Investors typically measure an asset’s potential to diversify a portfolio by its correlations with the portfolio’s other assets, but correlation is useful only if it provides a good estimate of how an asset’s returns co-occur cumulatively with the other asset returns over the investor’s...

We adopt deep learning models to directly optimize the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimize portfolio weights by updating model parameters. Instead of selecting individual assets, we...

We overcome an obstacle in mathematical optimization. In so doing, we explicitly derive the optimal values without assuming the smoothness of the value function. We apply our method to the portfolio model

Covariance appears throughout investment management, e.g., in risk reporting and control, portfolio construction, risk parity, smart beta, algorithmic trading, and hedging. It is usually represented via multi-factor model. The form’s fewer parameters and structure—comovement through...

We derive sufficient conditions for non-emptyness of the efficient set for Stochastic Dominance Relations, commonly applied in Economics and Finance, over sets of distributions on the real line. We do so via the use of the concept of stochastic spanning and its characterization via a saddle type...

* It has been estimated that the current size of the asset management industry is approximately US$58 trillion.* Portfolio optimization is one of the problems most frequently encountered by financial practitioners. It appears in various forms in the context of Trading, Risk Management and...

We introduce a generic solver for dynamic portfolio allocation problems when the market exhibits return predictability, price impact and partial observability. We assume that the price modeling can be encoded into a linear state-space and we demonstrate how the problem then falls into the LQG...

This paper develops an approximate closed-form optimal portfolio allocation formula for a spot asset whose variance follows a GARCH(1,1) process. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize the expected utility from terminal wealth under a...