ASSESSING FINANCIAL RISK TOLERANCE OF PORTFOLIO INVESTORS USING DATA ENVELOPMENT ANALYSIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For some investors their own personal investment counsellors address their investment strategy; for others automated means are used. To protect investors, the Canadian Government has enacted the "Know Your Client" Act requiring that all investment dealers and vendors of securities must know their clients and advise them on the appropriate investment strategy. This paper uses Data Envelopment Analysis (DEA) in a novel manner by applying it to a large data set of answers to a number of psychological questions. A Slacks Based Model was used to estimate investor risk tolerance. The model analyses the risk profile of the investor and can be used as a guide to match the risk rating of the investment vehicles for the client. Statistical comparisons were also carried out to show how risk tolerance relates to various demographic variables. Finally, the DEA results were validated through comparisons with the commercial system already in use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.004 | 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 it