INVESTMENT BEHAVIOUR AND RISK PERCEPTION: AN ANALYSIS FOR TURKISH MARKET
Notice bibliographique
Résumé
Purpose- The cognitive comprehension of financial indicators, risk aversion, risk perception, and investment behavior is defined as financial literacy. It's possible that a variety of characteristics, such as gender, age, income level, social standing, education, etc., will affect an investor's behavior. The purpose of this study is to highlight the behavior of investors in Turkish capital markets. The analysis is done on the results of two surveys, the first conducted in the fourth quarter of 2022 and the second in the first quarter of 2023. Methodology- This study's objective is to highlight investor behavior and risk perception in Turkish financial markets. In the most recent two consecutive quarters, the results of two surveys are analyzed and compared. Three sections comprise the surveys. A demographic question is asked in the first section. The second section asks questions concerning investment behavior, signs of financial stress, and confidence in regard to one's financial literacy. The final aspect contributes to the analysis of what people think of the Bitcoin market. In this study, Graphic analysis, Cronbach Alpha, Normality, and Mann-Whitney U tests are performed, respectively. First, the graphical analysis of the selected questions is made. Based on these graphs, the similarities and differences between the surveys are shown. Second, The reliability test is applied to the selected questions for the statistical modeling of the analysis. This test is determined as the Cronbach Alpha test. Third, the Normality test is applied to reveal which test to use in the next step. Two different tests are used for this analysis. These are the Kolmogorov-Smirnov and Shapiro-Wilk tests. Fourth, the Mann-Whitney U test is applied. At this stage, firstly, Mann-Whitney U and Wilcoxon W test statistics are examined. The ranks are calculated for each variable. Finally, the Mann-Whitney U test is applied, and the results are interpreted. Fifth, The results of the two surveys are compared. Findings- The findings show both similarities and differences among numerous variables. For instance, holding time is defined as the amount of time an investor holds an investment or as the time between purchasing it and selling it. Investors' risk aversion and financial literacy both influence the holding period. Riskier assets force investors to adjust their purchase or sell actions dynamically. The results show various portfolio diversification behaviours. While men prefer to start with foreign currency investments, women are more interested in making gold investments. Also, middle-aged investors invest more in cryptocurrencies and take more risks than younger investors. Conclusion- based upon the analysis, findings it may be concluded that respondents do differ in their investment preferences and risk-taking over the years. The findings show various portfolio diversification behaviors. While men prefer to invest in foreign currency, women are more interested in purchasing gold. Keywords: Investor behavior, risk perception, cryptocurrency market, Bitcoin, Mann-Whitney U test. JEL Codes: G1, G4, C4
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».