSTRACT FROM AUTHOR]

Given limited institutional resources, low-income populations often rely on social networks to improve their socioeconomic outcomes. However, it remains in question whether small-scale social interactions could affect large-scale economic inequalities in under-resourced contexts. Here, we leverage population-level data from one of the poorest South African settings to construct a large-scale, geographically defined, inter-household social network. Using a multilevel network model, we show that having social ties in close geographic proximity is associated with stable household asset conditions, while geographically distant ties correlate to changes in asset allocation. Notably, we find that localised network interactions are associated with an increase in wealth inequality at the regional level, demonstrating how macro-level inequality may arise from micro-level social processes. Our findings highlight the importance of understanding complex social connections underpinning inter-household resource dynamics, and raise the potential of large-scale social assistance programs to reduce disparities in resource-ownership by accounting for local social constraints. Yu and colleagues leverage population-level data to construct a large-scale, geographically defined, inter-household social network. Using a multilevel network model, they show that having social ties in close geographic proximity is associated with stable household asset conditions, while geographically distant ties correlate to changes in asset allocation. Notably, they find that localised network interactions are associated with an increase in wealth inequality at the regional level, demonstrating how macro-level inequality may arise from micro-level social processes. [ABSTRACT FROM AUTHOR]

The 2015 Paris Agreement has set out the climate change target of limiting global warming to 1.5 °C, which poses a serious challenge to countries to reduce emissions. As the world's largest carbon emitter, promoting the realization of the "dual-carbon" goal is the key to realizing China's green transformation and high-quality development. Chinese asset managers play active roles in the capital market as an important channel of asset allocation. Currently, the vast majority of Chinese asset managers hold high percentages of high-carbon industries in their portfolios, and lack quantitative data of their carbon footprints embodied in equity investments, which faces huge carbon-related risks. Therefore, it's an urgent need to comprehensively and scientifically measure financed emissions of Chinese asset managers, which is of great significance for asset managers' carbon risk management and sustainable investment. This paper develops a detailed inventory of carbon emissions for equity portfolios managed by Chinese asset managers from 2010 to 2020, which stands as a pivotal reference for in-depth analysis of emission characteristics. [ABSTRACT FROM AUTHOR]

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations. [ABSTRACT FROM AUTHOR]

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Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. We formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. To obtain strategies under the bounded leverage constraint among other realistic constraints, we propose a novel leverage-feasible neural network (LFNN) to represent the control, which converts the original constrained optimization problem into an unconstrained optimization problem that is computationally feasible with gradient descent, without dynamic programming. We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. We further validate the performance of the LFNN empirically by deriving a closed-form solution under jump-diffusion asset price models and show that a shallow LFNN model achieves comparable results on synthetic data. In the case study, we apply the LFNN approach to a four-asset investment scenario with bootstrap-resampled asset returns from the filtered high inflation regimes. The LFNN strategy is shown to consistently outperform the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the end of the investment horizon. [ABSTRACT FROM AUTHOR]

In order to deal with the aging problem, the pension system is actively transformed into the funded scheme. However, the funded scheme does not completely replace PAYGO (Pay as You Go) scheme and there exist heterogeneous mixes among PAYGO, EET (Exempt, Exempt, Taxed) and individual savings in different countries. In this paper, we establish the optimal mix by solving a Nash equilibrium between the pension participants and the government. Given the obligatory PAYGO and EET contribution rates, the participants choose the optimal asset allocation of the individual savings and the consumption policies to achieve the objective. The results extend the 'Samuelson-Aaron' criterion to age-dependent preference orderings. Under the baseline model, we identify three critical ages to distinguish the multiple outcomes of preference orderings based on heterogeneous characteristic parameters. The government is fully aware of the optimal feedback of the participants. It chooses the optimal PAYGO and EET contribution rates to maximize the overall utility of the participants weighted by each cohort's population. As such, the negative population growth rate leads to the decline of the PAYGO attractiveness as well as the increase of the older cohorts' weight in the government's decision-making. The optimal mix is the comprehensive result of the two effects. [ABSTRACT FROM AUTHOR]

