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The stock market currently remains one of the most difficult systems to model in finance. Hence, it is a challenge to solve stock portfolio allocation wherein an optimal investment strategy must be found for a curated collection of stocks that effectively maximizes return while minimizing the risk involved. Deep reinforcement learning approaches have shown promising results when used to automate portfolio allocation, by training an intelligent agent on historical stock prices. However, modern investors are actively engaging with digital platforms such as social media and online news websites to understand and better analyze portfolios. The overall attitude thus formed by investors toward a particular stock or financial market is known as market sentiment. Existing approaches do not incorporate market sentiment which has been empirically shown to influence investor decisions. In our paper, we propose a novel deep reinforcement learning approach to effectively train an intelligent automated trader, that not only uses the historical stock price data but also perceives market sentiment for a stock portfolio consisting of the Dow Jones companies. We demonstrate that our approach is more robust in comparison to existing baselines across standardized metrics such as the Sharpe ratio and annualized investment return.

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.

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.

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.

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.

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.

Are smokers more likely to seek and follow financial advice? We build on literature which shows that investors who smoke have self-control issues and trade impulsively. We empirically measure self-control failures by identifying cigarette addiction in a sample of German brokerage clients and show that smokers are more likely to delegate their decisions to financial advisors which work like a commitment device. Potential endogeneity issues and self-selection are addressed by using propensity score matching. Despite advisor incentives, such commitments work successfully in lowering overtrading, improving investment biases and increasing performance of low self-control investors.

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.

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.

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.

This paper investigates if opacity (as measured by derivatives usage) creates value for investors and the managers of hedge funds that charge performance fees. Since we do not identify a positive relation between opacity and managers’ revenue, it is not possible to state that opacity is a source of manager’s value creation for hedge fund investors and managers. However, considering that opacity is positively associated with risk-taking and negatively related with investors’ adjusted returns, we suggest policies aiming at protecting investors, especially those less qualified. We examine a unique and comprehensive database related to the positions in derivatives taken by managers, which was enabled due to specific disclosure regulatory demands of the Brazilian Securities Exchange Commission, where detailed information on hedge funds’ portfolio allocation should be provided on a monthly basis. RESUMO Este artigo investiga se a opacidade (mensurada pela utilização de derivativos) cria valor tanto para os investidores quanto para os gestores de fundos de hedge (que cobram taxas de performance). Como não foi verificada uma relação positiva entre opacidade e a receita do gestor, não é possível afirmar que ela crie valor para esses agentes ou mesmo para os investidores. Embora tenha sido constatado que a opacidade esteve positivamente associada à tomada de risco e negativamente relacionada com o retorno ajustado do fundo, foram sugeridas medidas de proteção para os investidores, especialmente os menos qualificados. Foi empregada uma base de dados única e abrangente relacionada a posições em derivativos em fundos. Isso foi possível devido às normas de divulgação da Comissão de Valores Mobiliários brasileira, que obrigam os gestores a publicar ao final de cada mês as informações detalhadas da alocação da carteira de cada fundo.

We examine how gender differences in investment risk tolerance, knowledge, confidence and portfolio cash allocations relate to the gender mix of investors and financial advisors among a sample of wealthy individuals in the UK. Our results demonstrate that gender effects are more nuanced than previously assumed. First, while even wealthy women consider themselves more conservative and allocate a higher proportion of their investable assets to cash than men, previous findings of lower investment knowledge and confidence do not extend to our sample. Second, having an advisor matters. Advised investors perceive themselves to have a higher risk tolerance and invest 10.6%-points more than self-directed investors. Finally, the investor-advisor gender combination matters, but only for female investors. Women with male advisors are more risk averse, feel less knowledgeable and less confident about their investment decisions. They also invest 11%-points less than women with female advisors. Indeed, female investors advised by women report the highest risk tolerance and make the lowest portfolio allocation to risk-free assets across the full sample, including men.

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.

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.

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.

This article draws on stylized facts to build a dynamic portfolio allocation model of sovereign wealth funds (SWF). We show that a traditional dynamic Merton allocation model allows for the stylized evidence that, on the one hand, the shares of monetary assets in such funds grow with the risk aversion of the state-investor in time, and on the other hand, these funds include the presence or absence of hedge funds correlated to the financial situation. One weakness of this model is its prediction of a lower risk/riskier asset ratio for sovereign stabilization funds and generational savings sovereign funds. This result contradicts the stylized fact of a lower risk/riskier asset ratio in stabilization funds than in generational savings funds. A dynamic model inspired by the theoretical framework of Bajeux-Besnainou et al. [1] is compatible with all the stylized facts.

