Enhancing Valuation and Pricing in Private Equity through Machine Learning  
Author Cheikh Ahmadou Bamba Fall

 
Co-Author(s) Yoshiyuki Higuchi

 
Abstract Financial Valuation, Machine Learning, Predictive Analytics, Discounted Cash Flow, Private Equity

 
Keywords This research explores the 􀀃 potential of machine learning in enhancing valuation and pricing strategies within the private equity sector. Leveraging insights from recent advancements in financial machine learning, we apply a comprehensive suite of algorithms including random forest model, support vector machine model, neural network model and linear regression model to analyze extensive financial datasets. These methodologies aim to overcome the limitations inherent in traditional valuation techniques by uncovering complex patterns and relationships embedded within the financial and market data. Our approach integrates rigorous data preprocessing, feature engineering techniques, and incorporates the Discounted Cash Flow (DCF) method to ensure robust and accurate valuations. The previous researches have demonstrated that machine learning models show better results than the conventional methods in predicting crucial financial outcomes such as company acquisitions, bankruptcies, and IPOs. This study underscores the potential of machine learning to provide enhanced precision and reliability in private equity valuations, offering valuable insights for investors and financial analysts alike.
   
    Article #:  DSBFI25-76
 
Proceedings of 3rd ISSAT International Conference on Data Science in Business, Finance and Industry
January 6-8, 2025 - Da Nang, Vietnam