Mutual fund investments are important if you want to build yourself financially in the long term. A mutual fund is a financial vehicle that is composed of money collected from investors for investing in bonds, stocks, and other assets. These are operated by professional oney managers who allocate fund assets to produce gains for fund investors. This article will help your journey of mutual fund investments. [ABSTRACT FROM AUTHOR]

We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individual variables for forecasting sector returns and analyze multivariate predictive regression models, including OLS, regularized regressions, principal component regressions, the three-pass regression filter, and forecast combinations. Using an out-of-sample Black–Litterman portfolio optimization framework and employing predicted returns as investors' 'views', we evaluate the benefits of sector return forecasts for investors. We find that portfolio optimization with sector return prediction models significantly outperforms portfolios using historical averages as well as passive benchmark portfolios. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine whether good asset allocation by a CEO leads to superior stock returns and, if so, how one might be able to identify CEOs that are good asset allocators. Employing US data from May 2001 to April 2019, we find that CEOs that invest the company's cash flows according to a value-investing style seem to outperform companies that do not. We find that high goodwill to assets and high operating margin (good asset allocator) companies outperform companies with high or low goodwill to assets and low operating margin (poor asset allocator) companies. The findings are corroborated with out-of-sample (May 2019–April 2023) robustness tests. When buying other businesses, value investor CEOs ensure that their consolidated operating margins remain high, as opposed to other firms managed by poor asset allocator CEOs who buy businesses that bring down operating margins, either because they overpay or due to an inability to materialize expected synergies. Using both summary statistics and regression analysis, the findings of this study help us identify companies that allocate assets like value investors and enable us to anticipate future stock performance. For example, if a company, on average, has a goodwill/assets ratio of 41.03%, and an operating margin of 21.38%, it is likely this firm would be at the top quartile in terms of stock return performance over at least the next three years. At the same time, if a firm has a low average goodwill/assets ratio (i.e., 1.95%), its operating margins, on average, should be 24.46%, if it wants to achieve a similar performance as that of firms with high goodwill/assets. Moreover, the future stock return predictability of high (low) goodwill/assets and high (low) operating margin firms, found in this study, can help an investor develop trading strategies that can lead to superior stock price performance by effectively taking long positions in (shorting) firms that are (not) managed by value investor CEOs. Finally, the paper's findings can also help investors in another way. For example, investors tend to be skeptical about companies with high goodwill/assets. The rule of thumb is to beware of companies carrying goodwill on their balance sheets that is more than 25% of assets. Based on our findings, this should not be a problem as long as the company's operating margin has remained high and is rising. [ABSTRACT FROM AUTHOR]

The aggregate portfolios used by end-clients (e.g., pension, endowment, or retired couple) are most often broadly diversified across stocks, bonds, and alternatives. Such portfolios are constructed and maintained using a widely accepted portfolio construction approach. This approach is frequently shorthanded as "modern portfolio theory" or MPT. A simplified version of this approach assumes random variable distributions (to represent the future returns for each asset category) and associated defining summary statistics (means, standard deviations, and correlations). This is the approach upon which all retail investment software tools are based and underlies most institutional asset allocation and asset/liability modeling efforts. For this approach to be successful, the statistical behavior of the underlying asset categories must satisfy certain behavioral properties. Specifically, (a) means must be sufficiently stable, (b) return distributions must not suffer from regime change, (c) distributions must not deliver risk without return over excessively long time periods, (d) the implementor cannot ex-post cherry pick the best geography and most attractive time period and then base the results on that after-the-fact handpicked-selection, (e) return distributions must not be defined by extreme episodic eras, and (f) excessive levels of trending and momentum must not be present. The objective of this article is to provide meaningful statistical evidence that each of these six requirements is violated. If true, then our industry's generally accepted portfolio construction process is suspect. The article ends with three suggestions for portfolio construction techniques that are generally immune to the issues raised and suggests why forward-looking capital market assumptions are unable to overcome the challenge. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature on carry trade by investigating the dynamic correlation and the dependence structure between the US-dollar carry trade and equity markets in the (Brazil, Russia, India, China and South Africa (BRICS)) economies during sample observations that include regular and crisis periods. Furthermore, the nonlinear Granger causality test based on the feed-forward neural networks (FFNN) model assesses how global volatility predicts the dynamic correlation between the US-dollar carry trade and equity markets in BRICS. The paper finds the dynamic correlations between carry trade and equity markets in BRICS are more pronounced during most global crises. Moreover, the results of the symmetrised Joe Clayton (SJC) copula model showed that the lower tail dependence between the two series is higher during the various crises. Furthermore, the results of the empirical analysis show that global volatility predicts the dynamic correlations between carry trade and equity markets in BRICS only during crises. Asset managers and investors can benefit from this paper's findings regarding portfolio diversification, risk management, asset allocation, and hedging when dealing with equity assets and carry trades. [ABSTRACT FROM AUTHOR]

