./mdx_sources/StructuralBreaks/structural_breaks.mdx

Structural breaks, like the transition from one market regime to another, is one example of such a confluence that is of particular interest.

What's the distinction between organized and unstructured data? We will learn how to work with unstructured financial data and then transform it into a structured dataset that can be used by $mathrmML$ algorithms. In general, you should avoid consuming someone else's processed dataset because you will most likely find what someone else already knows or will figure out shortly. Ideally, your starting point will be a collection of unstructured, raw data that you will analyse in order to generate relevant characteristics.

Structural Breaks

./mdx_sources/EntropyFeatures/entropy_features.mdx

When markets are not perfect, prices are formed with partial information, and as some agents know more than others.

Entropy Features

./mdx_sources/MicrostructuralFeatures/microstructural_features.mdx

The level of detail contained in FIX messages provides researchers with the ability to understand how market participants conceal and reveal their intentions.

Microstructural Features

./mdx_sources/FeatureImportance/feature_importance.mdx

Why repeating of backtest may fail? and how to prevent it. Why repeating a test over and over on the same data will likely lead to a  discovery?

One of the most common errors in financial research is taking some data, running it through an ML algorithm, backtesting the predictions, and repeating the process until a nice-looking backtest appears. Such pseudo-discoveries abound in academic journals, and even significant hedge funds are prone to falling into this trap. It makes no difference if the backtest is an out-of-sample walk-forward. The fact that we are repeating a test on the same data will almost certainly result in a  discovery. This methodological error is so well-known among statisticians that the American Statistical Association warns against it in its ethical guidelines (American Statistical Association [2016], Discussion #4). It usually takes around 20 iterations to find a () investment strategy with a standard significance level ( positive rate) of 5%.

Feature Importance

./mdx_sources/PortfolioConstruction/portfolio_construction.mdx

In practice, mean-variance optimal solutions tend to be concentrated and unstable. How to deal with the instability caused by the noise contained in the covariance matrix?

Portfolio Construction

In practice, mean-variance optimal solutions tend to be concentrated and unstable. How to deal with the instability caused by the noise contained in the covariance matrix?

Sat Mar 05 2022

Portfolio Construction

TODO