The objective of this Project is to develop stock earning driver model and predicting the stock market earning of S&P500 Organisation. For this purpose, I will use the daily closing price from 1st January, 2013 to 31st December, 2018 (4 years for training purpose and one years for validation purpose).
We will use a lot of different types of input data. Along with the stock’s historical trading data and technical indicators, we will use the newest advancements in NLP (using ‘Bidirectional Embedding Representations from Transformers’, BERT, sort of a transfer learning for NLP) to create sentiment analysis (as a source for fundamental analysis), Fourier transforms for extracting overall trend directions, stacked auto- encoders for identifying other high-level features, Eigen portfolios for finding correlated assets, autoregressive integrated moving average (ARIMA) for the stock function approximation, and many more, in order to capture as much information, patterns, dependencies, etc, as possible about the stock. As we all know, the more (data) the merrier. Predicting stock price movements is an extremely complex task, so the more we know about the stock (from different perspectives) the higher our changes are.