Buy and buy again: The impact of unique reference points on (re)purchase decisions
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
Abstract Behavioral finance has uncovered that investor engage emotionally when trading. We investigate how three psychological factors influence purchase and repurchase decisions: representativeness, the influence of prior gains, and reference points. Using trading data of 7200 UK investors we find that purchase decisions are influenced by representative heuristic and repurchase decisions are influenced by both representative heuristic and prior profitability. Further survival analysis showed that investors use the prior selling price as a unique reference point. Investors are more likely to repurchase a stock when trading above its reference point, but more likely to initiate the repurchase when trading below. Investors are influenced by previous experience and engage learning behavior when they seek to reinforce past success. As reference points are inferred but infrequently researched, this research adds to the literature and provides important and robust results for those engaging with financial planning clients.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it