Reference Prices and Turnover: Evidence from Small-Capitalization Stocks
Bibliographic record
Abstract
A large amount of literature in the field of social psychology and product pricing discusses the role of reference prices in affecting buyer’s price perception and purchase intention. Reference price denotes a standard against which the consumer compares the offer price of a product. In this paper, we investigate whether reference prices play any role in affecting the trading decision of stock market investors. We use firm-level, fixed-effect panel data methodology to empirically investigate whether investors respond to a violation of their internalized reference price range by executing a trading decision. Our results, based on a sample of Indian firms with small capitalization, show that investors respond to a violation of their internalized reference price range by executing a trading decision. However, consistent with the prior findings that investors suffer from myopic loss aversion, they continue to hold the positions when the reference price range is violated on the downside but sell stocks that have violated the high point of the reference price range. Our findings are robust for the reference price ranges that are constructed using the prior day’s trading prices, prior week’s trading prices, and prior year’s trading prices. The portfolio managers can develop a better understanding of expected trading intensity by incorporating reference price range in their models. The policymakers can use our results to find ways to improve the liquidity and efficiency of financial markets.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".