Vol. 5 No. 1 (2025): Issue 5
Articles

Prediction of Financial Loss due to Trust Equity with Lasso Regression

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  • Somchai Pairoj,
  • Naruemol Khamkha,
  • Apichai Chansilp,
  • Anouk Jansen
Somchai Pairoj
School of Business and Innovation, Khon Kaen University, Khon Kaen, Thailand
Naruemol Khamkha
Institute of Financial Risk Analysis, Udon Thani Rajabhat University, Udon Thani, Thailand
Apichai Chansilp
Center for Economic Forecasting, Suranaree University of Technology, Nakhon Ratchasima, Thailand

Published 2025-04-03

Keywords

  • Financial Loss,
  • Risk Management,
  • Trust Equity,
  • Lasso Regression,
  • Predictive Modeling

How to Cite

Pairoj, S., Khamkha, N., Chansilp, A., & Jansen, A. (2025). Prediction of Financial Loss due to Trust Equity with Lasso Regression. Economic and Financial Research Letters, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/EFRL/article/view/136

Abstract

Financial loss estimation plays a crucial role in risk management and decision-making for businesses. With the growing importance of trust equity in financial transactions, accurately predicting financial losses due to trust equity becomes essential. Current research lacks comprehensive models that can effectively predict such losses, leading to a gap in existing literature. This paper addresses this gap by proposing a novel approach using Lasso Regression to predict financial losses associated with trust equity. The innovative aspect of this work lies in the incorporation of trust equity data into the regression model, enhancing the accuracy of financial loss prediction. By applying this method to real-world financial datasets, we demonstrate its effectiveness and provide valuable insights for businesses to manage risks associated with trust equity more effectively.

