A Behavioral Theory of the Income-Oriented Investors: Evidence from Japanese Life Insurance Companies
Bibliographic record
Abstract
This study investigates the yield-seeking behavior of income-oriented institutional investors, who are essential players in financial markets. While external pressures compelling firms to “reach for yield” are well-documented, the firm-level behavioral drivers underlying this phenomenon remain largely underexplored. Drawing on the behavioral theory of the firm, this study argues that an investor’s performance relative to their social aspiration level (the peer average) influences their yield-seeking decisions, and that this effect is moderated by “portfolio slack,” defined as unrealized gains or losses. To test this theory in the context of persistent low-yield pressure, this study constructs and analyzes a panel dataset of Japanese life insurance companies from 2000 to 2019. The analysis reveals that these investors increase their portfolio income yield after underperforming their peers and decrease it after outperforming. Furthermore, greater portfolio slack amplifies yield increases after underperformance and mitigates yield decreases after outperformance. In contrast, organizational slack primarily mitigates yield reductions after outperformance. This research extends the behavioral theory of the firm to the asset management context by identifying distinct performance feedback responses and proposing portfolio slack as an important analytical construct, thereby offering key insights for investment managers and financial regulators.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".