CFP last date
20 August 2024
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2024

Submit your paper
Know more
Reseach Article

A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks

by Emmanuel Kwesi Baah, James Ben Hayfron-Acquah, Dominic Asamoah
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

@article{ 10.5120/ijca2021921554,
author = { Emmanuel Kwesi Baah, James Ben Hayfron-Acquah, Dominic Asamoah },
title = { A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2021 },
volume = { 183 },
number = { 20 },
month = { Aug },
year = { 2021 },
issn = { 0975-8887 },
pages = { 21-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number20/32040-2021921554/ },
doi = { 10.5120/ijca2021921554 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:20.214823+05:30
%A Emmanuel Kwesi Baah
%A James Ben Hayfron-Acquah
%A Dominic Asamoah
%T A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 20
%P 21-29
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. E. T. Jackson and K. Harji, “Accelerating impact: Achievements, challenges and what’s next in building the impact investing industry,” Rockefeller Found., no. July, p. 86, 2012.
  2. A. K. Höchstädter and B. Scheck, “What’s in a Name: An Analysis of Impact Investing Understandings by Academics and Practitioners,” J. Bus. Ethics, vol. 132, no. 2, pp. 449–475, 2015.
  3. A. Bugg-levine and J. Emerson, “Impact Investing: Transforming How We Make Money while Making a Difference An,” Innov. Technol. Gov. Glob., vol. 6, no. 3, pp. 9–18, 2011.
  4. J. Freireich and K. Fulton, Investing for social & environmental impact. New York, NY:, 2009.
  5. GIIN, “Annual Impact Investor Survey 2019,” 2019.
  6. GIIN, “Annual Impact Investor Survey 2018,” 2018.
  7. A. Nicholls, “The social enterprise investment fund(SEIF) evaluation,” no. December, 2010.
  8. M. Mendell and E. Barbosa, “Impact investing: a preliminary analysis of emergent primary and secondary exchange platforms,” J. Sustain. Financ. Invest., vol. 3, no. 2, pp. 111–123, 2013.
  9. N. Reeder and A. Colantonio, “Measuring Impact and Non-financial Returns in Impact Investing: A Critical Overview of Concepts and Practice,” EIBURS Work. Pap., no. 2013/01, pp. 1–44, 2013.
  10. L. Hehenberger, A.-M. Harling, and P. Scholten, A Practical Guide To Measuring and Managing Impact European Venture Philanthropy Association, no. June. 2015.
  11. S. Olsen and B. Galimidi, “Catalog of Approaches To Impact Measurement,” Rockefeller Found., no. May, 2008.
  12. U. Grabenwarter and H. Liechtenstein, In Search of Gamma - An Unconventional Perspective on Impact Investing. 2012.
  13. Y. Saltuk, A. Bouri, A. Mudaliar, and M. Pease, “Perspectives on Progress,” JP Morgan, no. January, pp. 1–28, 2013.
  14. I. So and A. Staskevicius, “Measuring the ‘impact’ in impact investing,” no. August, pp. 1–31, 2015.
  15. L. Aquino- and S. Doran, “Impact investing : challenges of impact measuring,” no. May, pp. 41–43, 2017.
  16. N. B. Verrinder, K. Zwane, D. Nixon, and S. Vaca, “Evaluative tools in impact investing: Three case studies on the use of theories of change,” African Eval. J., vol. 6, no. 2, pp. 1–9, 2018.
  17. Tideline, “NAVIGATING IMPACT INVESTING,” no. July, pp. 1–36, 2016.
  18. CISL, “In Search of Impact: Measuring the full value of capital,” UK, 2016.
  19. GIIN, “Annual impact investor survey 2016.,” New York, 2016.
  20. N. Watts and I. R. Scales, “Social impact investing, agriculture, and the financialization of development: Insights from sub-Saharan Africa,” World Dev., vol. 130, p. 104918, 2020.
  21. R. Vargas, A. Mosavi, and R. Ruiz, “Deep Learning: A Review,” Adv. Intell. Syst. Comput., no. October, 2018.
  22. F. Emmert-Streib, Z. Yang, H. Feng, S. Tripathi, and M. Dehmer, “An Introductory Review of Deep Learning for Prediction Models With Big Data,” Front. Artif. Intell., vol. 3, no. February, pp. 1–23, 2020.
  23. A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019.
  24. K. Sirinukunwattana, S. E. A. Raza, Y. W. Tsang, D. R. J. Snead, I. A. Cree, and N. M. Rajpoot, “Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1196–1206, 2016.
  25. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 36, 2015.
  26. X. Z. Wang, T. Zhang, and R. Wang, “Noniterative deep learning: Incorporating restricted boltzmann machine into multilayer random weight neural networks,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 7, pp. 1299–1308, 2019.
  27. Y. Bengio, “Deep learning of representations: Looking forward,” in Proceedings of the 1st International Conference on Statistical Language and Speech Processing, 2013, pp. 1–37.
  28. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798–1828, 2013.
  29. Y. Bengio and Y. Lecun, “Scaling Learning Algorithms towards AI,” in Large Scale Kernel Machines, Eds., no. 34, L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, Eds. Cambridge, MA: MIT Press, 2007, pp. 321–360.
  30. S. Ellis, T. Siesfeld, and D. Buelow, “Social capital : Measuring the community impact of corporate spending,” Deloitte Insights, no. 24, 2019.
  31. R. A. Alenius, “Measurement Process in Impact Investing : State of Practice in Europe,” Harvard Ext. Sch., 2016.
  32. J. Miao and L. Niu, “A Survey on Feature Selection,” Procedia Comput. Sci., vol. 91, no. Itqm, pp. 919–926, 2016.
  33. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
  34. Q. Chen, Z. Meng, X. Liu, Q. Jin, and R. Su, “Decision variants for the automatic determination of optimal feature subset in RF-RFE,” Genes (Basel)., vol. 9, no. 6, 2018.
  35. P. M. Granitto, C. Furlanello, F. Biasioli, and F. Gasperi, “Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products,” Chemom. Intell. Lab. Syst., vol. 83, no. 2, pp. 83–90, 2006.
  36. N. K. Poona, A. Van Niekerk, R. L. Nadel, and R. Ismail, “Random Forest (RF) Wrappers for Waveband Selection and Classification of Hyperspectral Data,” Appl. Spectrosc., vol. 70, no. 2, pp. 322–333, 2016.
  37. H. Jeon and S. Oh, “Hybrid-Recursive Feature Elimination for E ffi cient Feature Selection,” Appl. Sci., pp. 1–8, 2020.
  38. D. West, “Neural network credit scoring models,” Comput. Oper. Res., vol. 27, no. 11–12, pp. 1131–1152, 2000.
  39. P. Misra and A. S. Yadav, “Improving the classification accuracy using recursive feature elimination with cross-validation,” Int. J. Emerg. Technol., vol. 11, no. 3, pp. 659–665, 2020.
Index Terms

Computer Science
Information Sciences

Keywords

Impact investing random forest deep learning impact