Printed from https://ideas.repec.org/n/nep-big/2023-10-23.html
Report NEP-BIG-2023-10-23
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:
- Dangxing Chen, 2023, "Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models," Papers, arXiv.org, number 2309.13246, Sep.
- Marra de Artiñano, Ignacio & Riottini Depetris, Franco & Volpe Martincus, Christian, 2023, "Automatic Product Classification in International Trade: Machine Learning and Large Language Models," IDB Publications (Working Papers), Inter-American Development Bank, number 12962, Jul, DOI: http://dx.doi.org/10.18235/0005012.
- Albert Wong & Steven Whang & Emilio Sagre & Niha Sachin & Gustavo Dutra & Yew-Wei Lim & Gaetan Hains & Youry Khmelevsky & Frank Zhang, 2023, "Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms," Papers, arXiv.org, number 2309.00618, May.
- Paul Bilokon & Yitao Qiu, 2023, "Transformers versus LSTMs for electronic trading," Papers, arXiv.org, number 2309.11400, Sep.
- Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023, "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers, arXiv.org, number 2309.15552, Sep.
- Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023, "A Comprehensive Review on Financial Explainable AI," Papers, arXiv.org, number 2309.11960, Sep.
- Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-23.
- Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Krof & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2023, "AI Adoption in America: Who, What, and Where," Working Papers, Center for Economic Studies, U.S. Census Bureau, number 23-48, Sep.
- Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Papers, arXiv.org, number 2309.10546, Sep.
- Olivier de Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023, "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers, Banque de France, number 921.
- Sergio Caprioli & Emanuele Cagliero & Riccardo Crupi, 2023, "Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks," Papers, arXiv.org, number 2309.08652, Sep, revised Nov 2023.
- Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022, "Electricity price forecasting on the day-ahead market using machine learning," Post-Print, HAL, number hal-03621974, May, DOI: 10.1016/j.apenergy.2022.118752.
- Rotem Zelingher & David Makowski, 2022, "Forecasting Global Maize Prices From Regional Productions
[Prévision des prix mondiaux du maïs à partir des productions régionales]," Post-Print, HAL, number hal-03764942, Apr, DOI: 10.3389/fsufs.2022.836437. - Jiashu Lou, 2023, "Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT," Papers, arXiv.org, number 2309.11979, Sep.
- Paul Glasserman & Harry Mamaysky & Jimmy Qin, 2023, "New News is Bad News," Papers, arXiv.org, number 2309.05560, Sep.
- Axelsson, Birger & Song, Han-Suck, 2023, "Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model," Working Paper Series, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, number 23/10, Sep, revised 14 Nov 2023.
- Shuyang Wang & Diego Klabjan, 2023, "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers, arXiv.org, number 2309.00626, Jul.
- Georgios Petropoulos & Bertin Martens & Geoffrey Parker & Marshall Van Alstyne, 2023, "Platform Competition and Information Sharing," CESifo Working Paper Series, CESifo, number 10663.
- Salazar, Lina & Agurto Adrianzen, Marcos & Alvarez, Luis, 2023, "Estimating the Long-term Effects of a Fruit Fly Eradication Program Using Satellite Imagery," IDB Publications (Working Papers), Inter-American Development Bank, number 13038, Aug, DOI: http://dx.doi.org/10.18235/0005056.
- Wenting Liu & Zhaozhong Gui & Guilin Jiang & Lihua Tang & Lichun Zhou & Wan Leng & Xulong Zhang & Yujiang Liu, 2023, "Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data," Papers, arXiv.org, number 2309.16196, Sep.
- Sam Arts & Nicola Melluso & Reinhilde Veugelers, 2023, "Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text," Papers, arXiv.org, number 2309.16437, Sep, revised Dec 2024.