DEEP LEARNING APLICADO À NEGOCIAÇÃO DE AÇÕES POR ALGORITMOS: UMA REVISÃO DESCRITIVA DA LITERATURA
DOI:
https://doi.org/10.24325/issn.2446-5763.v6i17p237-268Keywords:
Deep Learning, Algorithmic Trading, Revisão da Literatura, Preços de açõesAbstract
Revisões da literatura relativamente recentes apresentam o Deep Learning como campo do Aprendizado de Máquina pouco explorado na área de negociação e predição de ativos por algoritmos (AT). Identifica-se uma oportunidade para a investigação das frentes de pesquisa e formulações epistemológicas emergentes sobre o tema. Este estudo tem por objetivo prospectar a produção científica sobre Deep Learning aplicado a sistemas de AT, identificando o estado da arte da pesquisa por meio de análise bibliométrica, revisão descritiva da literatura e análise das abordagens. Busca-se contribuir para aprimoramento do mercado de capitais quanto à liquidez, eficiência e capacidade preditiva de preços de ações, que possam se traduzir em redução de incertezas e menores custos de transação aos investidores.
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References
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CHABOUD, A. P.; CHIQUUOINE, B.; HJALMARSSON, E.; VEGA, C. Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance. v. 69, n. 5, 2014.
CHAN, E. P. Quantitative trading: how to build your own algorithmic trading business. Wiley, 2009.
CHONG, E.; HAN, H.; PARK, F. C. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, v. 83, pp. 187-205, 2017.
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DAMODARAN, A. Investment Valuation. Tools and Techniques for Determining the Value of Any Asset. 3rd ed. New Jersey: John Wiley & Sons, 2012.
EASLEY, D.; PRADO, M. M. L.; O’HARA, M. The Microstructure of the ‘Flash Crash’. Flow toxicity, Liquidity Crashes and the Probability of Informed Trading. The Journal of Portfolio Management, v. 37, n. 2, p. 118-128, 2011.
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HOCHREITER, S.; SCHMIDHUBER, J. Long Short-Term Memory. Neural Computation, 1997.
HU, Z.; LIU, W.; BIAN, J.; LIU, X.; LIU, T-Y. Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction. ACM Web Search and Data Mining, feb, 2018 .
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IEEE Spectrum. Hedge Funds Look to Machine Learning, Crowdsourcing for Competitive Advantage. 2017. Disponível em: <https://spectrum.ieee.org/tech-talk/computing/software/fintech-trends-hedge-funds-look-to-machine-learning-crowdsourcing-for-competitive-advantage> Acesso em 18 abr. 2019.
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KRAUS, M.; FEUERRIEGEL, S. Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, v. 104, pp. 38-48, 2017.
LÄNGKVIST, M.; KARLSSON, L.; LOUTFI, A. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters. v. 42, pp. 11-24. 2014.
LECUN, Y.; BENGIO, Y. Convolutional Networks for Images, Speech, and Time-Series. The handbook of brain theory and neural networks. MIT Press, 1995.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep Learning. Nature. Vol. 521, pp.436-444, 2015.
MIT TECHNOLOGY REVIEW. As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened. Nanette Byrnes. 7 fev. 2017. Disponível em <www.technologyreview.com/s/603431/as-goldman-embraces-automation-even-the-masters-of-the-universe-are-threatened/>. Acesso em 7 jun. 2018.
MURPHY, J. Technical Analysis of the Financial Markets. New York: Penguin Putnam, 1999.
NATENBERG, S. Option Volatility & Pricing. Advanced Trading Strategies and Techniques. McGraw-Hill, 1994.
NUTI, G.; MIRGHAEMI, M.; TRELEAVEN, P.;YINGSAEREE, C. Algorithmic trading. Computer-IEEE Computer Society, v. 44, n. 11, p. 61-69, 2011.
OKUBO, Y. Bibliometric Indicators and Analysis of Research Systems: Methods and Examples. Paris: OECD Publishing, 1997.
PARÉ, G.; TRUDEL, M-C.; JAANA, M.; KTSIOU, S. Synthesizing information systems knowledge: A typology of literature reviews. Information and Management. v. 52, n. 2, p 183-199, 2005.
