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...

We provide the first comprehensive analysis of option information for pricing the cross-section of stock returns by jointly examining extensive sets of firm and option characteristics. Using portfolio sorts and high-dimensional methods, we show that certain option measures have significant...

To analyze whether stock-market prices follow a random walk, the algebraic sign of their returns has been compared with a coin toss, which is a prominent example for a Bernoulli trial with equiprobable outcomes. Like coin tosses, signed returns lend themselves for a simple runs test for...

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...