Predict Stock Market's Fluctuating Behaviour : Role of Investor's Sentiments on Stock Market performance

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-11
Year of Publication : 2020
Authors : Uma Gurav, Prof.Dr.S.Kotrappa
DOI :  10.14445/22315381/IJETT-V68I11P209


MLA Style: Uma Gurav, Prof.Dr.S.Kotrappa  "Predict Stock Market's Fluctuating Behaviour : Role of Investor's Sentiments on Stock Market performance" International Journal of Engineering Trends and Technology 68.11(2020):72-80. 

APA Style:Uma Gurav, Prof.Dr.S.Kotrappa. Predict Stock Market's Fluctuating Behaviour : Role of Investor's Sentiments on Stock Market performance  International Journal of Engineering Trends and Technology, 68(11),72-80.

Long term historical records of the stock markets are widely used in technical research to define, understand and analyze stock market's time series trends and patterns which can be used to generate huge profits during trading sessions. Even though, technical analysis using different technical measures have been shown to be helpful in forecasting market patterns, formulating specific trading rule is a challenging task. In this research paper, we have tried to analyze investor's sentiments considering US presidential elections and effects of Covid 19 as an explicit fluctuating factor affecting stock market performance. In addition to this , in this research work ,we have tried to identify correct and better trading rules and trading points ,technical indicators to be considered using mathematical formulations, to determine when to buy or sell stocks.Thus, given dynamically varying stock market behaviour in high frequency trading environment, it is important to integrate market sentiments into forecasting operations. This paper combines sentiments into stock forecasting model using the log bilinear (LBL) model for short term stock market's sentiment pattern learning and recurrent neural (RNN) for long term sentiments pattern learning which achieves better performance then deep learning based stock price forecasting existing methodologies.


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Sentiment Analysis, machine learning, social networking platform, stock price forecasting, Time series Analysis