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FinTech: a literature review of emerging financial technologies and applications

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  • Gang Kou

    (Southwest University of Finance and Economics)

  • Yang Lu

    (Beijing Technology and Business University)

Abstract

Future financing has the potential to evolve into a forward-looking and ingenious system that can enrich diverse technologies. This investigation focuses on five attractive emerging technologies tied to the finance, namely artificial intelligence (AI), machine learning, blockchain, augmented reality (AR)/virtual reality (VR), and quantum mechanics. Within the financial milieu, there is an ongoing pursuit for more precise, optimized, secure, and agile solutions capable of managing multifarious financial undertakings. Emerging technologies present remarkable modalities for achieving these objectives. This scholarly exposition expounds upon the creation of emerging technologies within the financial world, while concurrently deliberating upon the prospective applications of these technologies within financial systems. Given the nascent stage of development that characterizes these emerging technologies, the advancement of financial performance emerges as a substantial challenge owing to both technological and operational advantages. Technology-oriented financing has emerged as an essential trajectory for shaping the future of finance.

Suggested Citation

  • Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-024-00668-6
    DOI: 10.1186/s40854-024-00668-6
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    as
    1. Jonathan Chiu & Thorsten V Koeppl, 2019. "Blockchain-Based Settlement for Asset Trading," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1716-1753.
    2. Zacharias Sautner & Laurence Van Lent & Grigory Vilkov & Ruishen Zhang, 2023. "Firm‐Level Climate Change Exposure," Journal of Finance, American Finance Association, vol. 78(3), pages 1449-1498, June.
    3. Arman Mergaliyev & Mehmet Asutay & Alija Avdukic & Yusuf Karbhari, 2021. "Higher Ethical Objective (Maqasid al-Shari’ah) Augmented Framework for Islamic Banks: Assessing Ethical Performance and Exploring Its Determinants," Journal of Business Ethics, Springer, vol. 170(4), pages 797-834, May.
    4. Feng Xiao & Jintao Ke, 2021. "Pricing, management and decision-making of financial markets with artificial intelligence: introduction to the issue," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-3, December.
    5. Chenlu Dang & Fan Wang & Zimo Yang & Hongxia Zhang & Yufeng Qian, 2022. "RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model," Operations Management Research, Springer, vol. 15(3), pages 662-675, December.
    6. Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
    7. Fabian Schär, 2021. "Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets," Review, Federal Reserve Bank of St. Louis, vol. 103(2), pages 153-174, April.
    8. Pedro Barroso & Konark Saxena, 2022. "Lest We Forget: Learn from Out-of-Sample Forecast Errors When Optimizing Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 35(3), pages 1222-1278.
    9. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    10. Mestel, Roland & Murg, Michael & Theissen, Erik, 2018. "Algorithmic trading and liquidity: Long term evidence from Austria," Finance Research Letters, Elsevier, vol. 26(C), pages 198-203.
    11. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    12. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    13. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2023. "The predictive power of Bitcoin prices for the realized volatility of US stock sector returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    14. Kuang-Hua Hu & Fu-Hsiang Chen & Ming-Fu Hsu & Gwo-Hshiung Tzeng, 2023. "Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-31, December.
    15. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    16. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    17. Dong, Ciwei & Chen, Chenyi & Shi, Xiutian & Ng, Chi To, 2021. "Operations strategy for supply chain finance with asset-backed securitization: Centralization and blockchain adoption," International Journal of Production Economics, Elsevier, vol. 241(C).
    18. Chod, Jiri & Lyandres, Evgeny, 2023. "Product market competition with crypto tokens and smart contracts," Journal of Financial Economics, Elsevier, vol. 149(1), pages 73-91.
