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Trading Simulation and Stock Market Prediction


Jayanth K N , Sapthagiri College of Engineering; Kavitha G, Sapthagiri College of Engineering; Jayanth G, Sapthagiri College of Engineering; G SivaSankar Reddy, Sapthagiri College of Engineering; Darshan P, Sapthagiri College of Engineering


Stock market prediction, social media, Sentiment Analysis, Deep Learning algorithms


Stock market prediction is one of the most attractive research topics since the successful prediction on the market’s future movement could lead to significant profit. Traditional short term stock market predictions are based on the analysis of historical market data, such as stock prices, moving averages or daily returns. Stock market prediction on the basis of public sentiments expressed on social media has been an intriguing field of research. In an elaborate way, positive news and tweets in social media about a company will encourage people to invest in the stocks of that company and as a result the stock price of that company will increase. A company whose stock prices increase but has a negative image in the mindset of its investors will discourage current investors and not attract any new potential investors. The stock market is a platform where an enormous amount of data exists and constantly needs to be scrutinized for potential business opportunities. Since the stock market involves so much data, data sets get so large and complex that it becomes difficult to analyze using traditional data processing applications. In order to overcome these challenges, we can extract the useful information from the stock market trading data to an understandable structure using Data Mining, and also use algorithms that learn from this data and automatically predict further trends. Using Deep Learning algorithms and Sentiment Analysis as the approaches, we implement and simulate a brokerage system and analyze the stock market while at the same time learning the fundamentals of investment, how sentiment can influence a company’s shares and most importantly, gain practical insight to the application of Deep Learning in the field of large-scale trading and financial enterprises.

Other Details:

Manuscript Id :IJSTEV6I12001
Published in :Volume : 6, Issue : 12
Publication Date: 01/07/2020
Page(s): 1-5
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