This module will provide the students with the fundamental analytical skills for finance. The module will comprise three main building blocks: introduction to statistic and probability theory, machine learning techniques and application to finance. In the first part of the module, students will deal with the basic principles of mathematics and statistics for data analysis such as random variables, univariate and multivariate discrete and continuous distributions, expectations and moments. In the second part the student will be exposed to basic machine learning techniques: supervised learning (linear regression, Lasso regression, logistic classification), unsupervised learning (PCA, k-means), neural network. Particular attention will be given to hypotheses testing, estimation and properties of estimators. The third part of the module focuses on application to finance: in particular the students will learn how to use machine learning to develop investment strategies across different markets (equities, rates, currencies) and properly backtest them. Focus will be on backtesting methodologies, strategies selection, backtest statistics and asset allocation. The module aims to build the foundational knowledge in order to enable students to use competently the most common machine learnings techniques for financial markets analysis and set the stage for more cutting-edge research in financial modelling. The module is highly linked to the Data Analysis and Quantitative Finance module and students are expected to be able to access financial and macro-economic data and to have basic knowledge of Python programming.

Learning Outcomes

Competency in basic econometrics, statistics and probability theory, and introduction to machine learning algorithms in financial analysis
	Understanding of basic mathematical principles and statistics, including random variables, univariate and multivariate discrete and continuous distributions, expectations and moments, hypotheses testing, estimation and properties of estimators, and time series
	Knowledge of machine learning algorithms with applications to financial analysis
	Ability to apply the above techniques to address an investment problem, including the ability to code the relevant algo in Python and analyze results.
Reading List:

M. Lopez De Prado: “Advances in Financial Machine Learning”, Wiley 2018
	T. Hastie, R. Tibshirani, J Friedman: “The elements of statistical learning”, Springer 2009