Report NEP-BIG-2021-04-19

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.

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

Other reports in NEP-BIG

The following items were announced in this report:

• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021. "Measuring National Life Satisfaction with Music," IZA Discussion Papers 14258, Institute of Labor Economics (IZA).
• Kai A. Konrad, 2019. "Attacking and Defending Multiple Valuable Secrets in a Big Data World," Working Papers tax-mpg-rps-2019-05, Max Planck Institute for Tax Law and Public Finance.
• Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Degang Wang, 2021. "Financial Markets Prediction with Deep Learning," Papers 2104.05413, arXiv.org.
• Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021. "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers 2104.06735, arXiv.org.
• Ling Qi & Matloob Khushi & Josiah Poon, 2021. "Event-Driven LSTM For Forex Price Prediction," Papers 2102.01499, arXiv.org.
• Seema Jayachandran & Monica Biradavolu & Jan Cooper, 2021. "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," CESifo Working Paper Series 8984, CESifo.
• Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020. "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography 2020-06, Gran Sasso Science Institute, Social Sciences, revised Oct 2020.
• Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
• Nazish Ashfaq & Zubair Nawaz & Muhammad Ilyas, 2021. "A comparative study of Different Machine Learning Regressors For Stock Market Prediction," Papers 2104.07469, arXiv.org.
• Daniel Straulino & Juan C. Saldarriaga & Jairo A. G'omez & Juan C. Duque & Neave O'Clery, 2021. "Uncovering commercial activity in informal cities," Papers 2104.04545, arXiv.org.
• Jean Jacques Ohana & Eric Benhamou & David Saltiel & Beatrice Guez, 2021. "Is the Covid equity bubble rational? A machine learning answer," Working Papers hal-03189799, HAL.
• Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
• Denuit, Michel & Charpentier, Arthur & Trufin, Julien, 2021. "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," LIDAM Discussion Papers ISBA 2021013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
• Donato Masciandaro & Davide Romelli & Gaia Rubera, 2021. "Monetary policy, Twitter and financial markets: evidence from social media traffic," BAFFI CAREFIN Working Papers 21160, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
• Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Hongwei Zhu, 2021. "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets," Papers 2104.04041, arXiv.org.
• Marianne Bertrand & Bruno Crépon & Alicia Marguerie & Patrick Premand, 2021. "Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Cote D’Ivoire," NBER Working Papers 28664, National Bureau of Economic Research, Inc.
• Margherita Pagani & Renaud Champion, 2020. "Making Sense of the AI Landscape," Post-Print hal-03188248, HAL.
• Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers 2104.06259, arXiv.org.
• Ekaterina Prytkova, 2021. "ICT's Wide Web: a System-Level Analysis of ICT's Industrial Diffusion with Algorithmic Links," Jena Economics Research Papers 2021-005, Friedrich-Schiller-University Jena.
• Fabrizio Lillo & Giulia Livieri & Stefano Marmi & Anton Solomko & Sandro Vaienti, 2021. "Analysis of bank leverage via dynamical systems and deep neural networks," Papers 2104.04960, arXiv.org.
• Trufin, Julien & Denuit, Michel, 2021. "Boosting cost-complexity pruned trees On Tweedie responses: the ABT machine," LIDAM Discussion Papers ISBA 2021015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
• Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
• Marco Due~nas & Federico Nutarelli & V'ictor Ortiz & Massimo Riccaboni & Francesco Serti, 2021. "Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms," Papers 2104.04570, arXiv.org, revised Nov 2024.
• Hainaut, Donatien & Trufin, Julien & Denuit, Michel, 2021. "Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link," LIDAM Discussion Papers ISBA 2021012, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).