The Application of Random Forest Algorithm for Company Valuation in the Advanced Materials Industry

Research Article
Open access

The Application of Random Forest Algorithm for Company Valuation in the Advanced Materials Industry

Yuexuan Li 1*
  • 1 Foster School of Business, University of Washington, Seattle, USA    
  • *corresponding author yl472@uw.edu
AEMPS Vol.192
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-195-2
ISBN (Online): 978-1-80590-196-9

Abstract

The advanced materials industry is a key driver of technological innovation, making accurate enterprise valuation essential for investment and market analysis. Traditional valuation methods like DCF, PE, and PB struggle with high-growth companies due to volatile cash flows and market dependencies. To address these challenges, this study applies a random forest algorithm to enhance valuation accuracy by leveraging financial data, market indicators, and industry-specific factors. By using bootstrap aggregation to randomly select samples and features, the random forest model, which is based on an ensemble learning approach with decision trees—improves predictive performance and minimizes overfitting. In order to quantify important valuation determinants, build a predictive model, and assess its performance using common error measures, this study gathers financial and market data from about 100 publicly traded businesses in the new materials sector. When compared to conventional techniques, the empirical study shows that the random forest model increases valuation accuracy and stability. The findings show that the model delivers a more accurate estimate of enterprise value, lessens sensitivity to market swings, and successfully captures nonlinear linkages in valuation.

Keywords:

enterprise valuation, Random Forest, advanced materials industry, machine learning, financial modeling

Li,Y. (2025). The Application of Random Forest Algorithm for Company Valuation in the Advanced Materials Industry. Advances in Economics, Management and Political Sciences,192,35-48.
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References

[1]. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29. https://doi.org/10.1177/1536867X20909688

[2]. Smith, R., & Jones, M. (2020). R&D investment and valuation challenges in the new material industry. Technological Innovation Review, 15(1), 77-98.

[3]. Doe, J. (2021). Policy impacts on emerging technology sectors: A case study of advanced materials. Economic Policy Review, 34(3), 45-67.

[4]. Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset (3rd ed.). Wiley.

[5]. Brealey, R. A., Myers, S. C., & Allen, F. (2018). Principles of corporate finance (12th ed.). McGraw-Hill Education.

[6]. Koller, T., Goedhart, M., & Wessels, D. (2020). Valuation: Measuring and managing the value of companies (7th ed.). Wiley. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35-65.

[7]. Fernandez, P. (2019). The limitations of discounted cash flow methods in valuing technology-driven firms. Financial Strategy Journal, 65(4), 23-39.

[8]. Brown, T., & White, L. (2022). Evaluating market-based valuation metrics in high-growth industries. Journal of Financial Analysis, 78(2), 112-130. Books

[9]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

[10]. Ling, X. (2023). Application of random forest algorithm in the valuation of biomedical enterprises [Doctoral dissertation, Jiangxi University of Finance and Economics]. Jiangxi, China

[11]. Kaggle. (2021). Machine learning datasets and competitions. Retrieved from https://www.kaggle.com

[12]. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 40(2), 139-157

[13]. Penman, S. H. (2016). Accounting for value. Columbia University Press.

[14]. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227.

[15]. Petersen, C., & Plenborg, T. (2012). Financial statement analysis. Pearson Education

[16]. Porter, M. E. (2008). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.

[17]. Moomoo. (2019). Company profile: Western Superconductor. Retrieved March 30, 2025, from https://www.moomoo.com/hans/stock/688122-SH/company

Cite this article

Li,Y. (2025). The Application of Random Forest Algorithm for Company Valuation in the Advanced Materials Industry. Advances in Economics, Management and Political Sciences,192,35-48.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICMRED 2025 Symposium: Effective Communication as a Powerful Management Tool

ISBN：978-1-80590-195-2(Print) / 978-1-80590-196-9(Online)
Editor：Lukáš Vartiak
Conference website: https://2025.icmred.org/Bratislava.html
Conference date: 30 May 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.192
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29. https://doi.org/10.1177/1536867X20909688

[2]. Smith, R., & Jones, M. (2020). R&D investment and valuation challenges in the new material industry. Technological Innovation Review, 15(1), 77-98.

[3]. Doe, J. (2021). Policy impacts on emerging technology sectors: A case study of advanced materials. Economic Policy Review, 34(3), 45-67.

[4]. Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset (3rd ed.). Wiley.

[5]. Brealey, R. A., Myers, S. C., & Allen, F. (2018). Principles of corporate finance (12th ed.). McGraw-Hill Education.

[6]. Koller, T., Goedhart, M., & Wessels, D. (2020). Valuation: Measuring and managing the value of companies (7th ed.). Wiley. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35-65.

[7]. Fernandez, P. (2019). The limitations of discounted cash flow methods in valuing technology-driven firms. Financial Strategy Journal, 65(4), 23-39.

[8]. Brown, T., & White, L. (2022). Evaluating market-based valuation metrics in high-growth industries. Journal of Financial Analysis, 78(2), 112-130. Books

[9]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

[10]. Ling, X. (2023). Application of random forest algorithm in the valuation of biomedical enterprises [Doctoral dissertation, Jiangxi University of Finance and Economics]. Jiangxi, China

[11]. Kaggle. (2021). Machine learning datasets and competitions. Retrieved from https://www.kaggle.com

[12]. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 40(2), 139-157

[13]. Penman, S. H. (2016). Accounting for value. Columbia University Press.

[14]. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227.

[15]. Petersen, C., & Plenborg, T. (2012). Financial statement analysis. Pearson Education

[16]. Porter, M. E. (2008). Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press.

[17]. Moomoo. (2019). Company profile: Western Superconductor. Retrieved March 30, 2025, from https://www.moomoo.com/hans/stock/688122-SH/company

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