Intelligent Market Prediction and Trend Analysis of Volatile Stocks by Long Short Term Memory

  • P. Jayasri Archana Devi, M. Jayanthi, R. Prasanth, P. S. Satheesh, M. Pavithra Rao, S. Selvakumaran

Abstract

Stock traders always wish to buy a stock at a low price and sell it at a higher price. However, when is the best time to buy or sell a stock is a challenging question. Stock investments can have a huge return or a significant loss due to the high volatilities of the stock prices. Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices.

The existing paper proposes a hierarchical coupled cross-market behavior analysis framework and a MCHMM to capture the complex hierarchical coupling relationships between various markets in different countries; the MCHMM-based forecasting model further predicts the movements of a market by considering the couplings with other markets across countries.

In our proposed project, we introduce the application of HMM in trading stocks based on the stock price predictions combined with powerful algorithms like ARIMA and LSTM. Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and computer scientists.The trends that once followed over a particular period will surely repeat in the future. The one day difference in close value of stocks for a certain period are found and its corresponding steady state probability distribution values are determined. The pattern of the stock market behaviour is then decided based on these probability values for a particular time. The goal is to figure out the hidden state sequence given the observation sequence so that the trend can be analysed using the steady state probability distribution values by using the integrated moving averages of the algorithm to produce highly optimized and efficient results.

Published
2020-01-13
How to Cite
P. Jayasri Archana Devi, M. Jayanthi, R. Prasanth, P. S. Satheesh, M. Pavithra Rao, S. Selvakumaran. (2020). Intelligent Market Prediction and Trend Analysis of Volatile Stocks by Long Short Term Memory. International Journal of Advanced Science and Technology, 29(2), 4838-4843. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/38083