The effect of aggregating bootstrap on the accuracy of neural network system for Islamic investment prediction

Nurul Hila Zainuddin

The effect of aggregating bootstrap on the accuracy of neural network system for Islamic investment prediction

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General Information

Title

The effect of aggregating bootstrap on the accuracy of neural network system for Islamic investment prediction

Type

Article

Main Author

Nurul Hila Zainuddin

Additional Authors

Shazlyn Milleana Shaharudin

Subject

Q Science (General)

ISSN

2331-9712

Language Code

eng

Bibliographic Details

Place of Production

Tanjung Malim

Publisher

Fakulti Sains dan Matematik

Year of publication

2021

Notes

Universal Journal of Accounting and Finance

Degree Type

Others

Corporate Name

Universiti Pendidikan Sultan Idris

HTTP Link

Access Web Resource

Abstract

Accurate prediction of the stock price is necessary for efficient financial decision making and reconstruction planning, especially during the COVID-19 crisis. Meanwhile, the ARIMA model with neural network approach is a hybrid statistical model that has been used widely in finance and statistics. It could solve the non-linearity problem in ARIMA. However, based on the unreliable sampling variation, the unbalance between the linearity and nonlinearity parts in the hybrid model could lead to inaccurate prediction. This matter motivates the purpose of this study, where the authors aim to balance the linearity and nonlinearity of the hybrid model towards predicting the Islamic investment during the Malaysia Movement Control Order (MCO). In this study, Islamic investment is analyzed using statistical based model of ARIMA. In order to balance linearity and nonlinearity parts of ARIMA which is outperforming the unreliable sampling variation, the aggregating bootstrap approach is used on ARIMA. The resampled linearity and nonlinearity parts will be declared as inputs of neural network system in order to predict the Islamic investment. Resampled nonlinearity part will be generated in this system and its sampling variation is examined. In addition, the performance of model procedure is estimated. It shows that the aggregating bootstrap gives smaller bias values and generates small weights in neural network system. In terms of prediction, applying the resampled procedure in neural network system eventually increases the precision of prediction estimation where it reduces the error estimation of MAE and MSE. Also, the prediction values continually align with actual values of investment at MCO phase. Balancing the linearity and nonlinearity part using the aggregating bootstrap in major procedure of prediction field contributes to high precise prediction of Islamic investment return estimation. The uncertainty of investment returns during MCO phase is a challenging phase and affects Malaysia financial trading. By considering using alternative procedure proposed in this study, i.e. helps in providing accuracy of investment prediction returns, a well-construct financial decisions and plans could be structured during MCO. However, this study limits to examine the effect of aggregating bootstrap. For further research, it is suggested to apply prediction on 10 years returns of Islamic investment using the proposed method. ? 2021 Horizon Research Publishing. All rights reserved.

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