Time Series Prediction : Predicting Stock Price

TL;DR

This paper compares four models for stock price prediction using SPX index data, highlighting the strengths of RNNs and introducing new online algorithms and trading strategies for practical application.

Contribution

It introduces an online to batch algorithm and discrepancy measure for time series prediction that do not require stationarity assumptions, along with practical trading strategies.

Findings

RNN outperforms linear models in stock prediction

The online to batch algorithm does not require stationarity assumptions

Trading strategies can create win-win and zero-sum situations

Abstract

Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized linear model requires lesser assumptions but is unable to outperform the martingale. In empirical testing, the RNN model performs the best comparing to other two models, because it will update the input through LSTM instantaneously, but also does not beat the martingale. In addition, we introduce an online to batch algorithm and discrepancy measure to inform readers the newest research in time series predicting method, which doesn't require any stationarity or non mixing assumptions in time series data. Finally, to apply these forecasting to practice, we introduce basic trading strategies that can create Win win and Zero sum situations.