PRADO, M. L. Advances in Financial Machine Learning. John Wiley & Sons, 2018.
ROSS, S. A.; WESTERFIELD, R. W.; JAFFE, J. F. Administração Financeira – Corporate Finance. São Paulo: ATLAS, 2011.
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SCHMIDHUBER, J. Deep learning in neural networks. Neural Networks. Vol. 61, p.85-117, 2014.
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SELVIN, S. et al. Stock price prediction using LSTM, RNN and CNN-sliding window model. Computing, Communications and Informatics, 2017.
SOHANGIR, S.; WANG, D.; POMERANETS, A.; KHOSHGOFTAAR, T. M. Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 2018.
TAKEUCHI, L.; LEE, Y-Y. Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks. Stanford.edu, 2013.
TALEB, N. Dynamic Hedging. Managing Vanilla and Exotic Options. New York: John Wiley & Sons, 1997.
TSANTEKIDIS, A. et al. Forecasting stock prices from the limit order book using convolutional neural networks. 2017 IEEE 19th Business Informatics, 2017.
VAN MAANEN, J. Reclaiming Qualitative Methods for Organizational Research. Administrative Science Quartely, v. 24, p. 520-526, dez. 1979.
VARGAS. M. R.; LIMA, B. S. L. P.; EVSUKOFF, A. G. Deep learning for stock market prediction from financial news articles. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017, p. 60-65, 2017.
VIDYAMURTHY, G. Pairs Trading. Quantitative Methods and Analysis. New Jersey: Wiley, 2004.
WIERSTRA, D.; SCHMIDHUBER, J.; GOMEZ, F. J. Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh: pp. 853–858, 2005.
WEB OF SCIENCE – CLARIVATE ANALYTICS Disponível em < www.webofknowledge.com>. Acesso em 30 Set. 2019.
BAO, W.; YUE, J.; RAO, Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 2017.
BENGIO, Y.; COURVILLE, A.; PASCAL, V. Representation learning: a review and new perspectives. ArXiv e-prints, 2012.
BERTO, R.M.V.S.; NAKANO; D.N. A produção científica nos anais do Encontro Nacional de Engenharia de Produção: um levantamento de métodos e tipos de pesquisa. ABEPRO Produção, v. 9, n. 2, p. 65-76, 2000.
BROGAARD, J.; HENDERSHOTT, T.; RIORDAN, R. High-Frequency Trading and the 2008 short-sale ban. Journal of Financial Economics. v. 124, 2017.
BRYMAN, A.; BELL, E. Business Research Methods. 3 ed. Oxford University Press, 2011.
CAUCHICK-MIGUEL, P. A.; FLEURY, A. Metodologia De Pesquisa em Engenharia de Produção. 2ª.ed. Capítulo 2 – Planejamento do Projeto de Pesquisa e Definição do Modelo Teórico. Elsevier, 2012.
CAVALCANTE, C. C. et al. Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications. v 55, 2016.
CHABOUD, A. P.; CHIQUUOINE, B.; HJALMARSSON, E.; VEGA, C. Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance. v. 69, n. 5, 2014.
CHAN, E. P. Quantitative trading: how to build your own algorithmic trading business. Wiley, 2009.
CHONG, E.; HAN, H.; PARK, F. C. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, v. 83, pp. 187-205, 2017.
CRESWELL, J. W. Research Design. Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications, 2014.
DAMODARAN, A. Investment Valuation. Tools and Techniques for Determining the Value of Any Asset. 3rd ed. New Jersey: John Wiley & Sons, 2012.
EASLEY, D.; PRADO, M. M. L.; O’HARA, M. The Microstructure of the ‘Flash Crash’. Flow toxicity, Liquidity Crashes and the Probability of Informed Trading. The Journal of Portfolio Management, v. 37, n. 2, p. 118-128, 2011.
ELTON, E.; GRUBER, M. Modern Portfolio Theory and Investment Analysis. 5 ed. New York: Wiley, 1995.
FILIPPINI, R. Operations management research: some reflections on evolution, models and empirical studies in OM. International Journal of Operations and Production Management, v. 17, n.7, p. 655-70, 1997.
