Futures contracts on the New York Mercantile Exchange are the most liquid instruments for trading crude oil, which is the world’s most actively traded physical commodity. Under normal market conditions, traders can easily find counterparties for their trades, resulting in an efficient market...

We develop a novel ranking methodology to rank the market forecaster. In particular, we distinguish forecasts by their specificity, rather than considering all predictions and forecasts equally important, and we also analyze the impact of the number of forecasts made by a particular forecaster....

With the advent in recent years of large financial data sets, machine learning and high-performance computing, analysts can backtest millions (if not billions) of alternative investment strategies. Backtest optimizers search for combinations of parameters that maximize the simulated historical...

In this paper I examine whether one can use analyst forecasts of macroeconomic variables to improve investors ex-ante allocation of wealth between stocks and bonds. Such forecasts provide a forward-looking approach which I find improves investor's information set for the myopic stock-bond...

We examine the empirical properties of the theoretical Black–Scholes–Merton (BSM) bankruptcy model. We evaluate the predictive ability of various existing modifications of the BSM model and extend prior studies by estimating volatility directly from market-observable returns on firm value.We...

We evaluated the performance of multivariate models for forecasting Value at Risk (VaR), Expected Shortfall (ES) and Expectile Value at Risk (EVaR). We used Historical Simulation (HS), Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedastic (DCC-GARCH) and copula...

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a...

Using 278 economic and financial variables we study the power of machine learning (ML) in predicting the daily CBOE implied volatility index (VIX). Designing and applying an automated three-step ML framework with a large number of algorithms we identify Adaptive Boosting as the best...

The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in...

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...