Report NEP-BIG-2023-05-29

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:

• Michael Kopp, 2023, "The impact of the AI revolution on asset management," Papers, arXiv.org, number 2304.10212, Apr, revised Apr 2023.
• Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Suhas Vijaykumar & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Shan Wang, 2023, "Hedonic Prices and Quality Adjusted Price Indices Powered by AI," Papers, arXiv.org, number 2305.00044, Apr, revised Jan 2026.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers, Banco de España, number 2240, Nov, DOI: https://doi.org/10.53479/23646.
• Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023, "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers, arXiv.org, number 2305.04811, Apr, revised Sep 2023.
• Ashish Dhiman, 2023, "UQ for Credit Risk Management: A deep evidence regression approach," Papers, arXiv.org, number 2305.04967, May, revised May 2023.
• Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023, "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1374, Mar, DOI: 10.17016/IFDP.2023.1374.
• Philippe Goulet Coulombe & Maximilian Gobel, 2023, "Maximally Machine-Learnable Portfolios," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 23-01, Apr, revised Apr 2023.
• Li Rong Wang & Hsuan Fu & Xiuyi Fan, 2023, "Stock Price Predictability and the Business Cycle via Machine Learning," Papers, arXiv.org, number 2304.09937, Apr.
• Antoine Jacquier & Zan Zuric, 2023, "Random neural networks for rough volatility," Papers, arXiv.org, number 2305.01035, May, revised Feb 2026.
• Aggarwal, Sakshi, 2023, "LSTM based Anomaly Detection in Time Series for United States exports and imports," MPRA Paper, University Library of Munich, Germany, number 117149, Apr.
• Andrew Na & Meixin Zhang & Justin Wan, 2023, "Computing Volatility Surfaces using Generative Adversarial Networks with Minimal Arbitrage Violations," Papers, arXiv.org, number 2304.13128, Apr, revised Dec 2023.
• Adamantios Ntakaris & Moncef Gabbouj & Juho Kanniainen, 2023, "Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting," Papers, arXiv.org, number 2304.09840, Apr, revised May 2023.
• Sabri Boubaker & Zhenya Liu & Ling Zhai, 2021, "Big data, news diversity and financial market crash," Post-Print, HAL, number hal-03511405, Jul, DOI: 10.1016/j.techfore.2021.120755.
• Faraz Sasani & Ramin Mousa & Ali Karkehabadi & Samin Dehbashi & Ali Mohammadi, 2023, "TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data," Papers, arXiv.org, number 2304.02094, Mar.
• David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023, "Augmented balancing weights as linear regression," Papers, arXiv.org, number 2304.14545, Apr, revised Jun 2024.
• Ali Lashgari, 2023, "Assessing Text Mining and Technical Analyses on Forecasting Financial Time Series," Papers, arXiv.org, number 2304.14544, Apr.
• Viktoriia Naboka-Krell, 2023, "Construction and Analysis of Uncertainty Indices based on Multilingual Text Representations," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202310.
• Akihiko Takahashi & Toshihiro Yamada, 2023, "Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1212, Apr.
• Shogo Fukui, 2023, "Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup," Papers, arXiv.org, number 2305.01201, May, revised Jun 2024.
• Sungwoo Kang & Jong-Kook Kim, 2023, "Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis," Papers, arXiv.org, number 2304.14870, Apr, revised Apr 2024.
• Mohamed Hamdouche & Pierre Henry-Labordere & Huyên Pham, 2023, "Generative modeling for time series via Schrödinger bridge," Working Papers, HAL, number hal-04063041, Apr.
• Salvatore Certo & Anh Pham & Nicolas Robles & Andrew Vlasic, 2023, "Conditional Generative Models for Learning Stochastic Processes," Papers, arXiv.org, number 2304.10382, Apr, revised Aug 2023.
• Akihiko Takahashi & Toshihiro Yamada, 2023, "Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus (Forthcoming in "Partial Differential Equations and Applications")(Revised version of CARF-F-547)," CARF F-Series, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, number CARF-F-560, May.
• David H. Kreitmeir & Paul A. Raschky, 2023, "The Unintended Consequences of Censoring Digital Technology -- Evidence from Italy's ChatGPT Ban," Papers, arXiv.org, number 2304.09339, Apr.
• Marina Diakonova & Corinna Ghirelli & Javier J. Pérez & Luis Molina, 2022, "The economic impact of conflict-related and policy uncertainty shocks: the case of Russia," Working Papers, Banco de España, number 2242, Nov, DOI: https://doi.org/10.53479/23707.
• Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022, "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers, Banco de España, number 2232, Sep.
• Adeliyi, Oluwaseyi & Adesoba, Adeola, 2022, "A Multi-method Approach to Analyze Australia-China Geopolitical Discourse on YouTube," OSF Preprints, Center for Open Science, number pe58w, Mar, DOI: 10.31219/osf.io/pe58w.
• Alejandro Bernales & Marcela Valenzuela & Ilknur Zer, 2023, "Effects of Information Overload on Financial Markets: How Much Is Too Much?," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.), number 1372, Mar, DOI: 10.17016/IFDP.2023.1372.
• Ali Shirazi & Fereshteh Sadeghi Naieni Fard, 2023, "Financial Hedging and Risk Compression, A journey from linear regression to neural network," Papers, arXiv.org, number 2305.04801, Apr.
• Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Pérez & Elena Vidal, 2022, "Using newspapers for textual indicators: which and how many?," Working Papers, Banco de España, number 2235, Oct.

Report NEP-BIG-2023-05-29

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: