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Record W3200280918 · doi:10.5267/j.ac.2021.6.015

The effect of essential information and disposition effect on shifting decision investment

2021· article· en· W3200280918 on OpenAlexvenueno aff
Sautma Ronni Basana, Zeplin Jiwa Husada Tarigan

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsDisposition effectModerationOptimismStock marketStock exchangeDispositionBusinessInvestment decisionsStock (firearms)Monetary economicsFinancial economicsEconomicsFinanceBehavioral economics

Abstract

fetched live from OpenAlex

The current pandemic era has given uncertainty to the country's economic growth and resulted in many countries experiencing a drastic decline in share prices. This condition impacts investors' perceptions of the funds that have invested in the stock market. This study investigates the effect of essential information and disposition effect on shifting decision investment with the character investor's moderation as the moderator variable. A survey was conducted on 252 investors who have invested in the Indonesian stock exchange. The Data processing used the partial least square (PLS) technique. This study indicates that essential information for investors in the pandemic era can increase the disposition effect in deciding beneficial share ownership. The essential information obtained by investors in the covid era regarding stock market movements and its internal performance in the stock market list can increase investor shifting decisions. The disposition effect can have a significant effect on shifting decision investors. Essential information related to stock price movements and its internal performance affects investors' courage to take risks and provide optimism for shifting decisions. Then the investor type does not affect the disposition effect on shifting decisions. This study contributes to the theory of financial behavior in decision making by considering psychological factors when uncertainty exists in the stock market.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.236
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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