Instruction Tuning Chronologically Consistent Language Models

Battle Count: 72

Directly addresses a critical methodological issue in LLM-based quantitative trading: lookahead bias from training data leakage. The paper provides a practical tool (ChronoGPT-Instruct) for establishing conservative lower bounds on predictive power, demonstrates that at least 54-62% of news return predictability persists without leakage, and shows prompt-based trading portfolio construction over 2007-2023. The Sharpe ratio comparisons (0.95 for ChronoGPT-Instruct vs. 1.53-1.76 for larger models) directly quantify the leakage component in trading signals. The envelope phenomenon finding suggests temporal alignment matters more than raw model capability for trading.

The paper provides a simple conversational chat interface and publicly available fixed model weights, making it accessible for researchers. However, understanding the underlying methodology requires knowledge of the no-training-leakage contract, loss decomposition with leakage terms, and the two-stage training pipeline. The prompt-based filtering algorithm and curriculum-based instruction finetuning add some complexity. For practitioners, using the models is straightforward (chat interface), but replicating the full training pipeline would be computationally expensive.

The paper provides fully open, fixed model weights on HuggingFace (https://huggingface.co/manelalab), publicly releases instruction-finetuning data, uses a simple conversational chat interface, and defines a clear knowledge-cutoff date ensuring strict temporal separation. The methodology is well-documented with formal mathematical definitions of the no-training-leakage contract.