Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Research Article
Open access

Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Lin Tian 1* , Lin Tian 2*
  • 1 University of California    
  • 2 University of California    
  • *corresponding author l4tian@ucsd.edu
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/13/20230759
AEMPS Vol.13
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-69-0
ISBN (Online): 978-1-915371-70-6

Abstract

Forecasting the stock market index has been an essential part of the investing process for the world’s investors, so predication for the composite stock market index of three different countries in North America were made for the investors to get references. The weekly data of three representative composite stock market index for each country from the past three months were chosen to generate the prediction for the next week’s performance of the stock market in each country through different time-series forecasting methods. The correlation between each index are calculated, indicating the short-term relationship between each country’s stock market. Three time-series predicting method is produced: SMA, WMA, and SES, one of the three methods with the least error by comparing the MSE and MAD would be selected for each stock index. Analyze the forecast results from the selected method for each country’s composite stock market index and compare them. The forecast results show that the composite stock market index for all three countries is going to decline in the following week. Several short-term relationships between different countries’ stock markets are revealed. The results and the discussion of this research tend to serve as a reference or an indicator for investors who have interests in multiple countries’ stock markets in the world.

Keywords:

time-series forecasting, composite stock market index, correlation, North American stock markets

Tian,L.;Tian,L. (2023). Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models. Advances in Economics, Management and Political Sciences,13,389-401.
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References

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[2]. Wong, W.-K., Penm, J., Terrell, R. D., & Ching, K. Y. The Relationship between Stock Markets of Major Developed Countries and Asian Emerging Markets. In: Journal of Applied Mathematics and Decision Sciences, vol. 8, no. 4, pp.201–218. (2004), https://doi.org/10.1207/s15327612jamd0804_1. Accessed Aug. 2022.

[3]. Syriopoulos, T. Risk and return implications from investing in emerging European stock markets. In: Journal of International Financial Markets, Institutions and Money, vol. 16, no. 3, pp. 283–299 (2006), https://doi.org/10.1016/j.intfin.2005.02.005. Accessed Aug. 2022.

[4]. Clark, J., & Berko, E. Foreign investment fluctuations and emerging market stock returns: The case of mexico. SSRN Electronic Journal. (1997) https://doi.org/10.2139/ssrn.993813.

[5]. G.Peter Zhang, Time series forecasting using a hybrid ARIMA and neural network model. In:Neurocomputing,Vol. 50, pp.159-175, ISSN0925-2312 (2003), https://doi.org/10.1016/S0925-2312(01)00702-0.Accessed Aug. 2022.

[6]. S&P 500®. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview.

[7]. S&P/TSX Composite index. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-tsx-composite-index/#overview.

[8]. IPC Mexico Stock market2022 data - 1987-2021 historical - 2023 forecast - quote. IPC Mexico Stock Market - 2022 Data - 1987-2021 Historical - 2023 Forecast - Quote. (n.d.). Retrieved August 22, 2022, from https://tradingeconomics.com/mexico/stock-market.

[9]. Yahoo Finance Homepage, https://finance.yahoo.com/, last accessed 2022/8/23.

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[13]. Y. Zhuang, L. Chen, X. S. Wang and J. Lian, "A Weighted Moving Average-based Approach for Cleaning Sensor Data," 27th International Conference on Distributed Computing Systems (ICDCS '07), 2007, pp. 38-38, doi: 10.1109/ICDCS.2007.83.

[14]. Aimran, A. N., & Afthanorhan, A. A comparison between single exponential smoothing (SES), double exponential smoothing (DES), Holt’s (brown) and adaptive response rate exponential smoothing (arres) techniques in forecasting Malaysia population. In: Global Journal of Mathematical Analysis, vol. 2, no. 4, 2014, pp. 276–280., https://doi.org/10.14419/gjma.v2i4.3253.

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[16]. Chai, T., & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (Mae)? – Arguments against avoiding RMSE in the literature. In: Geoscientific Model Development, vol. 7, no. 3, 2014, pp. 1247–1250., https://doi.org/10.5194/gmd-7-1247-2014.

