The Application of ARIMA Box-Jenkins Method for Forecasting the Daily Stock Price per Share of Hasbro, Inc.

Arifian Zulfikar Baihaqi, Dadang Amir Hamzah

Abstract


Stock is a type of securities that gives stockholders the ownership in a company. The stock price can be predicted and forecasted with several techniques and formula. This research is aim to help forecast stock price using ARIMA Box-Jenkins Method which can predict a stock price using time series analysis. Research is done to the Hasbro, Inc. monthly stock closing price data from 1 May 2017 – 1 December 2022 for forecasting the stock closing price of its company for the next 3 month. The finding of this work is  ARIMA (1,2,0) is the best model as the most accurate among the other model with a MAPE of 1.97%.  The forecast result is shown that Hasbro, Inc. stock price will fall in the next 3 months.

Keywords


Stock price; ARIMA Box-Jenkins; time series analysis; Hasbro

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References


S. Khan, & H. Alghulaiakh, ARIMA Model for Accurate Time Series Stocks Forecasting. 2019. [Online]. Available: https://thesai.org/Downloads/Volume11No7/Paper_65-ARIMA_Model_for_Accurate_Time_Series.pdf

Hasbro, Hasbro Fact Sheet. 2023 [Online]. Available: https://corporate.hasbro.com/en-us [Accessed on: April 16, 2023]

M.R. Islam, & M.M Rahman, Stock Market Prediction using ARIMA Model, 2013

G. E. P. Box, G. M. Jenkins, & Reinsel, , Time series analysis: forecasting and control. John Wiley & Sons, 2015

R. J. Hyndman, & G. Athanasopoulos, Forecasting: principles and practice. OTexts, 2018

Padhan, P. C., “Application of ARIMA Model for Forecasting Agricultural Productivity in India” JOURNAL OF AGRICULTURE & SOCIAL SCIENCES, 2012 [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=8bcde52ddac636e96b1fff541c5a871081ad71d9 [Accessed on: April 16, 2023]

D. J. Cryer, & K. Chan, Time Series Analysis with Applications in R, 2008 [Online]. Available: https://mybiostats.wordpress.com/wp-content/uploads/2015/03/time-series-analysis-with-applications-in-r-cryer-and-chan.pdf [Accessed on: April 16, 2023]

M.B. Khashei, M. Ardali, Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs), 2012

T.N. Filder, M.M. Muraya, & R.M. Mutwiri, Application of Seasonal Autoregressive Moving Average Models to Analysis and Forecasting of Time Series Monthly Rainfall Patterns in Embu County, Kenya. 2019.

J. S. Hunter, The Exponentially Weighted Moving Average. Vol 18, No 4, 1986

L. Mao, Y. Huang, X. Zhang, S. Li, & X. Huang, ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19. 2022

Has, Inc. stock historical prices & data – Yahoo finance. https://finance.yahoo.com/quote/HAS/history?period1=1649030400&period2=1680566400&interval=1wk&filter=history&frequency=1wk&includeAdjustedClose=true


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