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References

  1. D. Razak and N. H. A. Rahman, "The interaction effect of trust towards profit and loss sharing element in Musharakah financing for SMES," 2017.
  2. L. M. Collins, "Consumers' cognitive, affective, and behavioral responses toward a firm's recovery strategies when committing a transgression," 2016.
  3. N. Versal, "Public Banks in Ukraine: Supports and Challenges," PSN: Financial Institutions (Topic), 2015.
  4. C. Yap, "Pengaruh faktor risiko sistematis dan faktor fundamentalterhadap kinerja pasar saham perusahaan," 2008.
  5. P. B. BP, R. P. Yalamarti, "A Comparative Study on Financial Performance between Equity and Debt Schemes of HDFC Mutual Funds and SBI Mutual Funds," Journal of Accounting Research Business and Finance Management, 2023.
  6. M. Raza et al., "Assessing the Return and Risk Equity Mutual Funds: A Case Study from the Financial Markets of Pakistan," Journal of Accounting and Finance in Emerging Economies, 2023.
  7. M. Złoty, "The “Sheep Rush” phenomenon and the level of social trust during the financial crisis 2008+, the COVID-19 pandemic and chosen armed attacks in the 21st century," Nierówności społeczne a wzrost gospodarczy, 2024.
  8. I. Fery, "Impairment in value of psak 7 financial accounting standards, classification of accounting measurements in hedging companies in the banking sector due to corona virus," marginal : journal of management, accounting, general finance and international economic issues, 2022.
  9. O. Y. Atasel et al., "Impact of Environmental Information Disclosure on Cost of Equity and Financial Performance in an Emerging Market: Evidence from Turkey," Ekonomika, 2020.
  10. Y.-M. Jin, "A Study on the Reliability Differences in Social Coherence, Rumor Trust, and Corporate Response Strategies Due to the Negative Rumors of a Hair salon by Demographic Characteristics," 2021.
  11. R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the royal statistical society series b-methodological, vol. 58, pp. 267-288, 1996.
  12. U. Sharma, N. Gupta, and M. Verma, "Prediction of compressive strength of GGBFS and Flyash-based geopolymer composite by linear regression, lasso regression, and ridge regression," Asian Journal of Civil Engineering, vol. 24, pp. 3399-3411, 2023.
  13. H. Gholami, A. Mohammadifar, K. Fitzsimmons, Y. Li, and D. Kaskaoutis, "Modeling land susceptibility to wind erosion hazards using LASSO regression and graph convolutional networks," Frontiers in Environmental Science, vol. 11, 2023.
  14. A. Iparragirre, T. Lumley, I. Barrio, and I. Arostegui, "Variable selection with LASSO regression for complex survey data," Stat, vol. 12, 2023.
  15. N. Chintalapudi et al., "LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining," Bioengineering, vol. 9, 2022.
  16. K. Xu, Y. Gan, and A. Wilson, ‘Stacked Generalization for Robust Prediction of Trust and Private Equity on Financial Performances’, Innovations in Applied Engineering and Technology, pp. 1–12, 2024.
  17. Q. Zhu, ‘Autonomous Cloud Resource Management through DBSCAN-based unsupervised learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Jun. 2025, doi: 10.71070/oaml.v5i1.112.
  18. S. Dan and Q. Zhu, ‘Enhancement of data centric security through predictive ridge regression’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, May 2025, doi: 10.71070/oaml.v5i1.113.
  19. S. Dan and Q. Zhu, ‘Highly efficient cloud computing via Adaptive Hierarchical Federated Learning’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Apr. 2025, doi: 10.71070/oaml.v5i1.114.
  20. Q. Zhu and S. Dan, ‘Data Security Identification Based on Full-Dimensional Dynamic Convolution and Multi-Modal CLIP’, Journal of Information, Technology and Policy, 2023.
  21. Q. Zhu, ‘An innovative approach for distributed cloud computing through dynamic Bayesian networks’, Journal of Computational Methods in Engineering Applications, 2024.
  22. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  23. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  24. H. Yan, ‘Real-Time 3D Model Reconstruction through Energy-Efficient Edge Computing’, Optimizations in Applied Machine Learning, vol. 2, no. 1, 2022.
  25. Y. Shu, Z. Zhu, S. Kanchanakungwankul, and D. G. Truhlar, ‘Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods’, J. Phys. Chem. A, vol. 128, no. 31, pp. 6412–6422, Aug. 2024, doi: 10.1021/acs.jpca.4c03137.
  26. C. Kim, Z. Zhu, W. B. Barbazuk, R. L. Bacher, and C. D. Vulpe, ‘Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications’, Toxicology Letters, vol. 401, pp. 125–138, 2024.
  27. J. Shen et al., ‘Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation’, NeuroImage, vol. 297, p. 120739, 2024.
  28. K. F. Faridi et al., ‘Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real‐world practice’, Echocardiography, vol. 41, no. 2, p. e15774, Feb. 2024, doi: 10.1111/echo.15774.
  29. Z. Zhu, ‘Tumor purity predicted by statistical methods’, in AIP Conference Proceedings, AIP Publishing, 2022.
  30. Z. Zhao, P. Ren, and Q. Yang, ‘Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America’, Apr. 17, 2024, arXiv: arXiv:2404.11029. doi: 10.48550/arXiv.2404.11029.
