Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Uma Gurav; Nandini Sidnal

doi:10.1109/CTEMS.2018.8769290

Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Uma Gurav, Nandini Sidnal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Time series based model has been widely applied to estimate the future stock price, and aids investors' decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.

Original language	English
Title of host publication	Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018
Editors	S. K. Niranjan, Veena Desai, Vijay S. Rajpurohit, M N Nadkatti
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	380-385
Number of pages	6
ISBN (Electronic)	9781538677094
DOIs	https://doi.org/10.1109/CTEMS.2018.8769290
Publication status	Published - Dec 2018
Externally published	Yes
Event	1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018 - Belagavi, India Duration: 21 Dec 2018 → 23 Dec 2018

Publication series

Name	Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018

Conference

Conference	1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018
Country/Territory	India
City	Belagavi
Period	21/12/18 → 23/12/18

Keywords

Artificial neural network
Data mining
Deep learning
machine learning
prediction system
time series

Access to Document

10.1109/CTEMS.2018.8769290

Cite this

Gurav, U., & Sidnal, N. (2018). Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN). In S. K. Niranjan, V. Desai, V. S. Rajpurohit, & M. N. Nadkatti (Eds.), Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018 (pp. 380-385). Article 8769290 (Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CTEMS.2018.8769290

Gurav, Uma ; Sidnal, Nandini. / Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN). Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018. editor / S. K. Niranjan ; Veena Desai ; Vijay S. Rajpurohit ; M N Nadkatti. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 380-385 (Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018).

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title = "Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)",

abstract = "Time series based model has been widely applied to estimate the future stock price, and aids investors' decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.",

keywords = "Artificial neural network, Data mining, Deep learning, machine learning, prediction system, time series",

author = "Uma Gurav and Nandini Sidnal",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018 ; Conference date: 21-12-2018 Through 23-12-2018",

year = "2018",

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doi = "10.1109/CTEMS.2018.8769290",

language = "English",

series = "Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018",

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Gurav, U & Sidnal, N 2018, Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN). in SK Niranjan, V Desai, VS Rajpurohit & MN Nadkatti (eds), Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018., 8769290, Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 380-385, 1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018, Belagavi, India, 21/12/18. https://doi.org/10.1109/CTEMS.2018.8769290

Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN). / Gurav, Uma; Sidnal, Nandini.
Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018. ed. / S. K. Niranjan; Veena Desai; Vijay S. Rajpurohit; M N Nadkatti. Institute of Electrical and Electronics Engineers Inc., 2018. p. 380-385 8769290 (Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gurav, Uma

AU - Sidnal, Nandini

PY - 2018/12

Y1 - 2018/12

N2 - Time series based model has been widely applied to estimate the future stock price, and aids investors' decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.

AB - Time series based model has been widely applied to estimate the future stock price, and aids investors' decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.

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KW - Deep learning

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KW - prediction system

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M3 - Conference contribution

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T3 - Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018

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BT - Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018

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Y2 - 21 December 2018 through 23 December 2018

ER -

Gurav U, Sidnal N. Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN). In Niranjan SK, Desai V, Rajpurohit VS, Nadkatti MN, editors, Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 380-385. 8769290. (Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018). doi: 10.1109/CTEMS.2018.8769290

Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

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Keywords

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