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

This study examines corporate pension funds' search for yield through investments in private equity (PE). Using pension asset allocation data from 10‐K filings, we find that corporate pension funds significantly increase the PE share within their risky assets following underperformance relative to their expected return. This risk‐taking behavior is more pronounced for sponsoring firms that were more financially constrained with poorly funded plans in the previous year. Furthermore, we discover that pension asset allocation to PE does not significantly increase pension returns but does lower pension return volatility and tracking error relative to the expected return. [ABSTRACT FROM AUTHOR]

We introduce the formation of a network of money flows between assets in stock markets, which captures directed relations between assets in terms of how investors have re-allocated money in the stock exchange. Our approach is based on identifying a directed link, or money flow, that occurs when an investor funds a purchase of an asset by selling another asset(s). We extract investor-level money flow networks on daily basis from shareholder registration data, which are then aggregated for both financial institutional and retail investors. Overall, we have a time series of 877 daily networks from 2006 to 2009, which is exceptionally long data on temporal networks. Through our analysis of non-reciprocated triadic patterns in the aggregated money-flow networks, we find that these patterns are both recurrent and significant. However, they are not related to the 2008 financial crisis. Additionally, we observe that the counts on different triadic motifs exhibit not only an autoregressive process but are also interconnected contemporaneously and dynamically. These findings suggest the need for further research using sophisticated network models to provide a comprehensive representation of money flows in stock markets. [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]

Undoubtedly, financial markets are not only affected by common economic, financial and behavioral factors but also by other global risk shocks. In this paper, we investigate the relevance of global geopolitical risk in stock-bond relationships. Specifically, we assess the possibility and manner in which time-varying correlations of Islamic stocks and bonds (sukuk) returns are affected by geopolitical risk and whether they behave differently from their conventional counterparts. We do so by implementing a multivariate GARCH model under dynamic conditional correlations (DCCs) to daily data of the Gulf Cooperation Council (GCC) markets (2013-2019). Our outcomes unveil that geopolitical risks positively affect the dependence between Islamic stocks and sukuk, as well as their conventional counterparts. Regional rather than global geopolitical risk factors positively affect the volatility of conventional stock-bond correlation, while the volatility of Islamic stock-sukuk correlation decreases during times of high geopolitical risks. Our findings offer several prominent implications in terms of financial stability, asset allocation of portfolios and policy design. [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]

This article provides applications of derivatives to asset allocation and multi-asset management. The four applications include using futures for top-down asset allocation, deploying portable alpha strategies using derivatives to achieve desired convexity in payoff profiles, developing effective hedging strategies, and using derivatives for active speculative views by proprietary traders. [ABSTRACT FROM AUTHOR]

Infrastructure is the backbone of economies and societies, and it is not only governments that invest in digital, energy, transport, and utilities companies. Increasingly, institutional investors have increased their allocations to infrastructure as an asset class. In this article, the authors empirically evaluate four main motives to include infrastructure in the investment mix. Their results show that portfolio diversification, downside risk protection, and stable income yields are all valid reasons for investing in infrastructure. However, the evidence on infrastructure's inflation-hedging capabilities is less compelling, despite its significant role in asset-liability management frameworks. This article offers valuable empirical insights into infrastructure as an investable asset class that has important applications for portfolio construction. [ABSTRACT FROM AUTHOR]

This article explores diversification within the infrastructure investment asset class. The focus is on constructing well-diversified portfolios of unlisted infrastructure equity and the strategic asset allocation benefits of including infrastructure in a multi-asset class portfolio. The authors show that, despite common perceptions, simply increasing the number of assets, sectors, and geographies does not efficiently enhance diversification. Instead, achieving a well-diversified portfolio of infrastructure investments is possible with a limited number of bets, as long as the key risk factors found in these investments are used to build the portfolio accordingly. In a multi-asset class portfolio, they demonstrate that, whatever the investor profile and portfolio optimization objective, unlisted infrastructure equity can play a key role in the total portfolio, especially if it can be invested as an asset class, on a well-diversified basis, for example using the Smart Infra approach described in this article. [ABSTRACT FROM AUTHOR]

This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability. [ABSTRACT FROM AUTHOR]

War‐related expectations cause changes to investors' risks and returns preferences. In this study, we examine the implications of war and sanctions sentiment for the G7 countries' debt markets during the Russia‐Ukraine war. We use behavioural indicators across social media, news media, and internet attention to reflect the public sentiment from 1st January 2022 to 20th April 2023. We apply the quantile‐on‐quantile regression (QQR) and rolling window wavelet correlation (RWWC) methods. The quantile‐on‐quantile regression results show heterogenous impact on fixed income securities. Specifically, extreme public sentiment has a negative impact on G7 fixed income securities return. The wavelets correlation result shows dynamic correlation pattern among public sentiment and fixed income securities. There is a negative relationship between public sentiment and G7 fixed income securities. The correlation is time‐varying and highly event dependent. Our additional analysis using corporate bond data indicates the robustness of our findings. Furthermore, the contagion analysis shows public sentiment significantly influence G7 fixed income securities spillover. Our findings can be of great significance while framing strategies for asset allocation, portfolio performance and risk hedging. [ABSTRACT FROM AUTHOR]

Bank capital stands at the heart of banking regulation aimed at curbing bank risk-taking. This paper examines the impact of the Chinese version of Basel III capital regulation on bank risk-taking in China, by applying panel regressions to a large sample of Chinese commercial banks. Our findings indicate that more Tier 2 contingent convertible (CoCo) bonds as capital instruments in the capital composition may paradoxically increase, rather than decrease, bank risk-taking. This effect is robust to instrumental variable estimation. A channel analysis reveals that this unintended effect primarily arises from a leverage effect. This is due to the strong debt attributes of Chinese Tier 2 CoCo bonds, leading to lower core capital levels. Conversely, no evidence is found for the franchise value effect as a significant driver. Lastly, we confirm that capital constraints influence banks' capital adjustments and asset risk allocation aligning with regulatory aims. Our paper has significant policy implications for financial regulation, shedding light on the efficacy of Basel III capital regulation while highlighting the limitations of Tier 2 CoCo bonds as capital instruments in China. [ABSTRACT FROM AUTHOR]

