The Business Cycle And The Portfolio Composition Of Mutual Funds
Bibliographic record
Abstract
This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of mutual funds’ composition across the business cycle. The main purpose of the research is to determine whether mutual funds alter their investments across the business cycle. Systematization of the literary sources and approaches for solving the problem of the relationship between the business cycle and the composition of mutual funds indicates that five-star rated mutual funds may have an investment strategy that is different from lower-rated funds. Investigation of the topic of the relationship between the business cycle and composition of mutual funds in the paper is carried out in the following logical sequence: First, we classified each quarter as an “improving” or a “worsening” business condition period based on the Aruoba-Diebold-Scotti Business Conditions Index. As a result, we had seven “improving” and seven “worsening” business condition periods during our sample period. Then, we compared each star group (one-star to five-star) investments in common stocks, preferred stocks, convertible bonds, warrants, corporate bonds, municipal bonds, government bonds, other securities, and cash across the “improving” versus the “worsening” periods. The methodological tools utilized in this research were nonparametric tests. The objects of the research are the mutual funds listed in the CRSP quarterly mutual funds dataset for the 2003-2006 period. The paper presents the results of empirical analysis for these mutual funds, which showed that five-star funds tend to have a different strategy when compared to lower-rated funds. The research empirically confirms and theoretically proves that the five-star funds tend to invest more in riskier assets and they tend to better adjust to the conditions (i.e. invest more in common stocks and less in bonds in improving periods) when compared to the other groups. This explains their success: higher NAVs compared to the other groups and higher star ratings. On the other hand, our results show that the lower-rated funds do not adjust their investments in main asset classes like stock and bonds during “improving” versus “worsening” business condition periods. Overall, our results indicate that mutual funds’ star ratings and NAVs are linked to these funds’ success in their adaptation to the macro-economic environment. The results of the research can be useful for investment firms or individual investors that consider investing in U.S. mutual funds. Keywords: mutual fund, portfolio, business cycle, recession, net asset value.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".