Student-managed investment funds: a review and research agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose In recent years, student-managed investment funds (SMIFs), experiential learning programs at an increasing number of universities, have attracted significant scholarly interest. In this article, we review the academic literature on this pedagogy. Design/methodology/approach We use the systematic review method to assess a sample of 85 articles published in 30 journals during the period 1975 to 2020. Findings Our literature review reveals four streams of research: best practices and challenges, investment management, innovation and trends and SMIFs in a research setting. We also propose future research directions, including specific gaps in the literature, a focus on innovations to traditional programs, systematic investment performance and expansion into behavioral finance issues. Originality/value We contribute a comprehensive view of the body of scholarship on SMIFs, identifying existing streams of research and future research directions that will help guide the development of SMIF research into a cohesive and productive space.
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.
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it