    19. Faisal Mahmood & Umeair Shahzad & Ali Nazakat & Zahoor Ahmed & Husam Rjoub & Wing-Keung Wong, 2022. "The Nexus Between Cash Conversion Cycle, Working Capital Finance, And Firm Performance: Evidence From Novel Machine Learning Approaches," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-44, June.
    20. Emiliano S Pagnotta, 2022. "Decentralizing Money: Bitcoin Prices and Blockchain Security," The Review of Financial Studies, Society for Financial Studies, vol. 35(2), pages 866-907.
    21. Gao, Qiang & Lin, Mingfeng & Sias, Richard, 2023. "Words Matter: The Role of Readability, Tone, and Deception Cues in Online Credit Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 58(1), pages 1-28, February.
    22. G Andrew Karolyi & Stijn Van Nieuwerburgh, 2020. "New Methods for the Cross-Section of Returns," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1879-1890.
    23. Cong, Lin William & Li, Ye & Wang, Neng, 2022. "Token-based platform finance," Journal of Financial Economics, Elsevier, vol. 144(3), pages 972-991.
    24. Cynthia Weiyi Cai, 2018. "Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 965-992, December.
    25. Zhiqi Feng & Yongli Li & Xiaochen Ma, 2023. "Blockchain-oriented approach for detecting cyber-attack transactions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    26. Husam Rjoub & Tomiwa Sunday Adebayo & Dervis Kirikkaleli, 2023. "Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    27. Esra Bulut, 2022. "Blockchain-based entrepreneurial finance: success determinants of tourism initial coin offerings," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(11), pages 1767-1781, June.
    28. He, Zhiguo & Huang, Jing & Zhou, Jidong, 2023. "Open banking: Credit market competition when borrowers own the data," Journal of Financial Economics, Elsevier, vol. 147(2), pages 449-474.
    29. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
    30. Griffin, Dale & Li, Kai & Xu, Ting, 2021. "Board Gender Diversity and Corporate Innovation: International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(1), pages 123-154, February.
    31. Lixia Liu & Xueli Zhan, 2019. "Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-11, July.
    32. Longbing Cao & Qiang Yang & Philip S. Yu, 2020. "Data science and AI in FinTech: An overview," Papers 2007.12681, arXiv.org, revised Jul 2021.
    33. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    34. Lin William Cong & Zhiguo He, 2019. "Blockchain Disruption and Smart Contracts," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1754-1797.
    35. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    36. Jiri Chod & Nikolaos Trichakis & Gerry Tsoukalas & Henry Aspegren & Mark Weber, 2020. "On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption," Management Science, INFORMS, vol. 66(10), pages 4378-4396, October.
    37. Jiang, Rong & Kang, Yuanjie & Liu, Yongsong & Liang, Zhihong & Duan, Yunlong & Sun, Yani & Liu, Jialan, 2022. "A trust transitivity model of small and medium-sized manufacturing enterprises under blockchain-based supply chain finance," International Journal of Production Economics, Elsevier, vol. 247(C).
    38. Sokolov, Konstantin, 2021. "Ransomware activity and blockchain congestion," Journal of Financial Economics, Elsevier, vol. 141(2), pages 771-782.
    39. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    40. Marco Taboga, 2022. "Cross-country differences in the size of venture capital financing rounds: a machine learning approach," Empirical Economics, Springer, vol. 62(3), pages 991-1012, March.
    41. Hao Ni & Xin Dong & Jinsong Zheng & Guangxi Yu, 2021. "An Introduction to Machine Learning in Quantitative Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0275.
    42. Lin, Boqiang & Bai, Rui, 2022. "Machine learning approaches for explaining determinants of the debt financing in heavy-polluting enterprises," Finance Research Letters, Elsevier, vol. 44(C).
    43. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    44. Yael Grushka-Cockayne & Victor Richmond R. Jose & Kenneth C. Lichtendahl Jr., 2017. "Ensembles of Overfit and Overconfident Forecasts," Management Science, INFORMS, vol. 63(4), pages 1110-1130, April.
    45. Cynthia W. Cai & Martina K. Linnenluecke & Mauricio Marrone & Abhay K. Singh, 2019. "Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific," Abacus, Accounting Foundation, University of Sydney, vol. 55(4), pages 709-733, December.
    46. Chaklader, Barnali & Gupta, Brij B. & Panigrahi, Prabin Kumar, 2023. "Analyzing the progress of FINTECH-companies and their integration with new technologies for innovation and entrepreneurship," Journal of Business Research, Elsevier, vol. 161(C).