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[18]. Wong, W.-K., Penm, J., Terrell, R. D., & Ching, K. Y. The Relationship between Stock Markets of Major Developed Countries and Asian Emerging Markets. In: Journal of Applied Mathematics and Decision Sciences, vol. 8, no. 4, pp.201–218. (2004), https://doi.org/10.1207/s15327612jamd0804_1. Accessed Aug. 2022.

[19]. Syriopoulos, T. Risk and return implications from investing in emerging European stock markets. In: Journal of International Financial Markets, Institutions and Money, vol. 16, no. 3, pp. 283–299 (2006), https://doi.org/10.1016/j.intfin.2005.02.005. Accessed Aug. 2022.

[20]. Clark, J., & Berko, E. Foreign investment fluctuations and emerging market stock returns: The case of mexico. SSRN Electronic Journal. (1997) https://doi.org/10.2139/ssrn.993813.

[21]. G.Peter Zhang, Time series forecasting using a hybrid ARIMA and neural network model. In:Neurocomputing,Vol. 50, pp.159-175, ISSN0925-2312 (2003), https://doi.org/10.1016/S0925-2312(01)00702-0.Accessed Aug. 2022.

[22]. S&P 500®. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview.

[23]. S&P/TSX Composite index. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-tsx-composite-index/#overview.

[24]. IPC Mexico Stock market2022 data - 1987-2021 historical - 2023 forecast - quote. IPC Mexico Stock Market - 2022 Data - 1987-2021 Historical - 2023 Forecast - Quote. (n.d.). Retrieved August 22, 2022, from https://tradingeconomics.com/mexico/stock-market.

[25]. Yahoo Finance Homepage, https://finance.yahoo.com/, last accessed 2022/8/23.

[26]. Mader, H. M., & Nason, G. P. Stationary and non-stationary time series. In Statistics in Volcanology (pp. 129–130). essay, Geological Society. (2006).

[27]. Palachy, S. (2019, November 12). Detecting stationarity in time series data. Medium, Towards Data Science. Retrieved August 22, 2022, from https://towardsdatascience.com/detecting-stationarity-in-time-series-data-d29e0a21e638.

[28]. Sani, B., & Kingsman, B. G. Selecting the best periodic inventory control and demand forecasting methods for low demand items. In: Journal of the Operational Research Society,vol. 48, no. 7, pp. 700–713. (1997) https://doi.org/10.1038/sj.jors.2600418.

[29]. Y. Zhuang, L. Chen, X. S. Wang and J. Lian, "A Weighted Moving Average-based Approach for Cleaning Sensor Data," 27th International Conference on Distributed Computing Systems (ICDCS '07), 2007, pp. 38-38, doi: 10.1109/ICDCS.2007.83.

[30]. Aimran, A. N., & Afthanorhan, A. A comparison between single exponential smoothing (SES), double exponential smoothing (DES), Holt’s (brown) and adaptive response rate exponential smoothing (arres) techniques in forecasting Malaysia population. In: Global Journal of Mathematical Analysis, vol. 2, no. 4, 2014, pp. 276–280., https://doi.org/10.14419/gjma.v2i4.3253.

[31]. Rachmat, R., & Suhartono, S. Comparative analysis of single exponential smoothing and Holt's method for quality of hospital services forecasting in General Hospital. In: Bulletin of Computer Science and Electrical Engineering, vol. 1, no. 2, 2020, pp. 80–86. https://doi.org/10.25008/bcsee.v1i2.8.

[32]. Chai, T., & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (Mae)? – Arguments against avoiding RMSE in the literature. In: Geoscientific Model Development, vol. 7, no. 3, 2014, pp. 1247–1250., https://doi.org/10.5194/gmd-7-1247-2014.

Cite this article

Tian,L.;Tian,L. (2023). Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models. Advances in Economics, Management and Political Sciences,13,389-401.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies

ISBN：978-1-915371-69-0(Print) / 978-1-915371-70-6(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.confbps.org/
Conference date: 26 February 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.13
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Erkam Guresen, Gulgun Kayakutlu, Tugrul U. Daim. Using artificial neural network models in stock market index prediction. In: Expert Systems with Applications, vol. 38, Issue 8, pp. 10389-10397, ISSN 0957-4174. (2011), https://doi.org/10.1016/j.eswa.2011.02.068.Accessed Aug. 2022.

