Intangible Assets and US Stock Returns: An analysis using the Index Method, Panel Regression, and Machine Learning

Journal of Applied Economic Research. 2024. Vol. 23. No. 3. P. 833–854.

Ханиев А.

This study examines the impact of intangible assets on stock returns in the U.S. using the Drucker Institute indices, which assess companies based on customer satisfaction, employee engagement and development, innovation, social responsibility, and financial stability. The relevance of this study lies in the growing importance of considering non-financial indicators in investment decision-making. The objective is to determine how these indices affect stock returns across different sectors. The hypotheses posit that each index has a positive impact. The study employs both panel regression with fixed effects and machine learning methods using XGBoost with Shapley values to analyze data from U.S. companies for the period from June 30, 2016, to June 30, 2023. The results indicate that social responsibility has a broadly positive impact on stock returns across various sectors. Innovation significantly affects returns only in the technology sector. Customer satisfaction and financial stability exhibit varying effects depending on the sector, while employee engagement and development show only negative impacts in the energy sector. The significance of this research lies in its contribution to understanding the role of intangible assets in shaping stock performance. We show that investors can achieve both ethical satisfaction and higher financial returns by prioritizing investments in companies with strong social responsibility records. Additionally, we draw the attention of investors and researchers to the importance of considering sectoral affiliation when analyzing companies. The use of advanced analytical tools, such as XGBoost with Shapley values, underscores the potential of machine learning in uncovering complex relationships in financial data. This approach proves to be highly promising for future research.

Ключевые слова: машинное обучение, доходность акций, Stock returns, ESG, ESG, Machine Learning