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Collecting large asset managers' capital market assumptions, we revisit the relationships between subjective equity premium expectations, equity valuations, and financial portfolios. In contrast to the well-documented extrapolative expectations of retail investors, asset managers' equity premium expectations are countercyclical: they are high (low) when valuations are low (high). We find that asset managers' portfolios reflect their heterogeneous expectations: allocation funds of asset managers with larger U.S. equity premium expectations invest significantly more in U.S. equities. The sensitivity of portfolios to expectations seems to be muted by investment mandates and is smaller than the one predicted by a standard portfolio choice model. [ABSTRACT FROM AUTHOR]

We study the impact of monetary policy changes on portfolio decisions of U.S. households. Contrary to the predictions of canonical portfolio choice models, interest rate increases are related to increased equity ownership and larger wealth allocations to risky assets. Inflation hedging is a likely explanation for these findings. Interest rates have a stronger impact on the equity exposure of households that experience higher inflation, especially those with greater inflation awareness and potentially stronger hedging motives. Using household portfolios at a large discount brokerage house, we provide direct evidence that investors with greater inflation sensitivity overweight stocks with high hedging potential. This paper was accepted by Camelia Kuhnen, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.02197. [ABSTRACT FROM AUTHOR]

Life cycle asset allocation has recently gained popularity and is the default option in pension funds worldwide. A line of recent studies has refuted its glory and argues that a balanced fund with a fixed allocation throughout the investment horizon may yield better results. The question arises from these studies: What should be the balanced fund's optimum asset allocation weight? Therefore, the present study uses Genetic Algorithm to obtain the optimum asset allocation weight for India's defined contribution pension plan subscribers. To validate our result, the study compares the results of a Genetic Algorithm to the outcomes of widely used asset allocation techniques, notably the life cycle and equally weighted strategies. This study holds important policy implications as, given the size of the pension asset and the long investment duration, even a trivial increase in return will substantially impact investment outcomes, affecting the subscribers' well-being in old age. [ABSTRACT FROM AUTHOR]

We study the fundamental properties of the "Buy the dip" (BTD) investment heuristic. Looking into cash holdings versus a stock market exchange‐traded fund, we find that BTD does not necessarily maximize investors' real terminal wealth and is sensitive to market conditions at the beginning year of investment. While under certain conditions, BTD may improve risk‐adjusted performance over a passive investment policy or a classical dollar‐cost averaging approach, its optimality is subject to estimation risk. Given the vast popularity of BTD, our results have important implications for asset managers and retail investors alike. [ABSTRACT FROM AUTHOR]

The paper investigates the intricate relationship between oil and stock markets in the context of different oil price shocks and a country's energy profile. Focusing on five major oil-producing and consuming countries, the analysis reveals a significant, time-varying dimension in the oil-stock relationship. It highlights the importance of distinguishing between various types of oil shocks and a nation's reliance on oil revenues. The paper finds that countries heavily reliant on oil revenues exhibit robust responses to oil supply and demand shocks. In contrast, oil producers show more moderate reactions due to lower oil revenue dependence. This evidences the importance of recognizing the multifaceted impacts of oil shocks on different economies for making well-informed decisions in asset allocation, portfolio management, and policymaking. [ABSTRACT FROM AUTHOR]

A goals-based methodology built on an intertemporal CAPM (ICAPM) asset allocation framework provides a goal-relative definition of risk for portfolio selection and risk management, efficient capital allocation to fund goals, and efficient goal-aligned subportfolios that accrue to an efficient total portfolio. It offers an elegant goals-based solution for both high-net-worth investors with significant financial assets and retirement accumulators with significant human capital. [ABSTRACT FROM AUTHOR]

As another US presidential election approaches, investor interest in the impact of these elections on asset returns and the existence of a presidential puzzle reemerges. To help investors decide on whether or not they should consider presidential elections and related criteria in making tactical asset allocation decisions, we update prior literature to include more recent elections and we directly test for differences in real returns on various asset classes. Considered independently, there is evidence that the party of the President, political gridlock, and monetary policy continue to have a relationship to stock, bond, and T-Bill returns that continues to diminish over time. Examining these relationships in a simultaneous, multivariate setting yields similar results. In an efficient frontier portfolio setting, Democrats provide investors with better risk-return tradeoffs than Republicans for stock, government bond, and T-Bill allocations with higher allocation toward equities, but Republicans have provided better tradeoffs for conservative investors that are more weighted toward bonds and T-Bills. As the election approaches, active investors should remain cognizant of a presidential puzzle and related risk-return tradeoffs. [ABSTRACT FROM AUTHOR]

