Classification of S&P 500 Stocks Based on Correlating Market Trends

  • Minakshi Tomer
  • Vaibhav AnandEmail author
  • Raghav Shandilya
  • Shubham Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1164)


Stock market is a correlated entanglement of companies whose prices depend upon various factors including how well each individual company is doing, their stock’s performance and the general trend of the stock market. A simple linear prediction based upon the past performances of the stock is not enough to declare whether a particular stock will perform in a linear way. To predict the behavior of stocks, it is required to look at all of its correlated counterparts, compile their performance numbers and take a logical guess on whether the stock’s performance will be on the positive side or on the negative side. For that measure, data of all the stocks from Standard and Poor’s 500 Ratings (S&P 500) was collected. The novel idea in this paper is to classify a stock’s performance based upon its correlated features and predict that whether it should be bought, sold, or held.


Supervised classification Support vector machines Random forest classifiers Stock market Correlation 


  1. 1.
    J. Kalyani, P. Bharathi, P. Jyothi, Stock Trend Prediction Using News Sentiment Analysis (2016). arXiv:1607.01958
  2. 2.
    M. Poženel, D. Lavbič, Discovering Language of the Stocks (2019). arXiv:1902.08684Google Scholar
  3. 3.
    Z. Liu, M.D. Moghaddam, R.A. Serota, Distributions of Historic Market Data—Stock Returns (2017). arXiv:1711.11003
  4. 4.
    A. Chaudhuri, K. De, D Chatterjee, Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using Rough Fuzzy Multi Layer Perception Networks (2013). arXiv:1307.1895
  5. 5.
    X.Y. Fu, J.H. Du, Y.F. Guo, M.W. Liu, T. Dong, X.W. Duan., A Machine Learning Framework for Stock Selection (2018). arXiv:1806.01743Google Scholar
  6. 6.
    S Lu, J. Zhao, H. Wang, The Emergence of Critical Stocks in Market Crash (2019). arXiv:1908.07244
  7. 7.
    A. Elliot, C.H. Hsu, Time Series Prediction: Predicting Stock Price (2017). arXiv:1710.05751
  8. 8.
    Z. Kakushadze, W. Yu, Stock Market Visualization (2018). arXiv:1802.05264
  9. 9.
    J. Liu, F. Chao, Y.-C. Lin, C.-M. Lin, Stock Prices Prediction using Deep Learning Models (2019). arXiv:1909.12227
  10. 10.
    S. Arik, S.B. Eryilmaz, A. Goldberg, Supervised Classification-Based Stock Prediction and Portfolio Optimization (2014). arXiv:1406.0824Google Scholar
  11. 11.
    A. Filos, Reinforcement Learning for Portfolio Management (2019). arXiv:1909.09571
  12. 12.
    M.S., Lauretto, B.B.C. Silva, P.M. Andrade, Evaluation of a Supervised Learning Approach for Stock Market Operations (2013). arXiv:1301.4944
  13. 13.
    L. Khaidem, S. Saha, S.R. Dey, Predicting the Direction of Stock Market Prices using Random Forest (2016). arXiv:1605.00003
  14. 14.
    M. Velay, F. Daniel, Using NLP on News Headlines to Predict Index Trends (2018). arXiv:1806.09533

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Minakshi Tomer
    • 1
  • Vaibhav Anand
    • 1
    Email author
  • Raghav Shandilya
    • 1
  • Shubham Tiwari
    • 1
  1. 1.Department of Information TechnologyMaharaja Surajmal Institute of TechnologyNew DelhiIndia

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