Simulating financial time series using attention

Fu, Weilong; Hirsa, Ali; Osterrieder, Jörg Robert (2022). Simulating financial time series using attention Cornell University 10.48550/arXiv.2207.00493

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Financial time series simulation is a central topic since it extends the limited real data for training and evaluation of trading strategies. It is also challenging because of the complex statistical properties of the real financial data. We introduce two generative adversarial networks (GANs), which utilize the convolutional networks with attention and the transformers, for financial time series simulation. The GANs learn the statistical properties in a data-driven manner and the attention mechanism helps to replicate the long-range dependencies. The proposed GANs are tested on the S&P 500 index and option data, examined by scores based on the stylized facts and are compared with the pure convolutional GAN, i.e. QuantGAN. The attention-based GANs not only reproduce the stylized facts, but also smooth the autocorrelation of returns.

Item Type:	Working Paper
Division/Institute:	Business School > Institute for Applied Data Science & Finance > Finance, Accounting and Tax Business School
Name:	Fu, Weilong; Hirsa, Ali and Osterrieder, Jörg Robert0000-0003-0189-8636
Subjects:	H Social Sciences > HG Finance
Publisher:	Cornell University
Language:	English
Submitter:	Jörg Robert Osterrieder
Date Deposited:	22 Aug 2022 12:57
Last Modified:	22 Aug 2022 12:57
Publisher DOI:	10.48550/arXiv.2207.00493
Uncontrolled Keywords:	deep learning, generative adversarial networks, attention, time series, stylized facts
ARBOR DOI:	10.24451/arbor.17396
URI:	https://arbor.bfh.ch/id/eprint/17396