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International review of financial analysis, 2024-05, Vol.93, p.103120, Article 103120
2024

Details

Autor(en) / Beteiligte
Titel
Intermediate cross-sectional prospect theory value in stock markets: A novel method
Ist Teil von
  • International review of financial analysis, 2024-05, Vol.93, p.103120, Article 103120
Ort / Verlag
Elsevier Inc
Erscheinungsjahr
2024
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • This study examines the predictive power of performance persistence from the perspective of prospect theory using 12 months of return distributions. Performance persistence stems from unique information that differs from momentum, disposition effect, and firm-specific variables related to the trading behaviors of individual investors. The novel cross-sectional prospect theory value (CSPTV) measurement, designed to reflect cross-sectional comparisons across return distributions for all stocks, captures this unique information better than the existing prospect theory value (PTV), which is specific to a single stock. In other words, CSPTV improves the predictive power of prospect theory by providing performance with a larger magnitude and greater significance than PTV. Consequently, this study anticipates differentiated contributions from the CSPTV design, which expands the application scope of the existing prospect theory to the past 12-month return distribution and improves the predictive power of prospect theory in cross-sectional stock returns. •We expand the application of the prospect theory value (PTV) to the past 12-month return distribution•The predictive power of existing PTV using 12-month return distributions is confirmed.•Significant positive performance stems from unique information distinct from momentum.•We devised a novel method of cross-sectional prospect theory value (CSPTV).•The CSPTV improves the predictive power of prospect theory compared to that of existing PTV.
Sprache
Englisch
Identifikatoren
ISSN: 1057-5219
eISSN: 1873-8079
DOI: 10.1016/j.irfa.2024.103120
Titel-ID: cdi_elsevier_sciencedirect_doi_10_1016_j_irfa_2024_103120

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