Results 31 to 40 of about 160 (43)
$\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning
Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, compared with the expressive yet overfitting-prone black-box alphas.
Jiang, Shengyi +5 more
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Text mining arXiv: a look through quantitative finance papers
This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we
Bianchi, Michele Leonardo
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A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models.
Papapantoleon, Antonis, Rou, Jasper
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Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance.
Vidler, Alicia, Walsh, Toby
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Pricing and calibration in the 4-factor path-dependent volatility model
We consider the path-dependent volatility (PDV) model of Guyon and Lekeufack (2023), where the instantaneous volatility is a linear combination of a weighted sum of past returns and the square root of a weighted sum of past squared returns.
Gazzani, Guido, Guyon, Julien
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Calibrating the Heston Model with Deep Differential Networks
We propose a gradient-based deep learning framework to calibrate the Heston option pricing model (Heston, 1993). Our neural network, henceforth deep differential network (DDN), learns both the Heston pricing formula for plain-vanilla options and the ...
Amici, Giovanni +2 more
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We propose an efficient and easy-to-implement gradient-enhanced least squares Monte Carlo method for computing price and Greeks (i.e., derivatives of the price function) of high-dimensional American options.
Li, Guanglian, Yang, Jiefei
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Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle ...
Hirano, Masanori
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A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing
We generalize a semi-classical path integral approach originally introduced by Giachetti and Tognetti [Phys. Rev. Lett. 55, 912 (1985)] and Feynman and Kleinert [Phys. Rev.
Capriotti, Luca, Stedman, Mark
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