Emerging insights: Unveiling market efficiency in Mongolia's transforming economy
Bibliographic record
Abstract
The Efficient Market Hypothesis (EMH) posits that stock prices reflect all available information, preventing consistent outperformance in strong-form efficient markets. However, in inefficient markets, investors can achieve higher returns by exploiting informational advantages. This study evaluates whether the Mongolian capital market is weak-form efficient. Although the Mongolian Stock Exchange (MSE) has grown since its 1991 inception, it remains under-researched. Our analysis focuses on the MSE Top 20 Index's most frequent constituents between 2012 and 2023, assessing if their returns are independent and align with the random walk model—a characteristic of developed markets like the U.S. Using daily stock returns, we conducted statistical tests, including non-parametric (Kolmogorov-Smirnov for normality, Run test) and parametric analyses (autocorrelation under the random walk model). Results compellingly reject the random walk hypothesis, indicating weak-form inefficiency. This inefficiency implies a potential for investors to realize abnormal returns, especially through momentum-based strategies, challenging the EMH. Findings were consistent across three market cycles, enhancing robustness. Future research could apply advanced econometric models and compare results with other markets, offering deeper insights into the MSE's characteristics. This study opens new directions for strategic trading and market analysis within the Mongolian capital market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".