3 citations · 7 across the 12 of their papers we have counts for
12 papers
Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models
Weixian Waylon Li, Mengyu Wang, Tiejun Ma
Backtesting large language models (LLMs) on historical financial data is unreliable when their pre-training data include the evaluated events. An LLM trained in 2024 may already en…
Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
Mengyu Wang, Xiaoying Zhi, Zhiyi Li +4
While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constrains performance on specialize…
Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory
Weixian Waylon Li, Jiaxin Zhang, Xianan Jim Yang +2
Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete m…
Spectral Attention Steering for Prompt Highlighting
Weixian Waylon Li, Yuchen Niu, Yongxin Yang +3
Attention steering is an important technique for controlling model focus, enabling capabilities such as prompt highlighting, where the model prioritises user-specified text. Howeve…
One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning
Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho +2
Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (Q…
Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability
Weixian Waylon Li, Tiejun Ma
Identifying risky traders with high profits in financial markets is crucial for market makers, such as trading exchanges, to ensure effective risk management through real-time deci…
FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
Ying Li, Mengyu Wang, Miguel de Carvalho +2
Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard re…
GRAB: A Risk Taxonomy--Grounded Benchmark for Unsupervised Topic Discovery in Financial Disclosures
Ying Li, Tiejun Ma
Risk categorization in 10-K risk disclosures matters for oversight and investment, yet no public benchmark evaluates unsupervised topic models for this task. We present GRAB, a fin…
Pre-training Time Series Models with Stock Data Customization
Mengyu Wang, Tiejun Ma, Shay B. Cohen
Stock selection, which aims to predict stock prices and identify the most profitable ones, is a crucial task in finance. While existing methods primarily focus on developing model…
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model
Weixian Waylon Li, Yftah Ziser, Yifei Xie +2
Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub…
Modeling News Interactions and Influence for Financial Market Prediction
Mengyu Wang, Shay B. Cohen, Tiejun Ma
The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper intr…
MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction
Mengyu Wang, Tiejun Ma
It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, r…