Report NEP-CMP-2020-06-29

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:

• Jie Fang & Jianwu Lin, 2020. "Prior knowledge distillation based on financial time series," Papers 2006.09247, arXiv.org, revised Nov 2020.
• Žulj, Ivan, 2020. "Designing new models and algorithms to improve order picking operations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 121209, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Pietro Rossi & Flavio Cocco & Giacomo Bormetti, 2020. "Deep learning Profit & Loss," Papers 2006.09955, arXiv.org, revised Aug 2020.
• Takanobu Mizuta, 2020. "Does an artificial intelligence perform market manipulation with its own discretion? -- A genetic algorithm learns in an artificial market simulation," Papers 2005.10488, arXiv.org.
• Laura Leal & Mathieu Lauri`ere & Charles-Albert Lehalle, 2020. "Learning a functional control for high-frequency finance," Papers 2006.09611, arXiv.org, revised Feb 2021.
• T. van der Zwaard & L. A. Grzelak & C. W. Oosterlee, 2020. "A Computational Approach to Hedging Credit Valuation Adjustment in a Jump-Diffusion Setting," Papers 2005.10504, arXiv.org, revised Sep 2020.
• Matteo Gambara & Josef Teichmann, 2020. "Consistent Recalibration Models and Deep Calibration," Papers 2006.09455, arXiv.org, revised Jul 2021.
• Sobin Joseph & Lekhapriya Dheeraj Kashyap & Shashi Jain, 2020. "Shallow Neural Hawkes: Non-parametric kernel estimation for Hawkes processes," Papers 2006.02460, arXiv.org.
• Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020. "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers 2006.01911, arXiv.org, revised May 2021.
• Yun-Cheng Tsai & Chun-Chieh Wang, 2019. "Deep Reinforcement Learning for Foreign Exchange Trading," Papers 1908.08036, arXiv.org, revised Jun 2020.
• Christopher Ferrall, 2020. "Object Oriented (Dynamic) Programming: Replication, Innovation and "Structural" Estimation," Working Paper 1432, Economics Department, Queen's University.
• Karolina Sowinska & Pranava Madhyastha, 2020. "A Tweet-based Dataset for Company-Level Stock Return Prediction," Papers 2006.09723, arXiv.org.
• Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
• Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Post-Print hal-02007388, HAL.
• Jun-Hao Chen & Samuel Yen-Chi Chen & Yun-Cheng Tsai & Chih-Shiang Shur, 2020. "Adversarial Robustness of Deep Convolutional Candlestick Learner," Papers 2006.03686, arXiv.org.
• David Byrd & Sruthi Palaparthi & Maria Hybinette & Tucker Hybinette Balch, 2020. "The Importance of Low Latency to Order Book Imbalance Trading Strategies," Papers 2006.08682, arXiv.org.
• Marcin Chlebus & Maciej Stefan Świtała, 2020. "So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison," Working Papers 2020-16, Faculty of Economic Sciences, University of Warsaw.
• Itzhak Rasooly & Carlos Gavidia-Calderon, 2020. "The importance of being discrete: on the inaccuracy of continuous approximations in auction theory," Papers 2006.03016, arXiv.org, revised Aug 2022.
• Guang Zhang, 2020. "Pairs Trading with Nonlinear and Non-Gaussian State Space Models," Papers 2005.09794, arXiv.org.
• codagnone, cristiano & Bogliacino, Francesco & Gómez, Camilo Ernesto & Charris, Rafael Alberto & Montealegre, Felipe & Liva, Giovanni & Villanueva, Francisco Lupiañez & Folkvord, F. & Veltri, Giuseppe, 2020. "Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United K," SocArXiv x9m36, Center for Open Science.
• Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
• Chuangyin Dang & Qi Qi & Yinyu Ye, 2020. "Computations and Complexities of Tarski's Fixed Points and Supermodular Games," Papers 2005.09836, arXiv.org.
• Gallego, J & Prem, M & Vargas, J. F, 2020. "Corruption in the times of pandemia," Documentos de Trabajo 18178, Universidad del Rosario.
• Ljungqvist, Alexander & Bircan, Cagatay & Biesinger, Markus, 2020. "Value Creation in Private Equity," CEPR Discussion Papers 14676, C.E.P.R. Discussion Papers.
• Duso, Tomaso & Affeldt, Pauline & Szücs, Florian, 2020. "25 Years of European Merger Control," CEPR Discussion Papers 14548, C.E.P.R. Discussion Papers.
• Alain Naef, 2020. "Blowing against the Wind? A Narrative Approach to Central Bank Foreign Exchange Intervention," Working Papers 0188, European Historical Economics Society (EHES).
• Stetter, Christian & Mennig, Philipp & Sauer, Johannes, 2020. "Going Beyond Average – Using Machine Learning to Evaluate the Effectiveness of Environmental Subsidies at Micro-Level," 94th Annual Conference, April 15-17, 2020, K U Leuven, Belgium (Cancelled) 303699, Agricultural Economics Society - AES.
• Emerick, Kyle & Burke, Marshall & Maue, Casey, 2020. "Productivity dispersion and persistence among the world’s most numerous firms," CEPR Discussion Papers 14553, C.E.P.R. Discussion Papers.