The growth-optimal portfolio is designed to have maximum expected log return over the next rebalancing period. Thus, it can be computed with relative ease by solving a static optimization problem. ...The growth-optimal portfolio has sparked fascination among finance professionals and researchers because it can be shown to outperform any other portfolio with probability 1 in the long run. In the short run, however, it is notoriously volatile. Moreover, its computation requires precise knowledge of the asset return distribution, which is not directly observable but must be inferred from sparse data. By using methods from distributionally robust optimization, we design fixed-mix strategies that offer similar performance guarantees as the growth-optimal portfolio but for a finite investment horizon and for a whole family of distributions that share the same first- and second-order moments. We demonstrate that the resulting robust growth-optimal portfolios can be computed efficiently by solving a tractable conic program whose size is independent of the length of the investment horizon. Simulated and empirical backtests show that the robust growth-optimal portfolios are competitive with the classical growth-optimal portfolio across most realistic investment horizons and for an overwhelming majority of contaminated return distributions.
This paper was accepted by Yinyu Ye, optimization
.
This paper proposes a portfolio choice model in which investors are subject to liquidation risk and (endogenously) face higher costs in the event of joint liquidation (as was observed during the ...crisis of 2008 to 2009). The risk of joint liquidation creates an incentive for investors to choose heterogeneous portfolios and to rationally forgo diversification benefits. Joint liquidation risk is also reflected in asset prices, resulting in (1) assets with high idiosyneratic risk having low expected returns, and (2) assets that display high correlation with the portfolios of (liquidation-prone) investors having high expected returns.
Investors interested in the global financial market must analyze financial securities internationally. Making an optimal global investment decision involves processing a huge amount of data for a ...high‐dimensional portfolio. This article investigates the big data challenges of two mean‐variance optimal portfolios: continuous‐time precommitment and constant‐rebalancing strategies. We show that both optimized portfolios implemented with the traditional sample estimates converge to the worst performing portfolio when the portfolio size becomes large. The crux of the problem is the estimation error accumulated from the huge dimension of stock data. We then propose a linear programming optimal (LPO) portfolio framework, which applies a constrained ℓ1 minimization to the theoretical optimal control to mitigate the risk associated with the dimensionality issue. The resulting portfolio becomes a sparse portfolio that selects stocks with a data‐driven procedure and hence offers a stable mean‐variance portfolio in practice. When the number of observations becomes large, the LPO portfolio converges to the oracle optimal portfolio, which is free of estimation error, even though the number of stocks grows faster than the number of observations. Our numerical and empirical studies demonstrate the superiority of the proposed approach.
Household investment mistakes are an important concern for researchers and policymakers alike. Portfolio underdiversification ranks among those mistakes that are potentially most costly. However, its ...roots and empirical importance are poorly understood. I estimate quantitatively meaningful diversification statistics and investigate their relationship with key variables. Nearly all households that score high on financial literacy or rely on professionals or private contacts for advice achieve reasonable investment outcomes. Compared to these groups, households with below-median financial literacy that trust their own decision-making capabilities lose an expected 50 bps on average. All group differences stem from the top of the loss distribution.
Model Comparison with Sharpe Ratios Barillas, Francisco; Kan, Raymond; Robotti, Cesare ...
Journal of financial and quantitative analysis,
09/2020, Volume:
55, Issue:
6
Journal Article
Peer reviewed
Open access
We show how to conduct asymptotically valid tests of model comparison when the extent of model mispricing is gauged by the squared Sharpe ratio improvement measure. This is equivalent to ranking ...models on their maximum Sharpe ratios, effectively extending the Gibbons, Ross, and Shanken (1989) test to accommodate the comparison of nonnested models. Mimicking portfolios can be substituted for any nontraded model factors, and estimation error in the portfolio weights is taken into account in the statistical inference. A variant of the Fama and French (2018) 6-factor model, with a monthly updated version of the usual value spread, emerges as the dominant model.
We develop a novel method to dynamically hedge foreign exchange exposure in international equity and bond portfolios. The method exploits the time-series predictability of currency returns, which we ...show emerges from exploiting a forecastable component in global factor returns. The hedging strategy outperforms leading alternative approaches to currency hedging across a large set of performance metrics. Moreover, we find that exploiting currency return predictability via an independent currency portfolio delivers a high risk-adjusted return and provides superior diversification gains to global equity and bond investors relative to currency carry, value, and momentum investment strategies.
We formulate and carry out an analytical treatment of a single-period portfolio choice model featuring a reference point in wealth, S-shaped utility (value) functions with loss aversion, and ...probability weighting under Kahneman and Tversky's
cumulative prospect theory
(CPT). We introduce a new measure of loss aversion for large payoffs, called the
large-loss aversion degree
(LLAD), and show that it is a critical determinant of the well-posedness of the model. The sensitivity of the CPT value function with respect to the stock allocation is then investigated, which, as a by-product, demonstrates that this function is neither concave nor convex. We finally derive optimal solutions explicitly for the cases in which the reference point is the risk-free return and those in which it is not (while the utility function is piecewise linear), and we employ these results to investigate comparative statics of optimal risky exposures with respect to the reference point, the LLAD, and the curvature of the probability weighting.
This paper was accepted by Wei Xiong, finance.
We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients ...of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple and easily modified and extended to capture the effect of transaction costs, for example, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. We present an empirical implementation for the universe of all stocks in the CRSP- Compustat data set, exploiting the size, value, and momentum anomalies.
If an investor wants to form a portfolio of risky assets and can exert effort to collect information on the future value of these assets before he invests, which assets should he learn about? The ...best assets to acquire information about are ones the investor expects to hold. But the assets the investor holds depend on the information he observes. We build a framework to solve jointly for investment and information choices, with general preferences and information cost functions. Although the optimal research strategies depend on preferences and costs, the main result is that the investor who can first collect information systematically deviates from holding a diversified portfolio. Information acquisition can rationalize investing in a diversified fund and a concentrated set of assets, an allocation often observed, but usually deemed anomalous.
Cognitive abilities and portfolio choice Christelis, Dimitris; Jappelli, Tullio; Padula, Mario
European economic review,
2010, 2010-1-00, 20100101, Volume:
54, Issue:
1
Journal Article
Peer reviewed
Open access
We study the relation between cognitive abilities and stockholding using the recent Survey of Health, Ageing and Retirement in Europe (SHARE), which has detailed data on wealth and portfolio ...composition of individuals aged 50+ in 11 European countries and three indicators of cognitive abilities: mathematical, verbal fluency, and recall skills. We find that the propensity to invest in stocks is strongly associated with cognitive abilities, for both direct stock market participation and indirect participation through mutual funds and retirement accounts. Since the decision to invest in less information-intensive assets (such as bonds) is less strongly related to cognitive abilities, we conclude that the association between cognitive abilities and stockholding is driven by information constraints, rather than by features of preferences or psychological traits.
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