A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

Wu, Binghui; Duan, Tingting

doi:10.2991/ijcis.2017.10.1.23

Cited by 24 publications

(14 citation statements)

References 17 publications

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“…e maximum number of publications are available in the field of neural network and other computing techniques. e analysis of this category implies that a major part of the literature talks about studies on the combination of artificial intelligence and neural networks [14,57,[81][82][83]. Research related to predicting stock indices, such as genetic algorithms and neural networks, and linear and nonlinear models recognizes patterns by estimating coefficients and their statistical significance.…”

Section: Main Subjectmentioning

confidence: 99%

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sharma

Erkut

Jain

et al. 2020

Mathematical Problems in Engineering

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The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

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Section: Main Subjectmentioning

confidence: 99%

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sharma

Erkut

Jain

et al. 2020

Mathematical Problems in Engineering

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show abstract

“…Artificial neural networks (ANN) are in an innovation of man-made brainpower imaginative by the human cerebrum and nerves framework and generally use to the shape and troublesome upgrade marvels including a huge estimation of procedure factors [1][2][3]. Counterfeit neural systems are computational approach's that lead to in various analyzers [4].…”

Section: Introductionmentioning

confidence: 99%

Hamming neural network application with FPGA device

Mezher

2021

IJRES

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The Hamming neural network is a kind of counterfeit neural system that substance of two kinds of layers (feed forward layers and repetitive layer). In this study, two pattern entries are utilization in the binary number. In the first layer, two nerves were utilization as the pure line work. In the subsequent layer, three nerves and a positive line work were utilization. The Hamming Neural system calculation was also implemented in three reproduction strategies (logical gate technique, programming program encryption strategy and momentary square chart technique). In this study in programming of VHDL and FPGA machine was utilization.

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“…Zheng [9] used an Elman NN to forecast opening prices of the Shanghai Stock Exchange. Wu and Duan applied the Elman NN in predicting stock [10] and gold future markets [11], respectively. In the area of electricity prediction, Rani and Victoire [12] integrated the decomposition method and group search optimization algorithm into the Elman NN.…”

Section: Introductionmentioning

confidence: 99%

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

Wang

Tang

2021

Complexity

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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

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A Performance Comparison of Neural Networks in Forecasting Stock Price Trend

Cited by 24 publications

References 17 publications

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

Hamming neural network application with FPGA device

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

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