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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 introduce entrepreneurial spirit into an incomplete markets model of entrepreneurial firms and explain how it influences entrepreneurs’ allocation of wealth between consumption and asset portfolios, capital structure, investment, and business exit decisions with nondiversifiable risks. The study shows that entrepreneurs with an entrepreneurial spirit prefer to choose a relatively high debt level and tend to sell their firms earlier than those without an entrepreneurial spirit, which increases the default risk and credit spreads and leads to a higher discount rate of corporate bonds. Hence, our paper provides an alternative explanation for the credit spread puzzle and firms’ excessive risk management. [ABSTRACT FROM AUTHOR]

• The Augmented Tree-Based Conditional Sort approach is proposed for return prediction. • Momentum effect is found in the Chinese stock market during period from January 2005 to December 2022. • The corresponding momentum strategy robustly outperforms the market portfolio. • We locate year 2018 as a turning point from the reversal effect to the momentum effect. This paper proposes the Augmented Tree-Based Conditional Sort approach to investigate the return prediction problems. We find a momentum effect in the Chinese stock market using monthly data over 5000 stocks from January 2005 to December 2022. The corresponding momentum strategy robustly outperforms the market portfolio and earns an excess return of 10.60 % annually. Besides a full-period momentum effect, we find a sub-period reversal effect before year 2018 and locate year 2018 as a turning point from the reversal effect to the momentum effect. We attribute this phenomenon to the rapid increase of institutional investors since then. [ABSTRACT FROM AUTHOR]

• Examined how relative deprivation influences financial risk taking among Korean adults. • Relative deprivation was negatively associated with risk aversion. • The associations were significant for men and individuals who experienced increasing relative deprivation. • Relative deprivation was not linked to risky asset ownership and risky asset shares. • Offered empirical support for reference-dependent preference and social-comparison model. This study examines whether relative deprivation is associated with risk preference and behavior. Relative deprivation measured by the Yitzhaki and Deaton indices and financial risk-taking outcomes are obtained from a representative household survey of Korean adults. Results show that relative deprivation is negatively associated with risk aversion, and this association is significant for men and individuals who experienced increasing relative deprivation. However, relative deprivation is not associated with investment outcomes such as risky asset ownership and risky asset share of financial net worth. These results are consistent with reference-dependent preference and social-comparison model that incorporates social standing into utility function. [ABSTRACT FROM AUTHOR]

A portfolio allocation model is discussed in asset management with fuzziness. By perception-based extension for fuzzy random variables, the estimation methods of asset risks are introduced. Introducing an average value-at-risk for fuzzy random variables, this paper formulates a portfolios allocation model with average value-at-risks. This paper discusses maximization of the expected return under an average value-at-risk constraint with fuzzy random variables. A numerical example is given to demonstrate the results. [ABSTRACT FROM AUTHOR]

Markov decision processes have been applied in solving a wide range of optimization problems over the years. This study provides a review of Markov decision processes and investigates its suitability for solutions to portfolio allocation problems under vendor managed inventory in an uncertain market environment. The problem was formulated in the frame work of Markov decision process and a value iteration algorithm was implemented to obtain the expected reward and the optimal policy that maps an action to a given state. Two challenges were examined -the uncertainty about the value of the item which follows a stochastic model and the small state/action spaces that can be solved via value iteration. It was observed that the optimal policy is expected to always short the stock when in state 0 because of its large return. However, while the return is not as large as in state 0, the probability of staying in state 2 is high enough that the vendor should long the stock because he expects high reward for several periods. We also obtained the expected reward for each state every ten iterations using a discount factor of λ = 0.95. In spite of the small state/action spaces, the vendor is able to optimize its reward by the use of Markov decision process. [ABSTRACT FROM AUTHOR]

We analyze a methodology for portfolio selection based on the independent component analysis. In this paper parametric and non-parametric approaches are used for capturing the behavior of independent components that generate the distribution of asset returns. Although the setup is quite general, we focus mainly on the numerical issues encountered for parametric models and suggest the inclusion of a penalty function in the optimization problem. [ABSTRACT FROM AUTHOR]

The best asset allocation model is searched for. In this paper, we argue that it is unlikely to find an individual model which continuously outperforms its competitors. Rather one should consider a combined model out of a given set of asset allocation models. In a large empirical study using various standard asset allocation models, we find that (i) the best model depends strongly on the chosen data set, (ii) it is difficult to ex-ante select the best model, and (iii) the combination of models performs exceptionally well. Frequently, the combination even outperforms the ex-post best asset allocation model. The promising results are obtained by a simple combination method based on a bootstrap procedure. More advanced combination approaches are likely to achieve even better results. [ABSTRACT FROM AUTHOR]

