Report NEP-CMP-2023-01-23

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stan Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Prateek Jain & Alberto Garcia Garcia, 2022. "Quantum classical hybrid neural networks for continuous variable prediction," Papers 2212.04209, arXiv.org, revised Mar 2023.
• Xiaodong Li & Pangjing Wu & Chenxin Zou & Qing Li, 2022. "Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization," Papers 2212.14670, arXiv.org.
• Raj G. Patel & Chia-Wei Hsing & Serkan Sahin & Samuel Palmer & Saeed S. Jahromi & Shivam Sharma & Tomas Dominguez & Kris Tziritas & Christophe Michel & Vincent Porte & Mustafa Abid & Stephane Aubert &, 2022. "Quantum-Inspired Tensor Neural Networks for Option Pricing," Papers 2212.14076, arXiv.org, revised Mar 2024.
• Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
• MohammadAmin Fazli & Mahdi Lashkari & Hamed Taherkhani & Jafar Habibi, 2022. "A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management," Papers 2212.14477, arXiv.org.
• Item repec:bny:wpaper:0111 is not listed on IDEAS anymore

• John A. List & Ian Muir & Gregory K. Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," NBER Working Papers 30756, National Bureau of Economic Research, Inc.
• Asati, Akshita, 2022. "A Comparative Study On Forecasting Consumer Price Index Of India Amongst XGBoost, Theta, ARIMA, Prophet And LSTM Algorithms," OSF Preprints hyqsb, Center for Open Science.
• N'yoma Diamond & Grant Perkins, 2022. "Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning," Papers 2212.08734, arXiv.org, revised Dec 2022.
• Arthur Charpentier, 2022. "Quantifying fairness and discrimination in predictive models," Papers 2212.09868, arXiv.org.
• Hamid Nasiri & Mohammad Mehdi Ebadzadeh, 2022. "Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition," Papers 2212.14687, arXiv.org.
• Jean-Franc{c}ois Chassagneux & Junchao Chen & Noufel Frikha, 2022. "Deep Runge-Kutta schemes for BSDEs," Papers 2212.14372, arXiv.org.
• Maria Teresa Monteduro & Dalila De Rosa & Chiara Subrizi, 2023. "Did the policy responses to COVID-19 protect Italian households’ incomes? Evidence from incomes nowcasting in microsimulation models," Working Papers wp2023-16, Ministry of Economy and Finance, Department of Finance.
• Joseph Levitas & Konstantin Yavilberg & Oleg Korol & Genadi Man, 2022. "Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction," Papers 2212.09385, arXiv.org, revised Mar 2023.
• Majid Ahmadi & Nathan Durst & Jeff Lachman & John A. List & Mason List & Noah List & Atom T. Vayalinkal, 2022. "Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft," NBER Working Papers 30786, National Bureau of Economic Research, Inc.
• Dreher, Sandra & Eichfelder, Sebastian & Noth, Felix, 2022. "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," arqus Discussion Papers in Quantitative Tax Research 276, arqus - Arbeitskreis Quantitative Steuerlehre.
• Giovanni Dosi, 2023. "Why is economics the only discipline with so many curves going up and down? There is an alternative," LEM Papers Series 2023/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
• Akaki Mamageishvili & Edward W. Felten, 2022. "Efficient Rollup Batch Posting Strategy on Base Layer," Papers 2212.10337, arXiv.org, revised Feb 2023.
• Laureti, Lucio & Costantiello, Alberto & Leogrande, Angelo, 2022. "The fight against corruption at global level. A metric approach," MPRA Paper 115837, University Library of Munich, Germany.