Abstract
Prediction of stock prices is a difficult but extremely important problem that demands the development of algorithms for predicting trading opportunities by detecting patterns from past data. A related problem is the task of identifying inter-dependencies between different stocks, so that investment in one stock can be done when a related stock is performing well. The work till date on this problem seems mostly focused on theory or database techniques. We define three very simple indicators derived from stock data for this purpose, and show how they can be used in practice to successfully identify investor thumb rules in a quantitative manner.