Effect of Behavioral Biases on Investment Decision Making in Nepalese Stock Market with the Mediating Role of Investors’ Sentiment
Bibliographic record
Abstract
The current study explores the impact of behavioral biases such as market factors, herding behavior and awareness factors on investors’ decision-making on the Nepal Stock Exchange and also investigates the mediating role of investors’ sentiment in the relationship among the variables. To comprehend investor sentiment (IS) and how individual investors make decisions, this study introduced an innovative conceptual framework incorporating herding behavior, market dynamics, and awareness factors. A quantitative methodology was used to conduct the investigation, specifically employing a survey research design. This study made use of primary data that was collected by means of a structured questionnaire and gathered data from 408 individual investors. The convenience sample method was used, while structural equation modeling was used as a statistical tool to test hypothesis. It was determined how the market factor, herd behavior, and awareness affect investors' sentiment and decision-making by using structural equation modeling. The result showed that the most notable factors that affect investors’ sentiment were herding factors. Similarly, in the absence of a moderating variable, investors' sentiment is affected by all variables, including market factors, herding behavior, and awareness factors which have a significant impact on investors decision-making in the Nepal Stock Exchange. This study aims to assist individual investors in Nepal by guiding their investment choices and preventing sentiment-driven errors. It emphasizes the importance of understanding fellow investors' sentiment and suggests that awareness programs can enhance market understanding. Previous research has typically examined only a few factors at a time. This current study explores how various factors affecting investor sentiment—like market factors, herding behavior, and awareness—impact investment decision-making among individual investors in Nepal. Importantly, these factors have never been studied together in the context of how Nepalese individual investors make decisions about their investments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".