Report NEP-BIG-2019-10-07

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2019. "Too Much Data: Prices and Inefficiencies in Data Markets," NBER Working Papers 26296, National Bureau of Economic Research, Inc.
• Norbert Pfeifer, 2019. "Text-Based Rental Rate Predictions of Airbnb Listings," ERES eres2019_329, European Real Estate Society (ERES).
• Marcelo Cajias, 2019. "Can a machine understand real estate pricing? – Evaluating machine learning approaches with big data," ERES eres2019_232, European Real Estate Society (ERES).
• Kei Nakagawa & Masaya Abe & Junpei Komiyama, 2019. "A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy," Papers 1910.01491, arXiv.org.
• Peter Carr & Liuren Wu & Zhibai Zhang, 2019. "Using Machine Learning to Predict Realized Variance," Papers 1909.10035, arXiv.org.
• Qi Deng, 2019. "Artificial Intelligence BlockCloud (AIBC) Technical Whitepaper," Papers 1909.12063, arXiv.org.
• Sarah Perrin & Thierry Roncalli, 2019. "Machine Learning Optimization Algorithms & Portfolio Allocation," Papers 1909.10233, arXiv.org.
• Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
• Toyotaro Suzumura & Yi Zhou & Natahalie Baracaldo & Guangnan Ye & Keith Houck & Ryo Kawahara & Ali Anwar & Lucia Larise Stavarache & Yuji Watanabe & Pablo Loyola & Daniel Klyashtorny & Heiko Ludwig & , 2019. "Towards Federated Graph Learning for Collaborative Financial Crimes Detection," Papers 1909.12946, arXiv.org, revised Oct 2019.
• Marcelo Cajias, 2019. "New Technology and Data in Real Estate," ERES eres2019_155, European Real Estate Society (ERES).
• Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
• Yang Qu & Ming-Xi Wang, 2019. "The option pricing model based on time values: an application of the universal approximation theory on unbounded domains," Papers 1910.01490, arXiv.org, revised Apr 2021.
• Martijn Droes & Martin Hoesli & Steven C. Bourassa, 2019. "Heterogeneous Households and Market Segmentation in a Hedonic Framework," ERES eres2019_218, European Real Estate Society (ERES).
• 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 eres2019_171, European Real Estate Society (ERES).
• 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 1909.10578, arXiv.org.
• Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
• Arthur Charpentier, 2019. "Big Data, GAFA et Assurance," Working Papers hal-02294899, HAL.
• Andreas Kindt, 2019. "A Framework for the optimal Development and Application of Automated Valuation Models (AVMs)," ERES eres2019_240, European Real Estate Society (ERES).
• Draca, Mirko & Schwarz, Carlo, 2019. "How Polarized are Citizens? Measuring Ideology from the Ground-Up," CAGE Online Working Paper Series 432, Competitive Advantage in the Global Economy (CAGE).
• Helene Dernis & Petros Gkotsis & Nicola Grassano & Shohei Nakazato & Mariagrazia Squicciarini & Brigitte van Beuzekom & Antonio Vezzani, 2019. "World Corporate Top R&D investors: Shaping the Future of Technologies and of AI," JRC Research Reports JRC117068, Joint Research Centre.
• Bénédicte Apouey, 2019. "Intérêt des adhérents d'une mutuelle pour des services utilisant leurs données personnelles dans le cadre de la médecine personnalisée," PSE Working Papers halshs-02295392, HAL.
• Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2019. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Papers 1909.12592, arXiv.org, revised Feb 2021.
• Michael Poli & Jinkyoo Park & Ilija Ilievski, 2019. "WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series," Papers 1909.10801, arXiv.org.
• 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 1909.11532, arXiv.org.
• Item repec:ulb:ulbeco:2013/283916 is not listed on IDEAS anymore

• Yan Carriere-Swallow & Vikram Haksar, 2019. "The Economics and Implications of Data; An Integrated Perspective," IMF Departmental Papers / Policy Papers 19/16, International Monetary Fund.