CoChat
Sign Up
Cited by 31Stock Market Forecasting MethodsForecasting Techniques and ApplicationsFinancial Markets and Investment Strategies

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

Open access
Mashadihasanli, Tamerlan·2022·Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi
DOI 10.26650/jepr1056771
Open this paper in CoChat — free accountOpen PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.26650/jepr1056771

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ARIMA is a unique model with significant coefficients and passes all diagnostic tests.

    “ARIMA can be considered because it is a unique model with significant coefficients and passes all the diagnostic tests (Mashadihasanli 2022).”

    From Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023) · cites this paper as Mashadihasanli 2022

    Section 4.5 (Data Forecasting) says ARIMA (3,1,5) was the only model with significant coefficients that passed all diagnostic tests. That finding concerns the selected model for the Istanbul stock-market index, not ARIMA models generally, as the citing sentence suggests. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 32 · Cited by 31

Research works printed but not linked: 32

  1. Al-Zeaud, H. A. (2011). Modelling and forecasting volatility using ARIMA Model. European Journal of Economics, Finance & Administrative Science, 35, 109-125.
  2. Atsalakis, G. S., & Valavanis, K. P. (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert systems with Applications, 36(7), 10696-10707.
  3. Atsalakis, G. S., Dimitrakakis, E. M., & Zopounidis, C. D. (2011). Elliott wave theory and neuro-fuzzy systems, in stock market prediction: The WASP system. Expert Systems with Applications, 38(8), 9196-9206.
  4. Baba, N., & Kozaki, M. (1992). An intelligent forecasting system of stock price using neural networks. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks (Vol. 1, pp. 371-377).
  5. Chang, C. L., Sriboonchitta, S., & Wiboonpongse, A. (2009). Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation. Mathematics and computers in simulation, 79(5), 1730- 1744.
  6. Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE transactions on power systems, 18(3), 1014-1020.
  7. Datta, K. (2011). ARIMA forecasting of inflation in the Bangladesh Economy. IUP Journal of Bank Management, 10(4), 7-15.
  8. Etuk, H. E., Uchendu, B., & Udo, E. O. (2012). Box-Jenkins modeling of Nigerian stock prices data. Greener Journal of Science Engineering and Technological Research, 2(2), 32-38.
  9. Gupta, S., & Kashyap, S. (2015). Box Jenkins approach to forecast exchange rate in India. Prestige International Journal of Management and Research, 8(1), 1-11.
  10. Kamijo, K. I., & Tanigawa, T. (1990). Stock price pattern recognition-a recurrent neural network approach. In 1990 IJCNN international joint conference on neural networks (pp. 215-221).
  11. Kihoro, J. M., & Okango, E. L. (2014). Stock market price prediction using artificial neural network: an application to the Kenyan equity bank share prices. Journal of Agriculture, Science and Technology, 16(1), 160-171.
  12. Kock, A. B., & Teräsvirta, T. (2013). Forecasting the Finnish consumer price inflation using artificial neural network models and three automated model selection techniques. Finnish Economic Papers, 26(1), 13-24.
  13. Kumar, K., Yadav, A. K., Singh, M. P., Hassan, H., & Jain, V. K. (2004). Forecasting Daily Maximum Surface Ozone. Journal of the Air & Waste Management Association, 54(7), 80-814
  14. Liu, Q., Liu, X., Jiang, B., & Yang, W. (2011). Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC infectious diseases, 11(1), 1-7.
  15. Merh, N., Saxena, V. P., & Pardasani, K. R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Business Intelligence Journal, 3(2), 23-43.
  16. Mitra, S. K. (2009). Optimal combination of trading rules using neural networks. International business research, 2(1), 86-99.
  17. Mostafa, M. M. (2010). Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert systems with applications, 37(9), 6302-6309.
  18. Nandakumar, R., Uttamraj, K. R., Vishal, R., & Lokeswari, Y. V. (2018). Stock price prediction using long short term memory. International Research Journal of Engineering and Technology, 5(03), 1-9.
  19. Nyoni, T. (2018). Modeling and forecasting inflation in Kenya: Recent insights from ARIMA and GARCH analysis. Dimorian Review, 5(6), 16-40.
  20. Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
  21. Saibu, O. (2015). Determining optimal crude oil price benchmark in Nigeria: An empirical approach. Romanian Economic Journal Year XVIII no, 58.
  22. Sekreter, Α., & Gursoy, A. (2014). Combining forecasting method vs. individual forecasting methods: Evidence from Istanbul Stock Exchange National 100 Index. The Empirical Economics Letters, 13(7), 735-743.
  23. Shah, D., Campbell, W., & Zulkernine, F. H. (2018). A comparative study of LSTM and DNN for stock market forecasting. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4148-4155).
  24. Sterba, J., & Hilovska, K. (2010). The implementation of hybrid ARIMA neural network prediction model for aggregate water consumption prediction. Aplimat-Journal of Applied Mathematics, 3(3), 123-131.
  25. Subing, H. J. T., & Kusumah, R. W. R. (2017). An empirical analysis of internal and external factors of stock pricing: Evidence from Indonesia. Problems and Perspectives in Management, 15(4), 178-87.
  26. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics. Boston, MA: Pearson.
  27. Takahashi, T., Tamada, R., & Nagasaka, K. (1998). Multiple line-segments regression for stock prices and long- range forecasting system by neural network. In Proceedings of the 37th SICE Annual Conference. International Session Papers (pp. 1127-1132).
  28. Tsitsika, E. V., Maravelias, C. D., & Haralabous, J. (2007). Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models. Fisheries science, 73(5), 979-988.
  29. Uko, A. K., & Nkoro, E. (2012). Inflation forecasts with ARIMA, vector autoregressive and error correction models in Nigeria. European Journal of Economics, Finance & Administrative Science, 50, 71-87.
  30. Yaziz, S. R., Ahmad, M. H., Nian, L. C., & Muhammad, N. (2011). A comparative study on Box-Jenkins and Garch models in forecasting crude oil prices. Journal of applied sciences, 11(7), 1129-1135.
  31. Yoo, S. (2007). Neural Network Model vs. SARIMA Model in Forecasting Korean Stock Price Index (KOSPI). Issues in Information Systems, 8(3), 372-378.
  32. Yoon, Y., & Swales, G. (1991). Predicting stock price performance: A neural network approach. In Proceedings of the twenty-fourth annual Hawaii international conference on system sciences (Vol. 4, pp. 156-162).

Authors
Mashadihasanli, T.
Affiliations
Istanbul University (TR)
Corresponding author
Mashadihasanli, Tamerlan
ORCID
Mashadihasanli, Tamerlan
Venue
Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi
Publisher
Istanbul University
Published
2022-07-29
Volume
9
Issue
2
Pages
439-454
ISSN
2148-3876 (ISSN-L)
DOI
10.26650/jepr1056771
OpenAlex
W4288689678
Cited by
31
References
32
Citations by year
2026: 6 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 2
FWCI
3.83
Type
journal article
Language
tr
Open access
Licence
cc-by-nc
Locations
  • Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi (publishedVersion, cc-by-nc)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DergiPark (Istanbul University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
Keywords
ARIMA model, Borsa Istanbul, forecast accuracy, stock market volatility

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review