Document Type : Research Article


Faculty of Literature and Humanities, Hakim Sabzevari University, Sabzevar, Iran


Forecasting in the financial markets is vital for informed decision-making, risk management, efficient capital allocation, asset valuation, and economic stability. This study thoroughly examines forecasting techniques to predict the 30-day closing prices of APPLE in a select group of 100 prominent companies chosen based on their revenue profiles. list of 100 big Companies published by The Fortune Global 500. The evaluated forecasting methods encompass a broad spectrum of approaches, including Moving Average (MA), Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), Simple Linear Regression, Multiple Regression, Decision Trees, Random Forests, Neural Networks, and Support Vector Regression (SVR). The information on the dataset was downloaded from Yahoo Finance, and all methods were evaluated in Python. The MAPE method is used to measure the accuracy of the examined methods. Based on the selected dataset, Our findings reveal that SVR, Simple Linear Regression, Neural Networks, and ARIMA consistently outperform other methods in accurately predicting the 30-day APPLE closing prices. In contrast, the Moving Average method exhibits subpar performance, primarily due to its inherent limitations in accommodating the intricate dynamics of financial data, such as trends, seasonality, and unexpected shocks. In conclusion, this comprehensive analysis enhances our understanding of forecasting techniques and paves the way for more informed and precise decision-making in the ever-evolving realm of financial markets.


[1] Armaan, M. S. A., Antony A. S., 2019, A comparison of regression models for prediction of
graduate admissions, 2019 International Conference on Computational Intelligence in Data
Science (ICCIDS), Chennai, India.
[2] Armstrong, J. S. (Ed.). (2001), Principles of Forecasting: A Handbook for Researchers and
Practitioners (Vol. 30). Boston, MA: Kluwer Academic.
[3] Banerjee, D., 2014, Forecasting of Indian stock market using time-series ARIMA model. In
2014 2nd international conference on business and information management (ICBIM) (pp.
131-135). IEEE.
[4] Bharathi, S., & Geetha, A. (2017), ”Sentiment analysis for effective stock market prediction.”
In International Journal of Intelligent Engineering and Systems, 10(3), 146-154.
[5] Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992), ”A training algorithm for optimal
margin classifiers.” In Proceedings of the fifth annual workshop on computational learning
theory, 144-152. New York, NY, USA: ACM.
[6] Box, G., 2013, Box and Jenkins: time series analysis, forecasting and control. In A Very
British Affair: Six Britons and the Development of Time Series Analysis During the 20th
Century, London, UK, Palgrave Macmillan UK.
[7] Breiman, L., 2001, Random forests. Machine Learning 45, 5-32.

