This paper studies the predictability of stock returns using monthly data on eight markets over the period 1876-1913. In contrast to much of the existing literature I find broad predictability across stock markets. Market interest rates and seasonal dummies generally have predictive power, and...

Empirical Finance is in crisis: Our most important "discovery" tool is historical simulation, and yet, most backtests published in leading Financial journals are flawed.The problem is well-known to professional organizations of Statisticians and Mathematicians, who have publicly criticized the...

We prove that high simulated performance is easily achievable after backtesting a relatively small number of alternative strategy configurations, a practice we denote “backtest overfitting”. The higher the number of configurations tried, the greater is the probability that the backtest is...

In order to predict future relative results within a universe of equity portfolios, the authors hypothesize that it is possible to use selected portfolio characteristics as opposed to relying on past performance. This research uses Active Share and Concentration Coefficient data for universes of...

Most publications in Financial ML seem concerned with forecasting prices. While these are worthy endeavors, Financial ML can offer so much more. In this presentation, we review a few important applications that go beyond price forecasting:1. Portfolio construction2. Structural breaks3. Bet...

One of the most exciting recent developments in financial research is the availability of new administrative, private sector and micro-level datasets that did not exist a few years ago. The unstructured nature of many of these observations, along with the complexity of the phenomena they...

This article reviews ten notable financial applications where ML has moved beyond hype and proven its usefulness. This success does not mean that the use of ML in finance does not face important challenges. The main conclusion is that there is a strong case for applying ML to current financial...

Many investors rely on market experts and forecasters when making investment decisions, such as when to buy or sell securities. Ranking and grading market forecasters provides investors with metrics on which they may choose forecasters with the best record of accuracy for their particular market...

The purpose of this paper is to study the impact of beating analysts' forecasts and the impact of analysts' forecast dispersion on the pricing of firms' credit default swaps (CDSs). CDS premium is the compensation required by investors for bearing firms' credit default risk. Sell-side analysts...

Default correlation is a critical concept in risk management for fixed income investment, bank management, and insurance industry, working capital management, among many. We extend the Leland-Toft term structure model into a two-firm environment and predict the default correlation between two...