# Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling

arXiv: https://arxiv.org/abs/2602.19919 · HTML: https://mlsanity.com/paper/2602.19919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Preference alignment for recommenders and RAG, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 20 of 20)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://mlsanity.com/paper/2412.03104/) (2024)
- [Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement](https://mlsanity.com/paper/2503.01875/) (2025)
- [DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning](https://mlsanity.com/paper/2310.15205/) (2023)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework](https://mlsanity.com/paper/2403.12582/) (2024)
- [What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning](https://mlsanity.com/paper/2505.22148/) (2025)
- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Beyond the Black Box: Interpretability of LLMs in Finance](https://mlsanity.com/paper/2505.24650/) (2025)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Pre-training Time Series Models with Stock Data Customization](https://mlsanity.com/paper/2506.16746/) (2025)
- [FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph](https://mlsanity.com/paper/2508.00961/) (2025)
- [Fusing Narrative Semantics for Financial Volatility Forecasting](https://mlsanity.com/paper/2510.20699/) (2025)
- [StockMem: An Event-Reflection Memory Framework for Stock Forecasting](https://mlsanity.com/paper/2512.02720/) (2025)
- [DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets](https://mlsanity.com/paper/2511.00390/) (2025)
- [Unleashing Expert Opinion from Social Media for Stock Prediction](https://mlsanity.com/paper/2504.10078/) (2025)

## Built on by (most cited first, 1 of 1)

- [Buy the Rumor, Sell the News: When Is News Priced In?](https://mlsanity.com/paper/2608.14014/) (2026)

## Abstract (arXiv)

Financial market movements are often driven by discrete financial events conveyed through news, whose impacts are heterogeneous, abrupt, and difficult to capture under purely numerical prediction objectives. These limitations have motivated growing interest in using textual information as the primary source of trading signals in learning-based systems. Two key challenges hinder existing approaches: (1) the absence of large-scale, event-centric datasets that jointly model news semantics and statistically grounded market reactions, and (2) the misalignment between language model reasoning and financially valid trading behavior under dynamic market conditions. To address these challenges, we propose Janus-Q, an end-to-end event-driven trading framework that elevates financial news events from auxiliary signals to primary decision units. Janus-Q unifies event-centric data construction and model optimization under a two-stage paradigm. Stage I focuses on event-centric data construction, building a large-scale financial news event dataset comprising 62,400 articles annotated with 10 fine-grained event types, associated stocks, sentiment labels, and event-driven cumulative abnormal return (CAR). Stage II performs decision-oriented fine-tuning, combining supervised learning with reinforcement learning guided by a Hierarchical Gated Reward Model (HGRM), which explicitly captures trade-offs among multiple trading objectives. Extensive experiments demonstrate that Janus-Q achieves more consistent, interpretable, and profitable trading decisions than market indices and LLM baselines, improving the Sharpe Ratio by up to 102.0% while increasing direction accuracy by over 17.5% compared to the strongest competing strategies.