FinTech as a Game Changer: Overview of Research Frontiers

References

• Adamopoulos P , Ghose A , Todri V (2018) The impact of user personality traits on word of mouth: Text-mining social media platforms. Inform. Systems Res. 29(3):612–640.Link, Google Scholar
• Allen F , Karjalainen R (1999) Using genetic algorithms to find technical trading rules. J. Financial Econom. 51(2):245–271.Crossref, Google Scholar
• Anand A , Sharma R , Kohli R (2020) The effects of operational and financial performance failure on BI&A-enabled search behaviors: A theory of performance-driven search. Inform. Systems Res. 31(4):1144–1163.Link, Google Scholar
• Aoyagi J (2020) The dark side of regulating fast informed trading. Working paper, Department of Economics, University of California, Berkeley, Berkeley, CA.Google Scholar
• Aviv Y , Pazgal A (2005) A partially observed Markov decision process for dynamic pricing. Management Sci. 51(9):1400–1416.Link, Google Scholar
• Baldauf M , Mollner J (2020) High-frequency trading and market performance. J. Finance 75(3):1495–1526.Crossref, Google Scholar
• Ban G-Y , El Karoui N , Lim AEB (2018) Machine learning and portfolio optimization. Management Sci. 64(3):1136–1154.Link, Google Scholar
• Bao Y , Ke B , Li B , Yu YJ , Zhang J (2020) Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach. J. Accounting Res. 58(1):199–235.Crossref, Google Scholar
• Barclay M , Hendershott T , McCormick T (2003) Competition among trading venues: Information and trading on electronic communications networks. J. Finance 58(6):2637–2665.Crossref, Google Scholar
• Belloni A , Chernozhukov V , Hansen C (2013) Inference on treatment effects after selection among high-dimensional controls. Rev. Econom. Stud. 81(2):608–650.Crossref, Google Scholar
• Benjamini Y , Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. B . 57(1):289–300.Google Scholar
• Biais B , Foucault T , Moinas S (2015) Equilibrium fast trading. J. Financial Econom. 116(2):292–313.Crossref, Google Scholar
• Biais B , Bisière C , Bouvard M , Casamatta C (2019) The blockchain folk theorem. Rev. Financial Stud. 32:1662–1715.Crossref, Google Scholar
• Bianchi D , Bu¨chner M , Tamoni A (2020) Bond risk premiums with machine learning. Rev. Financial Stud. 34(2):1046–1089.Google Scholar
• Bogousslavsky V , Collin-Dufresne P , Saglam M (2020) Slow-moving capital and execution costs: Evidence from a major trading glitch. Preprint, submitted January 24, https://dx.doi.org/10.2139/ssrn.2613667.Google Scholar
• Brogaard J , Hendershott T , Riordan R (2014) High frequency trading and price discovery. Rev. Financial Stud. 28(8):2267–2306.Crossref, Google Scholar
• Brogaard J , Hendershott T , Riordan R (2017) High frequency trading and the 2008 short-sale ban. J. Financial Econom. 124(1):22–42.Crossref, Google Scholar
• Brogaard J , Hendershott T , Riordan R (2019) Price discovery without trading: Evidence from limit orders. J. Finance 74(4):1621–1658.Crossref, Google Scholar
• Brown NC , Crowley RM , Elliott WB (2020) What are you saying? Using topic to detect financial misreporting. J. Accounting Res. 58(1):237–291.Crossref, Google Scholar
• Budish EB , Cramton P , Shim J (2015) The high-frequency trading arms race: Frequent batch auctions as a market design response. Quart. J. Econom. 130(4):1547–1621.Crossref, Google Scholar
• Cai CW (2018) Disruption of financial intermediation by FinTech: A review on crowdfunding and blockchain. Accounting Finance 58(4):965–992.Crossref, Google Scholar
• Camerer CF , Nave G , Smith A (2019) Dynamic unstructured bargaining with private information: Theory, experiment, and outcome prediction via machine learning. Management Sci. 65(4):1867–1890.Link, Google Scholar
• Chaboud A , Chiquoine B , Hjalmarsson E , Vega C (2014) Rise of the machines: Algorithmic trading in the foreign exchange market. J. Finance 69(5):2045–2084.Crossref, Google Scholar
