Results 21 to 30 of about 160 (43)

An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution

, 2023
The purpose of this research is to devise a tactic that can closely track the daily cumulative volume-weighted average price (VWAP) using reinforcement learning.
Hong, Youngjoon, Kim, Jimyeong, Kim, Soohan, Sul, Hong Kee   +3 more
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Large Language Models in Finance: A Survey

, 2023
Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing ...
Chen, Hang, Ding, Han, Li, Yinheng, Wang, Shaofei   +3 more
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Two-stage Modeling for Prediction with Confidence

, 2022
The use of neural networks has been very successful in a wide variety of applications. However, it has recently been observed that it is difficult to generalize the performance of neural networks under the condition of distributional shift.
Chen, Dangxing
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FinBen: A Holistic Financial Benchmark for Large Language Models

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks.
Ananiadou, Sophia, Chen, Zhengyu, Dai, Yongfu, Deng, Zhiyang, Feng, Duanyu, Han, Weiguang, He, Yueru, Hu, Gang, Huang, Jiajia, Huang, Jimin, Jiang, Yuechen, Kang, Haoqiang, Kuang, Ziyan, Lai, Yanzhao, Li, Dong, Li, Haohang, Liu, Xiao-Yang, Liu, Zhiwei, Lopez-Lira, Alejandro, Luo, Zheheng, Peng, Min, Wang, Benyou, Wang, Hao, Xiang, Ruoyu, Xiao, Mengxi, Xie, Qianqian, Xiong, Guojun, Xu, Yijing, Yang, Kailai, Yao, Zhiyuan, Yu, Yangyang, Yuan, Chenhan, Zhang, Tianlin, Zhang, Xiao   +33 more
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Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing

, 2022
In this paper, we propose and analyze a novel combination of multilevel Richardson-Romberg (ML2R) and importance sampling algorithm, with the aim of reducing the overall computational time, while achieving desired root-mean-squared error while pricing ...
Chakrabarty, Siddhartha P., Sinha, Devang   +1 more
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A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options

In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate deep kernel learning and sparse variational Gaussian processes to address the
Ding, Deng, Lu, Weiguo, Wu, Xuan, Yuan, Gangnan, Zhuang, Jirong   +4 more
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Application of Black-Litterman Bayesian in Statistical Arbitrage

\begin{abstract} In this paper, we integrated the statistical arbitrage strategy, pairs trading, into the Black-Litterman model and constructed efficient mean-variance portfolios. Typically, pairs trading underperforms under volatile or distressed market
Zhou, Qiqin
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Neural option pricing for rough Bergomi model

The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model.
Li, Guanglian, Teng, Changqing
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A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models

We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new ...
Georgoulis, Emmanuil H., Papapantoleon, Antonis, Smaragdakis, Costas   +2 more
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Explainable AI in Request-for-Quote

In the contemporary financial landscape, accurately predicting the probability of filling a Request-For-Quote (RFQ) is crucial for improving market efficiency for less liquid asset classes.
Zhou, Qiqin
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