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A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 20 |
| Year of Publication: 2021 |
| Authors: Emmanuel Kwesi Baah, James Ben Hayfron-Acquah, Dominic Asamoah |
10.5120/ijca2021921554
|
Emmanuel Kwesi Baah, James Ben Hayfron-Acquah, Dominic Asamoah . A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks. International Journal of Computer Applications. 183, 20 ( Aug 2021), 21-29. DOI=10.5120/ijca2021921554
Abstract
Impacting investing is fast becoming an up-and-coming area in the finance industry. With the massive projection of investment that would go into this sector, there are present predicaments with the measurement of impact from impact investing, which casts doubts on the prospect of this concept. However, it is tagged as being characteristic of the future of investment. The challenge involves defining what to measure when to measure, and at what phase of investment. In this study, a combination of machine learning and deep learning models is used on the intended community to measure the pre-impact factors suitable to generating confidence for the full granting of funds for impact investing. The first phase employed a survey of the impact community to gather features useful for the pre-impact assessment using redundant feature elimination with random forest. A deep neural network is then used to predict the various classes chosen for the classification problem. The results indicate that this new approach creates confidence in the next phase of impact measurement. Thus, the critical features for measuring the impact outcomes are not humanly generated or biased towards individuals but have a mathematical model that selects these features and the accuracy, precision, and recall for all three models are very significant. The deep learning and machine learning models had a unique advantage in resolving pre-impact measurement from impact investing and proved promising for other investment phases with minimal human effort, cost-effectiveness and timeliness.
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