While helping one of my students, Oleg Karapaev, in his struggle with a paper on determinants of divergence between “fair” and observed prices in stocks (written as a part of his bachelor research project), I’ve made some observations perhaps worth sharing with blogosphere. I used some R code and ggplot2 + ggthemes by Jeffrey Arnolds to represent findings visually (important: to replicate code in full you should have an access to Bloomberg and Rbbg package to download data).
Class description: introductory course on developing trading algorithms and algotrading industry as a whole. Class is held in financial lab with 9 Bloomberg terminals; students are learning to fast-prototype algorithms using R language and real data from markets. Focus is on practice, but good understanding of underlying theory is a must. Knowledge of Bloomberg platform and R language is an advantage.
Prerequisites: basics of financial markets, technical and fundamental analysis, financial mathematics, modern portfolio theory, statistics and probability theory.
Modern portfolio theory assumes that decisions are made by individual agents. In reality most investors are involved in group decision-making. The paper renders group decision-making process by means of random forest algorithm, which could significantly improve prediction of weak learners by combining them into one model with superior performance. We combine technical, fundamental and sentiment analysis in order to generate views on different asset classes. Then the portfolio model is built using copula opinion-pooling under views generated by random forest. The model is backtested and results are compared with the ones obtained using traditional asset allocation techniques.