[2]. Wong, W.-K., Penm, J., Terrell, R. D., & Ching, K. Y. The Relationship between Stock Markets of Major Developed Countries and Asian Emerging Markets. In: Journal of Applied Mathematics and Decision Sciences, vol. 8, no. 4, pp.201–218. (2004), https://doi.org/10.1207/s15327612jamd0804_1. Accessed Aug. 2022.

[3]. Syriopoulos, T. Risk and return implications from investing in emerging European stock markets. In: Journal of International Financial Markets, Institutions and Money, vol. 16, no. 3, pp. 283–299 (2006), https://doi.org/10.1016/j.intfin.2005.02.005. Accessed Aug. 2022.

[4]. Clark, J., & Berko, E. Foreign investment fluctuations and emerging market stock returns: The case of mexico. SSRN Electronic Journal. (1997) https://doi.org/10.2139/ssrn.993813.

[5]. G.Peter Zhang, Time series forecasting using a hybrid ARIMA and neural network model. In:Neurocomputing,Vol. 50, pp.159-175, ISSN0925-2312 (2003), https://doi.org/10.1016/S0925-2312(01)00702-0.Accessed Aug. 2022.

[6]. S&P 500®. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview.

[7]. S&P/TSX Composite index. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-tsx-composite-index/#overview.

[8]. IPC Mexico Stock market2022 data - 1987-2021 historical - 2023 forecast - quote. IPC Mexico Stock Market - 2022 Data - 1987-2021 Historical - 2023 Forecast - Quote. (n.d.). Retrieved August 22, 2022, from https://tradingeconomics.com/mexico/stock-market.

[9]. Yahoo Finance Homepage, https://finance.yahoo.com/, last accessed 2022/8/23.

[10]. Mader, H. M., & Nason, G. P. Stationary and non-stationary time series. In Statistics in Volcanology (pp. 129–130). essay, Geological Society. (2006).

[11]. Palachy, S. (2019, November 12). Detecting stationarity in time series data. Medium, Towards Data Science. Retrieved August 22, 2022, from https://towardsdatascience.com/detecting-stationarity-in-time-series-data-d29e0a21e638.

[12]. Sani, B., & Kingsman, B. G. Selecting the best periodic inventory control and demand forecasting methods for low demand items. In: Journal of the Operational Research Society,vol. 48, no. 7, pp. 700–713. (1997) https://doi.org/10.1038/sj.jors.2600418.

[13]. Y. Zhuang, L. Chen, X. S. Wang and J. Lian, "A Weighted Moving Average-based Approach for Cleaning Sensor Data," 27th International Conference on Distributed Computing Systems (ICDCS '07), 2007, pp. 38-38, doi: 10.1109/ICDCS.2007.83.

[14]. Aimran, A. N., & Afthanorhan, A. A comparison between single exponential smoothing (SES), double exponential smoothing (DES), Holt’s (brown) and adaptive response rate exponential smoothing (arres) techniques in forecasting Malaysia population. In: Global Journal of Mathematical Analysis, vol. 2, no. 4, 2014, pp. 276–280., https://doi.org/10.14419/gjma.v2i4.3253.

[15]. Rachmat, R., & Suhartono, S. Comparative analysis of single exponential smoothing and Holt's method for quality of hospital services forecasting in General Hospital. In: Bulletin of Computer Science and Electrical Engineering, vol. 1, no. 2, 2020, pp. 80–86. https://doi.org/10.25008/bcsee.v1i2.8.

[16]. Chai, T., & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (Mae)? – Arguments against avoiding RMSE in the literature. In: Geoscientific Model Development, vol. 7, no. 3, 2014, pp. 1247–1250., https://doi.org/10.5194/gmd-7-1247-2014.

