tailieunhanh - Application of time series models in forecasting automobile sectors volatility for selected period.

This paper suggested a review on some of the most crucial works gives a meticulous view of recent machine learning (ML) techniques in the quantitative share price prediction showing that these are the methods transcend some traditional approaches. | Application of time series models in forecasting automobile sectors volatility for selected period. International Journal of Management IJM Volume 11 Issue 4 April 2020 pp. 5 14 Article ID IJM_11_04_002 Available online at http ijm JType IJM amp VType 11 amp IType 4 Journal Impact Factor 2020 Calculated by GISI ISSN Print 0976-6502 and ISSN Online 0976-6510 IAEME Publication Scopus Indexed APPLICATION OF TIME SERIES MODELS IN FORECASTING AUTOMOBILE SECTORS VOLATILITY FOR SELECTED PERIOD. Ajay S. Ghangare Assistant Professor DMT Department of Management Technology Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India Tanmay Gupta Assistant Professor DMT Department of Management Technology Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India Mr. Shubham Singh Student Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India ABSTRACT The Bombay Stock Exchange is extensive and fully regulated trading system in India. Exploration and forecasting of stock market time series data have developed considerable interest from the researchers over the last decade. Time Series modelling techniques perform pivotal role in prediction of data for future demands. Volatility forecasting has become crucial for investors policy holders retailers since bringing preciseness in estimating the future is very difficult. Automotive sector has gone through severe crash in their operations in last decade mostly due to policy changes policy paralysis and confusion among retailers about several new changes to be brought by the authority. This paper suggested a review on some of the most crucial works gives a meticulous view of recent machine learning ML techniques in the quantitative share price prediction showing that these are the methods transcend some traditional .