Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange

Hasnain Iftikhar; Murad Khan; Josué E. Turpo-Chaparro; Paulo Canas Rodrigues; Javier Linkolk López-Gonzales; Hasnain Iftikhar; Murad Khan; Josué E. Turpo-Chaparro; Paulo Canas Rodrigues; Javier Linkolk López-Gonzales

doi:10.3934/math.2024159

AIMS Mathematics

2024, Volume 9, Issue 2: 3264-3288. doi: 10.3934/math.2024159

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Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange

1.
Department of Mathematics, City University of Science and Information Technology Peshawar, Khyber Pakhtunkhwa 25000, Pakistan
2.
Department of Statistics, Quaid-i-Azam University, 45320, Islamabad, Pakistan
3.
Department of Statistics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan
4.
Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru
5.
Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil

Received: 26 October 2023 Revised: 06 December 2023 Accepted: 14 December 2023 Published: 03 January 2024
MSC : 62G30, 62E15

Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts.
- forecasting stock prices,
- Hodrick-Prescott filter,
- time series models,
- filtering-combination technique
Citation: Hasnain Iftikhar, Murad Khan, Josué E. Turpo-Chaparro, Paulo Canas Rodrigues, Javier Linkolk López-Gonzales. Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange[J]. AIMS Mathematics, 2024, 9(2): 3264-3288. doi: 10.3934/math.2024159

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Abstract

Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts.

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