查看更多>>摘要：While risk factor disclosures in 10-K filings have been criticized by practitioners as generic and boilerplate, recent studies indicate that these risk reports can be informative. This study contributes to the ongoing discussion by investigating whether risk factor disclosures contain valuable information that can be used to improve the estimation of the covariance matrix of stock returns. In particular, we examine the 10-K and 10-Q filings of firms listed in the Standard & Poor's 100 index from 2006 to 2020. We compute cosine similarity measures to compare risk factor reports and use them in linear regressions to estimate the covariance matrix of stock returns. Our estimators using risk report data outperform well-established sample-based estimators, such as the shrinkage estimator of Ledoit and Wolf. This indicates that risk factor disclosures are informative and contain information that is not already reflected in historical stock prices. This information can be used to improve portfolio selection and thus generate economic value.
查看更多>>摘要：The existing literature predominantly focuses on investor disagreements and their implications for financial returns, but it typically ignores the significant influence of noninvestors (ie, people who do not physically trade securities). In response to this oversight, our study investigates the synchronism and hysteresis between social and investor disagreements, underpinned by an analysis of millions of noninvestmentoriented comments sourced from the Sina Weibo social media platform. For the convenience of research and description, we introduce a novel metric - noninvestor disagreement - that captures this hysteresis. Further, when integrated into an asset pricing model, this metric demonstrates the ability to capture residuals that fall outside the purview of the Fama-French model. Building upon these findings, we outline several investment strategies that not only outperform the market, but also can be symbiotic with other investment strategies, suggesting an inclusive approach to strategic financial decision-making. In summary, this research augments the field of asset pricing and behavioral finance, offering a robust methodology for harnessing information typically overlooked by investors, thus enhancing the interpretive ability of asset pricing models.
查看更多>>摘要：It is well known that the convergence rate of classic nonparametric estimators for Euler capital allocation based on value-at-risk is lower than the standard rate. In this paper, we propose an alternative approach to Euler capital allocation, based on mean conditional value-at-risk (MCVaR), that involves adjusting the probability level so the total capital remains equal to the reference quantile-based capital level. The optimistic coefficient of the model incorporates the risk preferences of investors into the MCVaR-based allocation. We apply the nonparametric estimation for the new probability level and the new allocation, which could converge at the standard rate. Then, we derive the asymptotic normality of the proposed nonparametric estimator. In order to assess the performance of the method, we discuss the nonoverlapping block bootstrap and moving block bootstrap methods to real-world data and compare the estimates based on the MCVaR of various optimistic coefficients for the new level with those based on value-at-risk.
查看更多>>摘要：We demonstrate how a mixture of two skewed exponential power distributions of the type introduced by Fern ' andez, Osiewalski and Steel (referred to as the SEP3 density) can model the conditional forecasting of value-at-risk (VaR) and conditional valueat-risk (CVaR) to efficiently cover market risk at regulatory levels of 1% and 2.5%, as well as at the additional 5% level. Our data consists of a sample of market asset returns relating to a period of extreme market turmoil and showing typical leptokurtosis and skewness. The SEP3 mixture outcomes are benchmarked using various competing models, including the generalized Pareto distribution. Appropriate scoring functions quickly highlight valuable models, which undergo conventional backtests. As an additional backtest, we argue for and apply the CVaR part of the Patton-Ziegel-Chen optimality test to assess the conditional adequacy of CVaR. An additional aim of the paper is to present a "collaborative" framework that relies on both comparative and conventional backtesting tools, all in compliance with the recent Basel framework for market risk.
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