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Classification of intraday S&P500 returns with a Random Forest

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  • Lohrmann, Christoph
  • Luukka, Pasi

Abstract

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.

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  • Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:1:p:390-407
    DOI: 10.1016/j.ijforecast.2018.08.004
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. repec:bla:intfin:v:4:y:2001:i:2:p:221-55 is not listed on IDEAS
    3. Evan Hurwitz & Tshilidzi Marwala, 2011. "Suitability of using technical indicators as potential strategies within intelligent trading systems," Papers 1110.3383, arXiv.org.
    4. Adam Fadlalla & Farzaneh Amani, 2014. "Predicting Next Trading Day Closing Price Of Qatar Exchange Index Using Technical Indicators And Artificial Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 209-223, October.
    5. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    6. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    7. Lionel Page, 2012. "‘It ain’t over till it's over.’ Yogi Berra bias on prediction markets," Applied Economics, Taylor & Francis Journals, vol. 44(1), pages 81-92, January.
    8. Rothschild, David & Pennock, David M., 2014. "The extent of price misalignment in prediction markets," Algorithmic Finance, IOS Press, vol. 3(1-2), pages 3-20.
    9. Ian Domowitz & Jack Glen & Ananth Madhavan, 2001. "Liquidity, Volatility and Equity Trading Costs Across Countries and Over Time," International Finance, Wiley Blackwell, vol. 4(2), pages 221-255.
    10. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    11. Eero P䴤ri & Mika Vilska, 2014. "Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2851-2872, August.
    12. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
    13. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    14. Myoung Jong Kim & Ingoo Han & Kun Chang Lee, 2004. "Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 43-60, January.
    15. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    16. Bhaduri, Saumitra & Saraogi, Ravi, 2010. "The predictive power of the yield spread in timing the stock market," Emerging Markets Review, Elsevier, vol. 11(3), pages 261-272, September.
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    3. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    4. Bharat Kumar Meher & Abhishek Anand & Sunil Kumar & Ramona Birau & Manohar Sing, 2024. "Effectiveness of Random Forest Model in Predicting Stock Prices of Solar Energy Companies in India," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 426-434, March.
    5. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    6. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    7. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
    8. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    9. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.

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