Practical Applications of Factor Investing Using Capital Market Assumptions
Bibliographic record
Abstract
In <b><i>Factor Investing Using Capital Market Assumptions</i></b>, from the January 2022 quantitative special issue of <b><i>The Journal of Portfolio Management</i></b>, three Canadian researchers—<b>Redouane Elkamhi</b> and <b>Marco Salerno</b> of the <b>Rotman School of Management</b> at the University of Toronto and <b>Jacky Lee</b> of the <b>Healthcare of Ontario Pension Plan</b>—present a factor investing methodology far less complicated and expensive than existing practices by making use of publicly available capital market assumption reports. Most factor investing strategies require access to large databases and use costly proprietary mathematical models. In the hope of making factor investing possible for a wider range of investors, the team identified three macroeconomic factors that appear to be embedded in publicly available reports on capital market assumptions (CMAs): the rate of economic growth, the real interest rate, and the rate of inflation. Using these macroeconomic factors, they calculate asset class returns closely aligned to those in the CMAs. They then devised a formula to calculate the factor sensitivity of a sizable group of publicly traded asset subclasses, as well as a separate formula to price private assets. In addition, they developed a formula to reconcile their target factor weights with a portfolio that is built with constraints on diversification and risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".