Report NEP-FMK-2021-01-25
This is the archive for NEP-FMK, a report on new working papers in the area of Financial Markets. Erik Schlogl issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-FMK
The following items were announced in this report:
- Ruiqiang Song & Min Shu & Wei Zhu, 2021, "The 2020 Global Stock Market Crash: Endogenous or Exogenous?," Papers, arXiv.org, number 2101.00327, Jan.
- Tinic, Murat & Sensoy, Ahmet & Demir, Muge & Nguyen, Duc Khuong, 2020, "Broker Network Connectivity and the Cross-Section of Expected Stock Returns," MPRA Paper, University Library of Munich, Germany, number 104719, Nov.
- Nina Boyarchenko & Richard K. Crump & Anna Kovner & Or Shachar, 2021, "Corporate Bond Market Distress," Staff Reports, Federal Reserve Bank of New York, number 957, Jan.
- Yvo Mudde & Anna Samarina & Robert Vermeulen, 2021, "Spillover effects of sovereign bond purchases in the euro area," Working Papers, DNB, number 706, Jan.
- Alexis Marchal, 2020, "Risk & returns around FOMC press conferences: a novel perspective from computer vision," Papers, arXiv.org, number 2012.06573, Dec, revised Jan 2021.
- Paweł Sakowski & Anna Turovtseva, 2020, "Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-41.
- Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020, "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers, arXiv.org, number 2012.07245, Dec.
- Xavier Warin, 2021, "Deep learning for efficient frontier calculation in finance," Papers, arXiv.org, number 2101.02044, Jan, revised Feb 2022.
- Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp677, Dec.
- Le Trung Hieu, 2020, "Deep Reinforcement Learning for Stock Portfolio Optimization," Papers, arXiv.org, number 2012.06325, Dec.
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