Portfolio Construction

TL;DR A role‑based, debate‑driven LLM system—AlphaAgents—coordinates three specialist agents (fundamental, sentiment, valuation) to screen equities, reach consensus, and build a simple, equal‑weight portfolio. In a four‑month backtest starting 2024‑02‑01 on 15 tech names, the risk‑neutral multi‑agent portfolio outperformed the benchmark and single‑agent baselines; risk‑averse variants underperformed in a bull run (as expected). The real innovation isn’t the short backtest—it’s the explainable process: constrained tools per role, structured debate, and explicit risk‑tolerance prompts. ...

Financial markets don’t reward the loudest opinions. They reward the most timely, well-calibrated ones. FinDPO, a new framework by researchers from Imperial College London, takes this lesson seriously. It proposes a bold shift in how we train language models to read market sentiment. Rather than relying on traditional supervised fine-tuning (SFT), FinDPO uses Direct Preference Optimization (DPO) to align a large language model with how a human trader might weigh sentiment signals in context. And the results are not just academic — they translate into real money. ...