  31. Z. Zhao, P. Ren, and M. Tang, ‘Analyzing the Impact of Anti-Globalization on the Evolution of Higher Education Internationalization in China’, Journal of Linguistics and Education Research, vol. 5, no. 2, pp. 15–31, 2022.
  32. M. Tang, P. Ren, and Z. Zhao, ‘Bridging the gap: The role of educational technology in promoting educational equity’, The Educational Review, USA, vol. 8, no. 8, pp. 1077–1086, 2024.
  33. P. Ren, Z. Zhao, and Q. Yang, ‘Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China’, Apr. 17, 2024, arXiv: arXiv:2404.11034. doi: 10.48550/arXiv.2404.11034.
  34. P. Ren and Z. Zhao, ‘Parental Recognition of Double Reduction Policy, Family Economic Status And Educational Anxiety: Exploring the Mediating Influence of Educational Technology Substitutive Resource’, Economics & Management Information, pp. 1–12, 2024.
  35. Z. Zhao, P. Ren, and M. Tang, ‘How Social Media as a Digital Marketing Strategy Influences Chinese Students’ Decision to Study Abroad in the United States: A Model Analysis Approach’, Journal of Linguistics and Education Research, vol. 6, no. 1, pp. 12–23, 2024.
  36. Z. Zhao and P. Ren, ‘Identifications of Active Explorers and Passive Learners Among Students: Gaussian Mixture Model-Based Approach’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  37. Z. Zhao and P. Ren, ‘Prediction of Student Answer Accuracy based on Logistic Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Feb. 2025.
  38. Z. Zhao and P. Ren, ‘Prediction of Student Disciplinary Behavior through Efficient Ridge Regression’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Mar. 2025.
  39. Z. Zhao and P. Ren, ‘Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Apr. 2025.
  40. P. Ren and Z. Zhao, ‘Recognition and Detection of Student Emotional States through Bayesian Inference’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, May 2025.
  41. P. Ren and Z. Zhao, ‘Support Vector Regression-based Estimate of Student Absenteeism Rate’, Bulletin of Education and Psychology, vol. 5, no. 1, Art. no. 1, Jun. 2025.
  42. G. Zhang and T. Zhou, ‘Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model’, IAET, pp. 1–13, Sep. 2024, doi: 10.62836/iaet.v3i1.232.
  43. G. Zhang, W. Huang, and T. Zhou, ‘Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation’, Electrical Science & Engineering, vol. 6, no. 1, Art. no. 1, Oct. 2024, doi: 10.30564/ese.v6i1.7532.
  44. T. Zhou, G. Zhang, and Y. Cai, ‘Unsupervised Autoencoders Combined with Multi-Model Machine Learning Fusion for Improving the Applicability of Aircraft Sensor and Engine Performance Prediction’, Optimizations in Applied Machine Learning, vol. 5, no. 1, Art. no. 1, Feb. 2025, doi: 10.71070/oaml.v5i1.83.
  45. Y. Tang and C. Li, ‘Exploring the Factors of Supply Chain Concentration in Chinese A-Share Listed Enterprises’, Journal of Computational Methods in Engineering Applications, pp. 1–17, 2023.
  46. C. Li and Y. Tang, ‘Emotional Value in Experiential Marketing: Driving Factors for Sales Growth–A Quantitative Study from the Eastern Coastal Region’, Economics & Management Information, pp. 1–13, 2024.
  47. C. Li and Y. Tang, ‘The Factors of Brand Reputation in Chinese Luxury Fashion Brands’, Journal of Integrated Social Sciences and Humanities, pp. 1–14, 2023.
  48. C. Y. Tang and C. Li, ‘Examining the Factors of Corporate Frauds in Chinese A-share Listed Enterprises’, OAJRC Social Science, vol. 4, no. 3, pp. 63–77, 2023.
  49. W. Huang, T. Zhou, J. Ma, and X. Chen, ‘An ensemble model based on fusion of multiple machine learning algorithms for remaining useful life prediction of lithium battery in electric vehicles’, Innovations in Applied Engineering and Technology, pp. 1–12, 2025.
  50. W. Huang and J. Ma, ‘Predictive Energy Management Strategy for Hybrid Electric Vehicles Based on Soft Actor-Critic’, Energy & System, vol. 5, no. 1, 2025.
  51. J. Ma, K. Xu, Y. Qiao, and Z. Zhang, ‘An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms’, Journal of Computational Methods in Engineering Applications, pp. 1–12, 2022.
  52. W. Huang, Y. Cai, and G. Zhang, ‘Battery Degradation Analysis through Sparse Ridge Regression’, Energy & System, vol. 4, no. 1, Art. no. 1, Dec. 2024, doi: 10.71070/es.v4i1.65.
  53. Z. Zhang, ‘RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations’, Optimizations in Applied Machine Learning, vol. 4, no. 1, 2024.
  54. Z. Zhang, K. Xu, Y. Qiao, and A. Wilson, ‘Sparse Attention Combined with RAG Technology for Financial Data Analysis’, Journal of Computer Science Research, vol. 7, no. 2, Art. no. 2, Mar. 2025, doi: 10.30564/jcsr.v7i2.8933.
  55. P.-M. Lu and Z. Zhang, ‘The Model of Food Nutrition Feature Modeling and Personalized Diet Recommendation Based on the Integration of Neural Networks and K-Means Clustering’, Journal of Computational Biology and Medicine, vol. 5, no. 1, 2025.
  56. Y. Qiao, K. Xu, Z. Zhang, and A. Wilson, ‘TrAdaBoostR2-based Domain Adaptation for Generalizable Revenue Prediction in Online Advertising Across Various Data Distributions’, Advances in Computer and Communication, vol. 6, no. 2, 2025.
  57. A. Wilson and J. Ma, ‘MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection’, Optimizations in Applied Machine Learning, vol. 5, no. 1, 2025.