References
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- Asteriou, D., & Hall S. (2007). Applied Econometrics, Revised Edition. Palgrave Macmillan, New York, USA.
- Banerjee, D. (2014). Forecasting of Indian stock market using the time-series ARIMA model. Paper presented at the 2nd International Conference on Business & Information Management (pp. 131-135). Durgapur, India. IEEE.
- Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, California, USA.
- Chatfield, C. (1996). The Analysis of Time Series, 5th ed., Chapman & Hall, New York.
- Contreras, J., Espinola, R., Nogales, F., &Conejo, A. (2003). ARIMA Models to Predict Nextday Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014- 1020.
- Gay, R. (2016). Effect of Macroeconomic Variables on Stock Market Returns for Four Emerging Economies: Brazil, Russia, India, and China. International Business & Economics Research Journal, 15(3), 119-126.
- Gilbert, K. (2005). An ARIMA Supply Chain Model, Management Science, 51(2), 305-310.
- Gould, P. (1981). Letting the Data Speak for Themselves. Annals of the Association of American Geographers, 71(2), 166-176.
- Guha, B., &Bandyopadhyay, G. (2016). Gold Price Forecasting using ARIMA Model. Journal of Advanced Management Science, 4(2), 117-121.
- Gujarati, D., & Porter, D. (2004). Basic Econometrics, Fourth Edition, McGraw-Hill, New York, USA.
- Hamjah, M. (2014). Rice Production Forecasting in Bangladesh: An Application of Box-Jenkins ARIMA Model. Mathematical Theory and Modeling, 4(4), 1-11.
- Jadhav, V., Reddy, B., & Gaddi, G. (2017). Application of ARIMA Model for Forecasting Agricultural Prices. Journal of Agricultural Science and Technology, 19(5), 981-992.
- Manoj, K., & Madhu, A. (2014). An Application of Time Series ARIMA Forecasting Model for Predicting Sugarcane Production in India. Studies in Business and Economics, 9(1), 81-94.
- Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. Central Bank and Financial Services Authority of Ireland Technical Paper Series, 1998(3), 1-48.
- Mondal, P., Shit, L., & Goswami, S. (2014). Study of Effectiveness of Time Series Modeling (ARIMA) in Forecasting Stock Prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13- 29.
- Padhan, P. (2012). Application of ARIMA Model for Forecasting Agricultural Productivity in India. Journal of Agriculture & Social Sciences, 8(2), 50- 56.
- Raymond Y. (1997). An Application of the ARIMA Model to Real-Estate Prices in Hong Kong. Journal of Property Finance, 8(2), 152-163.
- Adebiyi, A., Adewumi, A., & Ayo, C. (2014, March). Stock Price Prediction Using the ARIMA Model. Paper presented at the 2014 UKSim-AMSS 16th International Conference on Computer Modeling and Simulation, Cambridge University, United Kingdom. Retrieved from http://ijssst.info/Vol-15/No-4/data/4923a105.pdf.
- Asteriou, D., & Hall S. (2007). Applied Econometrics, Revised Edition. Palgrave Macmillan, New York, USA.
- Banerjee, D. (2014). Forecasting of Indian stock market using the time-series ARIMA model. Paper presented at the 2nd International Conference on Business & Information Management (pp. 131-135). Durgapur, India. IEEE.
- Box, G., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, California, USA.
- Chatfield, C. (1996). The Analysis of Time Series, 5th ed., Chapman & Hall, New York.
- Contreras, J., Espinola, R., Nogales, F., &Conejo, A. (2003). ARIMA Models to Predict Nextday Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014- 1020.
- Gay, R. (2016). Effect of Macroeconomic Variables on Stock Market Returns for Four Emerging Economies: Brazil, Russia, India, and China. International Business & Economics Research Journal, 15(3), 119-126.
- Gilbert, K. (2005). An ARIMA Supply Chain Model, Management Science, 51(2), 305-310.
- Gould, P. (1981). Letting the Data Speak for Themselves. Annals of the Association of American Geographers, 71(2), 166-176.
- Guha, B., &Bandyopadhyay, G. (2016). Gold Price Forecasting using ARIMA Model. Journal of Advanced Management Science, 4(2), 117-121.
- Gujarati, D., & Porter, D. (2004). Basic Econometrics, Fourth Edition, McGraw-Hill, New York, USA.
- Hamjah, M. (2014). Rice Production Forecasting in Bangladesh: An Application of Box-Jenkins ARIMA Model. Mathematical Theory and Modeling, 4(4), 1-11.
- Jadhav, V., Reddy, B., & Gaddi, G. (2017). Application of ARIMA Model for Forecasting Agricultural Prices. Journal of Agricultural Science and Technology, 19(5), 981-992.
- Manoj, K., & Madhu, A. (2014). An Application of Time Series ARIMA Forecasting Model for Predicting Sugarcane Production in India. Studies in Business and Economics, 9(1), 81-94.
- Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. Central Bank and Financial Services Authority of Ireland Technical Paper Series, 1998(3), 1-48.
- Mondal, P., Shit, L., & Goswami, S. (2014). Study of Effectiveness of Time Series Modeling (ARIMA) in Forecasting Stock Prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13- 29.
- Padhan, P. (2012). Application of ARIMA Model for Forecasting Agricultural Productivity in India. Journal of Agriculture & Social Sciences, 8(2), 50- 56.
- Raymond Y. (1997). An Application of the ARIMA Model to Real-Estate Prices in Hong Kong. Journal of Property Finance, 8(2), 152-163.
Cite this article
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APA : Afeef, M., Ali, N., & Khan, A. (2018). Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique. Global Social Sciences Review, III(IV), 413-426. https://doi.org/10.31703/gssr.2018(III-IV).28
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CHICAGO : Afeef, Mustafa, Nazim Ali, and Adnan Khan. 2018. "Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique." Global Social Sciences Review, III (IV): 413-426 doi: 10.31703/gssr.2018(III-IV).28
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HARVARD : AFEEF, M., ALI, N. & KHAN, A. 2018. Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique. Global Social Sciences Review, III, 413-426.
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MHRA : Afeef, Mustafa, Nazim Ali, and Adnan Khan. 2018. "Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique." Global Social Sciences Review, III: 413-426
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MLA : Afeef, Mustafa, Nazim Ali, and Adnan Khan. "Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique." Global Social Sciences Review, III.IV (2018): 413-426 Print.
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OXFORD : Afeef, Mustafa, Ali, Nazim, and Khan, Adnan (2018), "Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique", Global Social Sciences Review, III (IV), 413-426
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TURABIAN : Afeef, Mustafa, Nazim Ali, and Adnan Khan. "Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique." Global Social Sciences Review III, no. IV (2018): 413-426. https://doi.org/10.31703/gssr.2018(III-IV).28