Many financial portfolios are not mean-variance-skewness-kurtosis efficient. We recommend tilting these portfolios in a direction that increases their estimated mean and third central moment and decreases their variance and fourth central moment. The advantages of tilting come at the cost of deviation from the initial optimality criterion. In this paper, we show the usefulness of portfolio tilting applied to the equally-weighted, equal-risk-contribution and maximum diversification portfolios in a UCITS-compliant asset allocation setting., Mean-variance-skewness-kurtosis; Non-normality; Portfolio allocation; Tilting; Statistics; Finance; Banking; Econometrics; Operations management; Business; Economics; Information science; Industry

This paper investigates how culture affects US global mutual funds’ financial performance. I show that cultural dimensions are crucial not only for portfolio allocation choices but also for assessing the performance of institutional investors. Choosing securities from familiar cultural proximate markets significantly enhances the performance of mutual funds. The results suggest that fund managers can create positive performance for their clients if their portfolio allocation decisions are titled towards choosing cultural proximate markets that exhibit lower levels of information asymmetry.

This paper considers a U.S. institutional investor who is implementing a long-term portfolio allocation using forecasts of financial returns. We compare the predictive performance of two competing macro-finance models ---an unrestricted Vector AutoRegression (VAR) and a fully-structural Dynamic Stochastic General Equilibrium (DSGE) model--- for horizons up to 15 years. Although the performances are similar for short horizons, the DSGE model outperforms the VAR at forecasting financial returns in the long term. This model also generates substantially higher Sharpe ratios. Although it contains fewer unknown parameters, it benefits from economically grounded restrictions that help anchor financial returns in the long term.

To enhance the portfolio allocation process, individuals need to understand their financial ability and psychological willingness to tolerate risks. To do this, their risk tolerance level must be quantified. This study used a survey questionnaire to collect data from 470 students at selected South African universities, and a binary logistic regression to test the effect of demographic factors on financial risk tolerance versus non-financial risk tolerance. Our findings suggest that the level of risk tolerance cannot be generalized across different risk domains. We also found that demographic factors affect the two domains of risk tolerance differently. Specifically, age did not have a significant influence on financial risk tolerance, while it significantly increased non-financial risk tolerance. Similarly, gender did not have any significant influence on non-financial risk tolerance, while it positively affect-ed financial risk tolerance. Furthermore, students in the fields of the humanities, engineering and IT showed a strong appetite for non-financial risks, but students in the commerce faculty preferred financial risks.

An accurate assessment of tail inequalities and tail asymmetries of financial returns is key for risk management and portfolio allocation. We propose a new test procedure for detecting the full ext...

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of f...

Value-at-Risk (VaR) is one of the main regulatory tools used for risk management purposes. However, it is difficult to compute optimal VaR portfolios; that is, an optimal risk-reward portfolio allocation using VaR as the risk measure. This is due to VaR being non-convex and of combinatorial nature. In particular, it is well known that the VaR portfolio problem can be formulated as a mixed integer linear program (MILP) that is difficult to solve with current MILP solvers for medium to large-scale instances of the problem. Here, we present an algorithm to compute near-optimal VaR portfolios that takes advantage of this MILP formulation and provides a guarantee of the solution's near-optimality. As a byproduct, we obtain an algorithm to compute tight lower bounds on the VaR portfolio problem that outperform related algorithms proposed in the literature for this purpose. The near-optimality guarantee provided by the proposed algorithm is obtained thanks to the relation between minimum risk portfolios satisfying a reward benchmark and the corresponding maximum reward portfolios satisfying a risk benchmark. These alternate formulations of the portfolio allocation problem have been frequently studied in the case of convex risk measures and concave reward functions. Here, this relationship is considered for general risk measures and reward functions. To illustrate the efficiency of the presented algorithm, numerical results are presented using historical asset returns from the US financial market., Comment: 21 pages, 7 figures

This study examines how the greenness of the firm affects the short- and long-term performance of IPOs. To measure the greenness of the firm, we develop the Greenness Index based on the emissions produced. We find that the greenness of the firms operating in services and financial sectors is higher than in other sectors. To examine the short- and long-run performance of IPOs, we classify our sample into high and low green firms. In the short-run, high green firms obtain a lower return than low green firms. However, high green firms perform better than low green firms in the long-run. This study also determines the factors that cause short- and long-run performance, and the results suggest that the firm’s greenness negatively influences initial returns and underperformance of IPOs. Finally, we develop a theoretical model in terms of the portfolio's allocation and assert that investors participate in high-green firms to optimize their portfolio.

