A hierarchical RL portfolio optimizer using FinBERT sentiment and market indicators reports 26% annualized return and Sharpe 1.2 on a 2018-2024 backtest, beating equal-weight and S&P 500 benchmarks.
'Finance Wizard' at the FinLLM Challenge Task: Financial Text Summarization
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abstract
This paper presents our participation under the team name `Finance Wizard' in the FinNLP-AgentScen 2024 shared task #2: Financial Text Summarization. It documents our pipeline approach of fine-tuning a foundation model into a task-specific model for Financial Text Summarization. It involves (1) adapting Llama3 8B, a foundation model, to the Finance domain via continued pre-training, (2) multi-task instruction-tuning to further equip the model with more finance-related capabilities, (3) finally fine-tuning the model into a task-specific `expert'. Our model, FinLlama3\_sum, yielded commendable results, securing the third position in its category with a ROUGE-1 score of 0.521.
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HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
A hierarchical RL portfolio optimizer using FinBERT sentiment and market indicators reports 26% annualized return and Sharpe 1.2 on a 2018-2024 backtest, beating equal-weight and S&P 500 benchmarks.