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Discovering Language of the Stocks

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  • Marko Pov{z}enel
  • Dejan Lavbiv{c}

Abstract

Stock prediction has always been attractive area for researchers and investors since the financial gains can be substantial. However, stock prediction can be a challenging task since stocks are influenced by a multitude of factors whose influence vary rapidly through time. This paper proposes a novel approach (Word2Vec) for stock trend prediction combining NLP and Japanese candlesticks. First, we create a simple language of Japanese candlesticks from the source OHLC data. Then, sentences of words are used to train the NLP Word2Vec model where training data classification also takes into account trading commissions. Finally, the model is used to predict trading actions. The proposed approach was compared to three trading models Buy & Hold, MA and MACD according to the yield achieved. We first evaluated Word2Vec on three shares of Apple, Microsoft and Coca-Cola where it outperformed the comparative models. Next we evaluated Word2Vec on stocks from Russell Top 50 Index where our Word2Vec method was also very successful in test phase and only fall behind the Buy & Hold method in validation phase. Word2Vec achieved positive results in all scenarios while the average yields of MA and MACD were still lower compared to Word2Vec.

Suggested Citation

  • Marko Pov{z}enel & Dejan Lavbiv{c}, 2019. "Discovering Language of the Stocks," Papers 1902.08684, arXiv.org.
    • Handle: RePEc:arx:papers:1902.08684
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    References listed on IDEAS

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    1. Tsung-Hsun Lu & Yung-Ming Shiu, 2012. "Tests for Two-Day Candlestick Patterns in the Emerging Equity Market of Taiwan," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(0), pages 41-57, January.
    2. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. Cristina Abad & Sten A. Thore & Joaquina Laffarga, 2004. "Fundamental analysis of stocks by two-stage DEA," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 231-241.
    4. repec:pri:cepsud:91malkiel is not listed on IDEAS
    5. Lu, Tsung-Hsun, 2014. "The profitability of candlestick charting in the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 65-78.
    6. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    7. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    8. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    9. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
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