This paper applies the mean-variance portfolio optimization (PO) approach and the stochastic dominance (SD) test to examine preferences for international diversification versus domestic diversification from American investors’ viewpoints. Our PO results imply that the domestic diversification...

This paper applies specific quantitative methods to demonstrate a general theoretical model for measuring strategic performance. The theoretical concepts are universal and measurable for all types of strategic activity by applying the methodology through alternative quantitative analytical...

Using a sample of domestic U.S. equity mutual funds, we find strong evidence that investors respond to managerial replacements. We find that the top performing funds that have a change in management subsequently have lower flows compared to funds of which the manager is retained. On top of that,...

We construct portfolios based on characteristic weights and develop a novel way to measure capacity of these portfolios to absorb capital. Our estimates suggest that portfolio capacity is the highest for fundamental-weights, whereas portfolios based on momentum, equal risk budget, and equal...

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

This presentation reconsiders Knight's Risk, Uncertainty, and Profit of 1921 in light of the emergence of the World Wide Web in early-1990s, Emanuel Derman's pioneering work in Model Risk Management at Goldman Sachs in mid-1990s, backlash against quantitative models in aftermath of the Global...

We design a novel empirical framework to examine market efficiency through out-of-sample(OOS) predictability. We frame the classic empirical asset pricing problem as a machine learningclassification problem. We construct classification models to predict return states. The prediction- based...

This paper propose a new panel stochastic dominance (SD) test-PDD test, the asymptotic properties are derived, which extends Davidson and Duclos (DD) SD test to a panel context. The PDD test also contributes to settle one of the demerits while working with financial derivatives time series: that...