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...

Early in the 1950s, academics and investors started proposing in earnest a variety of summary statistics to capture in a single number the quality of an investment. Sharpe Ratio became the most commonly used, and it's an important metric, but maximizing Sharpe Ratio doesn't always maximize...

Estimates of standard performance measures can be improved by using returns on assets not used to define those measures. Alpha, the intercept in a regression of a fund's return on passive benchmark returns, can be estimated more precisely by using information in returns on nonbenchmark passive...

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances....

Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and variance-covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions...

We propose a new method to improve density forecasts of the equity premium using information from options markets. We obtain predictive densities from stochastic volatility (SV) and GARCH models, which we then tilt using the second moment of the risk-neutral distribution implied by options...

In addition to giving a detailed description and explanation of the Black–Litterman (BL) model, this paper deals with estimation of the parameter tau. This parameter is the most mysterious one in the BL model, as the literature does not provide specific guidance on its calibration....

We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions,...

In this short note, we show investors one way to calculate ideal investment sizing by using two rules of thumb based on a simple outline of individual risk aversion. We illustrate these two heuristics, which are not widely appreciated, with thought experiments involving coin flips and ketchup &...

We examine 24 global factor premiums across the main asset classes via replication and new-sample evidence spanning 217 years of data. Replication yields ambiguous evidence within a unified testing framework with methods that account for p-hacking. The new-sample evidence reveals that the large...