Behavioral Finance

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. ...

As AI systems step into the boardroom and brokerage app, a new question arises: How do they think about money? In a world increasingly shaped by large language models (LLMs) not just answering questions but making decisions, we need to ask not just whether AI is accurate—but what kind of financial reasoner it is. A recent study by Orhan Erdem and Ragavi Pobbathi Ashok tackles this question head-on by comparing the decision-making profiles of seven LLMs—including GPT-4, DeepSeek R1, and Gemini 2.0—with those of humans across 53 countries. The result? LLMs consistently exhibit a style of reasoning distinct from human respondents—and most similar to Tanzanian participants. Not American, not German. Tanzanian. That finding, while seemingly odd, opens a portal into deeper truths about how these models internalize financial logic. ...