Do the Israeli Provident Funds have the Ability to Time the Bond and Stock Markets? An Analysis across Alternative Investments
Bibliographic record
Abstract
This paper investigates whether Israeli fund managers possess market-timing ability across asset classes over time, using 15 years of monthly data from the Israeli provident funds. We apply three methodologies based on return based and portfolio holdings approaches. Most of the early return-based timing methods and the most recent portfolio holdings measures suggest that U.S. mutual fund managers do not possess equity timing ability. Our study is the first to test this evidence on multi- asset class provident funds in the Israeli market and compare the timing ability of fund managers in each asset class according to different approaches. We introduce an alternative holdings method that combine the asset allocation theory with that of market timing and use "excess policy" holdings data to predict future market returns. In addition, previous studies mostly ignore the contribution of other instruments to timing decisions, which may cause any conclusions about managers' timing decisions to be incomplete. Hence, we test equity market timing with respect to all markets using a multiple market index model in the holdings approach. In line with previous research, our empirical results indicate significantly negative market timing in domestic equities according to all the measures used. On the other hand, provident fund managers on average seem to display some timing ability for government bonds.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".