Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naïve portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios.The full paper is...

For large portfolio managers, a sequence of single-period optimal positions is rarely multi-period optimal. In particular, transaction costs can prevent large portfolio managers from monetizing most of their forecasting power. The solution is to compute the trading trajectory that comes...

Growth Optimal Portfolio (GOP) theory determines the path of bet sizes that maximize long-term wealth. How it is also known in practice GOP is too risky. We explain in this talk that the reason is in practice the investment horizon is finite and practitioners account for risk more explicitly. We...

Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. We propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest...

* 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...

Most publications in Financial ML seem concerned with forecasting prices. While these are worthy endeavors, Financial ML can offer so much more. In this presentation, we review a few important applications that go beyond price forecasting:1. Portfolio construction2. Structural breaks3. Bet...

This paper introduces the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz's CLA in particular: Instability, concentration and underperformance.HRP applies modern mathematics (graph theory and machine learning...

Growth Optimal Portfolio (GOP) theory determines the path of bet sizes that maximize long-term wealth. This multi-horizon goal makes it more appealing among practitioners than myopic approaches, like Markowitz's mean-variance or risk parity. The GOP literature typically considers risk-neutral...

This article reviews ten notable financial applications where ML has moved beyond hype and proven its usefulness. This success does not mean that the use of ML in finance does not face important challenges. The main conclusion is that there is a strong case for applying ML to current financial...