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Despite the overwhelming evidence of time-varying risk aversion documented in recent literature, standard dynamic portfolio theories often adopt an assumption of constant (relative) risk aversion due to analytical tractability. In “Time Varying Risk Aversion and Dynamic Portfolio Allocation,” Li et al. explicitly consider the implications of time-varying risk aversion for dynamic portfolio allocation under the framework of regime-switching models. An investor with regime-dependent utility exhibits a decreasing relative risk aversion (DRRA) and has higher risk aversion when a bear market regime is more likely in the future. They develop an efficient dynamic programming algorithm that overcomes the challenges imposed by regime-dependent preference in obtaining time-consistent portfolio policies. The empirical results show that VIX is an important predictor of regime shifts and that investors with regime-dependent risk aversion achieve better investment performance than those with constant risk aversion.

The contributions of this paper are threefold. First, by combining dynamic programs and neural networks, we provide an efficient numerical method to solve a large multiperiod portfolio allocation problem under regime-switching market and transaction costs. Second, the performance of our combined method is shown to be close to optimal in a stylized case. To our knowledge, this is the first paper to carry out such a comparison. Last, the superiority of the combined method opens up the possibility for more research on financial applications of generic methods, such as neural networks, provided that solutions to simplified subproblems are available via traditional methods. The research on combining fast starts with neural networks began about four years ago. We observed that Professor Weinan E’s approach for solving systems of differential equations by neural networks had much improved performance when starting close to an optimal solution and could stall if the current iterate was far from an optimal solution. As we all know, this behavior is common with Newton- based algorithms. As a consequence, we discovered that combining a system of differential equations with a feedforward neural network could much improve overall computational performance. In this paper, we follow a similar direction for dynamic portfolio optimization within a regime-switching market with transaction costs. It investigates how to improve efficiency by combining dynamic programming with a recurrent neural network. Traditional methods face the curse of dimensionality. In contrast, the running time of our combined approach grows approximately linearly with the number of risky assets. It is inspiring to explore the possibilities of combined methods in financial management, believing a careful linkage of existing dynamic optimization algorithms and machine learning will be an active domain going forward. Relationship of the authors: Professor John M. Mulvey is Xiaoyue Li’s doctoral advisor.

In contrast to the disappearing dividends view prevalent in the literature, we document extensive dividend payments by firms and significant variability within firms and across 16 countries during 2000–2013. We predict that within-firm variability in dividends increases investor demand for forward-looking dividend information, and analysts respond by producing informative dividend forecasts. We find that analyst dividend forecasts are available for most dividend-paying firms and are more prevalent for firms with higher variability of dividends. Analyst dividend forecasts are more accurate than alternative proxies based on extrapolations of past dividends. Finally, dividend forecasts (1) are incrementally useful to investors beyond information in other fundamentals, such as earnings and cash flow forecasts, (2) help investors interpret earnings quality, and (3) are associated with investors' portfolio allocation decisions. Data Availability: Data are available from the public sources cited in the text.

Portfolio allocation is an important topic in financial data analysis. In this article, based on the mean-variance optimization principle, we propose a synthetic regression model for construction o...

In this paper, we examine the impact of including environmental, social and governance (ESG) criteria in the allocation of equity portfolios. We focus on the risk and return characteristics of the resulting ESG portfolios and investment strategies. Two specific measures are considered to quantify the ESG performance of a company; the ESG rating and the greenhouse gas (GHG) emission intensity. For both measures, we carry out empirical portfolio analyses with assets in either the STOXX Europe 600 or the Russell 1000 index. The ESG rating data analysis does not provide clear-cut evidence for enhanced performance of portfolios with either high or low ESG scores. We moreover illustrate that the choice of rating agency has a significant impact on the performance of the resulting ESG-constrained portfolios. Secondly, we study the impact of GHG emission reductions and increases. We show that emission reductions do not necessarily lead to increased risk or diminished returns, which gives confidence in a smooth transition towards the green economy pursued by the European Green Deal.

