Data science in financial markets

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections’ peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

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Predicting Indian Stock Prices through Sentiment Score and Data Mining Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4830.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1213-1221

Keyword(s):

Stock Market ◽

Financial Markets ◽

Stock Prices ◽

Data Science ◽

Investment Decisions ◽

Learning Techniques ◽

Elementary Methods ◽

Domain Expertise ◽

Sentiment Score ◽

Stock Market Investment

Much is being said about the use of artificial intelligence to assist in the financial markets but there is a surprising lack of actual data supporting one trading algorithm over another. While there are numerous research that explore the possibilities of machine learning and deep learning techniques for stock market prediction, due to the lack of inter-domain expertise (people who are experts in both data science and finance), most of these projects use relatively elementary methods and fail to account for many underlying constraints. This paper aims to create a stock market investment aide which predicts the price action of stock market instruments using neural networks that learn time series patterns in the historical price data, correlates the prediction with the sentiment score of the market for that day and thereby give the investor/trader a buy or sell signal. The model developed has been successful in predicting price action of the NIFTY50 index with an accuracy of 89 percent. The prediction aided with the correlation from the sentiment analysis can give the investor an added confidence while making investment decisions on the stock market.

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