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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]

How are ministerial portfolios distributed among coalition parties in Asian-Pacific democracies? Studies of power-sharing in Asia tend to focus on democratization processes rather than executive cabinets, although government coalitions occur regularly in the region. Using an original dataset of governments in 27 Asian-Pacific democracies from 1945 to 2018, I examine the bargaining advantage of the formateur party - the party managing the government formation process - over other parties entering government. We know from existing studies, mainly on Europe, that government parties holding larger shares of legislative seats receive larger shares of cabinet posts. I argue that portfolio allocation also depends on institutional context, and use the substantial institutional variation across countries in my dataset to test implications of this argument. I find that formateur parties have a greater advantage over coalition partners in presidential systems than in parliamentary ones, but that this advantage diminishes as political constraints facing the formateur increase. [ABSTRACT FROM AUTHOR]

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy's stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji's South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset's beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered. [ABSTRACT FROM AUTHOR]

Recent research suggests that party leaders can strategically impact the perceived left–right position of their parties by changing their selective emphasis on certain issues. We suggest that a party's ideological image can also be altered by the portfolio allocation of the coalition government in which the party participates. By controlling a portfolio, the party will have a more direct influence on the related issue and will frequently communicate the party's issue position publicly, thereby cultivating a perception of strong emphasis on the related issue. We run a cross-national party-level analysis showing that portfolio allocation matters with regard to the importance of the subdimensions for the general left–right dimension. In particular, the influence of sociocultural stances depends on the share of sociocultural portfolios. In addition, we show that the mechanism does not apply at the beginning of a government's tenure, but only after a year or longer in office. [ABSTRACT FROM AUTHOR]

Markowitz optimization plays a crucial role in modern portfolio theory. However, it is well known that Markowitz optimization is highly affected by estimation errors in the mean vector and covariance matrix, resulting in extreme and/or unrealistic portfolio weights, lack of diversification, and poor out-of-sample performance. In response to these challenges, Michaud and Michaud (1998) proposed a heuristic portfolio resampling approach aimed to deliver more diversified and better out-of-sample portfolio performance. This approach has received both criticism and praise among academics and practitioners, with the main critique being the unclear economic benefits of applying this method to empirical data. To contribute to this ongoing debate, our study assess the performance of the Michaud and Michaud (1998) portfolio resampling approach using data from the Brazilian stock market. Additionally, we evaluate whether a resampling approach based on factor structure can yield superior out-of-sample performance. Our findings indicate no evidence of superiority over Markowitz optimization, reinforcing criticisms of the portfolio resampling approach. [ABSTRACT FROM AUTHOR]

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g. size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios. [ABSTRACT FROM AUTHOR]

Growing research links household financial decisions to physical and mental health status within the nuclear family. We hypothesize that mental health issues (psychological distress) outside of the nuclear family unit are a unique contributor to household portfolio allocation decisions. We use panel data and find that having at least one sibling with psychological distress decreases the probability of risky asset ownership (stocks, mutual funds), decreases risky assets as a share of financial assets, and decreases total amount of risky asset holding. These results have important policy-related implications for understanding the connection between health, portfolio allocation, and wealth building. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors. We present an innovative element-aggregation method that aggregates matrix entries to estimate covariance matrices. This method is designed to be applicable to both sparse and non-sparse matrices, transcending the limitations of sparsity-based approaches. The computational simplicity of the method's implementation ensures low complexity, making it a practical tool for real-world applications. Theoretical analysis then confirms the method's consistency and effectiveness with its convergence rate in specific scenarios. Additionally, numerical experiments validate the method's superior algorithmic performance compared to conventional methods, as well as the reduction in relative estimation errors. Furthermore, empirical studies in financial portfolio optimization demonstrate the method's significant risk management benefits, particularly its ability to effectively mitigate portfolio risk even with limited sample sizes. [ABSTRACT FROM AUTHOR]

We commonly encounter the problem of identifying an optimally weight-adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behavior, shapes, number of modes, etc., of the resulting weight-adjusted empirical distribution. In this article, we substantially enhance the flexibility of such a methodology by introducing a nonparametrically imbued distributional constraint on the weights and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight-adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric, while allowing for subtle departures. The proposed scheme for the re-weighting of observations subject to constraints is reminiscent of the empirical likelihood and related ideas, but offers greater flexibility in applications where parametric distribution-guided constraints arise naturally. The versatility of the proposed framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task—namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms. [ABSTRACT FROM AUTHOR]

