Predicting Short-term Market Returns and Volatility Using Index of Consumer Sentiment
Bibliographic record
Abstract
This study examines the short-term predictive ability of Index of Consumer Sentiment (ICS) about Indian stock market returns. Monthly values of ICS, six broad market indices, and nine sectorial indices are collected. The paper finds significant contemporaneous co-movement between S&PBSE500 and other indices. There is no contemporaneous co-movement between ICS and 15 indices. With one lag, ICS Granger cause two sectorial indices and four broad market indices. The one period ahead predictive regression model finds that sentiment has some predictive power of small cap, mid cap and BSE500 index returns. The effect is negative and statistically significant. The predictive regression result indicates that following month of high consumer sentiment, small cap, midcap, and BSE500 index returns decline and vice-versa. However, there is no association between ICS and large cap index and sentiment has no predictive ability of large cap index. The result of variance model indicate that ARCH term and GARCH term are insignificant indicating that the market has no long memory and new shocks will not persist to many future periods. The paper finds no volatility clustering and volatility persistence except in case of small cap index. This paper finds presence of noise trade and investors over-reaction in small cap stocks. EGARCH result supports for the presence of leverage effect, and confirms negative impact of consumer sentiment on small cap stocks.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".