Why won't my financial advisor beat the market? Reflections on the ‘Black Swan’
Bibliographic record
Abstract
Every time we meet with our investment advisors, we go over the same things. We look at the amount of cash contributed during that quarter, the ratio of equities, debt, derivatives, commodities and other financial instruments. We explore the distribution of assets among economic sectors and geography. Finally, we get to the numbers that concern us most, the bottom line rate of return net of fees. Most of us listen to a prepared presentation discussing the implications of geopolitics, macroeconomics, trade negotiations and currency exchanges, among other things, on that rate of return. Most financial advisors will present a coherent viewpoint to proffer an understanding of the rate of return for that quarter. This effectively communicates that the financial professional has some insight that has guided the investment strategy. The implication is that this financial insight has offered the investor an edge and serves as justification for the financial advisor’s fees. The problem, however, is that if these financial advisors are so insightful, knowledgeable and informed, why do they rarely beat the market in the long term? Why can’t our investment portfolios generate the consistently high returns reaped by Warren Buffet, George Soros and David Einhorn? And why do we continue to pay these people significant sums when the rate of return is comparable with an exchange-traded fund, which costs a fraction of personalized investment management?
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".