The Impact of Fintech on Corporate Financial Risk: From the Perspective of Information Asymmetry

Murong Liu

doi:10.6981/FEM.202504_6(4).0012

Authors

Murong Liu

DOI:

https://doi.org/10.6981/FEM.202504_6(4).0012

Keywords:

Fintech; Corporate Financial Risk; Information Asymmetry; Financing Constraints; Financial Regulation.

Abstract

This study investigates the impact of Fintech development and adoption on corporate financial risk, focusing on the mediating role of information asymmetry between corporations and external stakeholders. It posits that Fintech innovations, including AI-driven credit scoring, blockchain-based solutions, and big data analytics, can potentially mitigate information asymmetry and, consequently, influence corporate financial risk profiles. The research employs empirical analysis to examine the relationship between Fintech adoption and various dimensions of corporate financial risk, such as liquidity, credit, market, and operational risk. It further explores how specific Fintech applications contribute to reducing information gaps between corporations and investors or creditors. The study also considers the moderating effects of corporate governance mechanisms on the Fintech-risk relationship. Furthermore, it addresses potential unintended consequences of Fintech adoption, including increased systemic risk and algorithmic bias. The expected results include empirical evidence demonstrating a negative relationship between Fintech adoption and corporate financial risk, identification of effective Fintech applications, quantification of the mediating effect of information asymmetry, and policy recommendations for responsible Fintech innovation. This research contributes to the understanding of how financial technology reshapes corporate finance and risk management in the context of information asymmetry.

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References

[1] Akerlof, G. A. (1970). The Market for "Lemons": Quality Uncertainty and the Market Mechanism. *The Quarterly Journal of Economics, 84*(3), 488-500.

[2] Altman, E. I., & Rijken, H. A. (2011). The Cost of Credit Information Asymmetry. *Foundations and Trends in Entrepreneurship, 7*(3), 145-255.

[3] Healy, P. M., & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. *Journal of Accounting and Economics, 31*(1-3), 405-440.

[4] Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. *Journal of Financial Economics, 3*(4), 305-360.

[5] Philippon, T. (2016). *The FinTech Opportunity*. National Bureau of Economic Research.

[6] Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. *The Journal of Finance, 52*(2), 737-783.

[7] Brown, L. D., & Caylor, M. L. (2006). Corporate governance and firm valuation. *Journal of Accounting and Economics, 42*(1-2), 91-110.

[8] Buchak, G., Matvos, T., Piskorski, T., & Seru, A. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. *Journal of Financial Economics, 130*(3), 453-483.

[9] Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of Management Review, 14(1), 57-74.

[10] Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. Journal of Law and Economics, 26(2), 301-325.

[11] Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.

[12] Lambert, R. A. (1986). Executive effort and the selection of risky projects. The RAND Journal of Economics, 17(1), 77-88.

[13] Larcker, D. F. (1983). Managerial incentives in multi-division firms: An agency theory analysis. Journal of Accounting Research, 21(1), 3-23.

[14] Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. The Journal of Finance, 52(2), 737-783.

[15] Walsh, J. P., & Seward, J. K. (1990). On measuring organizational stakeholders. Academy of Management Review, 15(2), 422-424.

[16] Bhattacharya, S. (1979). Imperfect Information, Dividend Policy, and "the Bird in the Hand" Fallacy. *The Bell Journal of Economics, 10*(1), 259-270.

[17] Catalini, C., & Gans, J. S. (2018). Initial Coin Offerings and the Value of Crypto Tokens. *SSRN Electronic Journal*.

[18] Healy, P. M., & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. *Journal of Accounting and Economics, 31*(1-3), 405-431.

[19] Myers, S. C., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. *Journal of Financial Economics, 13*(2), 187-221.

[20] Ross, S. A. (1977). The determination of financial structure: the incentive-signalling approach. *The Bell Journal of Economics, 8*(1), 23-40.

[21] Spence, A. M. (1973). Job Market Signaling. *The Quarterly Journal of Economics, 87*(3), 355-374.

[22] Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. *Information Fusion*, *58*, 82-115.

[23] Budish, E., Cramton, P., & Shim, I. (2015). The high-frequency trading arms race: Frequent batch auctions as a market design response. *The Quarterly Journal of Economics*, *130*(4), 1547-1621.

[24] Hileman, G., & Rauchs, M. (2017). Global blockchain benchmarking study. *Cambridge Centre for Alternative Finance*.

[25] Jagtiani, J., & John, K. (2018). Fintech: The impact on consumers and regulatory responses. *Journal of Economics and Business*, *100*, 1-6.

[26] Jones, C. M. (2018). What do we know about high-frequency trading?. *Journal of Economic Perspectives*, *32*(3), 59-76.

[27] Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. *The Journal of Finance*, *72*(3), 967-998.

[28] Kshetri, N. (2018). Blockchain and supply chain management: Theory, applications, and future directions. *Technovation*, *72*, 1-13.

[29] O'Neil, C. (2016). *Weapons of math destruction: How big data increases inequality and threatens democracy*. Crown.

[30] Philippon, T. (2016). *The fintech opportunity*. National Bureau of Economic Research.

[31] Tapscott, D., & Tapscott, A. (2016). *Blockchain revolution: How the technology behind bitcoin is changing money, business, and the world*. Penguin.

[32] Barocas, S., & Selbst, A. (2016). Big data's disparate impact. *California Law Review*, *104*(3), 671-732.

[33] Berger, A. N., & Udell, G. F. (1998). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. *Journal of Banking & Finance*, *22*(6-8), 773-798.

[34] Bharath, S. T., & Shumway, T. (2008). Forecasting bankruptcy using accounting data. *Review of Accounting Studies*, *13*(2-3), 139-178.