[8] Chandwani, D., & Saluja, M. S. (2014), ”Stock direction forecasting techniques: An empirical
study combining machine learning system with market indicators in the Indian context.” In
International Journal of Computer Applications, 92(11), 8-17.
[9] Chatfield, C., 2000, Time-series forecasting, CRC Press, 263pp.
[10] Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N. ,2018,
Forecasting stock market crisis events using deep and statistical machine learning techniques.
Expert Systems with Applications, 112, 353-371.
[11] Dharmawan, P. A. S., & Indradewi, I. G. A. A. D. (2021), ”Double exponential smoothing
brown method towards sales forecasting system with a linear and non-stationary data trend.”
In Journal of Physics: Conference Series, IOP Publishing.
[12] Dhyani, B., Kumar, M., Verma, P., Jain, A. ,2020, Stock market forecasting technique using
ARIMA model, International Journal of Recent Technology and Engineering, 8(6), 2694-2697.
[13] Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. (1997), ”Support
vector regression machines.” In Advances in Neural Information Processing Systems, 9, 155-
[14] Du, Y., 2018, Application and analysis of forecasting stock price index based on combination
of ARIMA model and BP neural network. In 2018 Chinese control and decision conference
(CCDC) 2854-2857. IEEE.
[15] Fildes, R., & Kourentzes, N. (2011), ”Validation and forecasting accuracy in models of climate
change.” In International Journal of Forecasting, 27(4), 968-995.
[16] Ghanbari, M., Arian, H. ,2019, Forecasting stock market with support vector
regression and butterfly optimization algorithm, arXiv preprint arXiv:1905.11462.
[17] H.-I. Lim, 2019, A Linear Regression Approach to Modeling Software Characteristics for
Classifying Similar Software, in 2019 IEEE 43rd Annual Computer Software and Applications
Conference (COMPSAC).
[18] Hansun, S., & Subanar, S. (2016). ”H-WEMA: A New Approach of Double Exponential
Smoothing Method.” In TELKOMNIKA (Telecommunication Computing Electronics and
Control), 14(2), 772-777.
[19] Hidayatulah, H., & Parasian, S. (2020), ”Comparison of forecasting accuracy rate of exponential smoothing method on admission of new students.” In Journal of Critical Review, 7(2),
[20] Hyndman, R. J., & Athanasopoulos, G. (2018), Forecasting: Principles and Practice. OTexts.
292 pp.
[21] Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002), ”A state space framework
for automatic forecasting using exponential smoothing methods.” In International Journal
of Forecasting, 18(3), 439-454.
[22] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., Alfakeeh, A. S. ,2020,
Stock market prediction using machine learning classifiers and social media, news. Journal of
Ambient Intelligence and Humanized Computing, 13, 1-24.
[23] Khemavuk, P., & Leenatham, A. (2020), ”A Conceptual Model for Uncertainty Demand Forecasting by Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Based on
Quantitative and Qualitative Data.” In International Journal of Operations and Quantitative
Management, 26(4), 285-302.
[24] Kucharavy, D., Damand, D., & Barth, M. (2023). ”Technological forecasting using mixed
methods approach.” In International Journal of Production Research, 61(16), 5411-5435.
[25] Kumar, M., Thenmozhi, M. (2014). ”Forecasting stock index returns using ARIMA-SVM,
ARIMA-ANN, and ARIMA-random forest hybrid models.” In International Journal of Banking, Accounting and Finance, 5(3), 284-308.
[26] Makridakis, S., Spiliotis, E., Assimakopoulos, V. (2018). ”Statistical and Machine Learning
forecasting methods: Concerns and ways forward.” In PloS one, 13(3), e0194889. https:
[27] Mallikarjuna, M., Rao, R. P. (2019). ”Evaluation of forecasting methods from selected
stock market returns.” In Financial Innovation, 5(1), 1-16.
[28] Meher, B. K., Hawaldar, I. T., Spulbar, C. M., Birau, F. R. (2021). ”Forecasting stock market
prices using mixed ARIMA model: A case study of Indian pharmaceutical companies.” In
Investment Management and Financial Innovations, 18(1), 42-54.
[29] Meneghini, M., Anzanello, M., Kahmann, A., & Tortorella, G. (2018). ”Quantitative demand
forecasting adjustment based on qualitative factors: case study at a fast food restaurant.” In
Sistemas & Gest˜ao, 13(1), 68-80.
[30] Mondal, P., Shit, L., Goswami, S. (2014). ”Study of effectiveness of time series Modelling
(ARIMA) in forecasting stock prices.” In International Journal of Computer Science, Engineering and Applications, 4(2), 1329.
[31] Naik, N., & Mohan, B. R. (2021). ”Novel stock crisis prediction techniquea study on Indian
stock market.” In IEEE Access, 9, 86230-86242.
[32] Peng, Z., Li X. (2018). ”Application of a multi-factor linear regression model for stock portfolio optimization.” In 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS).
[33] Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B.,
Ziel, F. (2022). ”Forecasting: theory and practice.” In International Journal of Forecasting,
38(3), 705-871.
[34] Roopa, H., Asha, T. (2019). ”A linear model based on principal component analysis for
disease prediction.” In IEEE Access, 7, pp. 105314-105318, 2019.
[35] Scornet, E., Biau G., Vert, J.-P. (2015). ”Consistency of random forests.” In Annals of
[36] Scott, A. J., Fred, C. (2001). Principles of forecasting: a handbook for researchers and
practitioners. Boston, MA: Kluwer Academic, 862 pp.
[37] Shah, D., Isah, H., & Zulkernine, F. (2019), ”Stock market analysis: A review and taxonomy
of prediction techniques.” In International Journal of Financial Studies, 7(2), 26.
[38] Shukor, S. A., Sufahani, S. F., Khalid, K., Abd Wahab, M. H., Idrus, S. Z. S., Ahmad,
A., & Subramaniam, T. S. (2021, May). ”Forecasting Stock Market Price of Gold, Silver,
Crude Oil, and Platinum by Using Double Exponential Smoothing, Holts Linear Trend, and
Random Walk.” In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012087).
IOP Publishing.
[39] Siami-Namini, S., Tavakoli, N., Namin, A. S. (2018). ”A comparison of ARIMA and LSTM
in forecasting time series.” In 17th IEEE International Conference on Machine Learning and
Applications (ICMLA), pp. 1394-1401.
[40] Sidqi, F., & Sumitra, I. D. (2019). ”Forecasting product selling using single exponential
smoothing and double exponential smoothing methods.” In IOP conference series: materials
science and engineering.
[41] Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K.
(2023). ”Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models:
A Systematic Review, Performance Analysis and Discussion of Implications.” In International
Journal of Financial Studies, 11(3), 94.
[42] Taylor, J. W. (2008). ”An evaluation of methods for very short-term time series forecasting.” In International Journal of Forecasting, 24(4), 635-642.
[43] Uyank, G. K., Guler, N. (2013). ”A study on multiple linear regression analysis.” In ¨ ProcediaSocial and Behavioral Sciences, 106, 234-240.
[44] Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., Fouilloy, A. (2017).
”Machine learning methods for solar radiation forecasting: A review.” In Renewable energy,
105, 569-582.
[45] Wager, S., & Athey, S. (2018). ”Estimation and inference of heterogeneous treatment effects
using random forests.” In Journal of the American Statistical Association, 113, 1228-1242.
[46] Wang, X., Sun X. (2016). ”An improved weighted naive Bayesian classification algorithm
based on multivariable linear regression model.” In 2016 9th International Symposium on
Computational Intelligence and Design (ISCID).
[47] Wu, J., Liu, C., Cui W., & Zhang Y. (2019). ”Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression.” In 2019 IEEE International Conference
on Power Data Science (ICPDS).
[48] Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). ”A hybrid deep learning based traffic
flow prediction method and its understanding.” In Transportation Research Part C: Emerging
Technologies, 90, 166-180.
[49] Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). ”Deep learning methods for forecasting COVID-19 time-series data: A Comparative study.” In Chaos, solitons & fractals, 140,
[50] Zhang, F., & O’Donnell, L. J. (2020). ”Support vector regression.” In Machine learning,
123-140, Academic Press.