Report NEP-BIG-2024-06-17

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Rachel Soloveichik, 2024, "Studies on the Value of Data," BEA Papers, Bureau of Economic Analysis, number 0124, Apr.
• Kea Baret & Amélie Barbier-Gauchard & Theophilos Papadimitriou, 2023, "Forecasting the Stability and Growth Pact compliance using Machine Learning," Post-Print, HAL, number hal-03121966, Oct, DOI: 10.1111/twec.13518.
• Daniel de Souza Santos & Tiago Alessandro Espinola Ferreira, 2024, "Neural Network Learning of Black-Scholes Equation for Option Pricing," Papers, arXiv.org, number 2405.05780, May.
• Ariel Neufeld & Philipp Schmocker & Sizhou Wu, 2024, "Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs," Papers, arXiv.org, number 2405.05192, May, revised Jan 2025.
• G. Ibikunle & B. Moews & D. Muravyev & K. Rzayev, 2024, "Data-driven measures of high-frequency trading," Papers, arXiv.org, number 2405.08101, May, revised Mar 2025.
• S. Borağan Aruoba & Thomas Drechsel, 2024, "Identifying Monetary Policy Shocks: A Natural Language Approach," NBER Working Papers, National Bureau of Economic Research, Inc, number 32417, May.
• Simone Brusatin & Tommaso Padoan & Andrea Coletta & Domenico Delli Gatti & Aldo Glielmo, 2024, "Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling," Papers, arXiv.org, number 2405.02161, May, revised Oct 2024.
• Rehse, Dominik & Valet, Sebastian & Walter, Johannes, 2024, "Using market design to improve red teaming of generative AI models," ZEW policy briefs, ZEW - Leibniz Centre for European Economic Research, number 06/2024.
• Xiaowei Chen & Hong Li & Yufan Lu & Rui Zhou, 2024, "Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective," Papers, arXiv.org, number 2405.00697, Apr, revised Aug 2024.
• Tomaz Cajner & Leland D. Crane & Christopher J. Kurz & Norman J. Morin & Paul E. Soto & Betsy Vrankovich, 2024, "Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2024-026, May, DOI: 10.17016/FEDS.2024.026.
• Tänzer, Alina, 2024, "The effectiveness of central bank purchases of long-term treasury securities: A neural network approach," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 204.
• W. Benedikt Schmal, 2024, "Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide," Papers, arXiv.org, number 2404.18499, Apr.
• Tian Tian & Liu Ze hui & Huang Zichen & Yubing Tang, 2024, "Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development," Papers, arXiv.org, number 2405.04692, May.
• Maria S. Mavillonio, 2024, "Textual Representation of Business Plans and Firm Success," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/308, May.
• Chuanhao Li & Runhan Yang & Tiankai Li & Milad Bafarassat & Kourosh Sharifi & Dirk Bergemann & Zhuoran Yang, 2024, "STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2393, May.
• Xue Wen Tan & Stanley Kok, 2024, "Explainable Risk Classification in Financial Reports," Papers, arXiv.org, number 2405.01881, May, revised Dec 2024.
• Christian Peukert & Florian Abeillon & Jérémie Haese & Franziska Kaiser & Alexander Staub, 2024, "Strategic Behavior and AI Training Data," CESifo Working Paper Series, CESifo, number 11099.
• Sendhil Mullainathan & Ashesh Rambachan, 2024, "From Predictive Algorithms to Automatic Generation of Anomalies," NBER Working Papers, National Bureau of Economic Research, Inc, number 32422, May.
• Attila Sarkany & Lukas Janasek & Jozef Barunik, 2024, "Quantile Preferences in Portfolio Choice: A Q-DRL Approach to Dynamic Diversification," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2024/21, May, revised May 2024.
• Nicholas Tenev, 2024, "De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data," Papers, arXiv.org, number 2405.00910, May.
• Sugat Chaturvedi & Kanika Mahajan & Zahra Siddique, 2023, "Using Domain-Specific Word Embeddings to Examine the Demand for Skills," Working Papers, Ashoka University, Department of Economics, number 107, Nov.
• Reilly Pickard & F. Wredenhagen & Y. Lawryshyn, 2024, "Optimizing Deep Reinforcement Learning for American Put Option Hedging," Papers, arXiv.org, number 2405.08602, May.
• Tian Tian & Jiahao Deng, 2024, "Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis," Papers, arXiv.org, number 2405.01913, May.
• Xiaoxuan Zhang & John Gibson, 2024, "Local economic effects of connecting to China's high-speed rail network: Evidence from spatial econometric models," Working Papers in Economics, University of Waikato, number 24/03, Jun.
• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers, Bank of Canada, number 24-15, May, DOI: 10.34989/swp-2024-15.
• Ali Mohammadjafari, 2024, "Comparative Study of Bitcoin Price Prediction," Papers, arXiv.org, number 2405.08089, May.
• Sylvain BARTHÉLÉMY & Virginie GAUTIER & Fabien RONDEAU, 2024, "Convolutional Neural Networks to signal currency crises: from the Asian financial crisis to the Covid crisis," Economics Working Paper Archive (University of Rennes & University of Caen), Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS, number 2024-01, Mar.
• Zhiyu Cao & Zachary Feinstein, 2024, "Large Language Model in Financial Regulatory Interpretation," Papers, arXiv.org, number 2405.06808, May, revised Jul 2024.
• Ashish Anil Pawar & Vishnureddy Prashant Muskawar & Ritesh Tiku, 2024, "Portfolio Management using Deep Reinforcement Learning," Papers, arXiv.org, number 2405.01604, May.
• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers, Bank for International Settlements, number 1188, May.
• Vansh Murad Kalia, 2024, "Packing Peanuts: The Role Synthetic Data Can Play in Enhancing Conventional Economic Prediction Models," Papers, arXiv.org, number 2405.07431, May.
• Item repec:cam:camjip:2416 is not listed on IDEAS anymore

• Reilly Pickard & Finn Wredenhagen & Julio DeJesus & Mario Schlener & Yuri Lawryshyn, 2024, "Hedging American Put Options with Deep Reinforcement Learning," Papers, arXiv.org, number 2405.06774, May.
• Disa M. Hynsjö & Luca Perdoni, 2024, "Mapping Out Institutional Discrimination: The Economic Effects of Federal “Redlining”," CESifo Working Paper Series, CESifo, number 11098.
• Theodoros Zafeiriou & Dimitris Kalles, 2024, "Comparative analysis of neural network architectures for short-term FOREX forecasting," Papers, arXiv.org, number 2405.08045, May.
• Serguei Maliar & Bernard Salanie, 2024, "Testing for Asymmetric Information in Insurance with Deep Learning," Papers, arXiv.org, number 2404.18207, Apr.
• Yusuke Narita & Kohei Yata, 2024, "Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2391, May.
• Wang, Tengyao & Dobriban, Edgar & Gataric, Milana & Samworth, Richard J., 2024, "Sharp-SSL: selective high-dimensional axis-aligned random projections for semi-supervised learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122552, May.
• Lonjezo Sithole, 2024, "A Locally Robust Semiparametric Approach to Examiner IV Designs," Papers, arXiv.org, number 2404.19144, Apr.

Report NEP-BIG-2024-06-17

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: