Forecasting daily stock market return using dimensionality reduction

Zhong, Xiao; Enke, David

doi:10.1016/j.eswa.2016.09.027

Cited by 302 publications

(189 citation statements)

References 56 publications

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“…Since one of the baseline algorithms uses PCA for dimension reduction, the performance of the algorithm with different number of principal components is tested. In order Algorithm Explanation 3D-CNNpred Our method 2D-CNNpred Our method PCA+ANN (Zhong & Enke, 2017) PCA as dimension reduction and ANN as classifier…”

Section: Resultsmentioning

confidence: 99%

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CNNpred: CNN-based stock market prediction using a diverse set of variables

Hoseinzade

Haratizadeh

2019

Expert Systems with Applications

383

204

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Section: Resultsmentioning

confidence: 99%

“…Authors of (Zhong & Enke, 2017) have applied PCA and two variations of it in order to extract better features. A collection of different features was used as input data while an ANN was used for prediction of S&P 500.…”

Section: Related Workmentioning

confidence: 99%

CNNpred: CNN-based stock market prediction using a diverse set of variables

Hoseinzade

Haratizadeh

2019

Expert Systems with Applications

383

204

View full text Add to dashboard Cite

“…There are various data representation methods such as zero-to-one scaling, standardization, log-scaling, and principal component analysis (PCA) (Atsalakis & Valavanis, 2009). More recently, nonlinear methods became popular: Zhong and Enke (2017) compare PCA and two of its nonlinear variants; fuzzy robust PCA and kernel-based PCA. In this paper, we consider three unsupervised data representation methods: PCA, the autoencoder (AE), and the restricted Boltzmann machine (RBM).…”

Section: Data Representation Methodsmentioning

confidence: 99%

Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

Chong

Han

Park

2017

Expert Systems with Applications

592

272

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Highlights• Deep learning networks are applied to stock market analysis and prediction.• A comprehensive analysis with different data representation methods is offered.• Five-minute intraday data from the Korean KOSPI stock market is used.• The network applied to residuals of autoregressive model improves prediction.• Covariance estimation for market structure analysis is improved with the network. AbstractWe offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods-principal component analysis, autoencoder, and the restricted Boltzmann machine-on the network's overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

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“…Thus, Principal Component Analysis (PCA) was used to reduce the dimensions. PCA is the most popular and classical unsupervised linear technique for dimensionality reduction, with unified Emotion Score (ES) as one output to express the emotion condition for further analysis (Zhong and Enke 2017).…”

Section: Principal Component Analysis and Emotion Scorementioning

confidence: 99%

Mapping the Sensitivity of the Public Emotion to the Movement of Stock Market Value: A Case Study of Manhattan

Kang

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We examined whether emotion expressed by users in social media can be influenced by stock market index or can predict the fluctuation of the stock market index. We collected the emotion data by using face detection technology and emotion cognition services for photos uploaded to Flickr. Each face's emotion was described in 8 dimensions the location was also recorded. An emotion score index was defined based on the combination of all 8 dimensions of emotion calculated by principal component analysis. The correlation coefficients between the stock market values and emotion scores are significant (R>0.59 with p < 0.01). Using Granger Causality analysis for cause and effect detection, we found that users' emotion is influenced by stock market value change. A multiple linear regression model was established (R-square=0.76) to explore the potential factors that influence the emotion score. Finally, a sensitivity map was created to show sensitive areas where human emotion is easily affected by the stock market changes. We concluded that in Manhattan region: (1) there is an obvious relationship between human emotion and stock market fluctuation; (2) emotion change follows the movements of the stock market; (3) the Times Square and Broadway Theatre are the most sensitive regions in terms of public emotional reaction to the economy represented by stock value.

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Forecasting daily stock market return using dimensionality reduction

Cited by 302 publications

References 56 publications

CNNpred: CNN-based stock market prediction using a diverse set of variables

CNNpred: CNN-based stock market prediction using a diverse set of variables

Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

Mapping the Sensitivity of the Public Emotion to the Movement of Stock Market Value: A Case Study of Manhattan

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