This paper explores the spillover and leverage effects, as well as trading volume dynamics, of Financial, Technology, and FinTech Exchange-Traded Funds (ETFs) using ARMA-GARCH and ARMA-EGARCH models. The study indicates that these ETF sectors and the stock market index have significant connections and interdependencies, which can transmit shocks and volatility. Market fluctuations significantly impact the ETF sectors, with negative shocks having a more significant impact on volatility. Furthermore, increased volatility is linked to increased trading activity, which indicates active investor adjustments in uncertain periods. The practical implications of these findings for investors, portfolio managers, and policymakers are aimed at supporting portfolio diversification, risk management, and asset allocation decisions. Understanding the effect of market volatility on trading volumes can help improve trading strategies and liquidity management. The paper increases comprehension of spillover and leverage effects and trading volume in ETFs, enabling stakeholders to make well-informed decisions in the financial markets. [ABSTRACT FROM AUTHOR]

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine the predictability of crude oil price volatility. The empirical results indicate that changes in the CPU, rather than the original CPU, can forecast oil volatility. The revealed volatility predictability is both in-sample and out-of-sample. Asset allocation exercise shows that this finding is also economically significant. Furthermore, the predictive ability is mainly concentrated during economic expansions and the CPU contains climate risk that is not available for other predictors. Finally, CPU change indicators can explain the changes in crude oil consumption, which is the economic source of their predictive power. [ABSTRACT FROM AUTHOR]

The Public Retirement Research Lab (PRRL) was established to provide reliable data on public-sector defined contribution (DC) retirement plan participants. The PRRL Database is the repository for the data collected by the PRRL. To fully understand the impact of ongoing plan participation, it is crucial to analyze a consistent group of participants (a longitudinal sample) who have been part of the database for an extended period. This paper presents the first longitudinal study that analyzes public retirement plan participants who were present every year from 2019 through 2021. The study tracks balances, contributions, asset allocations, and loan activity of 1.1 million plan participants who held positive defined contribution account balances in the years 2019 through 2021 in the PRRL Database, covering a three-year period. [ABSTRACT FROM AUTHOR]

This paper studies the optimal asset allocation problem of a defined contribution (DC) pension plan with a stochastic salary and value under a constraint within a stochastic volatility model. It is assumed that the financial market contains a risk-free asset and a risky asset whose price process satisfies the Stein–Stein stochastic volatility model. To comply with regulatory standards and offer a risk management tool, we integrate the dynamic versions of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and worst-case CVaR (wcCVaR) constraints into the DC pension fund management model. The salary is assumed to be stochastic and characterized by geometric Brownian motion. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. By using the Lagrange multiplier method and the dynamic programming method to maximize the constant absolute risk aversion (CARA) utility of terminal wealth, we obtain closed-form expressions of optimal investment strategies with and without a VaR constraint. Several numerical examples are provided to illustrate the impact of a dynamic VaR/CVaR/wcCVaR constraint and other parameters on the optimal strategy. [ABSTRACT FROM AUTHOR]

We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme. [ABSTRACT FROM AUTHOR]

Superannuation fees have come under public scrutiny in recent years with the belief they are too high. We examine the determinants of fees and their relationship with fund performance. Superannuation funds with higher investment fees have higher allocations to asset classes that trade in more complex markets. For-profit retail funds charge both higher investment and administration fees than other funds. Funds with higher investment fees do not generate higher returns than the least expensive funds. These findings suggest that superannuation members may not earn higher after-fee risk-adjusted returns by holding funds with higher fees. JEL Classification: G11 [ABSTRACT FROM AUTHOR]