FIX TRADING. FIX Protocol. Disponível em: <www.fixtrading.org>. Acesso em 07 jun. 2018.
GADRE-PATWARDHAN, S.; KATDARE V. V.; JOSHI M. R. A Review of Artificially Intelligent Applications in the Financial Domain. Cap. 1 in DUNIS, C. L.; MIDDLETON, P. W.; KARATHANASOPOULOS, A.; THEOFILATOS, K. Artificial Intelligence in Financial Markets. Palgrave MacMillan, 2016.
GOODEFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. MIT Press, 2016.
GUO, X.; LAI, T. L.; SHEK, H.; WONG, S. P. Quantitative trading - Algorithms, Analytics, Data, Models, Optimization. CRC Press. Taylor & Francis Group, 2017.
HARVARD UNIVERSITY - IACS (INSTITUTE for APPLIED COMPUTATIONAL SCIENCE). Symposium Data, Dollars, and Algorithms: The Computational Economy. Publ. 19-jan-2017. Disponível em: <iacs.seas.harvard.edu/event/annual-iacs-symposium-data-dollars-and-algorithms-computational-economy?delta=0> Acesso em 19-set-2018.
HASBROUCK, J.; SAAR, G. Low-latency trading. Journal of Financial Markets. v. 16, 2013.
HENDERSHOTT, T.; JONES, C. M.; MENKVELD, A. J. Does Algorithmic Trading Improve Liquidity? The Journal of Finance. v. 66 n. 1, 2011.
HENDERSHOTT, T.; RIORDAN, R. Algorithmic Trading and the Market for Liquidity. Journal of financial and quantitative analysis. v. 48, n. 4, p. 1001–1024, 2013.
HILPISCH, Y. Algorithmic Trading in Less Than 100 Lines of Python Code. 2018. Disponível em <www.Oreilly.Com/Learning/Algorithmic-Trading-In-Less-Than-100-Lines-Of-Python-Code> Acceso em 19 Set. 2018.
HOCHREITER, S.; SCHMIDHUBER, J. Long Short-Term Memory. Neural Computation, 1997.
HU, Z.; LIU, W.; BIAN, J.; LIU, X.; LIU, T-Y. Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction. ACM Web Search and Data Mining, feb, 2018 .
HULL, J. Options, Futures, and Other Derivatives. 10th ed. Pearson, 2018.
IEEE Spectrum. Hedge Funds Look to Machine Learning, Crowdsourcing for Competitive Advantage. 2017. Disponível em: <https://spectrum.ieee.org/tech-talk/computing/software/fintech-trends-hedge-funds-look-to-machine-learning-crowdsourcing-for-competitive-advantage> Acesso em 18 abr. 2019.
IEEE Xplore. Acesso em 30 set. 2019. Disponível em <https://ieeexplore.ieee.org> (2019).
JOHNSON, B. Algorithmic Trading & DMA – An introduction to direct access trading strategies. London: 4MyelomaPress, 2010.
KAUFMAN, P. J. Trading Systems and Methods. 5 ed. Wiley, 2013.
KEARNS, M.; NEVMYVAKA, Y. Machine Learning for Market Microstructure and High-Frequency Trading. In: EASLEY, D.; PRADO, M. M. L.; O’HARA, M. (Orgs.) High-Frequency Trading: New Reality for Traders, Markets and Regulators. Risk Books, 2013.
KHARE, K.; DAREKAR, O.; GUPTA, P.; ATTAR, V. Z. Short Term Stock Price Prediction using Deep Learning. 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings - RTEICT 2017, pp. 482-486, 2017.
KITCHENHAM, B. A.; CHARTERS, S. Guidelines for performing systematic literature reviews in software engineering. version 2.3. EBSE Technical Report – Keele University – University of Durham, 2007.
KITCHENHAM, B. A. et al.; Systematic literature reviews in software engineering – A tertiary study. Information and Software Technology, v. 52, pp. 792-805, 2010.
KRAUS, M.; FEUERRIEGEL, S. Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, v. 104, pp. 38-48, 2017.
