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Report NEP-FMK-2020-10-26
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:
- Zhifeng Liu & Toan Luu Duc Huynh & Peng-Fei Dai, 2020, "The impact of COVID-19 on the stock market crash risk in China," Papers, arXiv.org, number 2009.08030, Sep, revised Aug 2021.
- Steven J. Davis & Stephen Hansen & Cristhian Seminario-Amez, 2020, "Firm-Level Risk Exposures and Stock Returns in the Wake of Covid-19," CESifo Working Paper Series, CESifo, number 8594.
- Frederik P. Schlingemann & René M. Stulz, 2020, "Have Exchange-Listed Firms Become Less Important for the Economy?," NBER Working Papers, National Bureau of Economic Research, Inc, number 27942, Oct.
- Paul Glasserman & Kriste Krstovski & Paul Laliberte & Harry Mamaysky, 2020, "Choosing News Topics to Explain Stock Market Returns," Papers, arXiv.org, number 2010.07289, Oct.
- Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020, "Distillation of News Flow into Analysis of Stock Reactions," Papers, arXiv.org, number 2009.10392, Sep.
- Bruce Knuteson, 2020, "Strikingly Suspicious Overnight and Intraday Returns," Papers, arXiv.org, number 2010.01727, Oct.
- Alessandro Giovannelli & Daniele Massacci & Stefano Soccorsi, 2020, "Forecasting Stock Returns with Large Dimensional Factor Models," Working Papers, Lancaster University Management School, Economics Department, number 305661169.
- Bruno Spilak & Wolfgang Karl Hardle, 2020, "Tail-risk protection: Machine Learning meets modern Econometrics," Papers, arXiv.org, number 2010.03315, Oct, revised Aug 2021.
- Thomas Dierckx & Jesse Davis & Wim Schoutens, 2020, "Using Machine Learning and Alternative Data to Predict Movements in Market Risk," Papers, arXiv.org, number 2009.07947, Sep.
- Qi Zhao, 2020, "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers, arXiv.org, number 2010.07404, Oct.
- Artur Sokolovsky & Luca Arnaboldi, 2020, "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components," Papers, arXiv.org, number 2009.09993, Sep, revised Jun 2022.
- Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020, "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers, arXiv.org, number 2009.10819, Sep.
- Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020, "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers, arXiv.org, number 2010.01197, Sep.
- Cheng Peng & Young Shin Kim & Stefan Mittnik, 2020, "Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation," Papers, arXiv.org, number 2009.11367, Sep, revised Feb 2023.
- Bampinas, Georgios & Panagiotidis, Theodore & Politsidis, Panagiotis, 2020, "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," MPRA Paper, University Library of Munich, Germany, number 102846, Aug.
- neifar, malika, 2020, "Efficient Markets Hypothesis in Canada: a comparative study between Islamic and Conventional stock markets ," MPRA Paper, University Library of Munich, Germany, number 103175, Sep.
- neifar, malika, 2020, "Efficiency-Market Hypothesis: case of Tunisian and 6 Asian stock markets ," MPRA Paper, University Library of Munich, Germany, number 103232, Sep.
- Musaeva, Gulzhan & Masih, Mansur, 2018, "Granger-causal relationship between islamic stock markets and oil prices: a case study of Malaysia," MPRA Paper, University Library of Munich, Germany, number 102862, Oct.