Report NEP-FMK-2020-09-21

This is the archive for NEP-FMK, a report on new working papers in the area of Financial Markets. Kwang Soo Cheong issued this report. It is usually issued weekly.

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Other reports in NEP-FMK

The following items were announced in this report:

• A. Fronzetti Colladon & S. Grassi & F. Ravazzolo & F. Violante, 2020. "Forecasting financial markets with semantic network analysis in the COVID-19 crisis," Papers 2009.04975, arXiv.org, revised Jul 2023.
• Christoph E. Boehm & T. Niklas Kroner, 2020. "The US, Economic News, and the Global Financial Cycle," Working Papers 677, Research Seminar in International Economics, University of Michigan.
• Jan J. J. Groen & Michael Nattinger & Adam I. Noble, 2020. "Measuring Global Financial Market Stresses," Staff Reports 940, Federal Reserve Bank of New York.
• Abdulnasser Hatemi-J, 2020. "Bear Markets and Recessions versus Bull Markets and Expansions," Papers 2009.01343, arXiv.org, revised Nov 2020.
• Caio Vigo Pereira, 2020. "Portfolio Efficiency with High-Dimensional Data as Conditioning Information," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202015, University of Kansas, Department of Economics, revised Sep 2020.
• Qiao Zhou & Ningning Liu, 2020. "A Stock Prediction Model Based on DCNN," Papers 2009.03239, arXiv.org.
• Zhiqiang Ma & Grace Bang & Chong Wang & Xiaomo Liu, 2020. "Towards Earnings Call and Stock Price Movement," Papers 2009.01317, arXiv.org.
• Giovanni Calice & Carlo Sala & Daniele Tantari, 2020. "Contingent Convertible Bonds in Financial Networks," Papers 2009.00062, arXiv.org, revised Dec 2023.
• Olkhov, Victor, 2020. "Volatility Depend on Market Trades and Macro Theory," MPRA Paper 102434, University Library of Munich, Germany.
• Andrej Gill & Matthias Heinz & Heiner Schumacher & Matthias Sutter, 2020. "Trustworthiness in the Financial Industry," CESifo Working Paper Series 8501, CESifo.
• Zhengxin Joseph Ye & Bjorn W. Schuller, 2020. "Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning," Papers 2009.03094, arXiv.org.