Functional Mixed-type Clustering of Investors' Daily Returns During a Market Shock Change-point and Recovery
Bibliographic record
Abstract
We analyze a dataset of daily portfolio market positions spanning July 17th 2019 to February 19th 2021 for many client accounts of a financial dealer. For each client, we know demographic information (age, gender, marital status, income, retirement status, investment knowledge), desired risk preferences, and their market positions for July 17th 2019 to February 19th 2021. The daily unrealized returns of those portfolios are analyzed using a functional principal component approach to discover underlying variations in observed client returns and if those differences in portfolios resulted in different outcomes before, during, and after the short-duration 2020 market crash. The distinguishing factors between portfolio returns are, in the order of importance, performances during the crash, before the crash, overall throughout, during the recovery, and mitigating losses at the sacrifice of recovery. We conduct mixed-type categorical analysis by mixing 15 functional principal components with categorical data using k-prototypes clustering. Clients are grouped into six categories: two small groups with strong performances corresponding to the volatility of their positions and four large groups, all with smaller volatilities and a range of overall performances. We find that clients tended to overshoot their risk appetites in clusters: high-risk appetites tended toward over-risked; low-risk tended toward under-risk. Overall, performance was connected to increased risk throughout the market crash—if the clients did not take as much risk, they did not experience as strong a recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".