Funds Manager and Mutual Funds Characteristics on Mutual Funds Performance: Empirical Evidence of Equity Mutual Funds in Indonesia
Bibliographic record
Abstract
This study examines the effect of investment fund managers' characteristics in the form of tenure, and mutual fund characteristics with proxy turnover portfolios, market timing and stock selectivity on the performance of stock mutual funds. The research sample is 27 stock mutual funds in Indonesia that were active from 2013 to 2017. On the analysis of the relationships between the characteristics of investment managers and mutual funds characteristics on the performance of stock mutual funds, a series of OLS regressions were run. The panel data regression was included based on using the Eviews. All of the above were aimed at achieving portfolio optimization and realizing the maximization of the interests for fund management companies and investors. The main findings are as follows. Tenure does not affect the performance of stock mutual funds during the years 2013 to 2017, but if divided into 2 quadrants of tenure, namely tenure over 19 years and tenure under 19 years of work, the result is that tenure over 19 years has a positive effect on the performance of stock mutual funds, but tenure brought 19 years has no effect on the performance of equity funds, whereas mutual funds characteristics, which are proxied by portfolio turnover, market timing and stock selectivity, have a significant positive effect on the performance of equity funds in Indonesia. The primary limitation in the scope is the sample, because stock mutual funds that publish consistently Financial statements between 2013 and 2017 are few in number. These findings have important implications for fund management companies as input material that the investment strategy of the investment management team affects the performance of equity funds compared to the characteristics of investment managers with proxies for years of service. This paper proposes a new perspective to evaluate the relationship between the fund manager and mutual funds characteristicsanddivide 2 groups of working years, and calculate them with non-linear models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".