Improving the genetic-algorithm-optimized wavelet neural network for stock market prediction

Fang, Yu; Fataliyev, Kamaladdin; Wang, Lipo; Fu, Xiuju; Wang, Yaoli

doi:10.1109/ijcnn.2014.6889969

Cited by 37 publications

(15 citation statements)

References 13 publications

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“…The employed bees to explore pollen source around in accordance with the (5). The movement strategy of scout bees follows (6).…”

Section: B Model Constructionmentioning

confidence: 99%

“…However, a problem of PF used for demand forecasting is the parameters of polynomial function are difficult to determine. In some forecasting system, the parameters of the model optimized by optimization algorithms such as Genetic-Algorithm (GA) [6] and Artificial Bee Colony (ABC), can improve the prediction accuracy to a certain extent. ABC is a new meta-heuristic algorithm proposed by Dervis Karaboga and Bahriye et al [7].…”

Section: Introductionmentioning

confidence: 99%

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Demand forecasting for footwear products using wavelet transform and Artificial Bee Colony algorithm optimized Polynomial Fitting

Liu

Wang

et al. 2015

2015 11th International Conference on Natural Computation (ICNC)

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Product life cycle management plays a crucial role in the footwear products demand forecasting. However, it is difficult to predict the recession point of the product life cycle curve. This paper proposes an integrated forecasting system where wavelet transforms and Polynomial Fitting based on Artificial Bee Colony algorithm are combined for footwear products demand forecasting. First, the method of wavelet transform using the one dimensional discrete wavelet is applied to decompose the sales data and thus eliminate the noise. Second, the Polynomial Fitting is employed to simulate the product life cycle function of footwear products. Third, the Artificial Bee Colony algorithm is utilized to optimize the parameters of Polynomial Fitting. Finally, real-world evaluation on a Chinese shoes and apparels retailer demonstrates that the proposed system is highly promising. At the same time, the simulation results also show that the demand distribution during the same period are basically the same between similar stores, which illustrates good practice of this demand forecasting method.

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“…The employed bees to explore pollen source around in accordance with the (5). The movement strategy of scout bees follows (6).…”

Section: B Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand forecasting for footwear products using wavelet transform and Artificial Bee Colony algorithm optimized Polynomial Fitting

Liu

Wang

et al. 2015

2015 11th International Conference on Natural Computation (ICNC)

View full text Add to dashboard Cite

show abstract

“…By now, the investor sentiment index research is still not perfect and can not reach a unified framework. Moreover, F. A. de Oliveira [11], Ming Zhu [12], Shekhar Gupta [13], Y. Fang Systematic Risk Estimation of Bull and Bear Market [14], and Y. Q. He [15] studied on stock prediction with neural networks.…”

Section: Introductionmentioning

confidence: 99%

A New Investor Sentiment Index Model and Its Application in Stock Price Prediction and Systematic Risk Estimation of Bull and Bear Market

Zhang¹

2019

IJFBR

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Many studies in recent years have shown that investor sentiment affects investor decision-making, which in turn affects stock market volatility and the direction of stock market prices. Since behavioral finance researchers find that linear combinations of stock turnover and popularity indices can greatly reflect stock investor sentiment, this paper aims to construct a new investor sentiment index that can be reasonably applied to predict stock market risk by selecting rational factors. A new investor sentiment index model is first proposed by combining specific monthly new account ratio (SNIA), monthly turnover rate (TOR), popularity index AR, delayed yield (DY) and using principal component analysis approach. Secondly, the indicator is statistically tested. The results of the correlation analysis show that the investor sentiment index is positively correlated with the monthly rate of return, and the result of causal analysis reveals that the investor sentiment index is the Granger cause of the change in yield. Thirdly, a new method is designed to predict the stock price trend by using the presented investor sentiment index. Finally, based on VaR and CoVaR model the investor sentiment index can be utilized to forecast and estimate of systematic risk in the bull or bear market.

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“…For example, 4%, 13% and 14% out of the total input variables were derived from the trading volume information in [6,17,20] whereas 96%, 87%, and 86%, respectively, were derived from the price information. Considering that the trading volume is known to be informative in explaining the status of a stock market [6,21], there is room to make greater use of the trading volume for input variables in stock price prediction. The latter issue is about the modeling parameters in a learning stage.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks

Dinh

Kwon

2018

Informatics

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There have been many machine learning-based studies to forecast stock price trends. These studies attempted to extract input features mostly from the price information with little focus on the trading volume information. In addition, modeling parameters to specify a learning problem have not been intensively investigated. We herein develop an improved method by handling those limitations. Specifically, we generated input variables by considering both price and volume information with even weight. We also defined three modeling parameters: the input and the target window sizes and the profit threshold. These specify the input and target variables, between which the underlying functions are learned by multilayer perceptrons and support vector machines. We tested our approach over six stocks and 15 years and compared with the expected performance over all considered parameter specifications. Our approach dramatically improved the prediction accuracy over the expected performance. In addition, our approach was shown to be stably more profitable than both the expected performance and the buy-and-hold strategy. On the other hand, the performance was degraded when the input variables generated from the trading volume were excluded from learning. All these results validate the importance of the volume and the modeling parameters in stock trading prediction.

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Improving the genetic-algorithm-optimized wavelet neural network for stock market prediction

Cited by 37 publications

References 13 publications

Demand forecasting for footwear products using wavelet transform and Artificial Bee Colony algorithm optimized Polynomial Fitting

Demand forecasting for footwear products using wavelet transform and Artificial Bee Colony algorithm optimized Polynomial Fitting

A New Investor Sentiment Index Model and Its Application in Stock Price Prediction and Systematic Risk Estimation of Bull and Bear Market

An Empirical Study on Importance of Modeling Parameters and Trading Volume-Based Features in Daily Stock Trading Using Neural Networks

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