    47. Hinzen, Franz J. & John, Kose & Saleh, Fahad, 2022. "Bitcoin’s limited adoption problem," Journal of Financial Economics, Elsevier, vol. 144(2), pages 347-369.
    48. Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021. "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125653, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    49. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    50. Randy Priem, 2020. "Distributed ledger technology for securities clearing and settlement: benefits, risks, and regulatory implications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    51. Traczynski, Jeffrey, 2017. "Firm Default Prediction: A Bayesian Model-Averaging Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1211-1245, June.
    52. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    53. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    54. Pierre Jinghong Liang & Vitaly Meursault & Bryan B. Routledge & Madeline Marco Scanlon, 2021. "PEAD.txt: Post-Earnings-Announcement Drift Using Text," Working Papers 21-07, Federal Reserve Bank of Philadelphia.
    55. Li Zhang & Yongping Xie & Yang Zheng & Wei Xue & Xianrong Zheng & Xiaobo Xu, 2020. "The challenges and countermeasures of blockchain in finance and economics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 691-698, July.
    56. Fahad Saleh & Wei Jiang, 2021. "Blockchain without Waste: Proof-of-Stake [Proof of Work vs Proof of Stake]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1156-1190.
    57. Sophie Meralli, 2020. "Privacy-preserving analytics for the securitization market: a zero-knowledge distributed ledger technology application," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-20, December.
    58. Gagan Deep Sharma & Burak Erkut & Mansi Jain & Tuğberk Kaya & Mandeep Mahendru & Mrinalini Srivastava & Raminder Singh Uppal & Sanjeet Singh, 2020. "Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, December.
    59. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    60. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    61. John M. Griffin & Nicholas Hirschey & Samuel Kruger, 2023. "Do Municipal Bond Dealers Give Their Customers “Fair and Reasonable” Pricing?," Journal of Finance, American Finance Association, vol. 78(2), pages 887-934, April.
    62. Baker, Malcolm & Hoeyer, Mathias F. & Wurgler, Jeffrey, 2020. "Leverage and the Beta Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(5), pages 1491-1514, August.
    63. Akey, Pat & Grégoire, Vincent & Martineau, Charles, 2022. "Price revelation from insider trading: Evidence from hacked earnings news," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1162-1184.
    64. Grennan, Jillian & Michaely, Roni, 2021. "FinTechs and the Market for Financial Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(6), pages 1877-1907, September.
    65. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    66. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    67. Ali, Omar & Ally, Mustafa & Clutterbuck, & Dwivedi, Yogesh, 2020. "The state of play of blockchain technology in the financial services sector: A systematic literature review," International Journal of Information Management, Elsevier, vol. 54(C).
    68. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    69. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    70. Yu Song & Bo Chen & Xin-Yi Wang, 2023. "Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-20, December.
    71. Du, Shuili & Xie, Chunyan, 2021. "Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities," Journal of Business Research, Elsevier, vol. 129(C), pages 961-974.
    72. Lennart Ante & Philipp Sandner & Ingo Fiedler, 2018. "Blockchain-Based ICOs: Pure Hype or the Dawn of a New Era of Startup Financing?," JRFM, MDPI, vol. 11(4), pages 1-19, November.
    73. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    74. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    75. Ferreira, Daniel & Li, Jin & Nikolowa, Radoslawa, 2023. "Corporate capture of blockchain governance," LSE Research Online Documents on Economics 115618, London School of Economics and Political Science, LSE Library.
    76. Han, Hongdan & Shiwakoti, Radha K. & Jarvis, Robin & Mordi, Chima & Botchie, David, 2023. "Accounting and auditing with blockchain technology and artificial Intelligence: A literature review," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    77. Ferrati, Francesco & Muffatto, Moreno, 2021. "Entrepreneurial Finance: Emerging Approaches Using Machine Learning and Big Data," Foundations and Trends(R) in Entrepreneurship, now publishers, vol. 17(3), pages 232-329, April.
