Quant Letter: December 2023, Week-1

Weekly (25th Edition)

SSRN

Recently Published

Quantitative

Deep Reinforcement Learning for US Equities Trading: The study shows that Deep Reinforcement Learning can effectively interpret synthetic alpha signals in financial trading, outperforming the market benchmark. (2023-11-27, shares: 3.0)

Machine Learning for Path-Dependent Contracts: The study introduces a new method for pricing financial products with early-termination features using machine learning algorithms and Chebyshev interpolation techniques. (2023-11-28, shares: 5.0)

Historical Calibration of SVJD Models with Deep Learning: The paper suggests using deep neural networks to calibrate parameters of Stochastic Volatility Jump Diffusion models, proving to be more accurate, robust, and faster than other methods. (2023-12-01, shares: 2.0)

Machine Learning for Portfolio Performance: The study introduces a method to determine the impact of individual factors on portfolio performance, providing insights into the economic value of return predictability in machine learning models. (2023-11-29, shares: 2.0)

Financial

Competition between ETFs and Mutual Funds: The research indicates that less transparent active ETFs do not affect mutual fund investor flows, instead, the reputation of the cloned mutual funds helps the new ETFs attract more flows. (2023-12-05, shares: 2.0)