It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric dependency structures using a nonparametric measure of local dependence, the local Gaussian correlation (LGC). It is shown that the portfolio optimization process for financial returns with asymmetric dependence structures is straightforward using local covariance matrices. The new method is shown to outperform the equally weighted ("1/N") portfolio and the classical Markowitz portfolio when applied to historical data on six assets. • Asymmetric dependence between asset returns leads to smaller diversification effects. • We present a method for portfolio allocation that incorporates asymmetric dependence. • The portfolio optimization is straightforward using local covariance matrices. • The local covariance matrices are obtained by the local Gaussian correlation (LGC). • The method is shown to outperform the "1/N" portfolio and the Markowitz portfolio. [ABSTRACT FROM AUTHOR]

• Data of 19,336 brokerage clients with security transactions and account bookings. • Identification of smokers as empirical proxy of self-control failure. • Smokers are more likely to seek and follow financial advice. • Advice lowers smokers' turnover while increasing diversification. • Advice works as an effective commitment for investors with low self-control. 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. [ABSTRACT FROM AUTHOR]

It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric dependency structures using a nonparametric measure of local dependence, the local Gaussian correlation (LGC). It is shown that the portfolio optimization process for financial returns with asymmetric dependence structures is straightforward using local covariance matrices. The new method is shown to outperform the equally weighted ("1/N") portfolio and the classical Markowitz portfolio when applied to historical data on six assets. • Asymmetric dependence between asset returns leads to smaller diversification effects. • We present a method for portfolio allocation that incorporates asymmetric dependence. • The portfolio optimization is straightforward using local covariance matrices. • The local covariance matrices are obtained by the local Gaussian correlation (LGC). • The method is shown to outperform the "1/N" portfolio and the Markowitz portfolio. [ABSTRACT FROM AUTHOR]

• Data of 19,336 brokerage clients with security transactions and account bookings. • Identification of smokers as empirical proxy of self-control failure. • Smokers are more likely to seek and follow financial advice. • Advice lowers smokers' turnover while increasing diversification. • Advice works as an effective commitment for investors with low self-control. 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. [ABSTRACT FROM AUTHOR]

• We analyze sectoral connectedness in the US, from early 2013 to the end of 2020. • Extraordinary increase in total connectedness during the pandemic is found. • Pairwise connectedness configuration has considerably changed during COVID-19. • Most sectors maintained their pervious status as net sender/receiver during COVID-19. • Financials has shown to be relevant sender during the pandemic in several metrics. We examine volatility connectedness of 11 sectoral indices in the US using daily data from January 01, 2013 to December 31, 2020. We employ the connectedness measures of Diebold and Yilmaz (2009, 2012, 2014), unveiling changes in sectoral connectedness and stylized facts regarding specific sectors during the COVID-19 pandemic. Among several results, we find extraordinary increase in total connectedness, from early stages of international spread to the end of July 2020; some relevant changes in the pairwise connections between sectors, especially among the originally stronger ones. However, in a total net connectedness perspective, there is little evidence of structural changes. [ABSTRACT FROM AUTHOR]

Risk, return, and different measures of the Greenness. [Display omitted] 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. [ABSTRACT FROM AUTHOR]

• Alternative investments (cryptocurrencies) in traditional stock portfolios add value by enhancing their performance. • PU approach yields less concentrated portfolio solutions and is less sensitive to sample size. • Results suggest higher efficacy of cryptos in boosting returns of portfolios with small-cap companies stocks. • Except the USDT, no other cryptocurrency seems to maintain its efficacy in boosting portfolio returns in the longer, 250-day time horizon. This study attempts to analyze the ability of the top 10 cryptocurrencies in enhancing portfolio returns of the 10 worst-performing stocks in the S&P600, S&P400 and S&P100 indexes, to match those of the 10 best-performing stocks therein. Applying probabilistic utility approach with different algorithms and time horizons, we find that addition of cryptocurrencies to traditional stock portfolios adds value in terms of enhancing returns. This is consistent with the growing literature on the hedging properties of cryptocurrencies against traditional financial assets. [ABSTRACT FROM AUTHOR]