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This paper investigates the association and causality between household financial knowledge and their corresponding financial behaviours in financial asset allocation and financial market participation. The 2014 data of the China Family Panel Studies (CFPS) is used to examine the financial behaviours of Chinese households. Our empirical results suggest that the level of financial knowledge is positively correlated with the number of various types of financial assets. Additionally, by utilizing a multivariate probit model with binary responses, allowing for pairwise correlations among unobservable factors, it is found that household heads with higher financial knowledge are more likely to participate in certain financial markets, such as term deposits, mutual funds and stocks. When using the instrumental variable approach, it confirms that financial knowledge has some positive impacts on the number of assets and ownership of mutual funds and stocks. We argue that financial knowledge can be a predictor of financial behaviour, but it has its limitation for market participation. The removal of institutional barriers and the improvement in information discourse in China should also be implemented to help Chinese households participate the financial market. [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]

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]

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz's 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential. [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]

This study focuses on efficient asset allocations that properly include T-bills, T-bonds, and the S&P 500 stock index. It checks that their annual real rates of linear return are both normal and almost lognormal. It reexamines how efficient portfolios based on the rates of linear return may turn into efficient portfolios based on the rates of logarithmic return. It finds that each efficient asset allocation has the lowest possible standard deviation as well as the highest possible arithmetic and geometric means. It eventually reconsiders the relationship between the confidence interval of a geometric mean and an expected long-run capital accumulation. As a consequence, it bridges a gap in the scientific literature by enabling financial advisors to trade off the mean rate of return on a portfolio more rigorously against the value at risk. [ABSTRACT FROM AUTHOR]

Since the Global Financial Crisis, central banks have created an environment in which risk taking and leveraging were the predominant strategies in the markets. Unprecedented central bank stimulus has led to market dynamics in which the availability of market liquidity has replaced the fundamentals of the economy as a driver of investment returns. The COVID crisis ended a 40-year period of low inflation, declining interest rates, and decreasing macro volatility in developed markets. The rise of inflation changed the market dynamics, and the diversification effect between liquid bonds, credit, and equity markets weakened significantly. As the investment market has become less diversified, it has become necessary to find new sources of diversification in portfolios. The author proposes a functional framework for asset allocation under uncertainty of correlation regimes. The functional categories for the asset classes in a portfolio are return-seeking assets, diversifiers, and tail risk hedges. The framework exploits the characteristics of defensive and diversifying alternative risk premium strategies, as well as the artificial diversification effect of illiquid asset classes. [ABSTRACT FROM AUTHOR]

Ian Toner, from Verus, discusses the firm's investment strategy and how it adapts to changing market conditions. The firm uses the same investment team for all clients, but the application of the strategy varies based on the client's decisions. The process includes setting the strategic asset allocation, introducing short-term tilts, and active manager selection. The firm focuses on measuring twice and cutting once, with a long-term and risk-focused approach. They prioritize meeting investment goals while staying within the client's risk tolerance. The firm also emphasizes the importance of understanding and implementing ESG factors, although they have chosen not to sign any industry commitments. Toner's leadership style emphasizes discipline, skepticism, and humility. The firm considers global economic trends in their investment decisions and prepares for potential international crises through ongoing research. Toner advises aspiring CIOs to learn from others and embrace continuous learning. The biggest opportunities and threats in the asset management industry lie in technological changes, but the firm remains focused on meeting client needs. [Extracted from the article]

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

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]

Rational asset allocation is central to household wealth accumulation. This paper employs data derived from the 2019 China Household Finance Survey to methodically examine the influence of property tax expectations on the asset allocation decisions of households. This study finds that the expectation of property tax positively influences both the probability of holding risky financial assets and their proportion of household assets. The percentage of housing assets constitutes a substantial negative moderating factor affecting the relationship between property tax expectation and household investments in risky financial assets. The positive effect of property tax expectations is more significant in eastern China. A definite expectation of property tax causes a precautionary saving effect among households. This study highlights that forming reasonable expectations about property taxes can help households adjust their investment portfolios in advance, diversify their asset allocation, and mitigate the impact of changes in property tax policies. [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]

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]

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 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]

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]

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]

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]

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]

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]

Digital finance plays a crucial role in enhancing financial inclusion and decreasing income inequality within developing countries. Given the digital and financial attributes that characterize digital finance, digital financial literacy (DFL) is a critical factor that influences the extent to which this function can be exploited. There is relatively little empirical evidence linking DFL to rural income inequality. Based on the 2017 and 2019 China Household Finance Survey data and two-way fixed effect panel model, this study focuses on rural China and examines the effect of DFL on income inequality. Meanwhile, this study also explores the mechanism of this effect from the perspectives of financial asset allocation and entrepreneurship. The empirical results show that (1) increasing DFL within rural households contributes to decreasing income inequality; (2) DFL can decrease income inequality by enriching the variety of household financial assets and enlarging the proportion of risky financial assets in rural households; and (3) improving DFL can ameliorate rural income inequality by increasing the probability of entrepreneurship. The study's findings put forward fresh empirical evidence for understanding the relationship and mechanism that exist between DFL and income inequality, and more significantly, provide new suggestions for designing and enhancing financial policies that aim to decrease income inequality in developing countries. [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]

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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]