• Chau M , Li TMH , Wong WC , Xu JJ , Yip PSF , Chen H (2020) Finding people with emotional distress in online social media: A design combining machine learning and rule-based classification. MIS Quart. 44(2):933-955.Crossref, Google Scholar
• Chen MA , Wu Q , Yang B (2019) How valuable is FinTech innovation? Rev. Financial Stud. 32(5):2062–2106.Crossref, Google Scholar
• Chiu J , Koeppl T (2019) Blockchain-based settlement for asset trading. Rev. Financial Stud. 32:1716–1753.Crossref, Google Scholar
• Clarke J , Chen H , Du D , Hu YJ (2020) Fake news, investor attention, and market reaction. Inform. Systems Res. 32(1):3–52.Link, Google Scholar
• Colliard J-E , Hoffmann P (2017) Financial transaction taxes, market composition, and liquidity. J. Finance 72(6):2685–2716.Crossref, Google Scholar
• Cong LW , He Z (2019) Blockchain disruption and smart contracts. Rev. Financial Stud. 32:1754–1797.Crossref, Google Scholar
• Costello AM , Down AK , Mehta MN (2020) Machine + man: A field experiment on the role of discretion in augmenting AI-based lending models. J. Accounting Econom. 70(2-3).Crossref, Google Scholar
• Dong Y , Song S , Venkataraman S , Yao Y (2021) Mobile money and mobile technologies: A structural estimation. Inform. Systems Res. 32(1):18–34.Link, Google Scholar
• Du S , Zhu H (2017) What is the optimal trading frequency in financial markets? Rev. Econom. Stud. 84(4):1606–1651.Google Scholar
• Du M , Chen Q , Xiao J , Yang H , Ma X (2020) Supply chain finance innovation using blockchain. IEEE Trans. Engrg. Management 67(4):1045–1058.Crossref, Google Scholar
• Easley D , Lopez de Prado M , O’Hara M , Zhang Z (2020) Microstructure in the machine age. Rev. Financial Stud., ePub ahead of print July 7, https://doi.org/10.1093/rfs/hhaa078.Google Scholar
• Feng G , Giglio S , Xiu D (2020) Taming the factor zoo: A test of new factors. J. Finance 75(3):1327–1370.Crossref, Google Scholar
• Feng G , He J , Polson NG (2018) Deep learning for predicting asset returns. Preprint, submitted April 25, https://arxiv.org/abs/1804.09314.Google Scholar
• Feng G , Polson NG , Xu J (2021) Deep learning in characteristics-sorted factor models.Preprint, submitted March 24, https://dx.doi.org/10.2139/ssrn.3243683.Google Scholar
• Fleming M , Mizrach B , Nguyen G (2018) The microstructure of a US Treasury ECN: The BrokerTec platform. J. Financial Markets 40(1):2–22.Crossref, Google Scholar
• Foucault T , Hombert J , Roşu I (2016) News trading and speed. J. Finance 71(1):335–382.Crossref, Google Scholar
• Frank MZ , Sanati A (2018) How does the stock market absorb shocks? J. Financial Econom. 129(1):136–153.Crossref, Google Scholar
• Fu R , Huang Y , Singh P (2021) Crowd, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.Google Scholar
• Gao Q , Lin M , Wu D (2020) Educational crowdfunding and student performance: An empirical study. Inform. Systems Res. 32(1):53–71.Google Scholar
• Ge R , Zheng Z , Tian X , Liao L (2021) Human-robot interaction: Investors’ usage and performance of robo-advisors in crowdfunding. Inform . Systems Res. Forthcoming.Google Scholar
• Giglio S , Yuan L , Xiu D (2020) Thousands of alpha tests. Rev. Financial Stud., ePub ahead of print September 24, https://doi.org/10.1093/rfs/hhaa111.Google Scholar
• Goldstein I , Jiang W , Karolyi GA (2019) To FinTech and beyond. Rev. Financial Stud. 32(5):1647–1661.Crossref, Google Scholar
• Gomber P , Kauffman RJ , Parker C , Weber BW (2018) On the Fintech revolution: Interpreting the forces of innovation, disruption and transformation in financial services. J. Management Inform. Systems 35(1):220–265.Crossref, Google Scholar
• Gu S , Kelly B , Xiu D (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Google Scholar