The authors estimate the alpha and beta of private equity investments using their desmoothed quarterly returns. Although computing the alpha and beta using a lagged linear regression between private and public market returns is straightforward, reconstructing desmoothed private market returns is trickier. A time series of desmoothed private capital returns has application in asset allocation, risk modeling, and return attribution. The authors use a state space modeling framework in which the desmoothing and the parameter estimation are done simultaneously using Bayesian inference. This is likely the first attempt to solve a full Bayesian model that can simultaneously estimate a desmoothed time series of returns as well as perform a lagged linear regression to estimate capital asset pricing model (CAPM) style parameters for private capital. The authors perform extensive model checking, look out for the model's convergence, and resolve any issues leading to overfitting. The estimates are precise with very low Monte Carlo simulation errors and are also in alignment with those in the literature, which validates this model and desmoothing technique. [ABSTRACT FROM AUTHOR]

The growing trend of sovereign wealth and pension funds to allocate more toward private investments has made the management of asset allocation more complex. Traditional rebalancing methods, such as fixed-weights rebalancing, encounter problems when applied to private assets, as their illiquidity and lags in appraisal valuations pose challenges. During financial crises, the delayed and smoothed valuations of private assets lead them to be overweight in portfolios as public assets decline in values. Rebalancing the underweight public assets can increase leverage usage and, more importantly, deteriorate the fund's liquidity position. To address these challenges, this article proposes a holistic rebalancing strategy: rebalance a portfolio to the desired factor allocation by complementing the factor exposures of existing private assets with an allocation to public assets that overall delivers the required factor allocation. This approach safeguards the liquidity position of a fund during market downturns by maintaining a stable risk and leverage profile. It presents a dynamic and risk-aware approach for rebalancing portfolios with private assets. [ABSTRACT FROM AUTHOR]

This article begins with a discussion of private fund performance metrics and turns to the closely related topic of performance benchmarking, a crucial issue in light of the widely held belief that private equity and venture capital investments outperform publicly traded assets. The authors then discuss how to estimate the alpha and beta of private investment funds and move onto a second set of topics concerned with portfolio construction and risk management. They examine the distorting effects that serially correlated return streams can have on risk analysis and asset allocation decisions and finally conclude with a discussion of practical remedies that allocators can employ to mitigate the risks of owning long-dated illiquid assets. [ABSTRACT FROM AUTHOR]

