Report NEP-BIG-2024-07-15

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:

• Askitas, Nikos, 2024, "A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code," IZA Discussion Papers, IZA Network @ LISER, number 17014, May.
• Mark E. Schaffer, 2023, "pystacked and ddml: Machine learning for prediction and causal inference in Stata," Economics Virtual Symposium 2023, Stata Users Group, number 04, Nov.
• Alexander Bakumenko & Katev{r}ina Hlav'av{c}kov'a-Schindler & Claudia Plant & Nina C. Hubig, 2024, "Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs," Papers, arXiv.org, number 2406.03614, Jun.
• Milen Arro-Cannarsa & Rolf Scheufele, 2024, "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers, Swiss National Bank, number 2024-06.
• Bivas Dinda, 2024, "Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy," Papers, arXiv.org, number 2406.02604, Jun.
• Fabrice Murtin & Max Salomon-Ermel, 2024, "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities, OECD Publishing, number 27, Jun, DOI: 10.1787/cbdfb5d9-en.
• Kumar, Pradeep & Nicodemo, Catia & Oreffice, Sonia & Quintana-Domeque, Climent, 2024, "Machine Learning and Multiple Abortions," IZA Discussion Papers, IZA Network @ LISER, number 17046, Jun.
• Seulki Chung, 2024, "Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach," Papers, arXiv.org, number 2405.19849, May.
• Edward Sharkey & Philip Treleaven, 2024, "BERT vs GPT for financial engineering," Papers, arXiv.org, number 2405.12990, Apr.
• Sugarbayar Enkhbayar & Robert Ślepaczuk, 2024, "Predictive modeling of foreign exchange trading signals using machine learning techniques," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-10.
• Emily Silcock & Abhishek Arora & Luca D'Amico-Wong & Melissa Dell, 2024, "Newswire: A Large-Scale Structured Database of a Century of Historical News," Papers, arXiv.org, number 2406.09490, Jun.
• Francesco Audrino & Jonathan Chassot, 2024, "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers, arXiv.org, number 2406.08041, Jun.
• Junquera, Álvaro F. & Kern, Christoph, 2024, "From rules to forests: rule-based versus statistical models for jobseeker profiling," SocArXiv, Center for Open Science, number c7ps3, Jun, DOI: 10.31219/osf.io/c7ps3.
• Nicolás Forteza & Elvira Prades & Marc Roca, 2024, "Analysing the VAT cut pass-through in Spain using web-scraped supermarket data and machine learning," Working Papers, Banco de España, number 2417, May, DOI: https://doi.org/10.53479/36652.
• Adam Korniejczuk & Robert Ślepaczuk, 2024, "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-09.
• Simon D Angus & Lachlan O'Neill, 2024, "Paired completion: quantifying issue-framing at scale with LLMs," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2024-02, Jun.
• Antonio Briola & Silvia Bartolucci & Tomaso Aste, 2024, "HLOB -- Information Persistence and Structure in Limit Order Books," Papers, arXiv.org, number 2405.18938, May, revised Jun 2024.
• Jinglong Dai & Hanwei Li & Weiming Zhu & Jianfeng Lin & Binqiang Huang, 2024, "Data-Driven Real-time Coupon Allocation in the Online Platform," Papers, arXiv.org, number 2406.05987, Jun, revised Jun 2024.
• Arnone, Massimo & Angelillis, Barbara & Costantiello, Alberto & Leogrande, Angelo, 2024, "Graduates, Training and Employment Across the Italian Regions," MPRA Paper, University Library of Munich, Germany, number 121117, Jun.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2024, "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1499.
• Raeid Saqur & Anastasis Kratsios & Florian Krach & Yannick Limmer & Jacob-Junqi Tian & John Willes & Blanka Horvath & Frank Rudzicz, 2024, "Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models," Papers, arXiv.org, number 2406.02969, Jun, revised Feb 2025.
• Tohid Atashbar, 2024, "Reinforcement Learning from Experience Feedback: Application to Economic Policy," IMF Working Papers, International Monetary Fund, number 2024/114, Jun.
• Berger, Benedikt & Adam, Martin & Rühr, Alexander & Benlian, Alexander, 2024, "Watch Me Improve — Algorithm Aversion and Demonstrating the Ability to Learn," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 146095, Jun, DOI: 10.1007/s12599-020-00678-5.
• Sven Goluv{z}a & Tomislav Kovav{c}evi'c & Tessa Bauman & Zvonko Kostanjv{c}ar, 2024, "Deep reinforcement learning with positional context for intraday trading," Papers, arXiv.org, number 2406.08013, Jun.
• Daniel Vebman, 2024, "Quantifying the Reliance of Black-Box Decision-Makers on Variables of Interest," Papers, arXiv.org, number 2405.17225, May.
• Jochen Wulf & Jurg Meierhofer, 2024, "Utilizing Large Language Models for Automating Technical Customer Support," Papers, arXiv.org, number 2406.01407, Jun.
• Joshua Nielsen & Didier Sornette & Maziar Raissi, 2024, "Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 24-33, May.
• Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2024, "Random Subspace Local Projections," Papers, arXiv.org, number 2406.01002, Jun.
• Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024, "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers, IZA Network @ LISER, number 17055, Jun.
• Can Celebi & Stefan Penczynski, 2024, "Using Large Language Models for Text Classification in Experimental Economics," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS), School of Economics, University of East Anglia, Norwich, UK., number 24-01, Jun.
• Mick Dueholm & Aakash Kalyani & Serdar Ozkan, 2024, "Can Earnings Calls Be Used to Gauge Labor Market Tightness?," On the Economy, Federal Reserve Bank of St. Louis, number 98404, Jun.
• Joel Ong & Dorien Herremans, 2024, "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers, arXiv.org, number 2406.08742, Jun.
• Chang Zong & Hang Zhou, 2024, "Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism," Papers, arXiv.org, number 2406.06594, Jun, revised Dec 2024.
• Haavio, Markus & Heikkinen, Joni & Jalasjoki, Pirkka & Kilponen, Juha & Paloviita, Maritta & Vänni, Ilona, 2024, "Reading between the lines: Uncovering asymmetry in the central bank loss function," Bank of Finland Research Discussion Papers, Bank of Finland, number 6/2024.

Report NEP-BIG-2024-07-15

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: