Fundamental Analysis of Equity Instruments Using an Entity Embedding Neural Network

Abstract

Analysing equity instruments has become more and more important with the stock markets being more accessible. The 2 popular ways include technical analysis and fundamental analysis. While technical analysis involves studying patterns or trends over a period of time, fundamental analysis takes a more logical approach by valuing the instrument according to its underlying fundamentals such as the reported profits, current debt, etc., and is closer to the balance sheet. Fundamental Analysis puts great emphasis on quantifying the strength of the instrument using the measures that directly represent how the organisation that issues these instruments is performing. This paper aims to investigate how a high-capacity model such as a Deep Neural Network, specifically the Entity Embedding Neural Network maps fundamental and price data to predict a future price that best explains a security. Results show that the proposed approach has an R2 score of 0.9019, accuracy of 93.42%, and MSE loss of 0.047 which outperforms the results obtained by some of the other ways of modeling this data.