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Report NEP-FMK-2023-07-24
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:
- Marco Di Maggio & Francesco Franzoni & Shimon Kogan & Ran Xing, 2023, "Avoiding Idiosyncratic Volatility: Flow Sensitivity to Individual Stock Returns," NBER Working Papers, National Bureau of Economic Research, Inc, number 31360, Jun.
- Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023, "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers, arXiv.org, number 2306.10224, Jun, revised Oct 2025.
- Ian Berk & Massimo Guidolin & Monia Magnani, 2023, "Strong vs. Stable: The Impact of ESG Ratings Momentum and their Volatility on the Cost of Equity Capital," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 23202.
- Cassidy K. Buhler & Hande Y. Benson, 2023, "Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification," Papers, arXiv.org, number 2306.12639, Jun.
- Sander Lammers & Massimo Giuliodori & Robert Schmitz & Adam Elbourne, 2023, "Bank Funding, SME lending and Risk Taking," CPB Discussion Paper, CPB Netherlands Bureau for Economic Policy Analysis, number 447, Jul, DOI: 10.34932/868h-xy80.
- Guglielmo Maria Caporale & Kyriacos Kyriacou & Nicola Spagnolo, 2023, "Aggregate Insider Trading and Stock Market Volatility in the UK," CESifo Working Paper Series, CESifo, number 10511.
- Karol Chojnacki & Robert Ślepaczuk, 2023, "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-15.