In most cases, the valuation of the investments characterized by various stages with a high level of uncertainty is done through the compound Real Option Valuation (ROV). This decision making support can consider various types of uncertainty that can affects these investment phases, such as that linked to technology. Specifically, within the category of uncertain investments there are the broadband opportunities that can be valued as real options in order to quantify the risks associated with the investment. However, since ROV theory has no definitive way to determine model parameters based on market information, we propose one that can adjust them dynamically. In this paper, to include this aspect in the project valuation, we have unified the ROV with the Sentiment Analysis, a Natural Language Processing (NLP) technique that allows us to quantify the polarity of expressions in natural language numerically. In particular, the inherent risks related to the different phases of the project can be extracted from the information present in the surrounding environment and published in newspapers. From there, we obtain a sentiment score which, through appropriate manipulations, manages to modify the evaluation of the success probabilities of each stage. Then, we embed these success probabilities in the ROV in order to provide a valuation methodology that includes the impact of information on the investment decision.
The impact of polarity score on real option valuation for multistage projects
Antonio Di Bari;Domenico Santoro
;Giovanni Villani
2023-01-01
Abstract
In most cases, the valuation of the investments characterized by various stages with a high level of uncertainty is done through the compound Real Option Valuation (ROV). This decision making support can consider various types of uncertainty that can affects these investment phases, such as that linked to technology. Specifically, within the category of uncertain investments there are the broadband opportunities that can be valued as real options in order to quantify the risks associated with the investment. However, since ROV theory has no definitive way to determine model parameters based on market information, we propose one that can adjust them dynamically. In this paper, to include this aspect in the project valuation, we have unified the ROV with the Sentiment Analysis, a Natural Language Processing (NLP) technique that allows us to quantify the polarity of expressions in natural language numerically. In particular, the inherent risks related to the different phases of the project can be extracted from the information present in the surrounding environment and published in newspapers. From there, we obtain a sentiment score which, through appropriate manipulations, manages to modify the evaluation of the success probabilities of each stage. Then, we embed these success probabilities in the ROV in order to provide a valuation methodology that includes the impact of information on the investment decision.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