    78. Min Xu & Xingtong Chen & Gang Kou, 2019. "A systematic review of blockchain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-14, December.
    79. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    80. Easley, David & O'Hara, Maureen & Basu, Soumya, 2019. "From mining to markets: The evolution of bitcoin transaction fees," Journal of Financial Economics, Elsevier, vol. 134(1), pages 91-109.
    81. Christian Fisch & Michele Meoli & Silvio Vismara, 2022. "Does blockchain technology democratize entrepreneurial finance? An empirical comparison of ICOs, venture capital, and REITs," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 31(1-2), pages 70-89, February.
    82. Braggion, Fabio & Manconi, Alberto & Zhu, Haikun, 2020. "Credit and social unrest: Evidence from 1930s China," Journal of Financial Economics, Elsevier, vol. 138(2), pages 295-315.
    83. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    84. Jan De Spiegeleer & Dilip B. Madan & Sofie Reyners & Wim Schoutens, 2018. "Machine learning for quantitative finance: fast derivative pricing, hedging and fitting," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1635-1643, October.
    85. Malcolm Campbell-Verduyn & Francesco Giumelli, 2022. "Enrolling into exclusion: African blockchain and decolonial ambitions in an evolving finance/security infrastructure," Journal of Cultural Economy, Taylor & Francis Journals, vol. 15(4), pages 524-543, July.
    86. Engle, Robert F. & Campos-Martins, Susana, 2023. "What are the events that shake our world? Measuring and hedging global COVOL," Journal of Financial Economics, Elsevier, vol. 147(1), pages 221-242.
    87. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.
    88. Daniel Ferreira & Jin Li & Radoslawa Nikolowa, 2023. "Corporate Capture of Blockchain Governance," The Review of Financial Studies, Society for Financial Studies, vol. 36(4), pages 1364-1407.
    89. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    90. Raúl Gómez Martínez & Miguel Prado Román & Paola Plaza Casado, 2019. "Big Data Algorithmic Trading Systems Based on Investors’ Mood," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(2), pages 227-238, April.
    91. Jun-Hao Chen & Yun-Cheng Tsai, 2020. "Encoding candlesticks as images for pattern classification using convolutional neural networks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
    92. Chi-Ming Ho, 2023. "Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    93. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    94. Mahboubeh Faghih Mohammadi Jalali & Hanif Heidari, 2020. "Predicting changes in Bitcoin price using grey system theory," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-12, December.
    95. Bernard, Darren & Blackburne, Terrence & Thornock, Jacob, 2020. "Information flows among rivals and corporate investment," Journal of Financial Economics, Elsevier, vol. 136(3), pages 760-779.
    96. Francisco Javier García-Corral & José Antonio Cordero-García & Jaime de Pablo-Valenciano & Juan Uribe-Toril, 2022. "A bibliometric review of cryptocurrencies: how have they grown?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    97. Frattaroli, Marc, 2020. "Does protectionist anti-takeover legislation lead to managerial entrenchment?," Journal of Financial Economics, Elsevier, vol. 136(1), pages 106-136.
    98. Canhoto, Ana Isabel, 2021. "Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective," Journal of Business Research, Elsevier, vol. 131(C), pages 441-452.
    99. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    100. Fedyk, Anastassia & Hodson, James, 2023. "When can the market identify old news?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 92-113.
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    16. Kyohei Shibano & Gento Mogi, 2022. "An analysis of the acquisition of a monetary function by cryptocurrency using a multi-agent simulation model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    17. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    18. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    19. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    20. Bryan Kelly & Semyon Malamud & Kangying Zhou, 2024. "The Virtue of Complexity in Return Prediction," Journal of Finance, American Finance Association, vol. 79(1), pages 459-503, February.

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