After the completition of this course students will be able to:
Measure quantitative strategy profits in the light of Fama-French risk factor model.
Implement forecasting models using multiple Machine Learning tools: Gaussian Process, Rule Learning and Artificial Neural Networks.
Backtest portfolio allocation strategies employing tools from Online Machine Learning theory (Universal Portfolio)
Employ forecasting models based on the Sentiment Analysis (Natural Language Processing) of a rich proprietary dataset of Twitter posts. The dataset consist of all tweets of Down Jones stocks since Twitter inception.
The summer school will have lectures, practicals, and self-reflection sessions, which might culminate in additional homework.
The practicals make use of real time series (daily and high-frequency) and of our Twitter datasets (proprietary).
Day 1: Market Phenomenology and Stylized Facts
Common risk factors of stock returns
Exotic risk factors (currency carry, volatility premia, interest rate curve premia and so on)
Fama-French Model: The difference between portfolio $\alpha$ and $\beta$
Fitting the Fama-French Model for multiple stocks
Is it easy to generate true $\alpha$ investment strategies?
Do you agree with the Fama-French hypothesis regarding $\alpha$? How to test if stock prices follow Random Walks?
Day 2: Classical Time Series Methods
Stationary and non-stationary time series: $ARMA$ and $ARIMA$ models
The high predicability of market volatility: $GARCH$ models
Time series Cointegration: Quant's bread and butter
Testing Cointegration of stocks in the same sector/industry
Backtesting a simple trading strategy based on Autoregressive models
Cross-validating the praticals outcomes
Day 3: Introducing non-linearities and capturing Seasonal patterns