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Report NEP-CMP-2019-10-07
This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-CMP
The following items were announced in this report:
- Yangang Chen & Justin W. L. Wan, 2019, "Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions," Papers, arXiv.org, number 1909.11532, Sep.
- Sarah Perrin & Thierry Roncalli, 2019, "Machine Learning Optimization Algorithms & Portfolio Allocation," Papers, arXiv.org, number 1909.10233, Sep.
- Marcelo Cajias, 2019, "Can a machine understand real estate pricing? – Evaluating machine learning approaches with big data," ERES, European Real Estate Society (ERES), number eres2019_232, Jan.
- Peter Carr & Liuren Wu & Zhibai Zhang, 2019, "Using Machine Learning to Predict Realized Variance," Papers, arXiv.org, number 1909.10035, Sep.
- Norbert Pfeifer, 2019, "Text-Based Rental Rate Predictions of Airbnb Listings," ERES, European Real Estate Society (ERES), number eres2019_329, Jan.
- David Byrd, 2019, "Explaining Agent-Based Financial Market Simulation," Papers, arXiv.org, number 1909.11650, Sep.
- Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019, "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers, arXiv.org, number 1909.10660, Sep, revised Nov 2019.
- Jang, Jiwook & Dassios, Angelos & Zhao, Hongbiao, 2018, "Moments of renewal shot-noise processes and their applications," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 87428.
- Qi Deng, 2019, "Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper," Papers, arXiv.org, number 1909.12063, Sep.
- Kei Nakagawa & Masaya Abe & Junpei Komiyama, 2019, "A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy," Papers, arXiv.org, number 1910.01491, Oct.
- Naoki Awaya & Yasuhiro Omori, 2019, "Particle Rolling MCMC," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1126, Sep.
- Giovanni Mariani & Yada Zhu & Jianbo Li & Florian Scheidegger & Roxana Istrate & Costas Bekas & A. Cristiano I. Malossi, 2019, "PAGAN: Portfolio Analysis with Generative Adversarial Networks," Papers, arXiv.org, number 1909.10578, Sep.
- Marcelo Cajias & Jonas Willwersch & Felix Lorenz, 2019, "I know where you will invest in the next year – Forecasting real estate investments with machine learning methods," ERES, European Real Estate Society (ERES), number eres2019_171, Jan.
- Jakob J. Kolb & Finn Muller-Hansen & Jurgen Kurths & Jobst Heitzig, 2019, "Macroscopic approximation methods for the analysis of adaptive networked agent-based models: The example of a two-sector investment model," Papers, arXiv.org, number 1909.13758, Sep, revised Aug 2020.
- Samudra Dasgupta & Arnab Banerjee, 2019, "Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation," Papers, arXiv.org, number 1909.12904, Sep, revised Sep 2020.
- Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019, "Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests," Working Papers, University of Pretoria, Department of Economics, number 201972, Sep.