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Mining semantic features in patent text for financial distress prediction

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  • Jiang, Cuiqing
  • Zhou, Yiru
  • Chen, Bo

Abstract

Financial distress prediction has been a popular topic over the decades. Most studies have used accounting features from financial statements to predict financial distress. Compared to listed companies, unlisted public companies have longer financial disclosure cycles, less required disclosure of market trading information, and higher financial risk. However, they can also have a strong ability to innovate and great growth potential, attributes that cannot be fully reflected in financial statements. In this study, as a supplement to accounting features, we propose a framework for mining the statistical features and semantic features in patent text by comprehensively analyzing the patent's structured information, abstract, claims, citations, and specifications. The results of empirical evaluation confirm that patent features contain incremental information related to financial distress. This research broadens the feature space of financial distress research and expands the research on patent text. It also provides decision support for banks approving loans, investment decision-making, and patent pledges.

Suggested Citation

  • Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
  • Handle: RePEc:eee:tefoso:v:190:y:2023:i:c:s004016252300135x
    DOI: 10.1016/j.techfore.2023.122450
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    as
    1. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    2. Hochberg, Yael V. & Serrano, Carlos J. & Ziedonis, Rosemarie H., 2018. "Patent collateral, investor commitment, and the market for venture lending," Journal of Financial Economics, Elsevier, vol. 130(1), pages 74-94.
    3. van Zeebroeck, Nicolas & van Pottelsberghe de la Potterie, Bruno & Guellec, Dominique, 2009. "Claiming more: the Increased Voluminosity of Patent Applications and its Determinants," Research Policy, Elsevier, vol. 38(6), pages 1006-1020, July.
    4. Yan Anthea Zhang & Zhuo Emma Chen & Yuandi Wang, 2021. "Which patents to use as loan collaterals? The role of newness of patents' external technology linkage," Strategic Management Journal, Wiley Blackwell, vol. 42(10), pages 1822-1849, October.
    5. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03529325, HAL.
    6. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    7. Hsu, Po-Hsuan & Lee, Hsiao-Hui & Liu, Alfred Zhu & Zhang, Zhipeng, 2015. "Corporate innovation, default risk, and bond pricing," Journal of Corporate Finance, Elsevier, vol. 35(C), pages 329-344.
    8. Leonid Kogan & Dimitris Papanikolaou & Amit Seru & Noah Stoffman, 2017. "Technological Innovation, Resource Allocation, and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 665-712.
    9. Markus Simeth & Michele Cincera, 2016. "Corporate Science, Innovation, and Firm Value," Management Science, INFORMS, vol. 62(7), pages 1970-1981, July.
    10. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    11. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    12. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    13. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    14. Shane Magee, 2013. "The effect of foreign currency hedging on the probability of financial distress," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1107-1127, December.
    15. Sharon Belenzon, 2012. "Cumulative Innovation and Market Value: Evidence from Patent Citations," Economic Journal, Royal Economic Society, vol. 122(559), pages 265-285, March.
    16. Suman Lodh & Maria Rosa Battaggion, 2015. "Technological breadth and depth of knowledge in innovation: the role of mergers and acquisitions in biotech," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 24(2), pages 383-415.
    17. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    18. Liu, Bai & Ju, Tao & Bai, Min & Yu, Chia-Feng (Jeffrey), 2021. "Imitative innovation and financial distress risk: The moderating role of executive foreign experience," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 526-548.
    19. Gerard George & Reddi Kotha & Yanfeng Zheng, 2008. "Entry into Insular Domains: A Longitudinal Study of Knowledge Structuration and Innovation in Biotechnology Firms," Journal of Management Studies, Wiley Blackwell, vol. 45(8), pages 1448-1474, December.
    20. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    21. Bai, Qing & Tian, Shaonan, 2020. "Innovate or die: Corporate innovation and bankruptcy forecasts," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 88-108.
    22. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    23. Hu, Feng & Xi, Xun & Zhang, Yueyue, 2021. "Influencing mechanism of reverse knowledge spillover on investment enterprises’ technological progress: An empirical examination of Chinese firms," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    24. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    25. Mewes, Lars & Broekel, Tom, 2022. "Technological complexity and economic growth of regions," Research Policy, Elsevier, vol. 51(8).
