picking stocks by graph database (part 2: machine learning)

In our last post, we demonstrated a graph database created to enable study of the stock market, particularly the study of causality relationships. So how to proceed from there? At this stage we want to pick winning stocks, not write an academic paper, so our focus turns toward practical machine learning. Source Data We start […]

picking stocks by graph database (part one)

Historical stock price data comes readily available at daily resolution. So we calculated the Granger causality for each pair of stocks we hold data for, at one and two day lags (testing the question “does daily percent change in volume for stock X Granger cause daily percent change in adjusted close price for stock Y?”). […]