[35] Beaver, W. H. (1966). Financial ratios as predictors of failure. *Journal of Accounting Research*, *4*, 71-111.

[36] Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. *The Journal of Finance*, *29*(2), 449-470.

[37] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. *Nature Machine Intelligence*, *1*(5), 206-215.

[38] Cachin, C., & Vukolić, M. (2017). Blockchain consensus protocols in the wild. *arXiv preprint arXiv:1707.01873*.

[39] Deng, R., Cong, L. W., & Li, Y. (2020). Blockchain-based platforms for supply chain finance. *Financial Innovation, 6*(1), 1-21.

[40] Gelsomino, L. M., Mangiaracina, R., Perego, A., & Tumino, A. (2016). Supply chain finance: A literature review. *International Journal of Physical Distribution & Logistics Management, 46*(4), 348-366.

[41] Hofmann, E. (2005). Supply chain finance: a conceptual framework. *Supply Chain Management: An International Journal, 10*(5), 332-347.

[42] Kshetri, N. (2018). Blockchain and supply chain management: Potentials, challenges, and research directions. *International Journal of Information Management, 39*, 14-25.

[43] Wüst, K., Gervais, A., Ritzdorf, H., & Wattenhofer, R. (2017). Evaluating blockchain technologies for supply chain management. *Production & Manufacturing Research, 5*(1), 206-216.

[44] Aldridge, I. (2013). *High-frequency trading: A practical guide to algorithmic strategies and trading systems*. John Wiley & Sons.

[45] Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. *The Review of Financial Studies*, *27*(8), 2266-2306.

[46] Easley, D., López de Prado, M. M., & O’Hara, M. (2012). Flow toxicity and order imbalance. *Journal of Financial Economics*, *103*(2), 343-361.

[47] Foucault, T., Kozhan, R., & Tham, W. Y. (2016). Does microstructure matter for asset pricing?. *Financial Analyst Journal*, *72*(4), 28-47.

[48] Hasbrouck, J., & Saar, G. (2013). Low-latency trading. *Journal of Financial Markets*, *16*(4), 646-679.

[49] Johnson, S., Madhavan, A., Goldstein, D. H., & Kirk, G. (2013). Crash, what crash? *Journal of Financial Markets*, *16*(4), 701-728.

[50] Kirilenko, A. A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. *The Journal of Finance*, *72*(3), 967-998.

[51] Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. *Accounting Review*, *71*(4), 443-465.

[52] Byrd, J. W., & Hickman, K. A. (1992). Do outside directors monitor managers? Evidence from tender offer defenses. *Journal of Financial Economics*, *32*(2), 195-221.

[53] DeAngelo, L. E. (1981). Auditor size and audit quality. *Journal of Accounting and Economics*, *3*(3), 183-199.

[54] Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. *Journal of Law and Economics*, *26*(2), 301-325.

[55] Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. *Journal of Financial Economics*, *3*(4), 305-360.

[56] Watts, R. L., & Zimmerman, J. L. (1983). Agency problems, auditing, and the theory of the firm: Some evidence. *Journal of Law and Economics*, *26*(3), 613-633.

[57] Bushman, R. M., & Smith, A. J. (2001). Financial accounting information and corporate governance. *Journal of Accounting and Economics, 32*(1-3), 233-333.

[58] La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (2000). Investor protection and corporate governance. *Journal of Financial Economics, 58*(1-2), 3-27.

[59] Claessens, S., Frost, J., Turner, G., & Zhu, F. (2018). Fintech credit: Market structure, business models and financial stability implications. *Bank for International Settlements*.

[60] Financial Stability Board. (2017). *FinTech and financial stability: Initial considerations*. Financial Stability Board.

[61] Haldane, A. G., & May, R. M. (2011). Systemic risk in banking ecosystems. *Nature*, *469*(7330), 351-359.

[62] Tarca, A., La Torre, M., & Brighi, F. (2022). Fintech risk management: A literature review and research agenda. *European Management Journal*, *40*(5), 683-696.

[63] Barocas, S., & Selbst, A. (2016). Big data's disparate impact. *California Law Review, 104*(3), 671-732.

[64] Goodman, B., & Flaxman, S. (2017). European union regulations on algorithmic decision-making and a “right to explanation”. *AI Magazine, 38*(3), 50-57.

[65] Hu, Y., Chen, Y., Hong, L., & Lin, Y. (2019). Algorithmic bias in credit scoring: Evidence from a consumer lending platform. *Management Science, 65*(10), 4473-4491.

[66] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. *ACM Computing Surveys (CSUR), 54*(6), 1-35.

[67] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. *Nature Machine Intelligence, 1*(5), 206-215.

[68] Brown, M., Ongena, S., & Popov, A. (2020). Fintech credit: blessing or curse?. *Review of Finance*, *24*(1), 1-52.

[69] Goodman, B., & Flaxman, S. (2017). European union regulations on algorithmic decision-making and a “right to explanation”. *AI Magazine*, *38*(3), 50-57.

[70] Philippon, T. (2016). The Fintech opportunity. In *NBER Macroeconomics Annual 2015*, *30* (pp. 159-203). University of Chicago Press.

[71] Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. *Review of Financial Studies*, *32*(5), 1754-1797.

[72] Beck, T., & Demirgüç-Kunt, A. (2006). Small and medium-size enterprises: Access to finance as a growth constraint. *Journal of Banking & Finance, 30*(11), 2931-2943.

[73] Berger, A. N., & Udell, G. F. (2006). A more complete conceptual framework for SME finance. *Journal of Banking & Finance, 30*(11), 2945-2966.

[74] Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica, 47*(2), 263-291.