Do Google Trends and Shariah Compliant Stocks Co-Integrated? An Evidence from India
Abstract
The objective of the study is to understand the dynamic relationship between Shariah-compliant stocks and the Google search value index (GSVI). The search strength is identified by the search volume of Shariah-compliant stocks on Google. The sample for the study consists of Shariah-compliant stocks commonly available in all the three Shariah indices in India, sample stock data has been extracted on a weekly basis from Sept 2014 to Sept 2019. The results of the study are based on the diagnostic analysis suggests that there is no serial correlation as demonstrated by LM residual test, CUSUM test shows stability in data, coefficient Wald test is showing there is no short-run causality running between selected Shariah-compliant stocks and GSVI. The outcome suggests that there is a long-run equilibrium relationship existing between Shariah-compliant stocks and the Google search value index. Trace statistics has five co-integration equations and Max-Eigen statistics has one co-integration. The vector error correction model (VECM) suggests the acceptability of the model. There are many potential investment opportunities for investors in the Islamic stock market of India. The motive of Shariah is to provide an avenue for ethical and viable investment to the investors. This study will not only be advantageous for the Muslim investors but also the other investors, industrialist, Shariah-compliant advisor as well.
Keywords
Full Text:
PDFReferences
Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 8.
Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 253, 239-264.
Bijl, L., Kringhaug, G., Molnár, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 7.
Curme, C., Stanley, H.E., Moat, H.S., & Preis, T. (2014, 08 12). Quantifying the semantics of search behavior before stock market moves. PNAS, 111 (32), 6.
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. J. of Finance, 665,1461-1499.
Dimp, T., & Jank, S. (2011). Can internet search queries help to predict stock market volatility? 42.
Dimpfl, T., & Kleiman, V. (2017). Investor Pessimism and the German Stock Market: Exploring Google Search Queries. German Economic Review, 28.
Enders, W. (2014). Applied econometric time series (4th ed.). New York: John Wiley & Sons, Inc.
Engle, R.F., & Granger, C.W.J. (1987). Cointegration and Error-Correction: Representation, Estimation and Testing. Econometrica, 55(2), 251-276.
Fama, E.F. (1976). Foundations of finance: Portfolio decisions and securities Prices. Blackwell.
Gujarati, D. (2004). Basic of Econometrics (4th ed.). NewYork: McGraw-hill.
Huang, M.Y., Rojas, R.R., & Convery, P.D. (2019). Forecasting stock market movements using google trend searches. Empirical Economic , 1-19.
Irfan, M. (2016). A study of islamic stock indices and macroeconomic variables. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 10(7), 2557-2565.
Irfan, M. (2017). An empirical study of price discovery in commodities future market. Indian Journal of Finance, 11(3).
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169-209.
Joseph, K.,Wintoki, M.B., Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International J. of Forecasting, 27, 1116 – 1127.
Kahnemanm, D. (1973). Attention and effort. Prentice-Hall: Englewood Cliffs, NJ
Khan, M. A., & Ahmad, E. (2019). Measurement of Investor sentiment and its bi-directional contemporaneous and lead–lag relationship with returns: Evidence from Pakistan. Sustainability, 20.
Krishnan, H., V, G., & Sureshkumar, V. (2018). Google trends and stock returns: a sstudy of investor sntiments using big data. International Journal of Pure and Applied Mathematics, 6.
Latoeiro, P., B, S. R., & Veiga, H. (2013). Predictability of stock market activity using Google search queries. Statistics and Econometrics Series 05, 52.
Laurens, R.B., Glenn, K., & Eirik, S. (2015). Predictive power of google search volume on stock returns. 55.
Medeiros, O.R., Van Doornik, B.F., & Oliveira, G.R. (2011). Modeling and forecasting a firm’s financial statements with a VAR–VECM Model. Brazilian Business Review, 8(3), 20-39.
Methodology Document of NIFTY50 Shariah Index NIFTY500 Shariah Index NIFTY Shariah 25 Index. (2019, August). Retrieved from NSE indices.
Narita, F., & Yin, R. (2018). In search of information:use of google trends’ data to narrow information gaps for low-income developing countries. IMF Working Papers, 51.
Nasir, M.A., Huynh, T.L., Nguyen, S.P., & Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 13.
NIFTY Shariah 25. (2019, September 19). Retrieved from Nifty indices: https://www.niftyindices.com/indices/equity/thematic-indices/nifty-shariah-25
NIFTY50 Shariah index. (2019, September 19). Retrieved from Nifty indices: https://www.niftyindices.com/indices/equity/thematic-indices/nifty-50-shariah
NIFTY500 Shariah index. (2019, September 24). Retrieved from Nifty indices.
NSE Indices. (2018, August). Methodology Document of Shariah Index. Retrieved from NSE Indices: https://www.nseindia.com/content/indices/Method_Nifty_Shariah_Indices.pdf
Merton, R. (1987). A simple model of capital market equilibrium with incomplete information. Journal of Finance, 423, 483–510.
Oliveira, N., Cortez, P., & Areal, N. (2013). On the predictability of stock market behavior using stocktwits sentiment and posting volume. Progress in Artificial Intelligence , 355-365.
Preis, T., Moat, H.S., & Stanley, H.E. (2013). Quantifying Trading Behavior in Financial Markets Using Google Trends. Scientific Reports, 6.
Seeking Alpha. (2018, July 28). Portfolio strategy :Using google trends to predict stocks. Retrieved from Seekingalpha: https://seekingalpha.com/article/4191521-using-google-trends-predict-stocks
Selene, Y.X. (2018). Stock price forecasting using information from yahoo finance and google trend. Caliphonia: UC, Berkeley.
Sims, C.A. (1980). Macroeconomics and reality. The Econometric Society, 48(1), 1-48.
Smet, S.D. (2015-16). Predictive power of Google Trends analysis on Euronext Brussels stock performance. ACADEMIEJAAR, 94.
Stromberg, J. (2013, April 25). Google search terms can predict the stock market. Retrieved from www.smithsonianmag.com: https://www.smithsonianmag.com/science-nature/google-search-terms-can-predict-the-stock-market-41584532/
Takeda, F., & Wakao, T. (2014, April). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific Basin FInance Journal, 27, 1-18.
The Invest. (28, Feb 2019). Tips for using google trends data to predict market movement. Retrieved from The Invest: http://www.theinvestblog.com/tips-for-using-google-trends-data-to-predict-market-movement/
Tower, P.E. (2015). Google search volume index:predicting returns, volatility and trading volumeof tech stocks. Economics Honors Thesis, 32.
DOI: https://doi.org/10.18196/ijief.3228
Refbacks
Copyright (c) 2020 International Journal of Islamic Economics and Finance (IJIEF)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
International Journal of Islamic Economics and Finance (IJIEF)
International Program for Islamic Economics and Finance
Department of Economics
Faculty of Economics and Business
Universitas Muhammadiyah Yogyakarta
Pascasarjana Building, Ground Floor
Jl. Brawijaya (Ringroad Selatan), Kasihan, Bantul
D.I. Yogyakarta 55183, INDONESIA
Official email: ijief@umy.ac.id