Hassan, Md.R. and Nath, B. (2005) Stock Market Forecasting Using Hidden Markov Models: A New Approach. Proceeding of IEEE 5th International Conference on Intelligent Systems Design and Applications.

has been cited by the following article:

• TITLE: Implementing Trade Strategy with HMM Model: A Practice on Some Telecommunication Companies

  AUTHORS: Chuanzhong Sun

  KEYWORDS: Hidden Markov Model, Stock Trend Status, Trade Strategy

  JOURNAL NAME: Open Journal of Social Sciences, Vol.6 No.3, March 13, 2018

  ABSTRACT: Stock trend prediction has been a challenge task for many traders. For stock traders, promptly predicting the bearish or bullish market status is very helpful in reducing the losses and increasing returns. This paper presents a Hidden Markov Models (HMM) approach for determining stock trend status: “upper trend”, “low trend”, or “Medium”. And a simple trade strategy based on the estimated trend status is formulated. Then the strategy is applied to three telecommunication stocks: AT & T, Verizon, and China Mobile for trade performance evaluation. We use the real daily trading data of NYSE from October 25, 2016 to November 06, 2017 to test performance of the strategy. The results show that the strategy is actually very encouraging. For the generally bearish period of telecommunication sector, a 9.7% and 3.7% return rate can still be achieved for AT & T and Verizon. For China Mobile, although the return is negative, the final loss is still 2% less than if “Buy and Hold”.