The growth and popularity of defined contribution pensions, along with the government's increasing attention to retirement plan costs and investment choices provided, make it important to understand how people select their retirement plan investments. This paper shows how employees in a large firm altered their fund allocations when the employer streamlined its pension fund menu and deleted nearly half of the offered funds. Using administrative data, we examine the changes in plan participant investment choices that resulted from the streamlining and how these changes might affect participants’ eventual retirement wellbeing. We show that streamlined participants’ new allocations exhibited significantly lower within-fund turnover rates and expense ratios, and we estimate this could lead to aggregate savings for these participants over a 20-year period of $20.2 M, or in excess of $9,400 per participant. Moreover, after the reform, streamlined participants’ portfolios held significantly less equity and exhibited significantly lower risks by way of reduced exposures to most systematic risk factors, compared with their non-streamlined counterparts.

This article focuses on the cross-asset-class properties of quality as a factor, particularly its joint behavior across equities and fixed income. The authors aim to provide further insight into quality, a relatively newer factor, and to shed light on the implications for portfolio allocation. Their results show that the correlation between quality variants in equities and fixed income is actually historically lower than that between equities and fixed-income core benchmarks. A 60/40 allocation to the quality cross-asset-class mix offers a statistically significant return, with lower tail risk reflecting the greater possible diversification benefits from factor allocations compared to traditional cap-weighted allocations.

This paper focuses on three &ldquo, safe haven&rdquo, assets (gold, oil, and the Swiss Franc) and examines the impact of recent financial crises and some macroeconomic variables on their return co-movements during the last two decades. All financial crises produced significant increases in conditional correlations between these asset returns, thus revealing consistent portfolio shifts from more traditional towards safer financial instruments during turbulent periods. The world equity risk premium stands out as the most relevant macroeconomic variable affecting return co-movements, while economic policy uncertainty indicators also exerted significant effects. Overall, this evidence points out that gold, oil, and the Swiss currency played an important role in global investors&rsquo, portfolio allocation choices, and that these assets preserved their essential &ldquo, properties during the period examined.

Party elites in coalition governments are acutely aware that the deals they strike will be critically evaluated by their supporters, and that they risk losing support if they are perceived as ineffective negotiators. This has a powerful influence on the bargains parties strike. Because most supporters are unaware of the complex aspects of bargains and instead rely on simple heuristics to evaluate their most visible features, parties have incentives to meet supporter expectations primarily on easily observable outcomes. To do so, they make trade-offs on less observable outcomes. This implies that the more visible features of a bargain typically do not accurately reflect the relative success of parties in coalition negotiations. We evaluate our argument using original data on the office rewards and policy risks of portfolio allocation in 16 parliamentary democracies. Our findings support our argument, and they have important implications for the nature of representation under multiparty government.

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return with payout yields. The second is used to construct an optimized volatility estimate. Reward-risk timing with machine learning provides substantial improvements over the buy-and-hold in utility, risk-adjusted returns, and maximum drawdowns. This paper presents a new theoretical basis and unifying framework for machine learning applied to both return- and volatility-timing.

In this paper, we propose a new way to predict market returns for multi-assets (equity, fixed-income and commodity) by extracting features from macroeconomic data and performing machine learning algorithms for both regression and classification. Our approach aims to select robust models to build alternative risk premia portfolio. We apply machine learning algorithms to our investment universe and then apply different portfolio allocation methods. We discover the importance of integrating macroeconomic data to build portfolio, especially with classification techniques which enhance the Sharpe ratios of strategies.

We employ recent Survey of Consumer Finances (SCF) microdata from the US to analyze the impacts of confidence in one’s own financial knowledge, confidence in the economy, and objective financial literacy on investment in risky financial assets (equity and bonds) on both the extensive and intensive margins. Controlling for a rich set of covariates including risk aversion, we find that objective financial literacy is positively related to investment in risky assets as well as debt securities. Moreover, confidence in own financial skills additionally increases the probability of holding risky assets and bonds. While these relationships are rather robust for the extensive margin, they break down with regard to the conditional share of financial wealth in risky assets of those who actually hold them. The relevance of financial literacy as well as confidence varies considerably with the distribution of wealth as well as across several socio-economic dimensions such as age, education and race.

Our purpose is to test the selected groups of strategies during period ranging from 2000 to 2020 as a hedge to equities during market downturns. We made performance analysis and correlations to test the strategies’ relationship with equities. Moreover, we added essential safe haven assets to our analysis for better presentation of selected strategies’ ability to hedge against equity market downturns. Therefore, we could analyze performance in relationship not only with respect to equities but also to the safe haven assets which we used as a benchmark.

