Stock market index prediction using artificial neural network

Moghaddam, Amin Hedayati; Moghaddam, Moein Hedayati; Esfandyari, Morteza

doi:10.1016/j.jefas.2016.07.002

Cited by 268 publications

(134 citation statements)

References 11 publications

Supporting

Mentioning

111

Contrasting

Unclassified

Order By: Relevance

“…In this form data are not submitted as input variables to the neural network. On the base of raw data are generated the most popular technical indicators which are used as input data of the neural network for prediction [4].…”

Section: Selection Of Input Datamentioning

confidence: 99%

Artificial neural network intelligent method for prediction

Trifonov

Yoshinov

Pavlova

et al. 2017

AIP Conference Proceedings

View full text Add to dashboard Cite

Coupling and power transfer efficiency enhancement of modular and array of planar coils using in-plane ringshaped inner ferrites for inductive heating applications Journal of Applied Physics 122, 014902 (2017) Abstract. Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

show abstract

Section: Selection Of Input Datamentioning

confidence: 99%

Artificial neural network intelligent method for prediction

Trifonov

Yoshinov

Pavlova

et al. 2017

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…The rapid evolution of machine learning has advanced numerous research fields and industries, including safety-critical areas such as biometric security, autonomous driving, cybersecurity, health, and financial planning [1]- [7]. Technology and human life become increasingly intertwined, which has resulted in a growing priority with regards to the security of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Hardware Trojan Design on Neural Networks

Clements

Lao

2019

2019 IEEE International Symposium on Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural networks and other machine learning models. As the technology evolves, it is also plausible that machine learning or artificial intelligence will soon become consumer electronic products and military equipment, in the form of well-trained models. Unfortunately, the modern fabless business model of manufacturing hardware, while economic, leads to deficiencies in security through the supply chain. In this paper, we illuminate these security issues by introducing hardware Trojan attacks on neural networks, expanding the current taxonomy of neural network security to incorporate attacks of this nature. To aid in this, we develop a novel framework for inserting malicious hardware Trojans in the implementation of a neural network classifier. We evaluate the capabilities of the adversary in this setting by implementing the attack algorithm on convolutional neural networks while controlling a variety of parameters available to the adversary. Our experimental results show that the proposed algorithm could effectively classify a selected input trigger as a specified class on the MNIST dataset by injecting hardware Trojans into 0.03%, on average, of neurons in the 5th hidden layer of arbitrary 7-layer convolutional neural networks, while undetectable under the test data. Finally, we discuss the potential defenses to protect neural networks against hardware Trojan attacks.

show abstract

“…The impact of artificial intelligence on society has increased due to the availability of big data and rapid advances in computer technology. The application of machine learning, an aspect of artificial intelligence, in business and economic analysis has been explored in energy economics by Tso and Yau (2005), Weron (2014), Ziel and Steinert (2016), and Lago et al (2018); stock price forecasting by Zhang et al (1998), Hegazy et al (2013), Rather et al (2015), and Moghaddam et al (2016); early warning systems by Tanaka et al (2016); financial hazard map by Tanaka et al (2018); and credit risk assessment by Angelini et al (2008), Khashman (2009), Khashman (2010, Khemakhem and Boujelbene (2015), and Hamori et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Does Ensemble Learning Always Lead to Better Forecasts?

Hamori¹,

Hamori²

2020

AEF

View full text Add to dashboard Cite

Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.

show abstract

Stock market index prediction using artificial neural network

Cited by 268 publications

References 11 publications

Artificial neural network intelligent method for prediction

Artificial neural network intelligent method for prediction

Hardware Trojan Design on Neural Networks

Does Ensemble Learning Always Lead to Better Forecasts?

Contact Info

Product

Resources

About