no code implementations • 24 May 2024 • Jianyuan Zhong, Zhijian Xu, Saizhuo Wang, Xiangyu Wen, Jian Guo, Qiang Xu
In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation.
no code implementations • 15 Feb 2024 • Hang Yuan, Saizhuo Wang, Jian Guo
Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT.
no code implementations • 6 Feb 2024 • Saizhuo Wang, Hang Yuan, Lionel M. Ni, Jian Guo
Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence. However, tailoring these agents for specialized domains like quantitative investment remains a formidable task.
no code implementations • 18 Nov 2023 • Saizhuo Wang, Zhihan Liu, Zhaoran Wang, Jian Guo
Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios.
no code implementations • 7 Oct 2023 • Xuhui Jiang, Chengjin Xu, Yinghan Shen, Xun Sun, Lumingyuan Tang, Saizhuo Wang, Zhongwu Chen, Yuanzhuo Wang, Jian Guo
Knowledge graphs (KGs) are structured representations of diversified knowledge.
no code implementations • 31 Jul 2023 • Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel M. Ni, Heung-Yeung Shum, Jian Guo
One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors).
3 code implementations • 15 Jul 2023 • Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, Jian Guo
Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
no code implementations • 13 Dec 2022 • Jian Guo, Saizhuo Wang, Lionel M. Ni, Heung-Yeung Shum
Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1. 0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2. 0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3. 0, applying deep learning techniques to discover complex nonlinear pricing rules.
no code implementations • 7 Apr 2022 • Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X. Liu, Raheem Beyah, Ting Wang
To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.
no code implementations • 1 Jan 2021 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
no code implementations • 24 Oct 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
1 code implementation • 8 Jul 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
no code implementations • 25 Sep 2019 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu, Shouling Ji
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.