Efficacy of Technical Analysis to Assess Fair Value Gap: Evidence from Nepalese Commercial Bank
Bibliographic record
Abstract
This paper examines the efficacy of the selected technical indicators to identify fair value gaps meant for stock investment and trade optimization based on the technical signals of the Banking sector stocks listed in the Nepal Stock Exchange (NEPSE). The analysis focuses on Simple Moving Averages (SMA), Moving Average Convergence Divergence (MACD), Bollinger Bands (BB), Relative Strength Index (RSI), and Fibonacci Retracement as key indicators. Findings reveal that the selected technical indicators provide reliable ground to explore fair value gap and better market entry and exit signals for optimizing investment, aligning with standard technical analysis principles. Therefore, the study underscores the significance of using multiple indicators for robust decision-making for stock market investment, particularly in emerging markets like Nepal, where market inefficiencies and volatility is common phenomena. Moreover, the results have practical implications for all market participants including traders, investors, and analysts. Future research should may choose other indicators to support this strategy to develop more comprehensive trading models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".