Bursts and Regularization in Financial Markets: Deterministic or Stochastic?

We analyze empirical finance data, such as the Financial Stress Index, a number of asset classes (swaps, equity and bonds), market (emerging vs. developed), issuer (corporate vs. government bond), maturity (short vs. long) data, asking whether the recently observed alternations between calm periods and financial turmoil can be modelled in a low-dimensional deterministic manner, or whether they demand for their description a stochastic model. We find that a deterministic model performs at least as well as one of the best stochastic models, but may provide additional insight into the essential mechanisms that drive financial markets.

Characteristic-Sorted Portfolios: Estimation and Inference

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.

An Evaluation of Alternative Multiple Testing Methods for Finance Applications*

In almost every area of empirical finance, researchers confront multiple tests. One high-profile example is the identification of outperforming investment managers, many of whom beat their benchmarks purely by luck. Multiple testing methods are designed to control for luck. Factor selection is another glaring case in which multiple tests are performed, but numerous other applications do not receive as much attention. One important example is a simple regression model testing five variables. In this case, because five variables are tried, a t-statistic of 2.0 is not enough to establish significance. Our paper provides a guide to various multiple testing methods and details a number of applications. We provide simulation evidence on the relative performance of different methods across a variety of testing environments. The goal of our paper is to provide a menu that researchers can choose from to improve inference in financial economics. (JEL G0, G1, G3, G5, M4, C1)

E. L. SMITH’S ENDURING CONTRIBUTIONS TO FINANCIAL ECONOMICS

In 1924, Edgar Lawrence Smith published a monograph presenting evidence aimed at overturning the conventional view that equities were speculative and bonds were the only long-term investments. This was immediately so successful that such eminent commentators as Irving Fisher and Benjamin Graham agreed that the monograph had had a material impact on market psychology, playing an instrumental role in the Great Crash. In this article, we examine Smith’s approach in detail, arguing that he made significant, enduring contributions to finance theory, empirical finance, and portfolio management practice. He was influential in creating the “cult of the equity,” laid the foundations for the equity risk premium, and introduced a probability-based risk metric and equally weighted portfolios. His influence is felt nowadays not only in the methodology employed in empirical work but also in major aspects of the conventional approach to portfolio management.

Empirical Finance

The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six articles on machine learning, five articles based on traditional econometric analysis, and five articles on emerging markets. The large share of articles on the application of machine learning is in line with recent trends in finance research. This special issue provides a state-of-the-art overview of empirical finance from economic, financial, and technical points of view.