LÄNGKVIST, M.; KARLSSON, L.; LOUTFI, A. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters. v. 42, pp. 11-24. 2014.
LECUN, Y.; BENGIO, Y. Convolutional Networks for Images, Speech, and Time-Series. The handbook of brain theory and neural networks. MIT Press, 1995.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep Learning. Nature. Vol. 521, pp.436-444, 2015.
MIT TECHNOLOGY REVIEW. As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened. Nanette Byrnes. 7 fev. 2017. Disponível em <www.technologyreview.com/s/603431/as-goldman-embraces-automation-even-the-masters-of-the-universe-are-threatened/>. Acesso em 7 jun. 2018.
MURPHY, J. Technical Analysis of the Financial Markets. New York: Penguin Putnam, 1999.
NATENBERG, S. Option Volatility & Pricing. Advanced Trading Strategies and Techniques. McGraw-Hill, 1994.
NUTI, G.; MIRGHAEMI, M.; TRELEAVEN, P.;YINGSAEREE, C. Algorithmic trading. Computer-IEEE Computer Society, v. 44, n. 11, p. 61-69, 2011.
OKUBO, Y. Bibliometric Indicators and Analysis of Research Systems: Methods and Examples. Paris: OECD Publishing, 1997.
PARÉ, G.; TRUDEL, M-C.; JAANA, M.; KTSIOU, S. Synthesizing information systems knowledge: A typology of literature reviews. Information and Management. v. 52, n. 2, p 183-199, 2005.
PRADO, M. L. Advances in Financial Machine Learning. John Wiley & Sons, 2018.
ROSS, S. A.; WESTERFIELD, R. W.; JAFFE, J. F. Administração Financeira – Corporate Finance. São Paulo: ATLAS, 2011.
RUSSELL, S. J.; NORVIG, P. Artificial Intelligence - A Modern Approach. 3rd. ed. Pearson, 2016
SAUNDERS, M.; LEWIS, P; THORNHILL, A. Research Methods for Business Students. 6th edition, Pearson Education Limited. 2012
SCHMIDHUBER, J. Deep learning in neural networks. Neural Networks. Vol. 61, p.85-117, 2014.
SCOPUS – ELSEVIER Disponível em <www.scopus.com>. Acesso em 30 Set. 2019.
SELVIN, S. et al. Stock price prediction using LSTM, RNN and CNN-sliding window model. Computing, Communications and Informatics, 2017.
SOHANGIR, S.; WANG, D.; POMERANETS, A.; KHOSHGOFTAAR, T. M. Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 2018.
TAKEUCHI, L.; LEE, Y-Y. Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks. Stanford.edu, 2013.
TALEB, N. Dynamic Hedging. Managing Vanilla and Exotic Options. New York: John Wiley & Sons, 1997.
TSANTEKIDIS, A. et al. Forecasting stock prices from the limit order book using convolutional neural networks. 2017 IEEE 19th Business Informatics, 2017.
VAN MAANEN, J. Reclaiming Qualitative Methods for Organizational Research. Administrative Science Quartely, v. 24, p. 520-526, dez. 1979.
VARGAS. M. R.; LIMA, B. S. L. P.; EVSUKOFF, A. G. Deep learning for stock market prediction from financial news articles. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2017, p. 60-65, 2017.
VIDYAMURTHY, G. Pairs Trading. Quantitative Methods and Analysis. New Jersey: Wiley, 2004.
WIERSTRA, D.; SCHMIDHUBER, J.; GOMEZ, F. J. Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh: pp. 853–858, 2005.
WEB OF SCIENCE – CLARIVATE ANALYTICS Disponível em < www.webofknowledge.com>. Acesso em 30 Set. 2019.
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Published
2020-08-22
How to Cite
Shimabukuro, C. I., Galegale, N. V., Okano, M. T., & Langhi, C. (2020). DEEP LEARNING APLICADO À NEGOCIAÇÃO DE AÇÕES POR ALGORITMOS: UMA REVISÃO DESCRITIVA DA LITERATURA. South American Development Society Journal, 6(17), 237. https://doi.org/10.24325/issn.2446-5763.v6i17p237-268
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