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.

Groundwater is the most important natural source for supplying of drinking water around the world, especially in rural areas. The 14^th khoroo (sub-district) of Khan-Uul district of Ulaanbaatar is only one khoroos where its inhabitants engage in animal husbandry and agriculture, which play an important role in the district’s economic growth, faces an increasing shortage of groundwater due to population and economic growth in the agriculture sector in the sub-district. In this study, we present the hydro-chemical characteristics and spatial distribution of aquifer using GIS and multivariate statistical approaches. During the sampling periods, a total of 51 groundwater samples were collected from 46 deep wells and 5 shallow wells in the area between October and November 2019. Samples included parameters of anions and cations (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, SO₄^2-, NO₃^-, HCO₃^-) and EC. Data from all the samples were subject to cluster and component analysis. As a result, three clusters were defined and it was established that the dominant groundwater type is HCO₃^-Ca²⁺-Na⁺. The parameter of Mg²⁺ and Ca²⁺ were not set in the permissible limits of the second and third clusters. Principal component analysis (PCA) illustrated that the northern part of the study area is greater affected to anthropogenic activities and aquifer mineralogy. Moreover, it found that Ca²⁺ and Mg²⁺ correlated with each other, which could be helpful in site specific monitoring of groundwater quality. The results of the study will contribute to management and quality control of the groundwater in the city.

Энэхүү судалгааны зорилго нь Монгол Улсын хөрөнгийн зах зээл дэх хөрөнгө оруулагчдын хэт итгэлийн гажуудлыг шинжлэх бөгөөд судалгаагаар Вектор авторегрессийн (VAR) загвар ашиглан хэт итгэлийн гажуудлын үр нөлөө болон түүний үргэлжлэх хугацааг шинжлэхдээ зах зээлийн өгөөж, арилжааны хэмжээний хоорондын харилцаа, хэлбэлзлийн үзүүлэлтүүдийг ашигласан. Судалгааны үр дүнгээс харахад Монголын хөрөнгийн зах зээлд тооцдог ТОП-20 индексийн хүрээний хэт итгэлийн гажуудал тодорхой хэмжээгээр оршиж байгааг тогтоосон. Тухайлбал, хөрөнгө оруулагчид зах зээлийн өгөөжийн богино хугацааны өсөлтийг эерэг дохио гэж хүлээн авч, арилжааны идэвхээ нэмэгдүүлдэг боловч энэ нь эцэстээ тогтворгүй байдалд хүргэдэг. Судалгаагаар энэхүү нөлөө нь богино хугацаанд (ойролцоогоор 10 хоног) үргэлжилж, улмаар тогтворжиж байгааг харуулсан. Энэхүү судалгааны үр дүнгээр хөрөнгө оруулагчдын хэт итгэлийн гажуудлыг багасгаж, зах зээлийн үр ашгийг нэмэгдүүлэхэд судалгааны өгөгдөл, үр дүнг ашиглах боломжтой гэж үзэж байна.

This paper examines the dynamic relationship between economic indicators, law enforcement mechanisms, and property-related crimes in Mongolia using a time-series econometric approach. Relying on the theoretical frameworks of Becker’s economic model of crime and Cantor and Land’s motivation–opportunity hypothesis, the study explores the effects of unemployment, detection probability, and incarceration rates on four crime categories: total crime, theft, robbery, and fraud. An error correction model (ECM) is employed to capture both short-run fluctuations and long-run equilibrium relationships over the period 1992–2022. The empirical findings reveal that detection rates exert a statistically significant deterrent effect on robbery in the short term, while incarceration rates are effective in reducing theft. Unemployment shows a positive and significant long-run effect on theft prior to 2009 but weakens thereafter due to methodological changes in labor statistics. Fraud demonstrates a distinct response pattern, exhibiting negative associations with both incarceration and unemployment, and showing no sensitivity to detection probability. Diagnostic tests support the model’s robustness, with heteroskedasticity in the theft model addressed using robust standard errors. This study contributes to the literature by providing the first country-specific empirical evidence on crime determinants in Mongolia. It highlights the heterogeneous impact of economic and institutional factors on different crime types in a transition economy. The findings underscore the need for integrated policy responses that combine improvements in law enforcement with inclusive economic and social development strategies.