• Hendershott T , Madhavan A (2015) Click or call? Auction versus search in the over-the-counter market. J. Finance 70(1):419–447.Crossref, Google Scholar
• Hendershott T , Moulton PC (2011) Automation, speed, and stock market quality: The NYSE’s hybrid. J. Financial Markets 14(4):568–604.Crossref, Google Scholar
• Hendershott T , Jones C , Menkveld A (2011) Does algorithmic trading improve liquidity? J. Finance 66(1):1–33.Crossref, Google Scholar
• Hendershott T , Zhang X , Zhao J , Zheng Z (2017) Call for papers–Special issue of Information Systems Research: Fintech–Innovating the finance industry through emerging information technologies. Inform. Systems Res. 28(4):885–886.Link, Google Scholar
• Hoffmann P (2014) A dynamic limit order market with fast and slow traders. J. Financial Econom. 113(1):156–169.Crossref, Google Scholar
• Ilk N , Shang G , Fan S , Leon Zhao J (2021) Stability of transaction fees in Bitcoin: A supply and demand perspective, MIS Quart. Forthcoming.Google Scholar
• Iwasaki HY , Chen Y (2018) Topic sentiment asset pricing with DNN supervised learning. Econometric Modeling: Capital Markets - Asset Pricing. Available at https://dx.doi.org/10.2139/ssrn.3228485.Google Scholar
• Jackwerth JC , Menner M (2020) Does the Ross recovery theorem work empirically? J. Financial Econom. 137(3):723–739.Crossref, Google Scholar
• Jiang Z , Xu D , Liang J (2017) A deep reinforcement learning framework for the financial portfolio management problem. Working paper, University College London, London.Google Scholar
• Jones C (2013) What do we know about high-frequency trading? Columbia Business School Research Paper No. 13-11, Columbia Business School, New York.Google Scholar
• Jovanovic B , Menkveld AJ (2016) Middlemen in limit order markets. Preprint, submitted June 20, https://dx.doi.org/10.2139/ssrn.1624329.Google Scholar
• Kirilenko A , Kyle AS , Mehrdad S , Tuzun T (2017) The flash crash: High-frequency trading in an electronic market. J. Finance 72(3):967–998.Crossref, Google Scholar
• Kleinberg J , Lakkaraju H , Leskovec J , Ludwig J , Mullainathan S (2018) Human decisions and machine predictions. Quart. J. Econom. 26:237–293.Google Scholar
• Korajczyk RA , Murphy D (2019) High-frequency market making to large institutional trades. Rev. Econom. Stud. 32(3):1034–1067.Google Scholar
• Kozak S , Nagel S , Santosh S (2020) Shrinking the cross-section. J. Financial Econom. 135(2):271–292.Google Scholar
• Lacity M , Sabherwal R , Sørensen C (2019) Special issue editorial: Delivering business value through enterprise blockchain applications. MIS Quart. Executive 18(4):3.Google Scholar
• Leng J , Jiang P , Xu K , Liu Q , Zhao JL , Bian Y , Shi R (2019) Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing. J. Cleaner Production 234:767–778.Google Scholar
• Levina T , Levin Y , McGill J , Nediak M (2009) Dynamic pricing with online learning and strategic consumers: An application of the aggregating algorithm. Oper. Res. 57(2):327–341.Google Scholar
• Li K , Mai, F , Shen R , Yan X (2020) Measuring corporate culture using machine learning. Rev. Financial Stud., ePub ahead of print July 9, https://doi.org/10.1093/rfs/hhaa079.Google Scholar
• Lim AEB , Shanthikumar JG , Vahn G-Y (2012) Robust portfolio choice with learning in the framework of regret: Single-period case. Management Sci. 58(9):1732–1746.Google Scholar
• Linn SC , Tay NSP (2007) Complexity and the character of stock returns: Empirical evidence and a model of asset prices based on complex investor learning. Management Sci. 53(7):1165–1180.Google Scholar
• Lovejoy WS (1991) Computationally feasible bounds for partially observed Markov decision processes. Oper. Res. 39(1):162–175.Google Scholar
• Manela A , Moreira A (2017) News implied volatility and disaster concerns. J. Financial Econom. 123(1):137–162.Google Scholar
