The Machine Learning-Enhanced DCF Model and Probabilistic Cash Flow Forecasting

Traditional DCF models rely on a high level of subjective analysts' judgment in cash flow forecasting and traditionally assume perpetual constant growth rates and WACC, which are ill-suited to capture market volatility and tail risks. To bypass these pitfalls, this research suggests a hybrid modeling approach with LSTM networks to distill temporal trends from historical financial data and forecast future cash flows as a first input. These forecasts are then run through a Monte Carlo simulation in order to generate probabilistic distributions, with a jump-diffusion process also included to cover Black Swan events [13]. An empirical investigation with data of the top 500 publicly traded US companies demonstrates that: (1) the machine learning can enhance the valuation range of DCF models; (2) compared to the traditional DCF model, the new approach provides a more complete measure of tail risks [10], (3) statistical tests confirm that valuation results of the two models are different significantly. This study introduces a more integrated risk-return investment decision-making framework, ideally suited to high-volatility industries and periods of market uncertainty [9,14].