Following the coalition literature highlighting intraparty politics (e.g., Giannetti and Benoit 2009; Laver 1999; Strøm 2003), I address the well-known “portfolio allocation paradox” (Warwick and Druckman 2006) by introducing a new model of government formation based on two main assumptions. First, no actor has a structural advantage in the negotiations leading to government formation. Second, all actors who can deprive the coalition of a majority (or other critical threshold size) must be included in the negotiations—not just parties. Whereas standard bargaining models are inconsistent with Gamson’s Law, the model proposed here implies that equilibrium portfolio allocations should be mostly Gamsonian but with a small-party bias, as the empirical literature has long found. Empirically, I show that my model outperforms the literature’s standard specification (due to Browne and Franklin 1973). Moreover, one of the model’s new predictions—that candidate-centered electoral rules should promote more Gamsonian portfolio allocations—is supported.

Zusammenfassung Trotz nach wie vor bestehender Unterreprasentation werden immer mehr Ministerien in den deutschen Bundeslandern mit Frauen besetzt. Weniger betrachtet wurde bisher, ob Frauen in diesem Prozess bestimmte Ministerien haufiger besetzen, es also zu geschlechtsspezifischer Portfolioallokation kommt. Diese ist vor allem relevant, wenn die weiblich besetzten Ministerien weniger prestigetrachtig sind. Davon ausgehend untersucht diese Studie die Prestigedifferenz zwischen mannlich und weiblich besetzten Ministerien in den 16 Landern zwischen 2005 und 2018 sowie mogliche Erklarungsfaktoren. Die Prestigedifferenz wird uber den unterschiedlichen Anteil an Beamt*innenplanstellen der Ministerien mess- und vergleichbar gemacht. Die Studie kommt zu dem Ergebnis, dass Frauen trotz ihres wachsenden Anteils in Kabinetten vermehrt weniger prestigetrachtige Ministerien besetzen und somit die Prestigedifferenz ansteigt. Als Erklarungsfaktoren zeigen vor allem die Ministerienanzahl sowie der Frauenanteil im Kabinett einen Effekt. Keywords: Portfolioallokation, Frauenreprasentation, Landeskabinette, Ministeriumsprestige ----- Gender difference in Portfolio Allocation: A study of the German Lander Abstract Despite persisting underrepresentation, more and more ministries in the German federal states (Lander) are occupied by women. So far, less consideration has been given to whether women are more likely to occupy certain particular ministries in this process, i.e. whether gender-specific portfolio allocation occurs. This is particularly relevant in cases where the female-occupied ministries are less prestigious. Therefore, this study examines the prestige difference between male and female ministries in the German federal states between 2005 and 2018 as well as possible explanatory factors. Prestige difference is measured by the different proportion of posts for civil servants within the ministries. The study concludes that, despite the increasing proportion of women in cabinets, women are increasingly occupying less prestigious ministries - thus, the prestige gap increases. The number of ministries and the proportion of women in the cabinet itself can partially explain this outcome. Keywords: portfolio allocation, women’s representation, subnational cabinets, ministry prestige ----- Bibliographie: Riethmuller, Felicia: Geschlechterungleichheit bei der Besetzung von Ministerposten: Eine Untersuchung anhand der deutschen Bundeslander, DNGPS Working Paper, 2021, S. 1-24. https://doi.org/10.3224/dngps.v7i1.02

Purpose Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies. Design/methodology/approach The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020. Findings Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar. Originality/value This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

We investigate the portfolio allocation and risk contribution characteristics of agricultural commodities, and the volatility spillovers between agricultural commodities and global and regional equ...

Under a differential taxation system, portfolio allocation decisions are based not only on the risk-return relationship of assets but also on their tax characteristics. The higher the tax burden on...

Portfolio allocation strategies, and notably the mean-variance approach, use past returns to assign optimal weights. Even though both past and expected returns should come from the same distribution, a formal test of whether this holds in practice has not been conducted yet. Thus, the study examines if the daily returns of 242 companies with continuous trading in the S&P index come from the same distribution using the Kolmogorov-Smirnov, Cramér-Von Mises, and Wilcoxon rank-sum tests. The tests suggest that generally stock returns do come from the same distribution. However, the hypothesis is rejected during the Great Recession, with the rejection rate increasing as the forecast horizon increased. The rejection rate, using an array of macroeconomic variables, is found to record high levels of persistence. Although macroeconomic variables were not found to be statistically significant determinants of the rejection rate, market distress has a small but significant effect.

High-dimensional covariance matrices appear in many disciplines. Much literature has devoted to the research in high-dimensional constant covariance matrices. However, constant covariance matrices ...

