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...

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...

Over the last two centuries, technological advantages have allowed some traders to be faster than others. We argue that, contrary to popular perception, speed is not the defining characteristic that sets High Frequency Trading (HFT) apart. HFT is the natural evolution of a new trading paradigm...

Flow toxicity can be measured in terms of the probability that a liquidity provider is adversely selected by informed traders. In previous papers we introduced the concept of Volume-synchronized Probability of Informed Trading (the VPIN* metric), and provided a robust estimation procedure. In...

Recent progress in causal inference has opened a path, however difficult, for advancing financial economics beyond its current phenomenological stage. The goal of this article is to propose a hierarchy of empirical evidence, recognizing that not all types of observations have the same scientific...

Correlation matrices are ubiquitous in finance. Some key applications include portfolio construction, risk management, and factor/style analysis. Correlation matrices are usually estimated from historical empirical observations or derived from historically estimated factors. It is widely...

There are three fundamental ways of testing the validity of an investment algorithm against historical evidence: a) the walk-forward method; b) the resampling method; and c) the Monte Carlo method. By far the most common approach followed among academics and practitioners is the walk-forward...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Traditionally, the development of investment strategies has required domain-specific knowledge and access to restricted datasets. These two barriers exist by design: (a) Financial knowledge is hoarded by firms, and protected as trade secrets, and (b) Financial data is expensive, making it...