Which party controls which cabinet posts is an important determinant of how multi-party governments work. Existing research shows that parties' attention to policy domains in election manifestos is a key predictor of portfolio allocation. However, election manifestos are broad documents and typically published months before an election. This research note argues that policy emphasis in the last few weeks before the election matters for portfolio allocation, because parties can focus their message, react to exogenous events and use campaign communication as a commitment device. A test of this argument makes use of a novel dataset on party representatives' campaign statements. The findings show that the policy focus of campaign statements, especially those stating positions rather than referring to valence, predicts who will control a ministerial portfolio associated with the respective policy domain. [ABSTRACT FROM AUTHOR]

Effective denoising strategies are increasingly important for portfolio investors. Considering that the common ensemble empirical mode decomposition (EEMD)-based stepwise denoising algorithms suffer from white noise interference and ignore the effect of low-frequency redundant noise components on the portfolio, a novel complete EEMD with adaptive noise (CEEMDAN) denoising algorithm is proposed to improve the portfolio performance. Specifically, we apply CEEMDAN to decompose noisy prices into a series of intrinsic mode functions (IMFs). Then, a series of tests based on the correlations between the original noisy prices and the decomposed IMFs are performed to identify which IMFs are noisy modes. If the tests accept the null hypothesis, the IMFs are considered as noisy components. Finally, we use the soft-threshold technique to process the noisy components and sum the non-noisy components to construct the denoised prices. The empirical results show that under the dynamic minimum-CVaR framework, the proposed CEEMDAN denoising algorithm is not affected by white noise and outperforms the EEMD denoising and stepwise denoising algorithms in improving out-of-sample portfolio returns. Overall, the proposed CEEMDAN denoising is the optimal denoising algorithm, which can help investors improve portfolio performance to the greatest extent. [ABSTRACT FROM AUTHOR]

This paper addresses the way parties assign members to parliamentary committees in fragmented multiparty settings. Thus, it analyzes how the two most central institutions of parliamentary politics––political parties and parliamentary committees––interact with one another. To the best of our knowledge, no research into this subject has systematically explored the intersection of considerations based on individual legislator characteristics and coalition management in committee assignment. Using Israel as our case study, we show that legislators' expertise modestly shapes committee assignment patterns. However, parties in coalition often have another set of considerations to take into account when assigning members to committees. We show that parties in coalition do not only bargain on ministerial positions or committee chairs––they also bargain on their members' assignment to committees and use this resource to allow (or hinder) each other to augment influence and control in a given policy area, or to perform affective monitoring. [ABSTRACT FROM AUTHOR]

This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk--return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies. [ABSTRACT FROM AUTHOR]

This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using cellwise robust association measures, the minCluster portfolio is able to retrieve the underlying hierarchical structure in the data. Furthermore, it provides downside protection by using tail risk measures for portfolio optimization. We show through simulation studies and a real data example that the minCluster portfolio produces better out-of-sample results than meanvariances or other hierarchical clustering based approaches. Cellwise outlier robustness makes the minCluster method particularly suitable for stable optimization of portfolios in highly volatile markets, such as portfolios containing cryptocurrencies. [ABSTRACT FROM AUTHOR]

Most of the literature on life cycle investment portfolio analysis focuses on the allocation between risky stocks and safe bonds. We introduce a new risky asset class, cryptocurrency, to a standard consumption-investment life cycle model. Our model suggests that the optimal investment profile in cryptocurrencies declines with age. Young investors mainly invest in cryptocurrency. As age and wealth increase, investors transition to mostly stocks mid-career and mostly bonds in retirement. A welfare analysis shows significant utility losses from not participating in the cryptocurrency market or not adjusting cryptocurrency portfolio shares throughout the life cycle. [ABSTRACT FROM AUTHOR]

This article exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors. We present an innovative element-aggregation method that aggregates matrix entries to estimate covariance matrices. This method is designed to be applicable to both sparse and non-sparse matrices, transcending the limitations of sparsity-based approaches. The computational simplicity of the method’s implementation ensures low complexity, making it a practical tool for real-world applications. Theoretical analysis then confirms the method’s consistency and effectiveness with its convergence rate in specific scenarios. Additionally, numerical experiments validate the method’s superior algorithmic performance compared to conventional methods, as well as the reduction in relative estimation errors. Furthermore, empirical studies in financial portfolio optimization demonstrate the method’s significant risk management benefits, particularly its ability to effectively mitigate portfolio risk even with limited sample sizes.

