A Multi-Agent Approach to Investor Profiling Using Large Language Models
Bibliographic record
Abstract
Investor profiling is essential in financial advising, allowing advisors to tailor investment strategies based on individual risk preferences, experience, and financial goals. This research aims to automate and enhance the investor profiling process using large language models (LLMs) through interactive multi-agent conversations. Our approach involves designing an investor agent, which represents a pre-defined investor derived from a narrative generated by a large language model (LLM) with given attributes, and an advisor agent that engages in conversation to infer the hidden attributes of the investor. The advisor-agent dynamically adjusts its questions based on previous conversation context to maximize the accuracy of its attribute predictions. The advisor agent makes predictions once it acquires sufficient information and compares them against the ground truth. We conducted extensive simulations across thousands of investor attribute sets and evaluated the effectiveness of the advisor-agent’s predictions based on key metrics. Our results demonstrate that LLM can effectively approximate investor characteristics. This research contributes to the field of AI-driven financial advising and unveils the potential of conversational agents in refining investor assessment methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".