Stock price prediction using dynamic mode decomposition

Stock price prediction is a challenging problem as the market is quite unpredictable. We propose a method for price prediction using Dynamic Mode Decomposition assuming stock market as a dynamic system. DMD is an equation free, data-driven, spatio-temporal algorithm which decomposes a system to modes that have predetermined temporal behaviour associated with them. These modes help us determine how the system evolves and the future state of the system can be predicted. We have used these modes for the predictive assessment of the stock market. We worked with the time series data of the companies listed in National Stock Exchange. The granularity of time was minute. We have sampled a few companies across sectors listed in National Stock Exchange and used the minute-wise stock price to predict their price in next few minutes. The obtained price prediction results were compared with actual stock prices. We used Mean Absolute Percentage Error to calculate the deviation of predicted price from actual price for each company. Price prediction for each company was made in three different ways. In the first, we sampled companies belonging to the same sector to predict the future price. In the latter, we considered sampled companies from all sectors for prediction. In the first and second method, the sampling as well as the prediction window size were fixed. In the third method the sampling of companies was done from all sectors considered. The sampling window was kept fixed, but predictions were made until it crossed a threshold error. Prediction was found to be more accurate when samples were taken from all the sectors, than from a single sector. When sampling window alone was fixed; the predictions could be made for longer period for certain instances of sampling.