Despite the long history of calculating inflation betas, estimates of investment returns' response to the temporary and persistent components of inflation are strikingly absent. This article attempts to fill that void. Using officially reported inflation as well as real economic, survey, and time-series measures, the author decomposes inflation and estimates the reaction of stocks, bonds, and Treasury bills to its components. Persistent inflation is destabilizing, can harm economic growth, and, if this article's findings are reliable, is negatively associated with equity returns. But it is rare. Large relative-price changes are common, but equities may react indifferently to them. Portfolio inflation protection isn't free: It comes at the expense of allocations to higher-return asset classes. The author's findings might affect the amount of costly inflation insurance practitioners require. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation into the performance of multi-asset funds investing internationally. Based on a custom-built conditional multi-factor model, which includes several bond and equity-related factors, along with time-varying betas and alphas, we show that funds in our dataset significantly underperform from 2004 to 2021. This evidence holds even on a before-fee basis, with funds investing predominantly in bonds exhibiting significantly higher alphas than funds investing predominantly in equities. Since multi-asset funds may better hedge against market downturns than equity or bond funds, given their higher asset class diversification, we also evaluate performance for crisis and non-crisis phases separately. The results show that, during market crises, international multi-asset funds perform significantly better than in non-crisis times, achieving neutral performance. However, while funds that favour bond investments perform similarly across different market phases, funds that prioritize equity investments perform significantly better during crises. Consequently, multi-asset funds with higher bond holdings can be a better option during non-crisis periods, while funds with higher equity holdings should be preferred during market downturns. This somewhat puzzling finding seems to be related to funds' cash holdings. By providing a better understanding of the asset allocation decisions of international mutual fund managers, as well as of their impact on fund performance, this work has meaningful implications for investors. [ABSTRACT FROM AUTHOR]

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises. [ABSTRACT FROM AUTHOR]

A constant weight asset allocation is a popular investment strategy and is optimal under a suitable continuous model. We study the tracking error for the target continuous rebalancing strategy by a feasible discrete-in-time rebalancing under a general multi-dimensional Brownian semimartingale model of asset prices. In a high-frequency asymptotic framework, we derive an asymptotically efficient sequence of simple predictable strategies. [ABSTRACT FROM AUTHOR]

With the deepening of enterprise financialization, the trend of "moving away from reality to emptiness" has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies' financial investment in the manufacturing industry is "substitution" motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, the financial risk caused by the financial investment of state-owned enterprises is greater. [ABSTRACT FROM AUTHOR]

In the realm of financial transactions, the allocation of household assets often lacks proper guidance, resulting in suboptimal utilization and limited income for residents. This study aims to address this issue by introducing rational asset allocation strategies, improving efficiency, and increasing household income. Specifically, the study focuses on enhancing Deep Reinforcement Learning (DRIO algorithms, particularly the Deep Q-Network algorithm. The current network is optimized by employing a dual network structure, leading to improved performance. Moreover, the proposed model incorporates Grubbs' improved algorithm to effectively denoise stock data samples, enabling iterative training of diverse stock agents. the results demonstrate the following findings: (1) The application of the Grubbs' improved algorithm to the cumulative returns of different stocks significantly surpasses the results without noise reduction, (2) The Sharpe ratio of the stock agents using the Grubbs improvement algorithm is noticeably higher than that of the unimproved DRL algorithm. The Sharpe ratio of stock agents under the unimproved algorithm consistently remains below 1.0; (3) The maximum drawdown of the stock agents using the Grubbs' improved algorithm is significantly lower than that of the unimproved DRL algorithm. The unimproved algorithm exhibits relatively higher maximum drawdown rates. These findings indicate that Grubbs' improved algorithm possesses notable advantages in noise reduction and enhancing the cumulative returns of assets. [ABSTRACT FROM AUTHOR]

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

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]

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]

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 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]

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]

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]

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]

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]

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 investigate whether international operations enhance information links between firms and foreign investors. Exploiting novel subsidiary-level data and within-location variations, we show that, after expanding into another country, a firm attracts greater investment allocation from funds from that country than from other foreign funds. This increase is economically significant, equivalent to one-fifth of the average firm weight in a country-specific portfolio. The observed effect cannot be attributed to funds' influence, persists even when funds are already familiar with the firm, and helps them generate superior risk-adjusted returns. Our results suggest that firms' cross-border economic activities contribute to global financial interconnectedness. [ABSTRACT FROM AUTHOR]