We commonly encounter the problem of identifying an optimally weight-adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behavior, shapes, number of modes, etc., of the resulting weight-adjusted empirical distribution. In this article, we substantially enhance the flexibility of such a methodology by introducing a nonparametrically imbued distributional constraint on the weights and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight-adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric, while allowing for subtle departures. The proposed scheme for the re-weighting of observations subject to constraints is reminiscent of the empirical likelihood and related ideas, but offers greater flexibility in applications where parametric distribution-guided constraints arise naturally. The versatility of the proposed framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task—namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.

This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using cellwise robust association measures, the minCluster portfolio is able to retrieve the underlying hierarchical structure in the data. Furthermore, it provides downside protection by using tail risk measures for portfolio optimization. We show through simulation studies and a real data example that the minCluster portfolio produces better out-of-sample results than mean-variances or other hierarchical clustering based approaches. Cellwise outlier robustness makes the minCluster method particularly suitable for stable optimization of portfolios in highly volatile markets, such as portfolios containing cryptocurrencies.

Minority presidents form coalitions to secure legislative support. However, such support comes at the policy cost of delegating power to coalition partners. Given this dilemma, how do presidents decide on portfolio allocation? The argument is that presidents consider ministries' policy relevance and the characteristics of coalition parties when distributing positions. The president will allocate the most important ministries to aligned ministers (seeking policy control) and to the larger coalition parties (seeking legislative support), distributing the least policy-relevant ministries to clientelist parties. The article presents an original measure for policy salience based on topic modeling of presidential addresses and conditional logit models to test the hypotheses. The analysis consists of a comparative study of Brazil, Chile, and Colombia over 24 years. Results indicate that presidents use salience to sort ministries and decide portfolio allocation. This evidence for the president-specific salience argument shows the relevance of considering the qualitative aspects of portfolio allocation in multiparty presidential systems and how presidents share power with coalition partners without losing control over governmental decisions. [ABSTRACT FROM AUTHOR]

Numerous European leaders have formed cabinets that contain equal numbers of women and men. But forming a gender-equal team at the center of the European Union remains a particularly difficult undertaking. This article analyses the case of Ursula von der Leyen, who, in 2019, set out to form a gender-equal college of commissioners. It analyses why von der Leyen's first leadership project did not initially succeed and assesses the longer-term prospects of gender equality in the European Commission. Employing a framework drawn from studies of Commission leadership, commissioner selection, and gendered executives, it conceptualizes Commission presidents-elect as constrained selectors. Even with personal dispositions that support gender equality and access to powerful institutional resources, treaty-based rules and situational settings obstruct the ability of presidents-elect to achieve their representational goals. In the future, even presidents-elect who are strongly committed to gender equality will likely struggle to achieve fully balanced colleges. [ABSTRACT FROM AUTHOR]

The study intends to examine the cause-and-effect relationship between Covid-19 and the factors affecting investment behavior in a South Asian economy. The investment behavior is considered as an MCDM problem. To address this problem, the study employs MCDM approach i.e., a blend of both DEMATEL and Grey theory due to its potential to deal with subjective judgments of investors. The results indicate that Covid-19 is the leading cause behind financial stress, psychophysiological health outcomes, investors' perception about the market, and investors' strategy. Among sub-factors, portfolio allocation is the most influenced sub-factor. Alteration in portfolio is a major challenge for emerging countries which have become attractive destinations for global investors. Overall, the significant contribution of the paper is to establish the interlinkages among the factors affecting investment behavior, given the uncertainty triggered by the pandemic. Although the literature provides evidence on this problem during normal situations, analysis of investment behavior during severe crisis is still lacking. The research will be immensely useful to different stakeholders such as government, policymakers, financial advisors, and investors in making their strategic or operational decisions. [ABSTRACT FROM AUTHOR]

