Improving returns on stock investment through neural network selection

Quah, Tong-Seng; Srinivasan, Bobby

doi:10.1016/s0957-4174(99)00041-x

Cited by 119 publications

(29 citation statements)

References 1 publication

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“…The railway construction technology expert resource and investment should be integrated based on the national conditions of Asian and European countries, the factors needed to be considered for railway construction, and the principles of being scientifically, systematically, typically, and feasibly practical. The opinions of experts, market operation experts, and venture capital experts establish an evaluation index system, as shown in Figure 2 [8,9]. The risk is roughly divided into several aspects.…”

Section: Railway Construction Risk Evaluation Methodsmentioning

confidence: 99%

“…That is, take as few hidden layer neurons as possible. When a=0, m=4.24, take 5, and then the number (5,6,7,8,9,10,11) of hidden layer neurons is used to verify the error rate until the best error rate is obtained. Table 1 shows the error rate of each selected hidden layer neuron, and the error meets accuracy requirement (error < 5%) when the number of hidden layer neurons is 6.…”

Section: Model and Algorithmic Principles For Asiamentioning

confidence: 99%

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[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

et al. 2019

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Chinese railway construction project is an important part of the implementation of the “Belt and Road” strategy, and the risk evaluation of overseas railway construction is the primary link of the project. Firstly, this paper mainly analyzes the Asian and European countries along the railway construction project, establishes a railway construction project risk evaluation system, and synthesizes various risk factors. Secondly, it establishes two independent BP neural network models by using different training algorithms because of the different political, economic, and cultural elements between the two continents.

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Section: Railway Construction Risk Evaluation Methodsmentioning

confidence: 99%

Section: Model and Algorithmic Principles For Asiamentioning

confidence: 99%

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

et al. 2019

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“…Different variable selection procedures have been employed in research entailing stock return prediction. 5 Kaastra and Boyd (1996), Qi (1999), Quah and Srinivasan (1999) and Eakins and Stansell (2003) apply this procedure when modelling time-series data. An advantage of the moving-window procedure as opposed to a static procedure is that new information is updated and older information discarded as time proceeds.…”

Section: Step 2 Selecting Input Variablesmentioning

confidence: 99%

Application Of Cascade-Correlation Neural Networks In Developing Stock Selection Models For Global Equities

Hodnett

Hsieh

2012

IBER

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We investigate the potential of artificial neural networks (ANN) in the stock selection process of actively managed funds. Two ANN models are constructed to perform stock selection, using the Dow Jones (DJ) Sector Titans as the research database. The cascade-correlation algorithm of Fahlman and Lebiere (1990/1991) is combined with embedded learning rules, namely the backpropagation learning rule and the extended Kalman filter learning rule to forecast the cross-section of global equity returns. The main findings support the use of artificial neural networks for financial forecasting as an active portfolio management tool. In particular, fractile analysis and risk-adjusted return performance metrics provide evidence that the model trained via the extended Kalman filter rule had greater strength in identifying future top performers for global equities than the model trained via the backpropagation learning rule. There is no distinguishable difference between the performances of the bottom quartiles formed by both ANN models. The zero-investment portfolios formed by longing the top quartiles and simultaneously shorting the bottom quartiles or the market proxy exhibit statistically significant Jensens alpha and continues to accumulate positive returns over the out-of-sample period for both ANN models. On the other hand, the zero-investment portfolios formed by longing the bottom quartiles and simultaneously shorting the market proxy exhibit statistically significant Jensens alpha and continues to accumulate losses over the out-of-sample period for both ANN models. The implementation of the extended Kalman filter rule in training artificial neural networks for applications involving noisy financial data is recommended.

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“…The widely used artificial intelligence approaches include neural network (NN) [2][3][4], support vector machines (SVM) [5][6][7][8], fuzzy systems [9], linear regression, Kalman filtering [10], and hidden Markov models (HMM) [3]. All of these approaches are used for updating the model parameters.…”

Section: Introductionmentioning

confidence: 99%

An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

Liu

2014

Abstract and Applied Analysis

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Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular, and nonstationary. The size of these economic datasets is often very small. Many models based on grey system theory could be adapted to various economic time series data. However, some of these models did not consider the impact of recent data or the effective model parameters that can improve forecast accuracy. In this paper, we proposed the PRGM(1,1) model, a rolling mechanism based grey model optimized by the particle swarm optimization, in order to improve the forecast accuracy. The experiment shows that PRGM(1,1) gets much better forecast accuracy among other widely used grey models on three actual economic datasets.

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Improving returns on stock investment through neural network selection

Cited by 119 publications

References 1 publication

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

[Retracted] Application of BP Neural Network Model in Risk Evaluation of Railway Construction

Application Of Cascade-Correlation Neural Networks In Developing Stock Selection Models For Global Equities

An Intelligence Optimized Rolling Grey Forecasting Model Fitting to Small Economic Dataset

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