Kalman Filtering for Stocks Price Prediction and Control

Abstract

Stocks price analysis has been a critical area of research as the stock market is a very fluctuating market. Stocks price is affected by demand and supply dynamics making it difficult to forecast the price of a stock at a particular instant. The entire idea of predicting stocks price is to gain significant profits but predicting how the stock market will perform is a difficult task to carry out. In an attempt to do this, we construct a dynamical system for the stock’s price and simulate it using the Kalman filter. The dynamic tracking features of the filter here enable us to track the price of the Boeing stock. The stock price variation appears to be a maneuvering system from which we derive the state space model. Further, the robustness of the model is investigated by examining observability and controllability in the state space and proving that the system can be stabilized through state feedback. Finally, the forecasting result of 252 stock closing prices from January 01, 2021, to January 01, 2022, is provided by Kalman predictor and Python simulation. The evaluation of the prediction is done using absolute and relative error which gives relatively small values and thus makes the filter accurate for prediction.