Virtual volatility

We introduce the concept of virtual volatility. This simple but new measure shows how to quantify the uncertainty in the forecast of the drift component of a random walk. The virtual volatility also is a useful tool in understanding the stochastic process for a given portfolio. In particular, and as an example, we were able to identify mean reversion effect in our portfolio. Finally, we briefly discuss the potential practical effect of the virtual volatility on an investor asset allocation strategy.Comment: 15 pages, 2 figures, elsart.cls, Accepted to Physica A. Added few comments that clarify data used for empirical wor

Collective Origin of the Coexistence of Apparent RMT Noise and Factors in Large Sample Correlation Matrices

Through simple analytical calculations and numerical simulations, we demonstrate the generic existence of a self-organized macroscopic state in any large multivariate system possessing non-vanishing average correlations between a finite fraction of all pairs of elements. The coexistence of an eigenvalue spectrum predicted by random matrix theory (RMT) and a few very large eigenvalues in large empirical correlation matrices is shown to result from a bottom-up collective effect of the underlying time series rather than a top-down impact of factors. Our results, in excellent agreement with previous results obtained on large financial correlation matrices, show that there is relevant information also in the bulk of the eigenvalue spectrum and rationalize the presence of market factors previously introduced in an ad hoc manner.Comment: 4 pages with 3 figur

Gender, style diversity and their effect on fund performance

© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).This paper examines the performance of 358 European diversified equity mutual funds controlling for gender diversity. Fund performance is evaluated against funds’ designated market indices and representative style portfolios. Consistently with previous studies, proper statistical tests point to the absence of significant differences in performance and risk between female and male managed funds. However, perverse market timing manifests itself mainly in female managed funds and in the left tail of the returns distribution. Interestingly, at fund level there is evidence of significant overperformance that survives even after accounting for funds’ exposure to known risk factors. Employing a quantile regression approach reveals that fund performance is highly dependent on the selection of the specific quantile of the returns distribution; also, style consistency for male and female managers manifests itself across different quantiles. These results have important implications for fund management companies and for retail investors’ asset allocation strategies

The value premium and time-varying volatility

Numerous studies have documented the failure of the static and conditional capital asset pricing models to explain the difference in returns between value and growth stocks. This paper examines the post-1963 value premium by employing a model that captures the time-varying total risk of the value-minus-growth portfolios. Our results show that the time-series of value premia is strongly and positively correlated with its volatility. This conclusion is robust to the criterion used to sort stocks into value and growth portfolios and to the country under review (the US and the UK). Our paper is consistent with evidence on the possible role of idiosyncratic risk in explaining equity returns, and also with a separate strand of literature concerning the relative lack of reversibility of value firms' investment decisions

Revisiting Agency and Stewardship Theories: Perspectives From Nonprofit Board Chairs and CEOs

Using principal-agent theories, this study examined differences in the perceptions of nonprofit chief executive officers (CEOs) and board chairs on key governance aspects, including board performance, leadership, satisfaction with diversity, and board meetings. Using data from the CEOs and board chairs of 474 nonprofit organizations, we found statistically significant differences in the governance perceptions of these leaders of nonprofit organizations. The findings provide support for an agency theory explanation about the differing interests of principals (board chairs) and agents (CEOs). The findings suggest that these two sets of nonprofit actors frequently operate from different perspectives, potentially affecting the governance of their organizations. © 2016 Wiley Periodicals, Inc

The Wall Street walk when blockholders compete for flows

Effective monitoring by equity blockholders is important for good corporate governance. A prominent theoretical literature argues that the threat of block sale (“exit”) can be an effective governance mechanism. Many blockholders are money managers. We show that when money managers compete for investor capital, the threat of exit loses credibility, weakening its governance role. Money managers with more skin in the game will govern more successfully using exit. Allowing funds to engage in activist measures (“voice”) does not alter our qualitative results. Our results link widely prevalent incentives in the ever-expanding money management industry to the nature of corporate governance

Inflation and Nominal Financial Reporting: Implications for Performance and Stock Prices

The monetary unit assumption of financial accounting assumes a stable currency (i.e., constant purchasing power over time). Yet, even during periods of low inflation or deflation, nominal financial statements violate this assumption. I posit that, while the effects of inflation are not recognized in nominal statements, such effects may have economic consequences. I find that unrecognized inflation gains and losses help predict future cash flows as these gains and losses turn into cash flows over time. I also find significant abnormal returns to inflation-based trading strategies, suggesting that stock prices do not fully reflect the implications of the inflation effects for future cash flows. Additional analysis reveals that stock prices act as if investors do not fully distinguish monetary and nonmonetary assets, which is fundamental to determining the effects of inflation. Overall, this study is the first to show that, although inflation effects are not recognized in nominal financial statements, they have significant economic consequences, even during a period in which inflation is relatively low

Is investor sentiment contagious? International sentiment and UK equity returns

This paper contributes to a growing body of literature studying investor sentiment. Separate sentiment measures for UK investors and UK institutional investors are constructed from commonly cited sentiment indicators using the first principle component method. We then examine if the sentiment measures can help predict UK equity returns, distinguishing between “turbulent” and “tranquil” periods in the financial markets. We find that sentiment tends to be a more important determinant of returns in the run-up to a crisis than at other times. We also examine if US investor sentiment can help predict UK equity returns, and find that US investor sentiment is highly significant in explaining the UK equity returns

Principal component analysis for big data

Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. The complexity of big data often makes dimension reduction techniques necessary before conducting statistical inference. Principal component analysis, commonly referred to as PCA, has become an essential tool for multivariate data analysis and unsupervised dimension reduction, the goal of which is to find a lower dimensional subspace that captures most of the variation in the dataset. This article provides an overview of methodological and theoretical developments of PCA over the last decade, with focus on its applications to big data analytics. We first review the mathematical formulation of PCA and its theoretical development from the view point of perturbation analysis. We then briefly discuss the relationship between PCA and factor analysis as well as its applications to large covariance estimation and multiple testing. PCA also finds important applications in many modern machine learning problems, and we focus on community detection, ranking, mixture model and manifold learning in this paper.Comment: review article, in press with Wiley StatsRe