A Combination of NLP and Monte Carlo Technique to Improve Wind Investment Decisions

Investment decisions in wind projects can be tough considering the uncertain economic performance depending on the stochastic nature of revenues. Thanks to the recent innovation in Natural Language Processing (NLP), this work tries to present an innovative approach based on the Monte Carlo option pricing model and Sentiment Analysis. Treating it as a financial option, the idea is to price the managerial flexibility of changing investment decisions during the project lifetime depending on the wind investment’s profitability. In this way, the Monte Carlo options pricing technique is combined with the sentiment (polarity) score, allowing the modification of transition probabilities from one phase of the investment to another and, consequently, the profitability of the investment.