Time Series Prediction : Predicting Stock Price

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 S&P 500 index as input time series data. The mean (martingale) and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized linear model (GLM) 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 (OTB) algorithm and discrepancy measure to inform readers the state-of-art 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.