Abstract:
Stochastic processes are frequently used in the assessment and follow-up of activities depending on restricted and unstable data. Various kinds of activities, including prediction, speech and gestures recognition and involvement even in social interaction can be simulated using stochastical processes. The reasons for using Hybrid Hidden Markov Model to predict stocks are HMM have a solid statistical background and can easily handle highly volatile and time-invariant data. The hybrid model of HMM and wavelets overcomes the lack of a conventional prediction model that when forecasting is confined to a linear system. This study refers HMM to time series data for economic purposes to investigate the parameters which the model can predict. HMM has been using four visible states (Open, High, Low and Close) to predict the next day’s stock prices. The predicted stocks prices of the next day are used as the present-day parameters for the second forecast in the range of predictions. Stocks of different companies are predicted and the Mean Absolute Percentage Error is calculated by using actual and predicted prices. In comparison with existing stock forecast approaches, this hybrid approach reports significantly better accuracies. These results are compared with Hidden Markov Model (HMM), Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA) and Simple Vector Machine (SVM) to evaluate the performance of the model. This hybrid probabilistic model approach is the future model for stock trading as it reflects the stock price trends well.
