This study investigates whether geographic variation in religion-induced gambling norms affects aggregate market outcomes. We conjecture that gambling propensity would be stronger in regions with ...higher concentrations of Catholics relative to Protestants. Consistent with our conjecture, we show that in regions with higher Catholic–Protestant ratios, investors exhibit a stronger propensity to hold lottery-type stocks, broad-based employee stock option plans are more popular, the initial day return following an initial public offering is higher, and the magnitude of the negative lottery-stock premium is larger. Collectively, these results indicate that religion-induced gambling attitudes impact investors' portfolio choices, corporate decisions, and stock returns.
We analyze the relation between time-series predictability and factor investing. We use a large set of financial, macroeconomic, and technical variables to time-series-manage the market portfolio. A ...combination of the out-of-sample market excess return forecasts of all variables yields a managed market portfolio that generates alphas relative to cross-sectional factor models that exceed 5% per annum. More broadly, the relation between time-series evaluation measures and (multifactor) alphas is weakly positive but complex. The variables’ predictability for future returns is more important than that for volatility. Finally, we document that managed market portfolios based on lagged factor realizations also perform well.
This paper was accepted by Lukas Schmid, finance.
Supplemental Material:
The online appendix and data are available at
https://doi.org/10.1287/mnsc.2022.4459
.
•Wishart correlations shrinked towards zero imply portfolios with large Sharpe ratios.•Estimated Wishart correlations support equicorrelation.•Estimated Wishart correlations are stable over time ...jumping at times of crisis.
Portfolio selection based on high-dimensional covariance matrices is a key challenge in data-rich environments with the curse of dimensionality severely affecting most of the available covariance models. We challenge several multivariate Dynamic Conditional Correlation (DCC)-type and Stochastic Volatility (SV)-type models to obtain minimum-variance and mean-variance portfolios with up to 1000 assets. We conclude that, in a realistic context in which transaction costs are taken into account, although DCC-type models lead to portfolios with lower variance, modeling the covariance matrices as latent Wishart processes with a shrinkage towards the diagonal covariance matrix delivers more stable optimal portfolios with lower turnover and higher information ratios. Our results reconcile previous findings in the portfolio selection literature as those claiming for equicorrelations, a smooth dynamic evolution of correlations or correlations close to zero.
It is widely noted that market capitalisation weighted portfolios are inefficient and underperform an equal weighted portfolio over the long-term. However, at least since 2016, an equal weighted ...portfolio of stocks in the S&P500 has significantly underperformed the market capitalisation weighted portfolio. In this paper, we analyse this underperformance using stochastic portfolio theory. We show that the equal weighted portfolio does appear to outperform the market capitalisation weighted portfolio over the long-term but with periods of significant short-term underperformance. In addition, we find that concentration in the market capitalisation weighted portfolio has increased in recent years and has contributed to the recent underperformance together with a significantly lower level of diversification benefits. Furthermore, we highlight an approach to improve the performance of a portfolio by dynamically selecting a market cap or an equal weighting using a rudimentary linear regression model.
This paper studies the estimation of high-dimensional minimum variance portfolio (MVP) based on the high frequency returns which can exhibit heteroscedasticity and possibly be contaminated by ...microstructure noise. Under certain sparsity assumptions on the precision matrix, we propose estimators of the MVP and prove that our portfolios asymptotically achieve the minimum variance in a sharp sense. In addition, we introduce consistent estimators of the minimum variance, which provide reference targets. Simulation and empirical studies demonstrate the favorable performance of the proposed portfolios.
•Traditional mean-variance strategy is worse than monkey picking in high dimensions.•A linear-programming-based estimator/remedy for mean-variance strategy is devised.•Consistency results for the ...proposed approach in a multi-period setting are derived.•Simulation studies confirm the accuracy and efficiency of the proposed approach.•The proposed estimator outperforms other competitors in extensive empirical studies.
This paper studies the mean-variance (MV) portfolio problems under static and dynamic settings, particularly for the case in which the number of assets (p) is larger than the number of observations (n). We prove that the classical plug-in estimation seriously distorts the optimal MV portfolio in the sense that the probability of the plug-in portfolio outperforming the bank deposit tends to 50% for p ≫ n and a large n. We investigate a constrained ℓ1 minimization approach to directly estimate effective parameters that appear in the optimal portfolio solution. The proposed estimator is implemented efficiently with linear programming, and the resulting portfolio is called the linear programming optimal (LPO) portfolio. We derive the consistency and the rate of convergence for LPO portfolios. The LPO procedure essentially filters out unfavorable assets based on the MV criterion, resulting in a sparse portfolio. The advantages of the LPO portfolio include its computational superiority and its applicability for dynamic settings and non-Gaussian distributions of asset returns. Simulation studies validate the theory and illustrate its finite-sample properties. Empirical studies show that the LPO portfolios outperform the equally weighted portfolio and the estimated optimal portfolios using shrinkage and other competitive estimators.
We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by ...transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized versions of efficient allocations. The regulatory effect of transaction costs is studied in an econometric setting incorporating parameter uncertainty and optimally combining predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study, we illustrate that turnover penalization is more effective than commonly employed shrinkage methods and is crucial in order to construct empirically well-performing portfolios.
Anomalies and the Expected Market Return DONG, XI; LI, YAN; RAPACH, DAVID E. ...
The Journal of finance (New York),
February 2022, Letnik:
77, Številka:
1
Journal Article
Recenzirano
ABSTRACT
We provide the first systematic evidence on the link between long‐short anomaly portfolio returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of the ...aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high‐dimensional setting. We find that long‐short anomaly portfolio returns evince statistically and economically significant out‐of‐sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.
Using error-free data on life-cycle portfolio allocations of a large sample of Norwegian households, we document a double adjustment as households age: a rebalancing of the portfolio composition away ...from stocks as they approach retirement and stock market exit after retirement. When structurally estimating an extended life-cycle model, the parameter combination that best fits the data is one with a relatively large risk aversion, a small per-period participation cost, and a yearly probability of a large stock market loss in line with the frequency of stock market crashes in Norway.
► We study the impact of long memory on dependence between financial returns. ► Copulas are considered to model the dependence. ► Wavelets are used to select copula and check stability of copula ...parameter. ► Optimal portfolio is obtained by minimizing CVaR copula program. ► We find that persistence affects both dependence and efficient frontier.
In this paper, we seek to examine the effect of the presence of long memory on the dependence structure between financial returns and on portfolio optimization. First, we focus on the dependence structure using copulas. To select the best copula, in addition to the goodness of fit tests, we employ a graphical method based on visual comparison of the fitted copula density and the smoothed copula density estimated by wavelets. Moreover, we check the stability of the copula parameter. The empirical results show that the long memory affects the dependence structure. Second, we analyze the impact of this dependence structure on the optimal portfolio. We propose a new approach based on minimizing the Conditional Value at Risk and assuming that the dependence structure is modeled by the copula parameter. The empirical results show that our approach outperforms the traditional minimizing variance approach, where the dependence structure is represented by the linear correlation coefficient.
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