Long Short-Term Memory (LSTM) Networks for Real Options Analysis in Strategic Investment Decisions

The main objective of this research work is to employ the Long Short-Term Memory (LSTM) networks technique in the advancing of Real Options Analysis. These conventional models of financial analysis simply fail to reckon with certain dynamism as obtaining in the global markets and as such are not very helpful when making strategic plans. Real Options Analysis is still another noteworthy and potent approach because it captures the role of managers’ flexibility; nonetheless, it has issues associated with the modeling of market variables. Among the recurrent neural networks the most important ones are the LSTM networks, which use the long short-term memory and non-linear modelling. This involves data collection and data preprocessing In using time series data, creation of the LSTM network using python and data mining and finally the comparison of the results to the standard models. The research confirms that LSTM networks are capable of producing efficient and accurate predications for valuation of real options and more precise definition of investment strategies. The authors say that because the approach is not impervious to the limitations that define big data analyses including the fact that a great deal of data has to be analysed and the costs of processing are always high, the paper demonstrates a useful method of strategic investment decision making that is available to practically any field that wants to take advantage of big data analyses.