Machine learning techniques applied to the problem of financial market
forecasting struggle with dynamic regime switching, or underlying correlation
and covariance shifts in true (hidden) market variables. Drawing inspiration
from the success of reinforcement learning in robotics, particularly in agile
locomotion adaptation of quadruped robots to unseen terrains, we introduce an
innovative approach that leverages world knowledge of pretrained LLMs (aka.
'privileged information' in robotics) and dynamically adapts them using
intrinsic, natural market rewards using LLM alignment technique we dub as
"Reinforcement Learning from Market Feedback" (**RLMF**). Strong empirical
results demonstrate the efficacy of our method in adapting to regime shifts in
financial markets, a challenge that has long plagued predictive models in this
domain. The proposed algorithmic framework outperforms best-performing SOTA LLM
models on the existing (FLARE) benchmark stock-movement (SM) tasks by more than
15\% improved accuracy. On the recently proposed NIFTY SM task, our adaptive
policy outperforms the SOTA best performing trillion parameter models like
GPT-4. The paper details the dual-phase, teacher-student architecture and
implementation of our model, the empirical results obtained, and an analysis of
the role of language embeddings in terms of Information Gain.

通过强化学习算法从市场反馈中动态适应迁移和协方差偏移，从而提高金融市场预测精确性，并且在现有基准任务和最近提出的任务中表现优于其他模型。

教授机器人行走的同时也教给它们交易 —— 使用知情数据和 LLMs 进行制度自适应执行

What Teaches Robots to Walk, Teaches Them to Trade too -- Regime  Adaptive Execution using Informed Data and LLMs

We introduce and make publicly available the NIFTY Financial News Headlines
dataset, designed to facilitate and advance research in financial market
forecasting using large language models (LLMs). This dataset comprises two
distinct versions tailored for different modeling approaches: (i) NIFTY-LM,
which targets supervised fine-tuning (SFT) of LLMs with an auto-regressive,
causal language-modeling objective, and (ii) NIFTY-RL, formatted specifically
for alignment methods (like reinforcement learning from human feedback (RLHF))
to align LLMs via rejection sampling and reward modeling. Each dataset version
provides curated, high-quality data incorporating comprehensive metadata,
market indices, and deduplicated financial news headlines systematically
filtered and ranked to suit modern LLM frameworks. We also include experiments
demonstrating some applications of the dataset in tasks like stock price
movement and the role of LLM embeddings in information acquisition/richness.
The NIFTY dataset along with utilities (like truncating prompt's context length
systematically) are available on Hugging Face at
this https URL

介绍并公开提供了 NIFTY 金融新闻头条数据集，旨在促进和推进利用大型语言模型（LLMs）进行金融市场预测的研究。该数据集包含两个针对不同建模方法的版本：NIFTY-LM 和 NIFTY-RL。每个版本都提供了经过筛选和排名、附带全面元数据、市场指数的高质量数据，以适应现代 LLM 框架。同时还包括了一些应用实例，如股价走势预测以及 LLM 嵌入在信息获取与丰富性中的角色。