Long-Term Returns: a Reality Check for Pension Funds and Retirement Savers
Bibliographic record
Abstract
Expectations for investment returns play an important role in establishing business capital cost and capital structure, as well as influencing individual savings behaviour, risk-taking, and long-term funding of institutional obligations such as pensions. Proper and realistic forecasting makes for better long-term investment decisions improving retirement planning. In this Commentary, we demonstrate why pension plan administrators and individual savers should avoid using historical rates of returns to forecast future returns, and provide our own forecast for long-term investment returns on a balanced portfolio of bonds and stocks using current and prospective market information. Our empirical analysis of Canadian data provides substantial evidence that forecasts based on past performance should not form a basis for decision-making, as they consistently point in the wrong direction. The history of stock and bond markets is punctuated with extreme situations – such as the recent global financial crisis – that make drawing on the outcome of these events inappropriate as a predictor of future performance. Thus, relying on historical performance to inform long-run return forecasts in pricing future pension liabilities is almost certain to be misleading. Prospectively, using information available as of February 2013, we predict long-term returns in the neighbourhood of 2.5 percent (0.5 percent real) on long-term bonds and of 6.9 percent (4.8 percent real) on stocks. For a balanced portfolio (50/50 split), we therefore expect a real return of 2.7 percent for the next decade. To incorporate potential risks to this scenario, we have performed a series of long-term simulations that give a sense of varied possible outcomes. We found significant downside risks. There is a 25 percent probability that portfolio returns will be lower than forecasted by more than one percentage point on a 30-year horizon, and lower by more than 2 percentage points on a 10-year horizon. Finally, we draw implications for pension funds and individual savers. The use of more realistic investment return expectations would reveal bigger pension liability for some defined-benefit pension plans. They also mean individuals should save more for their retirement to avoid a larger-than-expected drop in their retirement lifestyles.
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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 | 0.011 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".