Report NEP-BIG-2021-02-15

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:

• Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
• Christian M. Dahl & Torben Johansen & Emil N. S{o}rensen & Simon Wittrock, 2021. "HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition," Papers 2101.10862, arXiv.org, revised Mar 2022.
• Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.
• Yi Wei, 2021. "Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction," Papers 2101.10942, arXiv.org, revised Mar 2021.
• Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2021. "Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach," Papers 2101.09394, arXiv.org, revised Jan 2022.
• Pierre-Jean Benghozi & Hugues Chevalier, 2019. "The present vision of AI… or the HAL syndrome," Post-Print hal-03101149, HAL.
• John Ery & Loris Michel, 2021. "Solving optimal stopping problems with Deep Q-Learning," Papers 2101.09682, arXiv.org, revised Jun 2024.
• Mark Kiermayer, 2021. "Modeling surrender risk in life insurance: theoretical and experimental insight," Papers 2101.11590, arXiv.org, revised Aug 2021.
• Jeffrey Grogger & Ria Ivandic & Tom Kirchmaier, 2020. "Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases," CEP Discussion Papers dp1676, Centre for Economic Performance, LSE.
• Vera Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2020. "Constructing Daily Economic Sentiment Indices Based on Google Trends," KOF Working papers 20-484, KOF Swiss Economic Institute, ETH Zurich.
• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021. "Measuring National Happiness with Music," CAGE Online Working Paper Series 537, Competitive Advantage in the Global Economy (CAGE).
• In-Koo Cho & Jonathan Libgober, 2021. "Machine Learning for Strategic Inference," Papers 2101.09613, arXiv.org.
• Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
• Ines Wilms & Jacob Bien, 2021. "Tree-based Node Aggregation in Sparse Graphical Models," Papers 2101.12503, arXiv.org.
• Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
• Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
• Michele Cantarella & Nicolò Fraccaroli & Roberto Volpe, 2020. "Does Fake News Affect Voting Behaviour?," CEIS Research Paper 493, Tor Vergata University, CEIS, revised 17 Jun 2020.
• Jansen, Mark & Nguyen, Hieu & Shams, Amin, 2020. "Rise of the Machines: The Impact of Automated Underwriting," Working Paper Series 2020-19, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
• Chengyu Huang & Sean Simpson & Daria Ulybina & Agustin Roitman, 2019. "News-based Sentiment Indicators," IMF Working Papers 2019/273, International Monetary Fund.
• Shree Saha & Sudha Narayanan, 2020. "A Simplified measure of nutritional empowerment using machine learning to abbreviate the Women's Empowerment in Nutrition Index (WENI)," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-031, Indira Gandhi Institute of Development Research, Mumbai, India.