S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

被引：21

作者：

Challa, Madhavi Latha ^{[1
]}

Malepati, Venkataramanaiah ^{[2
]}

Kolusu, Siva Nageswara Rao ^{[3
]}

机构：

[1] CMR Coll Engn & Technol, Dept CSE, Hyderabad, India

[2] SG Govt Degree & PG Coll, Dept Commerce, Piler, Andhra Pradesh, India

[3] Vignan Fdn Sci Technol & Res, Dept Management Studies, Guntur, Andhra Pradesh, India

来源：

FINANCIAL INNOVATION | 2020年 / 6卷 / 01期

关键词：

Efficient market hypothesis; Bombay stock exchange; ARIMA; KPSS; S& P BSE Sensex; Forecasting; P BSE IT; CHINESE STOCK MARKETS; VARIANCE-RATIO TESTS; TIME-SERIES; LONG MEMORY; COMBINATION; HYPOTHESIS; EFFICIENT; PREMIUM; MODELS; PREDICTABILITY;

D O I：

10.1186/s40854-020-00201-5

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

引用

页数：19