Firms that have losses are expected to sell tax-free securities and replace them with taxable securities since they can no longer benefit from tax savings. However, after the most recent financial crisis, firms’ decisions to rebalance their investment portfolios may have led to additional losses during a period of stressed financial performance and increased insurance regulation. This study examines portfolio allocation behaviour in the property and casualty insurance industry. The results show that investment limitations imposed by insurance regulators can inhibit desired investment allocation post the financial crisis.

We decompose economic uncertainty into "good" and "bad" components according to the sign of innovations. Our results indicate that bad uncertainty provides stronger predictive content regarding future market volatility than good uncertainty. The asymmetric models with good and bad uncertainties forecast market volatility in a better way than the symmetric models with overall uncertainty. The combination for asymmetric uncertainty models significantly outperforms the benchmark of autoregression, as well as the combination for symmetric models. The revealed volatility predictability is further demonstrated to be economically significant in the framework of portfolio allocation.

The structure and organisation of the machinery of government is key to theambitions of political coalitions. When portfolio allocation and agencificationare a function of political choice, political volatility should also affect internalstructure of government administrations. This study tests the effects of politicalturnover of individual ministers and of the political ideology of coalitionson a dataset of intra-ministerial changes in Dutch ministries between 1980and 2014. Findings indicate that the turnover of political heads of departmentsand the shifts in policy preferences between successive coalitionsindeed affects the internal structure of ministerial departments. Political variableshave a strong impact, particularly changes in the left–right position ofthe government. A clear pattern for how precisely politics affect the structuraldesign of public organisations remains absent, in spite of the robustness ofthe findings. Most ministries experience significant effect of executive turnover,sometimes increasing the hazards of intra-organisational transitions andsometimes increasing stability. It turns out that ministers can substantially rearrangetheir organisations in line with their policy preferences but do notnecessarily do so. Sometimes the effect of liberal ideology dominates, sometimesthe effect of the policy preferences with respect to a specificdomain prevails.

Robust portfolio optimization refers to finding an asset allocation strategy whose behavior under the worst possible realizations of the uncertain inputs, e.g., returns and covariances, is optimized. The robust approach is in contrast to the classical approach, where one estimates the inputs to a portfolio allocation problem and then treats them as certain and accurate. In this paper we provide a categorized bibliography on the application of robust mathematical programming to the portfolio selection problem. With no similar surveys available, one of the aims of this review is to provide quick access for those interested, but maybe not yet in the area, so they know what the area is about, what has been accomplished and where everything can be found. Toward this end, a total of 148 references have been compiled and classified in various ways. Additionally, the number of Scopus© citations by contribution and journal is recorded. Finally, a brief discussion of the review’s major findings is provided and some solid leads on future directions are given.

This paper examines the relationship between crude oil prices and stock market returns in Turkey taking into account volatility spillovers that are exemplified by second moment effects. Using weekly data from 1990 to 2017 and time varying causality-in-mean and causality-in-variance tests and taking into account structural breaks, we model each series as an EGARCH process in order to capture any leverage effects in the volatility of returns. Empirical results suggest crude oil prices as measured by Brent benchmark have significant effects on stock market returns in Turkey. While we fail to document significant spillover effects stemming from oil prices in the entire sample, there are significant spillover effects from crude oil price changes to stock market returns in 1993 and 2008–09. These results suggest that government policies must take into account risk spillover effects between markets and that investors are better off monitoring crude oil markets in portfolio allocation decisions.

As defined contribution (DC) plans become more popular than defined benefit (DB) plans, American workers are increasingly responsible for their retirement savings. Because retirement plan participants' portfolio allocation is constrained by the available funds in the plan, the construction of a plan's investment menu has become extremely important. No research has evaluated fund selection in retirement plans or compared plans involving an advisor with self-directed plans. To fill this research gap, this study employs cross-sectional, nationwide data that include 5,570 retirement plans with 100 or more participants in 2013, 2014 and 2015. Results show that in most cases, using advisors is not related to plan performance. Plan sponsors should require advisors to periodically evaluate the performance of plans under their management using objective measures.

Whereas much of the existing literature on coalition formation has focused on the disparity between Gamsonian payoffs and the outcomes of bargaining models, dangers of ministerial drift, party ideal points, issue salience/emphasis, and negotiation complexity, very little has been said on the role of the way public opinion affects political behavior via expectation of future electoral returns. Following the logic of “issue yield,” this article argues that politicians allocate ministerial portfolios according to the distribution of public opinion in the hopes of obtaining better returns in subsequent elections and find compelling evidence linking public opinion with portfolio allocation.