The recent integration of digital technology and financial services has given rise to the newly emerging modality of digital finance. However, does digital finance improve the efficiency of financial services while influencing the investment behavior of brick-andmortar businesses? With the help of the data about Chinese listed companies, this paper uses multiple regression analysis, instrumental variables, and other methods to empirically test whether and how digital finance affects the financial asset allocation decisions of brick-and-mortar enterprises. The findings suggest that digital finance has a galvanizing effect on financial asset allocation. However, this effect mainly stems from the fact that firms allocate more illiquid financial assets and has a dampening effect on liquid financial assets. Path analysis shows that easing financing constraints is a causal pathway through which digital finance dampens firms’ liquid financial asset allocation. Moreover, rising risk exposure levels partially mediate the stimulus of digital finance, motivating firms to allocate illiquid financial assets. This paper contributes to the research on the economic consequences of digital finance and provides policy recommendations on how digital finance can better serve the real economy. [ABSTRACT FROM AUTHOR]

Water infrastructures are critical assets that ensure communities served by them have access to clean and reliable water. Because the cost of creating a water infrastructure is relatively high and the service life of infrastructures ranges from a few decades to more than a century, the process of systematic maintenance and rehabilitation is essential for preserving them at an optimal functioning capacity. From an asset management perspective, water infrastructure maintenance and rehabilitation (M&R) decision-making primarily involves assessing the condition and performance of the infrastructure, accounting for aspects such as complying with regulatory requirements, allocating resources based on system constraints, and mitigating network failure risks. The existing frameworks for water infrastructure maintenance and rehabilitation rely on models that allocate resources for M&R operations based on network condition and criticality under budgeting constraints. However, existing M&R frameworks have not included social equity (e.g., fair and just distribution of water resources) as a variable that directs or influences the M&R decisions. To improve the equitability of water infrastructures, a framework that incorporates social equity into the M&R decision-making is needed to attain equitable resource allocations for M&R projects. Hence, in this study, we developed a framework that optimizes resource allocations for water infrastructure M&R decisions by directly incorporating social equity objectives. The developed social equity objectives were derived from four social equity theories, utilitarianism, Rawlsianism, egalitarianism, and socialism, and integrated into three different policies A, B, and C, to (1) maximize performance, (2) maximize performance and improve access to disadvantaged groups, and (3) minimize the gap in performance between the disadvantaged group and the rest of network, respectively. The developed framework was applied to a numerical case study of a water distribution pipeline network in the City of Portland, Oregon, and the results of the modeling were discussed. The case study findings indicate that Policies B and C, which are equity inclined, without causing considerable economic setbacks result in a smaller performance disparity between the condition scores of assets in privileged (advantaged) versus underprivileged (disadvantaged) neighborhoods. The study results provide evidence that quantitatively incorporating social equity into the decision-making of water infrastructure M&R can improve asset allocations to disadvantaged communities without significantly compromising the broader network's performance. [ABSTRACT FROM AUTHOR]

We propose a novel ranking-based approach combined with long short-term memory (LSTM) networks to generate investor views in the well-known Black – Litterman (BL) model. This approach can effectively distinguish high-quality assets with the potential for future growth. In addition, it discards any information contained in the absolute differences between asset prices to mitigate the negative impact of these estimation errors on the BL model. Our findings suggest that the BL model based on our approach can achieve better out-of-sample performance. [ABSTRACT FROM AUTHOR]

This paper examines the impact of environmental news coverage in US newspapers on stock markets. Using textual analysis with a dictionary‐based approach, we obtain several measures of attention, tonality and uncertainty in the coverage of environmental news in major US newspapers. We consider different weighting schemes to account for the visibility and relevance of the text sources, and several sets of newspapers to measure the possible impact of their editorial line. Our results show that greater attention to environmental news in the US media reduced the excess returns of carbon‐intensive stocks and increased their volatility over the last decade, especially when the coverage was uncertain. The opposite result holds for the most virtuous green assets. Restricting the corpus of texts to conservative newspapers mitigates the impact of coverage. Overall, our results illustrate how rising environmental concerns lead investors to change their asset allocation. [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.