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...

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...

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...

Financial ML offers the opportunity to gain insight from data:* Modelling non-linear relationships in a high-dimensional space* Analyzing unstructured data (asynchronous, categorical)* Learning complex patterns (hierarchical, non-parametric)* Focusing on predictability over parametric...

Machine learning (ML) algorithms utilize the power of computers to solve tasks that are beyond the grasp of classical statistical methods. However, ML is often perceived as a black-box, hindering its adoption. This seminar demonstrates the use of Shapley values to interpret the outputs of ML...

We develop a detailed epidemiological multi-factor model, the K-Susceptible-Exposed-Infected-Removed (K-SEIR) model, as well as several simpler sub-models, as its building blocks. The general model enables us to account for all the relevant COVID-19 features, its disparate impact on different...

Classical statistics (e.g., Econometrics) relies on assumptions that are often unrealistic in finance. Two critical assumptions are that the researcher has perfect knowledge about the model's specification, and that the researcher knows all the variables involved in a phenomenon (including all...

Many quantitative firms have suffered substantial losses as a result of the COVID-19 selloff. In this note, we highlight three lessons that quantitative researchers could learn from this crisis. First, researchers should develop more nowcasting methods, and pay less attention to forecasts....

Six weeks after becoming a pandemic, COVID-19 has caused over 150,000 deaths across 210 countries. Governments around the world have instituted universal lockdowns to curve the spread of this serious disease. While it is obvious that extended universal lockdowns have saved lives that otherwise...

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...