We study optimal life-cycle portfolio allocation and its application to target-date fund (TDF) design. We show that optimal TDF allocation must be explicitly linked to a savings rate; for example, a higher savings rate generating higher financial wealth accumulation should necessarily come with a more conservative TDF. Further, we quantify the extent to which accumulated wealth, the investing environment, and participant characteristics affect TDF allocations and compare the resulting optimal allocations against the observed universe of US TDF products. Plan sponsors and asset managers can use these findings to improve TDF selection and design. Key Findings ▪ We quantify how observed savings rates in 401k plans affect a TDF’s optimal allocation. Using a state-of-the-art life-cycle academic model, we find strong effects: for a moderately risk-averse participant, a halving in savings rate from 10% to 5% increases its optimal equity allocation by 10%–15%. ▪ We document the effects of real-life variations in other key determinants of TDF optimal allocations: participant’s risk aversion, relation between wages and equity returns, outside retirement income, start of savings, and expected returns. We measure the resulting wealth and consumption experience at and in retirement. ▪ We set these optimal glide paths against the glide paths pursued by US TDF mutual funds and observe a much narrower range for the observed ones, thus leaving some constituencies without an appropriate investment choice. Further, the optimal glide paths tend to slow the equity decline as they approach retirement; most observed ones do the opposite.

The last decade has challenged the paradigm of the hedge fund industry as a unique performer. In this chapter three main factors are identified that have affected the operation of hedge funds: competition from mutual funds, the market environment, and tighter regulation. Recent trends in the financial industry have moved asset managers closer to hedge funds by introducing similar underlying strategies, such as liquid alternative funds, to directly compete with hedge funds. Such strategies can achieve performance similar to that of hedge funds, thus introducing more competition for hedge funds. Moreover, it is shown that several hedge fund styles that have traditionally worked well in crisis times—even in the last decade—are also strategies that can be replicated by liquid alternatives. Together with tighter regulation and a strong market environment, these developments continue to put pressure on the hedge fund industry. The chapter’s empirical findings add to the existing debate on the performance of hedge funds and the direct competition from liquid alternatives.

We develop a new framework of optimal consumption and portfolio choice at industry portfolio level under dynamic and asymmetric correlations between industry and market portfolios. We derive in closed-form the optimal consumption and investment strategies under regime-dependent correlations environment. Overall, we find that ignoring time-varying and asymmetric correlations between portfolios can be costly to investors when applied to a construction of the optimal portfolio. Finally, we empirically test the performance of the model-based investment strategy.

This article highlights the electoral effects of holding salient portfolios within a coalition government. For voters, holding ministries can be seen as a symbol of a party’s success within the coalition. As a voting heuristic, parties not controlling the portfolios on issues important to their platforms signal their failure to achieve these goals. Following this perspective, we hypothesize that the difference between coalition parties that hold salient portfolios and those that do not partially predicts the extent of the electoral cost of coalition participation. Using a data set that covers 11 European parliamentary democracies between 1966 and 2002, we show that for junior coalition partners there is an electoral reward for holding their most salient portfolio. There is also an electoral benefit for a junior partner to hold a larger number of portfolios if they do not control their most salient portfolio. Conversely, holding their most salient portfolio and a larger number of additional ministries results in greater electoral losses in the subsequent parliamentary election. These results indicate that parties’ success at negotiating for their policy priorities in coalition governments holds consequences for their future electoral success.

We show that the statistical properties of gold are negatively correlated with equities and that including gold in a portfolio will provide diversification benefits. As there is no consensus on the proportion of gold that should be included in a strategic portfolio allocation we propose a visual tool that associates a performance metric with a range of possible asset weighting schemes—a Sharpe ratio response surface. This very surface shows that a target performance metric can be achieved with a large number of different allocations. We further argue that the rebalancing approach based on the surface closest to the benchmark surface under the Hausdorrf distance metric should be selected. Using a data sample between 1990 and 2018, we find that annual rebalancing with a 44-week lookback period achieves the minimum distance from the benchmark surface.

