Energy income and optimal investment policy for the Alberta Heritage Savings Trust Fund
Bibliographic record
Abstract
The present paper illustrates the impact of the inclusion of the economic value of government energy income in the optimal asset al1oc:ation decision for the Alberta Heritage Savings Trust Fund. Existing investment policy does not consider exogenous income when determining asset mixes. The result is an over-allocation of capital to energy stocks, higher volatility and lower expected utility among all government assets. Two tests are conducted to examine the differences in asset allocations when energy resources are considered to be a nontradable asset in an expanded portfolio and when the allocation decision only involves financial assets. The first test assumes that the existing asset mix is ideal. An optirniser is calibrated to produce parameter estimates that result asset weights consistent with exi:sting industry sector weights as of March 31, 2005. The second test uses parameter variables estimated using historical data. Associated Sharpe Ratios are compared to determine whether the investor receives an economic benefit from the new portfolio weights. When compared to existing industry weight!; within the financial portfolio, the optimal portfolio mix decision will always exclude energy stocks except in those instances where the nontradable asset is smaller than the optimal allocation to this industry sector. In the latter case, the values are highly improbable and so I conclude that the Alberta Heritage Savings kust Fund should not be investing its funds in the energy sector. I also show that over allocation. prevents the fund from more effectively diversifying its holdings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".