    26. Wanke, Peter & Barros, Carlos P. & Faria, João R., 2015. "Financial distress drivers in Brazilian banks: A dynamic slacks approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 258-268.
    27. Pierre-Alexandre Balland & David Rigby, 2017. "The Geography of Complex Knowledge," Economic Geography, Taylor & Francis Journals, vol. 93(1), pages 1-23, January.
    28. Mastrogiorgio, Mariano & Gilsing, Victor, 2016. "Innovation through exaptation and its determinants: The role of technological complexity, analogy making & patent scope," Research Policy, Elsevier, vol. 45(7), pages 1419-1435.
    29. Yu-Shan Chen & Chun-Yu Shih, 2011. "Re-examine the relationship between patents and Tobin’s q," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 781-794, December.
    30. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    31. Jan W. Rivkin, 2000. "Imitation of Complex Strategies," Management Science, INFORMS, vol. 46(6), pages 824-844, June.
    32. Ahmed Al‐Hadi & Bikram Chatterjee & Ali Yaftian & Grantley Taylor & Mostafa Monzur Hasan, 2019. "Corporate social responsibility performance, financial distress and firm life cycle: evidence from Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(2), pages 961-989, June.
    33. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03380580, HAL.
    34. Arts, Sam & Hou, Jianan & Gomez, Juan Carlos, 2021. "Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures," Research Policy, Elsevier, vol. 50(2).
    35. Griffin, Paul A. & Hong, Hyun A. & Ryou, Ji Woo, 2018. "Corporate innovative efficiency: Evidence of effects on credit ratings," Journal of Corporate Finance, Elsevier, vol. 51(C), pages 352-373.
    36. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    37. Olav Sorenson & Jan W. Rivkin & Lee Fleming, 2010. "Complexity, Networks and Knowledge Flows," Chapters, in: Ron Boschma & Ron Martin (ed.), The Handbook of Evolutionary Economic Geography, chapter 15, Edward Elgar Publishing.
    38. Ugo Rizzo & Nicolò Barbieri & Laura Ramaciotti & Demian Iannantuono, 2020. "The division of labour between academia and industry for the generation of radical inventions," The Journal of Technology Transfer, Springer, vol. 45(2), pages 393-413, April.
    39. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.
    40. Nicolas van Zeebroeck, 2011. "The puzzle of patent value indicators," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(1), pages 33-62.
    41. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    42. Righi, Cesare & Simcoe, Timothy, 2019. "Patent examiner specialization," Research Policy, Elsevier, vol. 48(1), pages 137-148.
    43. Marco, Alan C. & Sarnoff, Joshua D. & deGrazia, Charles A.W., 2019. "Patent claims and patent scope," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    44. Catalina Martínez, 2011. "Patent families: When do different definitions really matter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 39-63, January.
    45. Snehal Awate & Ram Mudambi, 2018. "On the geography of emerging industry technological networks: the breadth and depth of patented innovations," Journal of Economic Geography, Oxford University Press, vol. 18(2), pages 391-419.
    46. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    47. Gao, Wenlian & Chou, Julia, 2015. "Innovation efficiency, global diversification, and firm value," Journal of Corporate Finance, Elsevier, vol. 30(C), pages 278-298.
    48. Chen, Hongshu & Zhang, Guangquan & Zhu, Donghua & Lu, Jie, 2017. "Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 39-52.
    49. Nakamura, Hiroko & Suzuki, Shinji & Sakata, Ichiro & Kajikawa, Yuya, 2015. "Knowledge combination modeling: The measurement of knowledge similarity between different technological domains," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 187-201.
    50. Lee, Hwang Hee & Oh, Frederick Dongchuhl, 2020. "Corporate innovation and credit default swap spreads," Finance Research Letters, Elsevier, vol. 32(C).
    51. Strumsky, Deborah & Lobo, José, 2015. "Identifying the sources of technological novelty in the process of invention," Research Policy, Elsevier, vol. 44(8), pages 1445-1461.
    52. Masatoshi Kato & Koichiro Onishi & Yuji Honjo, 2022. "Does patenting always help new firm survival? Understanding heterogeneity among exit routes," Small Business Economics, Springer, vol. 59(2), pages 449-475, August.
    53. Carl Benedikt Frey & Peter Neuhäusler & Knut Blind, 2020. "Patents and corporate credit risk," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 29(2), pages 289-308.
    54. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    55. Sai Yayavaram & Wei-Ru Chen, 2015. "Changes in firm knowledge couplings and firm innovation performance: The moderating role of technological complexity," Strategic Management Journal, Wiley Blackwell, vol. 36(3), pages 377-396, March.
    56. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    57. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    58. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    59. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    60. Yu, Gun Jea & Hong, KiHoon, 2016. "Patents and R&D expenditure in explaining stock price movements," Finance Research Letters, Elsevier, vol. 19(C), pages 197-203.
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