A Machine Learning Approach to Sector Based Market Efficiency

In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency. By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the basic comparisons of the weak form efficiency of four sectors over the same time period: utilities, healthcare, technology and energy. Finally, Within these sectors, I was able to detect inefficiencies in the stock market up to four years closer to modern day than previous studies.