In the absence of transaction costs and the presence of independent returns, a buy-and-hold strategy theoretically generates higher expected returns than a fixed-weight strategy, where the portfolio weights are regularly readjusted/rebalanced to some initial level. This higher expected return comes with higher volatility. The resulting trade-off leads to different rankings of the Sharpe ratio depending on the statistical moments of the assets. We also focus on Maximum Drawdown. We theoretically discuss causes affecting the ranking of the Sharpe ratio, and we introduce an easy-to-implement methodology to deal with proportional transaction costs. Under transaction costs, the buy-and-hold strategy as the cheaper approach should be the winner. In various simulation experiments, we investigate the relevance of transaction costs on rebalancing strategies. Eventually, we consider several realistic portfolios with a risk-free asset, bonds, stock indices, commodities and real estate that allow us to demonstrate that in practice rebalancing has value. [ABSTRACT FROM AUTHOR]

This paper examines how the financial globalization affects international equity mutual funds' portfolio choices in emerging markets. By examining the monthly holdings of 155 international funds, we first show that these funds actively engage in a rebalancing strategy to maintain their risk preferences upon realization of excess return changes. We also document robust evidence that these funds' propensity of rebalancing is larger in a country whose equity market is more strongly correlated with the global market. The results help understand how the financial globalization may raise the portfolio risk of the international funds' equity holdings in emerging economies.

Recently, with the increasing interest in investments in financial stock markets, several methods have been proposed to automatically trade stocks and/or predict future stock prices using machine learning techniques, such as reinforcement learning (RL), LSTM, and transformers. Among them, RL has been applied to manage portfolio assets with a sequence of optimal actions. The most important factor in investing in stocks is the utilization of past stock price data. However, existing RL algorithms applied to stock markets do not consider past stock data when taking optimal actions, as RL is formulated based on the Markov property. In other words, it means that the existing RL algorithm infers action based on the current state only. To resolve this limitation, we propose Transformer Actor-Critic with Regularization (TACR) using decision transformer to train the model with the correlation of past MDP (Markov Decision Process) elements using an attention network. In addition, a critic network is added to improve the performance by updating the parameters based on the evaluation of an action. For an efficient learning method, we train our model using an offline RL algorithm through suboptimal trajectories. To prevent overestimating the value of actions and reduce learning time, we train TACR through a regularization technique with an added behavior cloning. The experimental results using various stock market datasets show that TACR performs better than other state-of-the-art methods in terms of the Sharpe ratio and profit.

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 monthly excess returns with macroeconomic factors including payout yields. The second is used to estimate the prevailing volatility. 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 unifying framework for machine learning applied to both return- and volatility-timing.

The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take into account these market trends; however, the bull and bear market states tend to be unobserved and can only be assigned retrospectively. We fit a linked hidden Markov model (LHMM) to relative stock price changes for S&P 500 stocks from 2011–2016 based on weekly closing values. The LHMM consists of a multivariate state process whose individual components correspond to HMMs for each of the 12 sectors of the S&P 500 stocks. The state processes are linked using a Gaussian copula so that the states of the component chains are correlated at any given time point. The LHMM allows us to capture more heterogeneity in the underlying market dynamics for each sector. In this study, stock performances are evaluated in terms of capital gains using the LHMM by utilizing historical stock price data. Based on the fitted LHMM, optimal stock portfolios are constructed to maximize capital gain while balancing reward and risk. Under out-of-sample testing, the annual capital gain for the portfolios for 2016–2017 are calculated. Portfolios constructed using the LHMM are able to generate returns comparable to the S&P 500 index. [ABSTRACT FROM AUTHOR]

We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework. [ABSTRACT FROM AUTHOR]

Portfolio allocation decisions over the life cycle depend, among many factors, on the retirement income as well as risks and choices faced in retirement. However, due to computational complexity, many retirement factors are typically assumed away. By building on the standard life cycle investment-consumption model that includes a more realistic progressive retirement income program, I discuss how each aspect of retirement income affects optimal portfolio allocation over the life cycle. I find that all investors across all scenarios maintain high levels of stocks in their portfolios at a young age. However, investors who face low net replacement rates, risk of forced retirement, or retirement income uncertainty hedge these risks by accumulating higher private savings and reducing risky portfolio shares at an earlier age. In a realistic setting with early forced retirement risk and endogenous retirement timing, optimal equity portfolio share temporarily increases once the investor becomes eligible for retirement. Retirement income's dependency on workers' lifetime labor earnings is not, however, an important factor in portfolio allocation over the life cycle. [ABSTRACT FROM AUTHOR]

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 an impact on the performance of ESG-constrained portfolios. The analysis on GHG intensities shows that portfolios with reduced emissions do not necessarily have increased risk or diminished returns. [ABSTRACT FROM AUTHOR]

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability.

