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Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye

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  • Tamerlan Mashadihasanli

    (Istanbul University, Institute of Social Sciences/Department of Economics (English),Istanbul, Turkiye)

Abstract

Because of its critical position in open economies and its extremely high volatility, the stock market price index has been a popular subject of market research. In modern financial markets, traders and practitioners have had trouble predicting the stock market price index. In order to solve this problem, some methods have been researched by researchers and suitable methods have been found. To analyze and forecast monthly stock market price index, a variety of statistical and econometric models are extensively used. Thus, this study aims to investigate the application of autoregressive integrated moving averages (ARIMA) for forecasting monthly stock market price index in Istanbul for the period from 2009- M01 to 2021-M03. As compared to all other tentative models, the research showed that the ARIMA (3,1,5) model is the best fit model for predicting the stock market price index. Forecasting is conducted by using the developed model ARIMA (3,1,5) and the results indicated that the forecasted values are very similar to the actual ones, reducing forecast errors. In general, the stock market price index in Istanbul; showed a downwards trend over the forecasted period. The results of the study can set an example for researchers and practitioners working in the stock market and can be a guide for economic decision units and investors in the stock market.

Suggested Citation

  • Tamerlan Mashadihasanli, 2022. "Stock Market Price Forecasting Using the Arima Model: an Application to Istanbul, Turkiye," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 439-454, July.
    • Handle: RePEc:ist:iujepr:v:9:y:2022:i:2:p:439-454
    DOI: 10.26650/JEPR1056771
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    References listed on IDEAS

    as
    1. Kanchan Datta, 2011. "ARIMA Forecasting of Inflation in the Bangladesh Economy," The IUP Journal of Bank Management, IUP Publications, vol. 0(4), pages 7-15, November.
    2. Saibu Olufemi Muibi, 2015. "Determining Optimal Crude Oil Price Benchmark in Nigeria: An Empirical Approach," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 18(58), pages 51-80, December.
    3. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    4. Chang, Chia-Lin & Sriboonchitta, Songsak & Wiboonpongse, Aree, 2009. "Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1730-1744.
    5. Anders Bredahl Kock & Timo Teräsvirta, 2013. "Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques," Finnish Economic Papers, Finnish Economic Association, vol. 26(1), pages 13-24, Spring.
    6. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    7. Nyoni, Thabani & Nathaniel, Solomon Prince, 2018. "Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models," MPRA Paper 91351, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    ARIMA; forecasting; stock market price index; time series; Turkiye JEL Classification : E47 ; G17 ; E37;
    All these keywords.

    JEL classification:

    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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