This paper examines the role of time‐varying jump intensities in forming mean‐variance portfolios. We find that compared with the no‐jump or constant‐jump models, the model which incorporates time‐varying jump intensities better fits the dynamics of the assets returns, and yields mean‐variance portfolios with higher Sharpe ratios. Our research suggests that using a better econometric model that captures non‐normal features in the data has benefits for portfolio allocation even for a mean‐variance investor.

The problem of optimal investment under a multivariate utility function allows for an investor to obtain utility not only from wealth, but other (possibly correlated) attributes. In this paper we implement multivariate mixtures of exponential (mixex) utility to address this problem. These utility functions allow for stochastic risk aversions to differing states of the world. We derive some new results for certainty equivalence in this context. By specifying different distributions for stochastic risk aversions, we are able to derive many known, plus several new utility functions, including models of conditional certainty equivalence and multivariate generalisations of HARA utility, which we call dependent HARA utility. Focusing on the case of asset returns and attributes being multivariate normal, we optimise the asset portfolio, and find that the optimal portfolio consists of the Markowitz portfolio and hedging portfolios. We provide an empirical illustration for an investor with a mixex utility function of wealth and sentiment.

This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.

Subjective mortality beliefs affect pre- and post-retirement consumption and savings decisions, as well as portfolio allocation. New survey evidence shows that individuals overestimate their mortality at short horizons and survival rate at long horizons. For example, a 28-year-old male with a 99.4% chance of surviving beyond 5 years believes he will do so with 92.8% probability. A 68 year old with a 71.4% probability of living to 78 believes he has an 82.4% chance of living that long. The formation of these beliefs across age cohorts can be attributed to overweighting salient causes-of-death. This bias matters empirically: Survival expectations correlate with heterogeneity in financial education and investment behavior. Embedded in a run-of-the-mill life-cycle model, these beliefs cause the young to under-save and retirees to not fully draw down their assets. In addition, for reasonable levels of risk-tolerance, the required excess rate of return on equity is in line with historical averages once subjective beliefs are accounted for.

As a key consequence of government formation negotiations among executive and legislative actors, portfolio allocation offers a window to understand the impact of constitutional design and presidential prerogatives on cabinet dynamics across democratic regime types. This article uses Shugart and Carey’s emphasis on the implications of regime distinctions and institutional variation in presidential powers for executive-legislative relations as a starting point for an examination of the extent to which presidents influence government formation outcomes in parliamentary and semi-presidential systems. Many presidents in these political systems have some influence on government formation, which enables them to shape cabinet composition. Yet, whether these powers advantage presidential parties in reaping more cabinet spoils than their proportional share has yet to be investigated. Using data on 442 government formation situations in 23 European parliamentary and semi-presidential democracies during 1945–2015, this study finds that parties of strong presidents generally, or presidents formally empowered to choose a formateur, are more likely to be advantaged in the allocation of cabinet seats than their peers who are not allied with the head of state.

In this paper, we define foreign (domestic) bias as the deviation of foreign (domestic) investors’ actual portfolio allocation in a bond market from the same bond market’s weight in global bond market. We investigate the determinants of foreign and domestic investment bias in 41 global bond markets. Our evidence indicates that foreign investors significantly overweigh markets that offer better risk-return profiles. Such return driven behavior of foreign investors is especially pronounced in emerging bond markets. Foreign investors are also found to avoid volatility in highly controlled bond markets. Our evidence sheds some light on the importance of improving investor profile in emerging markets.

Given any set of exogenously provided efficient portfolios, we develop a dynamic programming algorithm that constructs an optimal portfolio trading strategy to maximize the probability of attaining an investor’s specified goal wealth at the end of a designated timeframe. Our algorithm can also accommodate periodic infusions or withdrawals of any size with no degradation in runtime performance. We explore how the terminal wealth distribution is sensitive to restrictions on the segment of the portfolio’s efficient frontier made available to the investor. Because our algorithm’s optimal strategy is on the efficient frontier, allowed to depend on the investor’s wealth, and allowed to depend on the investor’s individual goals and specifications, we show that it soundly beats the performance of target date funds for attaining investors’ goals. These optimal goals-based wealth management strategies are useful for modern day FinTech offerings, both advisor-driven or robo-driven.

