Essays on How Cultural Factors Affect the Sentiment and Behavior of Financial Market Participants
Bibliographic record
Abstract
The aim of this dissertation is to explore how cultural factors influence the sentiment and behavior of financial market participants. This dissertation consists of three chapters that encompass sports events, cultural dimensions, institutional investors, financial analysts, and earnings announcements. Chapter 1 is the introduction, Chapter 2 to 4 are the main content, and Chapter 5 concludes. In Chapter 2, I construct the Sports Mood Index (SMI) of 49 metropolitan areas in USA and Canada based on the performance of Big 4 professional sports teams and build the firm-level SMI based on institutional investors’ holdings as a proxy for investors’ mood. Under sports-induced bad mood settings, earnings announcement premium becomes higher because of increased uncertainty avoidance premium under pessimism, and post-earnings-announcement drift (PEAD) becomes lower because of the reversal effect. A standard deviation increase in SMI leads to a 22 bps increase of earnings announcement premium and a 16 bps decrease of PEAD in the following week. Whereas sports-induced good mood has no significant impact on the trading behavior of institutional investors, sports-induced bad mood leads to inattention. Institutional investors with sports-induced bad mood underreact to Standardized Unexpected Earnings (SUE) facing both positive and negative news, as evidenced by lower abnormal trading volume around earnings announcement days. The results remain valid after controls for SMI in the metropolitan areas of firm headquarters and are more pronounced if institutional investors located in NYC metropolitan area are excluded or if the market is facing high illiquidity. The SMI calculated based on dedicated institutional investors’ holdings has a greater impact on earnings announcement premium and abnormal trading volume than the SMI calculated based on quasi-indexers and transitory institutional investors. In Chapter 3, I explore how sports-induced bad mood affects the sentiment and behavior of sell-side financial analysts. Under sports-induced bad mood settings, sell-side analysts tend to issue more pessimistic forecasts in both earnings forecasts and price targets. Sports-induced bad mood also leads to inattention. Analysts under sports-induced bad mood have larger forecast errors and are slower or less likely to respond to earnings announcements. The results are robust to various measurements of pessimism, forecast errors and activity levels, and samples without analysts located in NYC. In Chapter 4, I examine whether Hofstede’s cultural dimensions influence the forecasting behavior of financial analysts, and how cultural diversity affects the quality of consensus forecasts. Combining earnings forecast, price target and recommendation samples, I find that individualism has a positive effect on boldness, whereas uncertainty avoidance has a negative effect; long-term oriented analysts are likely to have lower forecasting errors; and indulgent analysts tend to respond to earnings announcements slower. The quality of consensus forecasts would be better, if firms are covered by more culturally diversified analysts, which is associated with improved individual forecasting results. A standard deviation increase in diversity leads to a 45 bps decrease of the consensus forecast error. The effect of diversity is non-linear, that the benefits of diversity decline as the levels of diversity increase.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".