Report NEP-BIG-2019-01-07

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Rastin Matin & Casper Hansen & Christian Hansen & Pia M{o}lgaard, 2018. "Predicting Distresses using Deep Learning of Text Segments in Annual Reports," Papers 1811.05270, arXiv.org.
• María Gil & Javier J. Pérez & A. Jesús Sánchez & Alberto Urtasun, 2018. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," Working Papers 1842, Banco de España.
• Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
• Begler, A. & Gavrilova, T., 2018. "Artificial Intelligence Methods for Knowledge Management Systems," Working Papers 15106, Graduate School of Management, St. Petersburg State University.
• Zhang, Bin & Jin, Zhiye & Peng, Zhidao, 2018. "Bridging the Digital Divide: Making the Digital Economy Benefit to the Entire Society," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190412, International Telecommunications Society (ITS).
• Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2018. "A Big data analytical framework for portfolio optimization," Papers 1811.07188, arXiv.org, revised Nov 2018.
• David Saltiel & Eric Benhamou, 2018. "Trade Selection with Supervised Learning and OCA," Papers 1812.04486, arXiv.org.
• Qiang Zhang & Rui Luo & Yaodong Yang & Yuanyuan Liu, 2018. "Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series," Papers 1811.03711, arXiv.org.
• Javier Franco-Pedroso & Joaquin Gonzalez-Rodriguez & Maria Planas & Jorge Cubero & Rafael Cobo & Fernando Pablos, 2018. "The ETS challenges: a machine learning approach to the evaluation of simulated financial time series for improving generation processes," Papers 1811.07792, arXiv.org.
• Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2019. "Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data," Papers 1901.00227, arXiv.org, revised Aug 2019.
• Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
• Babak Mahdavi-Damghani & Konul Mustafayeva & Stephen Roberts & Cristin Buescu, 2018. "Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning," Papers 1812.10183, arXiv.org, revised Oct 2019.
• Daniel Muller, 2018. "Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps," Papers 1811.06606, arXiv.org, revised Nov 2018.
• Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
• Pater, Robert & Szkola, Jaroslaw & Kozak, Marcin, 2018. "A method for measuring detailed demand for workers' competences," Economics Discussion Papers 2018-83, Kiel Institute for the World Economy (IfW Kiel).