• Menkveld A (2013) High frequency trading and the new market makers. J. Financial Markets 16(4):712–740.Google Scholar
• Menkveld A (2016) The economics of high-frequency trading: Taking stock. Annual Rev. Financial Econom. 8:1–24.Google Scholar
• Nakamoto S (2008) Bitcoin: A peer-to-peer electronic cash system. Available online at. https://bitcoin.org/bitcoin.pdf.Google Scholar
• O’Hara M (2015) High frequency market microstructure. J. Financial Econom. 116(2):257–270.Google Scholar
• Oran O (2015) Goldman Sachs asks shareholders to ‘like’ tech push in San Francisco. Accessed October 20, 2020, https://www.reuters.com/article/us-goldman-sachs-annualmeeting/goldman-sachs-asks-shareholders-to-like-tech-push-in-san-francisco.Google Scholar
• Polk C , Thompson S , Vuolteenaho T (2006) Cross-sectional forecasts of the equity premium. J. Financial. Econom. 81(1):101–141.Google Scholar
• Ruf J , Wang W (2020) Neural networks for option pricing and hedging: A literature review. J. Comput. Finance Google Scholar
• Satell G , Abedl-Magied Y (2020) AI fairness isn’t just an ethical issue. Accessed October 20, 2020, https://hbr.org/2020/10/ai-fairness-isnt-just-an-ethical-issue.Google Scholar
• Silver D , Huang A , Maddison CJ , Guez A , Sifre L , van den Driessche G , Schrittwieser J , et al. . (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489.Google Scholar
• Securities and Exchange Commission (2010) Concept Release on Equity Market Structure, Release No. 34-61358; File No. S7-02-10, https://www.sec.gov/rules/concept/2010/34-61358.pdf.Google Scholar
• Simester D , Timoshenko A , Zoumpoulis SI (2020) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Google Scholar
• Shkilko A , Sokolov K (2020) Every cloud has a silver lining: Fast trading, microwave connectivity and trading costs. J. Finance 75(6):2899–2927.Google Scholar
• Tapscott A (2020) Financial Services Revolution: How Blockchain is Transforming Money, Markets, and Banking (Barlow Book Publishing Inc, Toronto).Google Scholar
• van Kervel V , Menkveld A (2019) High-frequency trading around large institutional orders. J. Finance 74(3):1091–1137.Google Scholar
• Voshmgir S (2019) Token Economy: How Blockchain and Smart Contracts Revolutionize the Economy (BlockchainHub Berlin).Google Scholar
• Vovk VG (1990) Aggregating strategies. Fulk M, Case J, eds. Proc. Third Annual Workshop Comput. Learn. Theory (Morgan Kaufmann, San Mateo, CA), 371–383.Google Scholar
• Wang Z , Zheng,Z , Tang S , Jian W (2021) Blockchain enabled data sharing for supply chain finance. Production Oper. Management. Forthcoming.Google Scholar
• Weber B (2006) Adoption of electronic trading at the International Securities Exchange. Decision Support Systems 41(4):728–746.Google Scholar
• Wu W , Chen J , Yang Z , Tindall ML (2020) A cross-sectional machine learning approach for hedge fund return prediction and selection. Management Sci., ePub ahead of print September 30, https://doi.org/10.1287/mnsc.2020.3696.Google Scholar
• Xu SX , Zhang X (2013) Impact of Wikipedia on market information environment: Evidence on management disclosure and investor reaction. MIS Quart. 37(4):1043–1068.Google Scholar
• Yang L , Zhu H (2020) Back-running: Seeking and hiding fundamental information in order flows. Rev. Financial Stud. 33(4):1484–1533.Google Scholar
• Zhang X , Zhang L (2015) How does the internet affect the financial market? An equilibrium model of internet facilitated feedback trading. MIS Quart. 39(1):17–38.Google Scholar
• Zhao JL , Fan S , Yan J (2016) Overview of business innovations and research opportunities in blockchain and introduction to the special issue. Financial Innovation 2(1):28.Google Scholar
• Zheng YJ , Zhou XH , Sheng WG , Xue Y , Chen SY (2018) Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks 102:78–86.Google Scholar