Forecasting financial time series with Boltzmann entropy through neural networks
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DOI: 10.1007/s10287-022-00430-2
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- LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
- Wu, Chuanzhen, 2021. "Window effect with Markov-switching GARCH model in cryptocurrency market," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
- Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
- Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
- Farmer, J. Doyne & Axtell, Robert L., 2022. "Agent-Based Modeling in Economics and Finance: Past, Present, and Future," INET Oxford Working Papers 2022-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
- Tesfatsion, Leigh S., 2002.
"Agent-Based Computational Economics: Growing Economies from the Bottom Up,"
Staff General Research Papers Archive
5075, Iowa State University, Department of Economics.
- Tesfatsion, Leigh, 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," ISU General Staff Papers 200201010800001427, Iowa State University, Department of Economics.
- Tesfatsion, Leigh, 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," ISU General Staff Papers 200201010800001251, Iowa State University, Department of Economics.
- Howitt, Peter & Clower, Robert, 2000. "The emergence of economic organization," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 55-84, January.
- Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
- Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
- repec:cdl:ucsbec:13-89 is not listed on IDEAS
- Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
- Justin A. Sirignano, 2019. "Deep learning for limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 549-570, April.
- Ymir Mäkinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2033-2050, December.
- Borland, Lisa, 2016. "Exploring the dynamics of financial markets: from stock prices to strategy returns," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 59-74.
- Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
- Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
- LeRoy, Stephen F, 1989. "Efficient Capital Markets and Martingales," Journal of Economic Literature, American Economic Association, vol. 27(4), pages 1583-1621, December.
- Michael Rollins & Dave Cliff, 2020. "Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order," Papers 2009.06905, arXiv.org.
- Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
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Keywords
Neural networks; Price forecasting; LSTM; Boltzmann entropy; Financial markets; Cryptocurrency;All these keywords.
JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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