Researchers have studied factors that influence the sustainability of retirement withdrawals (e.g., withdrawal rate, withdrawal rules, volatility, asset allocation, taxes, longevity) for 30 years. The frequency of withdrawal patterns (e.g., annual, quarterly, monthly) has escaped inquiry. This study uses Monte Carlo simulation to show that, despite intuitive reasons to believe that dividing retirement withdrawals into smaller amounts over more frequent intervals might control volatility or sequence of return risk, withdrawal frequency has no effect on retirement withdrawal sustainability. This result is robust to simulated markets characterized by: (1) a random walk, (2) simulated markets that are autocorrelated, (3) historical returns series randomly chosen from historical return records, and (4) historical returns in their original sequence. It also highlights factors (e.g., time in market, matching withdrawals to spending patterns, and maximizing optionality) that can enhance value or increase retiree utility. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic return connectedness among Decentralized Finance (DeFi) assets [Chainlink (LINK), Maker (MAKER), Basic Attention Token (BAT), and Synthetix (SNX)] and Centralized Financial assets (CeFi), specifically MSCI World, MSCI US, MSCI Europe, MSCI East Europe, MSCI Asia, and MSCI Far East. The Time-Varying Parameter Vector Autoregressions (TVP-VAR) framework is adopted. Our research findings demonstrate that economic events create variations in the dynamic connectedness of a system. There were peaks throughout the COVID-19 pandemic. Furthermore, the results indicate that DeFi assets like BAT and LINK are the most common shock transmitters, while MAKER and SNX are the most common shock receivers. Nonetheless, the pairwise connectedness for LINK and MSCI Far EAST (FarEAST) shows that may not only influence other markets but are also equally receptive to innovations that occur in the majority of these markets due to their high pairwise links. The retrieved evidence has major implications for investors in terms of the creation of strategies for the optimization of portfolios and asset allocation, the minimizing of downside risk, and hedging techniques. [ABSTRACT FROM AUTHOR]

Using the US Survey of Consumer Finances, we explore the empirical relationship between borrowing constraints and financial portfolio allocation by American households. To help motivate our empirical analysis we initially draw insights from a mean-variance model of optimal portfolio allocation with three tradable asset classes defined by increasing risk, and establish a link between borrowing restrictions and portfolio allocation in the presence of background risk. Our main contribution, however, lies in estimating the role that borrowing constraints play in shaping households' financial portfolio allocation. This is achieved using an ordered fractional probit model. In addition to revealing the significant empirical role played by household borrowing constraints in determining portfolio allocation, our analysis helps us to resolve ambiguity surrounding the behaviour of the medium-risk asset in our motivational theoretical framework. Further, the empirical distribution of medium-risk assets is found to be remarkably similar to that for high-risk assets, suggesting the presence of a more general 'risk puzzle', which our borrowing constraints measures partially explain. [ABSTRACT FROM AUTHOR]

This article introduces a practical method to maximize expected after-tax wealth and to fine-tune risk management utilizing linear programming instead of traditional Markowitz mean-variance optimization (MVO). Our simpler approach simultaneously optimizes asset allocation and asset location while considering capital gain impacts at the tax lot level. It is designed to be applied to portfolios individually. The approach's primary benefits include: explicit tax consideration across multiple types of accounts and tax-lot analysis within the optimization model, the ability to control risk under a variety of scenarios, clearer client communication via explicit quantification of the change in expected future after-tax wealth and the performance of the portfolio under selected scenarios, elimination of model-based and rule-of-thumb allocations, and better portfolios through customization. The technique can both identify a client's risk budget and rebalance portfolios. [ABSTRACT FROM AUTHOR]

In this paper, the impact of financial asset allocations on digital transformation is empirically explored by selecting a sample of 1,263 Chinese A‐share manufacturing enterprises. In addition, the maturity difference, moderating effect, and heterogeneity of financial assets are analyzed. The results show an "inverted U‐shaped" correlation between financial asset allocations and the digital transformation of enterprises. The short‐term financial assets provide a "reservoir" for the application of digital technology, while the long‐term financial assets cause a crowding‐out effect. Moreover, economic policy uncertainty exerts a moderating effect and enhances the positive impact of financial asset allocations on digital transformation. [ABSTRACT FROM AUTHOR]

This paper examines the potential profit of bull flag trading rules in the Shanghai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shanghai Stock Exchange Composite Index (SSE) during the period of 1991–2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions. [ABSTRACT FROM AUTHOR]

In this manuscript, we consider a risk-preference investor allocating some amount of capital to the dependent risky asset, where the responding asset will occur default if the stochastic return is less than some predetermined threshold. Then, we present sufficient conditions of the increasing convex order on capital allocation with dependent risky assets when the stochastic return is right tail weakly stochastic arrangement increasing. Finally, some numerical examples are given as illustrations.

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models.