[17]. Erkam Guresen, Gulgun Kayakutlu, Tugrul U. Daim. Using artificial neural network models in stock market index prediction. In: Expert Systems with Applications, vol. 38, Issue 8, pp. 10389-10397, ISSN 0957-4174. (2011), https://doi.org/10.1016/j.eswa.2011.02.068.Accessed Aug. 2022.

[18]. Wong, W.-K., Penm, J., Terrell, R. D., & Ching, K. Y. The Relationship between Stock Markets of Major Developed Countries and Asian Emerging Markets. In: Journal of Applied Mathematics and Decision Sciences, vol. 8, no. 4, pp.201–218. (2004), https://doi.org/10.1207/s15327612jamd0804_1. Accessed Aug. 2022.

[19]. Syriopoulos, T. Risk and return implications from investing in emerging European stock markets. In: Journal of International Financial Markets, Institutions and Money, vol. 16, no. 3, pp. 283–299 (2006), https://doi.org/10.1016/j.intfin.2005.02.005. Accessed Aug. 2022.

[20]. Clark, J., & Berko, E. Foreign investment fluctuations and emerging market stock returns: The case of mexico. SSRN Electronic Journal. (1997) https://doi.org/10.2139/ssrn.993813.

[21]. G.Peter Zhang, Time series forecasting using a hybrid ARIMA and neural network model. In:Neurocomputing,Vol. 50, pp.159-175, ISSN0925-2312 (2003), https://doi.org/10.1016/S0925-2312(01)00702-0.Accessed Aug. 2022.

[22]. S&P 500®. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview.

[23]. S&P/TSX Composite index. S&P Dow Jones Indices. (n.d.). Retrieved August 22, 2022, from https://www.spglobal.com/spdji/en/indices/equity/sp-tsx-composite-index/#overview.

[24]. IPC Mexico Stock market2022 data - 1987-2021 historical - 2023 forecast - quote. IPC Mexico Stock Market - 2022 Data - 1987-2021 Historical - 2023 Forecast - Quote. (n.d.). Retrieved August 22, 2022, from https://tradingeconomics.com/mexico/stock-market.

[25]. Yahoo Finance Homepage, https://finance.yahoo.com/, last accessed 2022/8/23.

[26]. Mader, H. M., & Nason, G. P. Stationary and non-stationary time series. In Statistics in Volcanology (pp. 129–130). essay, Geological Society. (2006).

[27]. Palachy, S. (2019, November 12). Detecting stationarity in time series data. Medium, Towards Data Science. Retrieved August 22, 2022, from https://towardsdatascience.com/detecting-stationarity-in-time-series-data-d29e0a21e638.

[28]. Sani, B., & Kingsman, B. G. Selecting the best periodic inventory control and demand forecasting methods for low demand items. In: Journal of the Operational Research Society,vol. 48, no. 7, pp. 700–713. (1997) https://doi.org/10.1038/sj.jors.2600418.

[29]. Y. Zhuang, L. Chen, X. S. Wang and J. Lian, "A Weighted Moving Average-based Approach for Cleaning Sensor Data," 27th International Conference on Distributed Computing Systems (ICDCS '07), 2007, pp. 38-38, doi: 10.1109/ICDCS.2007.83.

[30]. Aimran, A. N., & Afthanorhan, A. A comparison between single exponential smoothing (SES), double exponential smoothing (DES), Holt’s (brown) and adaptive response rate exponential smoothing (arres) techniques in forecasting Malaysia population. In: Global Journal of Mathematical Analysis, vol. 2, no. 4, 2014, pp. 276–280., https://doi.org/10.14419/gjma.v2i4.3253.

[31]. Rachmat, R., & Suhartono, S. Comparative analysis of single exponential smoothing and Holt's method for quality of hospital services forecasting in General Hospital. In: Bulletin of Computer Science and Electrical Engineering, vol. 1, no. 2, 2020, pp. 80–86. https://doi.org/10.25008/bcsee.v1i2.8.

[32]. Chai, T., & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (Mae)? – Arguments against avoiding RMSE in the literature. In: Geoscientific Model Development, vol. 7, no. 3, 2014, pp. 1247–1250., https://doi.org/10.5194/gmd-7-1247-2014.

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