Anticipating the Creditworthiness of an Organization using R
Dhanagopal R.1, Menaka R.2, Mathana J. M.3, Eric Clapten J.4
1Dr. Dhanagopal R.*, Associate Professor, Department of ECE, Chennai Institute of Technology, Kundrathur, Chennai, India.
2Dr. Menaka R., Professor, Department of ECE, Chennai Institute of Technology, Kundrathur, Chennai, India.
3Dr. Mathana J. M., Professor, Department of ECE, Chennai Institute of Technology, Kundrathur, Chennai, India.
4Eric Clapten J., Department of ECE, Chennai Institute of Technology, Kundrathur, Chennai, India.
Manuscript received on December 12, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 421-427 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7407129219/2020©BEIESP | DOI: 10.35940/ijitee.B7407.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Numerous start-ups are being created day-by-day. Government also welcomes those by providing funds and loans. As India’s economy grows at tremendous pace there is a need for analytical models to help investors track down and predict the performance of industry. Thus, predictive models help us to find and make an informed decision about the financial markets in the future. It allows investors to predict the right shares to obtain profitable investments, banks to invest on repayable customers, mutual funds providers to predict the credit worthiness and shares in order to obtain accuracy about investments and outcomes etc. while there are many models that have been created and perfected by numerous banks and credit rating agencies with their own software tool and data analytics processing there are no such models and systems exists for common retail stock mutual fund investors. This paper mainly focuses on building an open source user friendly model that predict the future performance of concern industry based on the historical records of financial data that is available in BSE/NSE market for various stake holders by focusing on different performance parameters of the concerned company. This prediction is done using R. The Descriptive and predictive models have been created using the financial data collected for more than 3000 companies and tested on accuracy with various statistical methods like ROC.
Keywords: Financial Data, CRA. Bank, R language.
Scope of the Article: Natural Language Processing