2023, Vol. 8, Issue 5, Part C
BSE Sensex forecasting using a machine learning approach
Author(s): B. Ramana Murthy, Shaik Nafeez Umar, K Vijaya Kumar and G Tejaswini Reddy
Abstract: Prediction of stock prices has played an important role in the financial decision-makers of investors. The BSE Sensex oscillates based on fewer influential factors with respect to time intervals and also financial parameters. Since the last decade’s prediction of stock prices contributes to and challenging task to yield significant profit for companies. In this study, the Neural Network Auto-Regressive (NNAR) model has been used to predict BSE Sensex daily closing price data. Models were evaluated using performance measures like Mean Absolute Percentage Error (MAPE) and, Mean Square Error (MSE). The results showed that the NNAR(1-7-1) model gives more appropriate for model building with respective of a 91% of accuracy in predicting the stock prices in India.
Pages: 209-212 | Views: 144 | Downloads: 10Download Full Article: Click Here
How to cite this article:
B. Ramana Murthy, Shaik Nafeez Umar, K Vijaya Kumar, G Tejaswini Reddy. BSE Sensex forecasting using a machine learning approach. Int J Stat Appl Math 2023;8(5):209-212.
