Asset Price Forecasting using Recurrent Neural Networks

TL;DR

This paper explores using LSTM neural networks for stock price forecasting, compares it with traditional models like ARIMA, and develops a unified framework for time series prediction with a focus on mathematical rigor.

Contribution

It introduces a mathematical framework for neural networks applied to regression tasks and analyzes the practical challenges of using LSTM for stock forecasting.

Findings

LSTM models face a recurring 'forecasting lag' in stock prediction.

ARIMA outperforms LSTM in certain stock forecasting scenarios.

Unified terminology and criteria for time series forecasting are proposed.

Abstract

This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices of two other stocks that lie in rather close correlation with GS. Other models such as ARIMA were used as benchmark. Empirical results manifest the practical challenges when using LSTM for forecasting stocks. One of the main upheavals was a recurring lag which we called "forecasting lag". The second purpose is to develop a more general and objective perspective on the task of time series forecasting so that it could be applied to assist in an arbitrary that of forecasting by ANNs. Thus, attempts are made for distinguishing previous works by certain criteria (introduced by a review paper written by Ahmed Tealab) so as to summarise those including effective information. The summarised information is then unified and expressed through a common terminology that can be applied to different steps of a time series forecasting task. The last but not least purpose of this thesis is to elaborate on a mathematical framework on which ANNs are based. We are going to use the framework introduced in the book "Neural Networks in Mathematical Framework" by Anthony L. Caterini in which the structure of a generic neural network is introduced and the gradient descent algorithm (which incorporates backpropagation) is introduced in terms of their described framework. In the end, we use this framework for a specific architecture, which is recurrent neural networks on which we concentrated and our implementations are based. The book proves its theorems mostly for classification case. Instead, we proved theorems for regression case, which is the case of our problem.