The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Frank C. Park - One of the best experts on this subject based on the ideXlab platform.

  • deep learning networks for stock Market Analysis and prediction
    Expert Systems With Applications, 2017
    Co-Authors: Eunsuk Chong, Frank C. Park
    Abstract:

    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. We 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 methodsprincipal component Analysis, autoencoder, and the restricted Boltzmann machineon the networks 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.

  • Deep learning networks for stock Market Analysis and prediction: Methodology, data representations, and case studies
    Expert Systems with Applications, 2017
    Co-Authors: Eunsuk Chong, Chulwoo Han, Frank C. Park
    Abstract:

    We 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.

Eunsuk Chong - One of the best experts on this subject based on the ideXlab platform.

  • deep learning networks for stock Market Analysis and prediction
    Expert Systems With Applications, 2017
    Co-Authors: Eunsuk Chong, Frank C. Park
    Abstract:

    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. We 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 methodsprincipal component Analysis, autoencoder, and the restricted Boltzmann machineon the networks 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.

  • Deep learning networks for stock Market Analysis and prediction: Methodology, data representations, and case studies
    Expert Systems with Applications, 2017
    Co-Authors: Eunsuk Chong, Chulwoo Han, Frank C. Park
    Abstract:

    We 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.

Brian Fabo - One of the best experts on this subject based on the ideXlab platform.

  • Prospects for utilisation of non-vacancy Internet data in labour Market Analysis—an overview
    IZA Journal of Labor Economics, 2016
    Co-Authors: Karolien Lenaerts, Miroslav Beblavý, Brian Fabo
    Abstract:

    Along with the advancement of the Internet in the last decade, researchers have increasingly identified the web as a research platform and a data source, pointing out its value for labour Market Analysis. This article presents a review of online data sources for this field. Specifically, the article introduces web-based research, focusing on the potential of relatively new data sources such as Google Trends, social networks (LinkedIn, Facebook and Twitter) and Glassdoor (surveys). For these data sources, a review is done and recent empirical applications are listed. Web-based data can further our understanding of the dynamics of the labour Market. JEL codes : E4, J2

Tim H Dodd - One of the best experts on this subject based on the ideXlab platform.

  • emerging wine Market in the dominican republic consumer Market Analysis
    Wine Economics and Policy, 2013
    Co-Authors: Natalia Velikova, Olga I Murova, Tim H Dodd
    Abstract:

    Abstract Existing wine literature focuses largely on developing wine producing countries, often, overlooking Markets that are not wine producing regions. These Markets, however, may comprise, lucrative Markets for export. The current study seeks to gain insights into one such Market. Specifically, the study focuses on wine consumer behavior and wine Market Analysis in the Dominican, Republic. Quantitative methodology incorporated a consumer survey conducted through superMarkets, and liquor stores intercepts in the Dominican Republic. Additionally, secondary data were used for Market Analysis.

Karolien Lenaerts - One of the best experts on this subject based on the ideXlab platform.

  • Prospects for utilisation of non-vacancy Internet data in labour Market Analysis—an overview
    IZA Journal of Labor Economics, 2016
    Co-Authors: Karolien Lenaerts, Miroslav Beblavý, Brian Fabo
    Abstract:

    Along with the advancement of the Internet in the last decade, researchers have increasingly identified the web as a research platform and a data source, pointing out its value for labour Market Analysis. This article presents a review of online data sources for this field. Specifically, the article introduces web-based research, focusing on the potential of relatively new data sources such as Google Trends, social networks (LinkedIn, Facebook and Twitter) and Glassdoor (surveys). For these data sources, a review is done and recent empirical applications are listed. Web-based data can further our understanding of the dynamics of the labour Market. JEL codes : E4, J2