Forecasting Stock Index using Deep Learning and how it can be applied in the financial sector

Abstract: The idea of predicting the stock market has existed for hundreds of years. From the pre-industrial age of japan investors used candlestick patterns to predict the movement of rice prices, to the modern age of high frequency robot traders. The rise of computational power and the availability of high resolution data in massive scale has given investors the opportunity to use neural networks in a more advanced and compelling ways. The recent introduction of TensorFlow has made it even easier to implement this technology in business. This report focuses solely on building up a Neural Network, unsupervised, to be trained and applied to predict a major stock market index. The data is minutely based and is used for training and testing. The result showcases how the model learns and adapts based on the training data, and after several epochs the model can adapt to even more complex parts of the graph. The stock data carries lots of noise which affects the training and results. Furthermore, the architecture of the neural network is vital for the model’s capability. This model was built to run on CPU, which also effects the model’s efficiency, as of recently GPUs are proven to be more effective. The second part, which focuses on the economical aspect of the model and how it can be implemented, discusses the cost of investing in machine learning, and how much the investment must yield per year to breakeven. Furthermore, the mistrust of algorithms and the skepticism regarding machine learning within the bank is also brought up and discussed, as the biggest challenge is not actually creating a sophisticated model but convincing the analyst of its usefulness.