The aim of this study is to adapt a well-known interactive and multi-criteria decision-making method, TODIM, to the portfolio allocation process. The proposed method is applied to empirical US equity data by employing variance, correlation and returns calculated on different observation periods as decision criteria. A total of 440 different configurations are applied to analyze the impact of several parameters in TODIM. Based on the results for the test period, outperforming TODIM configurations are elected. In the validation period, it is empirically demonstrated that portfolios based on outperforming TODIM configurations yield significantly better results than equally weighted portfolios (1/N) and insignificantly inferior results than the minimum variance portfolio (MVP) in terms of the Sharpe ratio. However, TODIM may still be a better choice than MVP for investors sensitive to concentration risk and turnover costs. (C) 2019 Elsevier Ltd. All rights reserved.

This paper proposes a new measure of belief dispersion for the Chinese stock market based on the closing price data from mobile and PC trading terminals. Our results show that our belief dispersion measure has significant predictive content for aggregate market volatility both in-sample and out-of-sample. The volatility predictability is robust to different realized volatility measures, forecasting horizons, and benchmark models. Our belief dispersion measure also helps improve the density prediction. Overall, we find that investors with mean-variance preferences who use belief dispersion information to generate volatility forecasts can improve their portfolio performance over longer horizons.

Bayesian inference is proposed for volatility models, targeting financial returns, which exhibit high kurtosis and slight skewness. Rotated GARCH models are considered which can accommodate the multivariate standard normal, Student t, generalised error distributions and their skewed versions. Inference on the model parameters and prediction of future volatilities and cross-correlations are addressed by Markov chain Monte Carlo inference. Bivariate simulated data is used to assess the performance of the method, while two sets of real data are used for illustration: the first is a trivariate data set of financial stock indices and the second is a higher dimensional data set for which a portfolio allocation is performed.

How do political parties divide coalition payoffs in multiparty governments? Perhaps the most striking answer to this question is Gamson’s Law, which suggests a strong fairness norm in the allocation of office payoffs among coalition partners. Building upon recent advancements in portfolio allocation research, we extend this approach in three important ways. First, we study fairness with regard to the allocation of policy (rather than office) payoffs. Second, we introduce measures to assess the fairness of the division of policy payoffs following two norms: envy-freeness and equitability. Third, we explore why some allocations of ministerial portfolios deviate from fairness norms. Based on an original data set of party preferences for individual portfolios in Western and Central Eastern Europe, we find substantial variation in the fairness of policy payoffs across cabinets. Moreover, coalitions are more likely to arrive at envy-free and equitable bargaining outcomes if (1) these fair allocations are based on an allocation of cabinet positions that is proportional to party size and if (2) the bargaining power is distributed evenly among government parties. The results suggest that fairness is not a universal norm for portfolio allocation in multiparty governments, but in fact depends on the cabinet parties’ bargaining positions.

This paper surveys the literature on the relationship between health and household portfolio allocation and provides updated empirical analysis based on recent data. Prior research finds robust evidence for cross-sectional correlations between measures of health status and portfolio decisions, but establishing the causal pathways and underlying mechanisms has proven more difficult and complex. Analysis from the most recently available 2016 and 2018 waves of the Health and Retirement Study yields results that are consistent with existing literature. Households with worse self-reported health have a lower probability of holding various types of financial assets and invest a higher share of their portfolios in safe assets, relative to other asset categories. However, there is only weak evidence that new health shocks to a household change portfolio holdings. The paper concludes with a discussion of the implications of this research and directions for future work.

This study proposes a theoretical model for measuring the greenness factors of a firm. We develop the multifactor utility function and find that the proportion of investment in green bonds is higher if greenness factors account for by a firm and vice versa. Moreover, we further develop the global aspects of greenness measures which identify how much level of greenness is maintained by a firm to make the environment green. In terms of reduction in emissions based on global measures, we report that the proportion of investment in green bonds is higher. This study argues that the difference between firm-related and global measures of greenness refers to distortion in portfolio allocation. Lastly, we compare the results of five Asian countries and report that Japanese firms are appropriately following the greenness measures while the firms operating in developing countries including Indonesia, Malaysia, Pakistan, and Thailand are far behind in implementing the greenness measures.