We show that mutual funds use information acquired by participating in the equity lending market to make portfolio allocation decisions. Using data from German mutual funds on their stock-level lending decisions, we find that funds lending shares are more likely to exit positions relative both to stocks that they do not lend and to funds that do not lend. Lenders also avoid losses by better timing the closure of long positions than for stocks they do not lend. Finally, we show information acquisition in the lending market allows lenders to front-run public disclosure of large short positions. The results suggest that the securities lending market provides a mechanism for mutual funds to acquire information.

Using an hourly dataset on retail investor security positions from Robinhood Markets, we find that ESG disclosures are irrelevant to retail investors’ portfolio allocation decisions. The response to ESG press releases by retail investors is no different than the routine portfolio adjustments that occur on non-event days. In contrast, these same investors make economically meaningful changes to their portfolios in response to press releases that do not pertain to ESG, especially those that pertain to earnings announcements. Our findings are not due to a lack of statistical power or other data shortcomings. Our conclusions contrast with evidence from experimental studies that retail investors respond favorably to ESG disclosures. In addition, while prior ESG studies have generally viewed investors as a single homogeneous group, our findings suggest that it is important to distinguish between retail and institutional investors.

Robo-Advisors guide investors through an automated investment advisory process, recommend personalized portfolio assignments based on their individual risk-affinity as well as investment goals and rebalance the portfolio automatically over time. Giving basic investment advice to customers, it can provide a useful way to reduce risk by diversifying and mitigating biases, while keeping a certain degree of performance at low costs. To verify these claims we conduct a sophisticated analysis of recommended portfolios of 36 Robo-Advisors, based on six distinct model customers with different risk-affinities and investment horizons, resulting in 216 recommended portfolios. We find that the analyzed Robo-Advisors provide distinct recommended portfolios for the different risk/investment horizon combinations, while sharing similarities in used products for portfolio allocation. We also find issues within the recommended portfolios, e.g. a low degree of distinctiveness between different investment horizons and a high amount of equities even in the short-term investment horizon.

The design of government portfolios – that is, the distribution of competencies among government ministries and office holders – has been largely ignored in the study of executive and coalition politics. This article argues that portfolio design is a substantively and theoretically relevant phenomenon that has major implications for the study of institutional design and coalition politics. The authors use comparative data on portfolio design reforms in nine Western European countries since the 1970s to demonstrate how the design of government portfolios changes over time. Specifically, they show that portfolios are changed frequently (on average about once a year) and that such shifts are more likely after changes in the prime ministership or the party composition of the government. These findings suggest a political logic behind these reforms based on the preferences and power of political parties and politicians. They have major implications for the study of institutional design and coalition politics.

This paper re-examines the static and time-varying asymmetries in quantile-specific relationship between gold returns and gold mining companies' stock returns. In doing so, we use novel cross-quantilogram approach that is built on quantiles of both distributions and control for the effect of crude oil price changes, overall stock market movements, stock market volatility and economic policy uncertainty. We find that the gold returns negatively predict the gold stock returns around the median quantile. Whereas the gold returns positively depend on the gold mining companies’ stock returns in the low and high quantiles. We therefore, support for an embedded real option in gold for gold mining companies which is time-varying and occurs in very short-run. This finding is useful for devising portfolio allocation and diversification strategies.

This article describes the journey of a family office to create and invest into a global stock market portfolio and get exposure to the world’s growth through equity ownership. We explored many products in the marketplace and considered many prevailing theories on portfolio allocation and optimization. However, we decided to follow our own advice, and set out to build a global equity portfolio that provides balanced exposure to ongoing economic, cultural,and investment growth. We explain here the logical sequence of events, our methodology, and our thinking as we organically arrived at an allocation and rebalancing algorithm from a multigenerational viewpoint. Very importantly, we also provide a description of the practical application to replicate the allocation in a cost-efficient manner by means of ETFs.

This article compares the results of Austria’s Household Finance and Consumption Survey (HFCS) on savings deposits and estimates on total financial assets with administrative records from the national accounts for the household sector. The microdata that are newly generated through the HFCS and the detailed (internally available) breakdown of savings deposits in the existing macrodata (financial accounts) lend themselves to a more in-depth analysis of the similarities and differences in these two sources. Comparing the data shows that the HFCSbased aggregate estimates are lower than the financial accounts data, which is in line with evidence from the literature. The article also shows, however, that the survey adequately captures the underlying patterns at the microlevel in terms of the overall financial portfolio allocation and the distribution of savings deposits over detailed breakdowns. Moreover, a simulation based on the HFCS data demonstrates the effect that the inclusion of savings deposits in the most affluent tail of the distribution has on common statistics. Undercoverage above all of the upper deposit ranges suggests an underestimation or bias in the statistics. This underestimation, however, can be shown to be relatively minor, particularly in the case of robust statistical measures, such as the median or percentile ratios.