Three cabinets (Conte I, Conte II, and Draghi) entered office during the 18th legislative term in Italy. In spite of the significant ideological differences between them, no full alternation of parties in government occurred. The largest party in parliament – the Five Star Movement – participated in all three cabinets, while the League and the Democratic Party took part in two of them (the League in the Conte I and Draghi, the Democratic Party in the Conte II and Draghi); other minor parties entered the Conte II and Draghi as well. Did party continuity lead to ministerial stability? This article puts the 18th legislative term in perspective, through a longitudinal comparison of all Italian partisan cabinets from 1994 to 2022 (15 cabinets). In particular, it aims to account for continuity and changes within the Italian ministerial elite across different cabinets, also controlling for the gendered aspect of cabinet reselections and promotions. It answers the following questions. What makes ministerial reselection likely? Do political and personal background count in being reappointed and promoted? Based on original data, the analysis shows that remarkable previous political experience, age, and time matter. In contrast, the type of portfolio held in cabinet and gender do not have a significant impact. In this context, the 18th legislative term appears in line with the general pattern, but it distances itself from other terms defined by party continuity across multiple cabinets. The article contributes to the debates about personnel turnover, representation, and policy-makers’ stability in democratic cabinets.

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability. [ABSTRACT FROM AUTHOR]

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 monthly excess returns with macroeconomic factors including payout yields. The second is used to estimate the prevailing volatility. 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 unifying framework for machine learning applied to both return- and volatility-timing. [ABSTRACT FROM AUTHOR]

Target-date funds in corporate retirement plans grew from $5 billion in 2000 to $734 billion in 2018, partly because federal regulation sanctioned these as default investments in automatic enrollment plans. We show that adopters delegated pension investment decisions to fund managers selected by plan sponsors. Inclusion of these funds in retirement saving menus raised equity shares, boosted bond exposures, curtailed cash/company stock holdings, and reduced idiosyncratic risk. The adoption of low-cost target-date funds may enhance retirement wealth by as much as 50% over a 30-year horizon. [ABSTRACT FROM AUTHOR]

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 of portfolio allocation, and an easy to implement approach to generate the synthetic sample for the model. Compared with the regression approach in existing literature for portfolio allocation, the proposed method of generating the synthetic sample provides more accurate approximation for the synthetic response variable when the number of assets under consideration is large. Due to the embedded leave-one-out idea, the synthetic sample generated by the proposed method has weaker within sample correlation, which makes the resulting portfolio allocation more close to the optimal one. This intuitive conclusion is theoretically confirmed to be true by the asymptotic properties established in this article. We have also conducted intensive simulation studies in this article to compare the proposed method with the existing ones, and found the proposed method works better. Finally, we apply the proposed method to real datasets. The yielded returns look very encouraging. [ABSTRACT FROM AUTHOR]

We study asset pricing implications of a revealing and tractable formulation of smooth ambiguity investor preferences in a continuous-time environment. Investors do not observe a hidden Markov state and instead make inferences about this state using past data. We show that ambiguity about this hidden state distribution alters investor decisions and equilibrium asset prices. Our continuous-time formulation allows us to apply recursive filtering and Hamilton–Jacobi–Bellman methods to solve the modified decision problem. Using such methods, we show how characterizations of portfolio allocations and local uncertainty-return tradeoffs change when investors are ambiguity-averse. [ABSTRACT FROM AUTHOR]

Ministerial portfolios that promise high status, broad public visibility, and extensive financial and personnel resources continue to be men's domains. In this article, we shed light on gender inequality in ministerial selection processes by studying the duration from a minister's original appointment as a member of cabinet until he or she receives responsibility for a highly prestigious portfolio. We argue that the time it takes for ambitious politicians to prove themselves suitable for this type of cabinet position depends on their sex and the degree to which the policy area for which they are responsible reinforces stereotypical expectations about their personality traits. Empirical evidence from event history analysis of original data including detailed information on all ministerial careers in 27 European countries between 1990 to 2018 supports these propositions. These findings reveal that even highly qualified women politicians who are already members of the executive face additional barriers during their political careers. [ABSTRACT FROM AUTHOR]

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment. [ABSTRACT FROM AUTHOR]