Covid-19 has impacted the financial markets dramatically. The risk and return expectations of investors have changed, leading them to reallocate their portfolios. This paper aims to analyse the impact of Covid-19 on the portfolio allocation decisions of individual investors. The study examines the perceptions of investors about various investment avenues before and during the period of extreme uncertainty caused by the COVID-19 pandemic. The data were collected from individual investors residing in Delhi and Mumbai. AHP is used to rank the investment preferences of the respondents. The results show that due to the present financial crisis pertaining to COVID-19, investors have started reallocating their portfolios. Since the returns on risky assets are not as expected, investors are moving towards a conservative portfolio. However, the case of transition from risky to risk-free assets is not the same in the case of all investors.

Stock portfolio allocation is one of the most challenging and interesting problems of modern finance. Recently, deep reinforcement learning applications have shown promising results in automating portfolio allocation. However, most current approaches use a single agent learning model which could inadequately capture the complex dynamics arising from the interactions of many traders in today’s stock market. In this paper, we explore the applicability of multi-agent deep reinforcement learning to this problem by implementing single-agent, 2-agent, 3-agent, and 4-agent deep deterministic policy gradients (DDPG) algorithms in a competitive setting. Upon analyzing the results obtained using standardized metrics, we observe that there is a significant improvement in the performance of our learning models with the introduction of multiple agents.

Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider risk-averse, return-seeking as well as diversificationpreferring investors who trade along different allocation frequencies, namely daily, weekly or monthly. Out-of-sample performance and diversification benefits are studied for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. To account for low liquidity in CC markets, we incorporate liquidity constraints via the LIBRO method. Our results show that CCs can improve the risk-return profile of portfolios. In particular, a maximum-diversification strategy (maximizing the Portfolio Diversification Index, PDI) draws appreciably on CCs, and spanning tests clearly indicate that CC returns are non-redundant additions to the investment universe. Though our analysis also shows that illiquidity of CCs potentially reverses the results.

Portfolio managers regularly have views on capital markets that span multiple periods. A portfolio manager, for example, may expect a recession with high probability over the coming period, followed by a recovery in the subsequent period. Currently, there are few methods for optimal portfolio allocation across these multiple periods that match how practitioners operate in the real world. Herein, we present a multi-period optimization method using a goals-based framework that allows practitioners to develop multi-period capital market expectations and optimize their portfolios across those periods.

Trotz nach wie vor bestehender Unterrepräsentation werden immer mehr Ministerien in den deutschen Bundesländern mit Frauen besetzt. Weniger betrachtet wurde bisher, ob Frauen in diesem Prozess bestimmte Ministerien häufiger besetzen, es also zu geschlechtsspezifischer Portfolioallokation kommt. Diese ist vor allem relevant, wenn die weiblich besetzten Ministerien weniger prestigeträchtig sind. Davon ausgehend untersucht diese Studie die Prestigedifferenz zwischen männlich und weiblich besetzten Ministerien in den 16 Ländern zwischen 2005 und 2018 sowie mögliche Erklärungsfaktoren. Die Prestigedifferenz wird über den unterschiedlichen Anteil an Beamt*innenplanstellen der Ministerien mess- und vergleichbar gemacht. Die Studie kommt zu dem Ergebnis, dass Frauen trotz ihres wachsenden Anteils in Kabinetten vermehrt weniger prestigeträchtige Ministerien besetzen und somit die Prestigedifferenz ansteigt. Als Erklärungsfaktoren zeigen vor allem die Ministerienanzahl sowie der Frauenanteil im Kabinett einen Effekt. Despite persisting underrepresentation, more and more ministries in the German federal states (Länder) are occupied by women. So far, less consideration has been given to whether women are more likely to occupy certain particular ministries in this process, i.e. whether gender-specific portfolio allocation occurs. This is particularly relevant in cases where the female-occupied ministries are less prestigious. Therefore, this study examines the prestige difference between male and female ministries in the German federal states between 2005 and 2018 as well as possible explanatory factors. Prestige difference is measured by the different proportion of posts for civil servants within the ministries. The study concludes that, despite the increasing proportion of women in cabinets, women are increasingly occupying less prestigious ministries - thus, the prestige gap increases. The number of ministries and the proportion of women in the cabinet itself can partially explain this outcome.

We study optimal life-cycle portfolio allocation and its application to target-date fund (TDF) design. We show that optimal TDF allocation must be explicitly linked to a savings rate; for example, a higher savings rate generating higher financial wealth accumulation should necessarily come with a more conservative TDF. Further, we quantify the extent to which accumulated wealth, the investing environment, and participant characteristics affect TDF allocations and compare the resulting optimal allocations against the observed universe of U.S. TDF products. Plan sponsors and asset managers can use these findings to improve TDF selection and design.

