Report NEP-BIG-2021-07-26

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

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report:

• Juyong Lee & Youngsang Cho, 2021, "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Papers, arXiv.org, number 2107.06174, Jun.
• Angelo Garangau Menezes & Saulo Martiello Mastelini, 2021, "MegazordNet: combining statistical and machine learning standpoints for time series forecasting," Papers, arXiv.org, number 2107.01017, Jun.
• Sohrab Mokhtari & Kang K. Yen & Jin Liu, 2021, "Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning," Papers, arXiv.org, number 2107.01031, Jun.
• Helmut Wasserbacher & Martin Spindler, 2021, "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers, arXiv.org, number 2107.04851, Jul.
• Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021, "Stock price prediction using BERT and GAN," Papers, arXiv.org, number 2107.09055, Jul.
• Naudé, Wim & Bray, Amy & Lee, Celina, 2021, "Crowdsourcing Artificial Intelligence in Africa: Findings from a Machine Learning Contest," IZA Discussion Papers, Institute of Labor Economics (IZA), number 14545, Jul.
• Supriya Bajpai, 2021, "Application of deep reinforcement learning for Indian stock trading automation," Papers, arXiv.org, number 2106.16088, May.
• Dan Wang & Zhi Chen & Ionut Florescu, 2021, "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers, arXiv.org, number 2107.10306, Jul.
• Francesca Micocci & Armando Rungi, 2021, "Predicting Exporters with Machine Learning," Working Papers, IMT School for Advanced Studies Lucca, number 03/2021, Jul, revised Jul 2021.
• Cynthia Pagliaro & Dhagash Mehta & Han-Tai Shiao & Shaofei Wang & Luwei Xiong, 2021, "Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective," Papers, arXiv.org, number 2107.05592, Jul.
• Ren'e Carmona & Mathieu Lauri`ere, 2021, "Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance," Papers, arXiv.org, number 2107.04568, Jul.
• Li, Wei & Paraschiv, Florentina & Sermpinis, Georgios, 2021, "A data-driven explainable case-based reasoning approach for financial risk detection," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2021-010.
• Kyle Colangelo & Ying-Ying Lee, 2019, "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP72/19, Dec.
• Naftali Cohen & Srijan Sood & Zhen Zeng & Tucker Balch & Manuela Veloso, 2021, "Visual Time Series Forecasting: An Image-driven Approach," Papers, arXiv.org, number 2107.01273, Jul, revised Nov 2021.
• Orkun Saka & Barry Eichengreen & Cevat Giray Aksoy, 2021, "Epidemic Exposure, Fintech Adoption, and the Digital Divide," CESifo Working Paper Series, CESifo, number 9173.
• CHARISI Vasiliki & COMPANO Ramon & DUCH BROWN Nestor & GOMEZ GUTIERREZ Emilia & KLENERT David & LUTZ Michael & MARSCHINSKI Robert & TORRECILLA SALINAS Carlos, 2021, "What future for European robotics?," JRC Research Reports, Joint Research Centre, number JRC125343, Jul.
• Orkun Saka & Barry Eichengreen & Cevat Giray Aksoy, 2021, "Epidemic Exposure, Fintech Adoption, and the Digital Divide," NBER Working Papers, National Bureau of Economic Research, Inc, number 29006, Jul.
• Mohammad Rasouli & Michael I. Jordan, 2021, "Data Sharing Markets," Papers, arXiv.org, number 2107.08630, Jul, revised Jul 2021.
• Mathieu Rosenbaum & Jianfei Zhang, 2021, "Deep calibration of the quadratic rough Heston model," Papers, arXiv.org, number 2107.01611, Jul, revised May 2022.
• Claudia Noack & Tomasz Olma & Christoph Rothe, 2021, "Flexible Covariate Adjustments in Regression Discontinuity Designs," Papers, arXiv.org, number 2107.07942, Jul, revised Apr 2025.
• Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021, "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers, arXiv.org, number 2107.05201, Jul, revised Oct 2021.
• Jean-Franc{c}ois Chassagneux & Mohan Yang, 2021, "Numerical approximation of singular Forward-Backward SDEs," Papers, arXiv.org, number 2106.15496, Jun.
• FORTES, Roberta & Le Guenedal, Theo, 2020, "Tracking ECB's communication: Perspectives and Implications for Financial Markets," MPRA Paper, University Library of Munich, Germany, number 108746, Dec.
• Wing Fung Chong & Haoen Cui & Yuxuan Li, 2021, "Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning," Papers, arXiv.org, number 2107.03340, Jul, revised Oct 2022.
• Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2021, "Emotions in Macroeconomic News and their Impact on the European Bond Market," Papers, arXiv.org, number 2106.15698, Jun.
• Stich, Christoph & Tranos, Emmanouil & Nathan, Max, 2021, "Modelling Clusters From The Ground Up: A Web Data Approach," SocArXiv, Center for Open Science, number j2w8v, May, DOI: 10.31219/osf.io/j2w8v.
• Ziwei Cong & Jia Liu & Puneet Manchanda, 2021, "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers, arXiv.org, number 2107.01629, Jul, revised Sep 2022.

Report NEP-BIG-2021-07-26

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

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: