Report NEP-CMP-2019-07-22

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:

• Jacobo Roa-Vicens & Cyrine Chtourou & Angelos Filos & Francisco Rullan & Yarin Gal & Ricardo Silva, 2019. "Towards Inverse Reinforcement Learning for Limit Order Book Dynamics," Papers 1906.04813, arXiv.org.
• Francois Belletti & Davis King & Kun Yang & Roland Nelet & Yusef Shafi & Yi-Fan Chen & John Anderson, 2019. "Tensor Processing Units for Financial Monte Carlo," Papers 1906.02818, arXiv.org, revised Jan 2020.
• A Itkin, 2019. "Deep learning calibration of option pricing models: some pitfalls and solutions," Papers 1906.03507, arXiv.org.
• Donovan Platt, 2019. "Bayesian Estimation of Economic Simulation Models using Neural Networks," Papers 1906.04522, arXiv.org.
• Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
• Jeremy D. Turiel & Tomaso Aste, 2019. "P2P Loan acceptance and default prediction with Artificial Intelligence," Papers 1907.01800, arXiv.org.
• Michael A. Kouritzin & Anne MacKay, 2019. "Branching Particle Pricers with Heston Examples," Papers 1907.00219, arXiv.org, revised Nov 2019.
• Yuxuan Huang & Luiz Fernando Capretz & Danny Ho, 2019. "Neural Network Models for Stock Selection Based on Fundamental Analysis," Papers 1906.05327, arXiv.org.
• Laliotis, Dimitrios & Buesa, Alejandro & Leber, Miha & Población García, Francisco Javier, 2019. "An agent-based model for the assessment of LTV caps," Working Paper Series 2294, European Central Bank.
• Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.
• Maarten Buis, 2019. "Agent based models in Mata," German Stata Users' Group Meetings 2019 04, Stata Users Group.
• Lotfi Boudabsa & Damir Filipovic, 2019. "Machine learning with kernels for portfolio valuation and risk management," Papers 1906.03726, arXiv.org, revised May 2021.
• Damien Ackerer & Natasa Tagasovska & Thibault Vatter, 2019. "Deep Smoothing of the Implied Volatility Surface," Papers 1906.05065, arXiv.org, revised Oct 2020.
• Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
• Hung Ba, 2019. "Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks," Papers 1907.03355, arXiv.org.
• Brandon Da Silva & Sylvie Shang Shi, 2019. "Style Transfer with Time Series: Generating Synthetic Financial Data," Papers 1906.03232, arXiv.org, revised Dec 2019.
• Bertin Martens & Songül Tolan, 2018. "Will this time be different? A review of the literature on the Impact of Artificial Intelligence on Employment, Incomes and Growth," JRC Working Papers on Digital Economy 2019-04, Joint Research Centre (Seville site).
• Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
• Emir Hrnjic & Nikodem Tomczak, 2019. "Machine learning and behavioral economics for personalized choice architecture," Papers 1907.02100, arXiv.org.
• Berk, Istemi & Çam , Eren, 2019. "The Shift in Global Crude Oil Market Structure: A model-based analysis of the period 2013–2017," EWI Working Papers 2019-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
• Rukmal Weerawarana & Yiyi Zhu & Yuzhen He, 2019. "Learned Sectors: A fundamentals-driven sector reclassification project," Papers 1906.03935, arXiv.org.
• Crowley, Patrick M. & Hudgins, David, 2019. "U.S. Macroeconomic Policy Evaluation in an Open Economy Context using Wavelet Decomposed Optimal Control Methods," Research Discussion Papers 11/2019, Bank of Finland.
• Rémy Le Boennec & Fouad Hadj Selem & Ghazaleh Khodabandelou, 2019. "La mobilité individuelle motorisée dans les déplacements domicile-travail : préférence modale ou choix contraint ? Une approche par le machine learning," Post-Print hal-02160862, HAL.
• Karen Turner & Gioele Figus & Kim Swales & L. (Lisa B.) Ryan & et al., 2019. "Can the composition of energy use in an expanding economy be altered by consumers’ responses to technological change?," Open Access publications 10197/10840, School of Economics, University College Dublin.
• Xin Qian & Yudong Chen & Andreea Minca, 2019. "Clustering Degree-Corrected Stochastic Block Model with Outliers," Papers 1906.03305, arXiv.org.
• Clement Gastaud & Theophile Carniel & Jean-Michel Dalle, 2019. "The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage," Papers 1906.03210, arXiv.org.
• Tianyao Chen & Xue Cheng & Jingping Yang, 2019. "Common Decomposition of Correlated Brownian Motions and its Financial Applications," Papers 1907.03295, arXiv.org, revised Nov 2020.
• Imad Chahboun & Nathaniel Hoover, 2019. "Variable Annuities: Underlying Risks and Sensitivities," Supervisory Research and Analysis Working Papers RPA 19-1, Federal Reserve Bank of Boston.
• Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
• Catherine D'Hondt & Rudy De Winne & Eric Ghysels & Steve Raymond, 2019. "Artificial Intelligence Alter Egos: Who benefits from Robo-investing?," Papers 1907.03370, arXiv.org.
• Zihao Zhang & Stefan Zohren & Stephen Roberts, 2019. "Extending Deep Learning Models for Limit Order Books to Quantile Regression," Papers 1906.04404, arXiv.org.
• Michael Lechner & Gabriel Okasa, 2019. "Random Forest Estimation of the Ordered Choice Model," Papers 1907.02436, arXiv.org, revised Sep 2022.
• Bertin Martens, 2018. "The impact of data access regimes on artificial intelligence and machine learning," JRC Working Papers on Digital Economy 2019-05, Joint Research Centre (Seville site).
• Fabrice Daniel, 2019. "Financial Time Series Data Processing for Machine Learning," Papers 1907.03010, arXiv.org.
• Matias Barenstein, 2019. "ProPublica's COMPAS Data Revisited," Papers 1906.04711, arXiv.org, revised Jul 2019.