In this article, the authors present a conceptual framework named adaptive seriational risk parity (ASRP) to extend hierarchical risk parity (HRP) as an asset allocation heuristic. The first step of HRP (quasi-diagonalization), determining the hierarchy of assets, is required for the actual allocation done in the second step (recursive bisectioning). In the original HRP scheme, this hierarchy is found using single-linkage hierarchical clustering of the correlation matrix, which is a static tree-based method. The authors compare the performance of the standard HRP with other static and adaptive tree-based methods, as well as seriation-based methods that do not rely on trees. Seriation is a broader concept allowing reordering of the rows or columns of a matrix to best express similarities between the elements. Each discussed variation leads to a different time series reflecting portfolio performance using a 20-year backtest of a multi-asset futures universe. Unsupervised learningbased on these time-series creates a taxonomy that groups the strategies in high correspondence to the construction hierarchy of the various types of ASRP. Performance analysis of the variations shows that most of the static tree-based alternatives to HRP outperform the single-linkage clustering used in HRP on a risk-adjusted basis. Adaptive tree methods show mixed results, and most generic seriation-based approaches underperform. Key Findings ▪ The authors introduce the adaptive seriational risk parity (ASRP) framework as a hierarchy of decisions to implement the quasi-diagonalization step of hierarchical risk parity (HRP) with seriation-based and tree-based variations as alternatives to single linkage. Tree-based variations are further separated in static and adaptive versions. Altogether, 57 variations are discussed and connected to the literature. ▪ Backtests of the 57 different HRP-type asset allocation variations applied to a multi-asset futures universe lead to a correlation matrix of the resulting 57 portfolio return time series. This portfolio return correlation matrix can be visualized as a dendrogram using single-linkage clustering. The correlation hierarchy reflected by the dendrogram is similar to the construction hierarchy of the quasi-diagonalization step. Most seriation-based strategies seem to underperform HRP on a risk-adjusted basis. Most static tree-based variations outperform HRP, whereas adaptive tree-based methods show mixed results. ▪ The presented variations fit into a triple artificial intelligence approach to connect synthetic data generation with explainable machine learning. This approach generates synthetic market data in the first step. The second step applies an HRP-type portfolio allocation approach as discussed in this article. The third step uses a model-agnostic explanation such as the SHAP framework to explain the resulting performance with features of the synthetic market data and with model selection in the second step.

To characterize ambiguity we use machine learning to impose guidance and discipline on the formulation of expectations in a data-rich environment. In addition, we use the bootstrap to generate plausible synthetic samples of data not seen in historical real data to create statistics of interest pertaining to uncertainty. While our approach is generic we focus on robust portfolio allocation problems as an application and study the impact of risk versus uncertainty in a dynamic mean-variance setting. We show that a mean-variance optimizing investor achieves economically meaningful wealth gains (33%) across our sample from 1996-2019 by internalizing our uncertainty measure during portfolio formation.

This paper studies how households make portfolio allocation decisions to save for children's college, and how these decisions affect children's college enrollment and student debt. I first document that more than 85% of households allocate college savings to risky assets, in particular more than 60% to stocks. I also demonstrate that college savings motives explain approximately one-third of stock market participation probability outside of retirement accounts for households with children. My results show that the allocation of college savings to stocks is associated with higher children's college enrollment rates and lower student loan participation rates after controlling for family and student characteristics. Exploiting exogenous variation in the portfolio allocation of 529 accounts, I find that holding a 10% greater share of 529 accounts in stocks during the school years increases the accumulated amount of college savings up to 20% and leads to a higher probability of enrollment in a selective four-year college and a lower student loan participation for 529 account beneficiaries from middle- and high-income households.

Medical expenditure risk is widely believed to reduce households' willingness to bear other risks and in turn alter their behavior. In this paper, we investigate the role of health insurance in household financial decision. To this end, we consider a double-multinomial discrete-factor model of insurance choice and portfolio allocation. Using data from a Chinese household survey, we find that enrolling in a health insurance scheme with better policies is associated with a higher probability of owning risky assets. This positive effect is stronger for households with lower risk aversion. Our findings suggest that risk attitudes could indirectly influence portfolio outcomes through affecting households' responsiveness to changes in medical expenditure risk.