Assessing and Predicting Returns of Equity Portfolio using the ARIMA Model
Riya Agarwal1, Shailee Choudhary2, Rinku Dixit3
1Riya Agarwal, Scholar, New Delhi Institute of Management, New Delhi, India.
2Prof. Shailee Choudhary, Department of Business Analytics, New Delhi Institute of Management, New Delhi, India.
3Prof. (Dr.) Rinku Sharma Dixit, Department of Business Analytics, New Delhi Institute of Management, New Delhi, India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 66-76 | Volume-8 Issue-5, June 2019 | Retrieval Number: D6538048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Predicting the Stock prices has always attracted the attention and interest of researchers and analysts worldwide and has led to the development of better prediction models. Share Market is unpredictable, since there are no significant rules to estimate or predict the prices of shares. Famous methods like technical analysis, fundamental analysis, etc. are all used to attempt to predict the price in the share market but none of these have proved as an accurately correct and consistently acceptable prediction tool.In recent years, the banking industry of India has been seeing many ups and downs due to world economic depression and it has impacted the stock prices of many banks. A smart investor can take the benefits of high volatility of NSE bank stock prices. Through time series analysis and statistical analysis, this research has tried to forecast the stock returns of banking industry. Results obtained revealed that statistical analysis by using efficient frontier and capital allocation line through Sharpe ratio has potential for short term prediction and can compete favorably with the existing techniques like technical analysis for stock prediction. Also, the Autoregressive Integrated Moving Average (ARIMA) model has been used which uses the historical data points which further enhances the accuracy in comparison to the fundamental analysis, as it considers the share prices and analyses the historical trend of the extracted data and then forecasts the prices
Keywords: Modern Portfolio Theory, Portfolio Management, Capital Allocation Line, Frontier Graphs, Covariance matrix, ARIMA, Risks, Returns.
Scope of the Article: Data Management