An Analysis on the Effects of Economic Conditions on Investment Behavior: Focusing on Level of Finance Knowledge, Income-Expenditure Balance and Liquidity Constraints
Bibliographic record
Abstract
In this study, we investigated the factors that influence investor's propensity to invest which called investment behavior. Factors known to have an impact on individual investment decisions are psychological and cognitive errors, socioeconomic and environmental factors, and financial, economic and environmental factors. Among those factors, financial and environmental factors including the level of knowledge in terms of financial economy, harmonization of income and expenditure, and liquidity constraints are empirically investigated. Among the personal factors the liquidity constraints has been turned out to have significant impact on decision-making about investments. The findings that liquidity constraints have an impact on the investment behavior and there are differences between investment behaviors according to the purposes of investment, and the liquidity constraints has impact on them have significant meanings. Generally, investment behaviors are kinds of inherent characteristics, in other words, hard to be changed or affected. But the study results indicate that investment behaviors can be affected personal economic conditions. So, when advices or consulting regarding investments is made, not only the person’s investment behavior but also the person’s economic conditions, especially if the person is under liquidity constraints should be considered. Also, investors should take into accounts his or her economic conditions when making investment decisions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".