Predicting the Variable Relationship Between R&D Investment and Market Value in Technology Companies

In today's highly competitive technology industry landscape, tech companies continue to seek opportunities to enhance their existing product and service portfolio and identify new areas to diversify their business to stay ahead of the competition. Over the past few decades, new product and service discovery through novel Innovation has become a key factor for any company to sustain its competitive advantage. Since investing in research and development (R&D) is widely recognized as a key factor for driving innovation, companies have started to allocate a significant amount of their revenue to R&D. However, allocating a fixed percentage of revenue to R&D might not be optimal, since the optimal level of R&D investment is often constrained by several other financial and non-financial factors, including industrial uncertainty, complex financial interdependencies, and rapidly shifting market dynamics driven by new technological innovations. This Praxis focuses on investigating whether machine learning (ML) can be leveraged as a decision-making framework to optimize R&D investments and predict market value with high accuracy by utilizing other key financial metrics.

Author

• Jinasundera, Sudirukkuge Tharanga

Language

• en

Keyword

• Neural Networks
• Decision-Making Tools
• Market Value Prediction
• R&D Investment
• Technology Industry
• Machine Learning

Date created

• 2026

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Blackford, J.P
• Emmons, Debra

Committee Member(s)

• Bierce, Jonathan

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/qv33rx821