stdClass Object ( [id] => 7733 [paper_index] => EW201908-01-002918 [title] => SENTIMENT INTENSITY OF MARKET NEWS: IS IT A POSITIVE SIGNAL FOR PREDICTIVE ANALYSIS OF STOCK PRICES? [description] =>
  1. Silivia Amaro, “Sell-offs could be down to machine that controls 80% of the US stock market, fund manager says”, www.cnbc.com, 5th Dec 2018.
  2. Moazzam Khoja, “Are High Frequency Traders independently informed? The study of the role of HFTs around Informational Events”, March 6, 2019.
  3. O’Hara, M., “High frequency market microstructure”,.Journal of Financial Economics (2015), http: //dx.doi.org/10.10
  4. Philip Treleaven, Michal Galas and Vidhi Lalchand, “Algorithmic Trading Review”, Communications of the ACM, November 2013, Vol.56, No.11, Page:79.
  5. Terrence Hendershott, Charled M. Jones and Albert J. Menkveld, “Does Algorithmic Trading Improve Liquidity?” The Journal of Finance, Vol. LXVI, No.1, February 2011.
  6. Adam Atkings, Mahesan Niranjan, Enrico Gerding, “Financial news predicts stock market volatility better than close price”, The Journal of Finance and Data Science, 4(2018) p120-137.
  7. Hiransha M, Gopalakrishnan E.A, Vijay Krishna Menon, Soman K.P, “NSE Stock Market Prediction Using Deep-Learning Models”, Procedia Computer Science 132 (2018) 1351–1362.
  8. Aditya Bhartwaj, Yogendra Narayan, Vanraj, Pawan, Maitreyee Dutta, “Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty”, Procedia Computer Science 132 (2018) 1351–1362.
  9. Li Guo, Feng Shi, Jun Tu, “Textual analysis and machine leaning: Crack unstructured data in finance and accounting”, The Journal of Finance and Data Science 2 (2016) p153 170.
  10. Bruno Miranda Henrique, Vinicius Amorim Sobreiro, Herbert Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices”, The Journal of Finance and Data Science 4 (2018) p183-201.
  11. Xiandong Li, Haoran Xie, Li Chen, Jianping Wang, Xiaotie Deng, “News impact on stock price return via sentiment analysis”, Knowledge-Based Systems 69 (2014) 14–23
  12. Axel Groß-Klußmann, Nikolaus Hautsch, “When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions”, Journal of Empirical Finance 18 (2011) 321–340
  13. Michael Hagenau, Michael Liebmann, Dirk Neumann, “ Automated news reading: Stock price prediction based on financial news using context-capturing features”, Decision Support Systems, Volume 55, Issue 3, June 2013, Pages 685-697
  14. Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
  15. Seabold, Skipper, and Josef Perktold. “Statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference. 2010.
[author] => Leben Johnson Mannariat [googlescholar] => https://scholar.google.co.in/citations?user=KeqZGcIAAAAJ&hl=en [doi] => [year] => 2019 [month] => August [volume] => 7 [issue] => 8 [file] => eprapub/EW201908-01-002918.pdf [abstract] =>

Technical Analysis: the prediction of stock prices using patterns in historical stock charts is nothing short of art. Quantitative analysts spend most of their time pouring over patterns to predict the future direction and price, to maximize profits or mitigate risk. Some analysts use these signals along with Fundamental Analysis and market news, to make trading decisions for short and long term trading strategies. Algorithm Trading is the process of using computerized mathematical models, based on certain patterns, rules and factors. With the exponential growth in Algorithm trading, currently capturing ~80% of market volume and the growth in computing power, sophisticated mathematical models is the order of the day. These strategies have grown over the period from simple Excel rule-based programs to Artificial Intelligence using deep neural networks.

Market news pertaining to the stock or macro economic factors, while having an impact on the stock price and important for trading decisions was always elusive to factor in quickly, due to it being voluminous and its inherent subjectivity in nature. Natural Language Processing (NLP) of news can provide a powerful signal that can augment trading decisions. This paper aims to explore the sensitivity intensity of relevant historical news through VADER NLP and use it as an additional signal to assess the excess predictive power over and above historical time-series stock price prediction, using neural network algorithms. This model is developed using Python and Tensorflow Keras neural network libraries.

KEYWORDS: Natural Language Processing, Sentiment Analysis, Algorithm Trading, AI/ML, Stock Trading.

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