Emotional Reactions of Students to Perceptions of Negative Trends and Small Losses in Short-Term Stock Market Simulation
Notice bibliographique
Résumé
Decision-making on stock markets is a complex experience characterized by strong emotions (Nofsinger, 2017). Price fluctuations are stimuli that can cause a wide range of psychological reactions in investors (Shiv et al., 2005). In particular, the perception of loss is a central area in behavioral finance: loss aversion (Kahneman & Tversky, 1979) suggests that the psychological impact of a loss is stronger than a gain of the same size. Traditionally, attention has focused on significant financial losses or bear markets, where intense emotions such as fear and panic are commonly found (Shiller, 2014). However, our study suggests addressing a less examined side of the emotional experience: the psychological and behavioral reactions of individuals to the perception of negative trends and small losses. Our goal is to understand if and how perceived unfavorable small stock market movements could induce significant emotional responses, particularly among novice investors. For this purpose, we used a qualitative exploratory study with eight students participating in a short-term (three-day) stock market simulation. During this period, the stock market index on which the students based their investments declined by 0.36%. This fluctuation does not constitute a bear market in the financial sense generally recognized. Nevertheless, our findings show that participants actively perceived a ‘general negative trend’ and exhibited significant emotional reactions, even facing small financial losses. Based on semi-structured interviews, our article aims at: 1) identifying the range of emotions felt by participants during the simulation; 2) describing the development of these emotions in response to perceived market variations; and 3) conducting a thematic analysis of their influence on participants' decisions, with a particular focus on emotional regret related to small losses and reactions to uncertainty and perceived losses. Our results demonstrate a significant change in emotions over time. From an initial interest and a relative emotional detachment, students show a growing emotional commitment to market fluctuations. Moreover, as the experiment progresses and disappointments increase, the emotional picture is largely defined by negative emotions, notably fear in relation to potential losses, as well as sadness and disappointment related to unfavorable results. Market surprises, particularly sudden falls, lead to intense reactions, which can result in panic and impulsive decisions. Given the inability to improve the financial situation, the experience can also result in abandonment and resignation. By examining these dynamics, we aim at contributing to understanding emotional impact in behavioral finance, beyond major crisis scenarios.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».