Min-soo Jeong
Yonsei University · Medicine
About the Lab
Professor Min-soo Jeong's research lab specializes in interdisciplinary studies at the intersection of data science, financial engineering, and materials science. The lab focuses on developing advanced predictive models for energy demand forecasting, incorporating uncertainty quantification and nonparametric estimation techniques to improve long-term projections. In financial modeling, the lab pioneers innovative portfolio selection strategies that integrate sentiment analysis, macroeconomic indicators like interest rates, and behavioral finance insights to enhance investment performance. Additionally, the lab contributes to materials engineering by modeling diffusion processes in heat-treated steels, particularly in vacuum carburizing with acetylene, to optimize material properties.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
12Down syndrome (DS) is associated with many neural defects, including reduced brain size and impaired neuronal proliferation, highly contributing to the mental retardation. Those typical characteristics of DS are closely associated with a specific gene group "Down syndrome critical region" (DSCR) on human chromosome 21. Here we investigated the molecular mechanisms underlying impaired neuronal proliferation in DS and, more specifically, a regulatory role for dual-specificity tyrosine-(Y) phosphor
A predictive model for the carbon concentration profile in vacuum carburized steels with acetylene was proposed. The model involves the process and boundary conditions based on the characteristic of vacuum carburizing with acetylene and carbon diffusivity with an alloying element effect. In order to verify the predictive model, the carbon concentration profile of a cylindrical SCM415 steel specimen vacuum carburized with acetylene was calculated using the finite element method. The carbon concen
This paper proposes an adjusted portfolio selection model: SA-M portfolio selection model. SA-M portfolio model is based on Markowitz’s mean-variance portfolio selection model, but it adjusts the total proportion of capital invested in risky assets considering the result of news sentiment analysis. We applied KR-FinBERT to news headline data which were gathered from 8 different economic newspapers. The performance of this model was evaluated by using historical monthly stock return data of South
에너지 수요에 대한 예측은 그 중요성으로 인해 지금까지 많은 연구자들의 관심을 받아왔다. 이러한 에너지 수요의 전망은 향후 더욱 더 중요해질 것으로 예상하지만 모형을 기반으로 하는 예측에는 다양한 불확실성이 존재할 수 있다. 예를 들어 예측에는 예측모형의 불완전성으로 인한 불확실성이 존재할 수 있고, 또한 예측모형에서 사용되는 설명변수의 불확실성에서 오는 불확실성도 존재할 수 있다. 본 연구에서는 이러한 다양한 불확실성들을 보정할 수 있는 기법들을 제시하고, 이러한 기법들을 적용한 비모수 추정 모형을 이용하여 장기 에너지 수요를 예측하였다. 에너지 예측시 편의를 발생시킬 수 있는 여러 가지 다양한 요인이 존재하는데, 본 연구에서는 먼저 GDP 등의 설명변수 예측에서 발생할 수 있는 오차를 줄이기 위한 비모수 기법을 제시하고 적용하였다. 또한 비모수 비선형적인 모형을 추정할 때 발생하는 편의를 추가적으로 보정하여 보다 개선된 추정값을 얻을 수 있도록 하였다. 본 연구에서 제시한 예측
본 연구에서는 연속시간 회귀모형에서 오차항이 정상성(stationarity)을갖지 않는 경우의 추정량에 대해 분석한다. 이산시간 I(1) 과정의 경우 이미 Choi, Hu and Ogaki(2008)에서 다루었지만, 연속시간 모형은 단순한 I(1)을 기준으로 분석한 결과와는 다르게 모형의 특성에 따라 상이한결과들이 다양하게 나타난다. 연속시간 모형은 이산시간 모형과는 달리 조건에 따라 OLS(ordinary least squares) 추정량이 일치성(consistency)을 갖기도 하고 GLS(generalized least squares) 추정량이 비일치성을 갖는 경우도 존재하게 되는데, 본 연구에서는 두 추정량이 일치성을 지닐 조건을 각각 도출하고 각 조건들의 의미를 살펴본다. 또한 한국과 미국의 주가지수 동조화 분석에 본 연구의 회귀모형을 적용하여, 기존의 공적분(cointegration) 분석이나 수익률모형 분석은 시계열의 추세적인 연관성을 설명하는데 충분하지 않을 수도 있음
This study introduces a novel portfolio construction algorithm, named the Interest rate-based portfolio combination (IPc), uniquely incorporating dynamic interest rate as an indicator to guide asset allocation decisions. Extensive literature substantiates the existence of an inverse relationship between the interest rate and the profitability of the stock market, although this relationship has not been widely exploited in the realm of portfolio optimization research. Leveraging this relationship
This paper examines whether stock excess return predictability is dependent upon the stock market volatility. The paper introduces a two-state regime switching model with endogenous feedback effect for the stock return predictability test. To model regime switching, this paper adopted a new approach proposed by Chang et al. (2017), allowing an endogenous feedback effect channel through which the underlying time series affect the next period volatility regime. This paper shows that modeling such
This study empirically proves the superiority of Fund-H, which has been built on the basis of Markowitz portfolio selection model. We use 49 stock items and 22 commodity items for 20 years to check the performance of Fund-H and benchmarks. We first compare the expected annual return rate of the stock market and the commodity market, then invest on the market with the higher expected return rate. Exponentially weighted moving average method has been used to calculate the expected return rate, thu
In this paper, we address the issue of the time-varying relationship between health and long-term income and show that income profile over time is more important than the permanent income of a specific period in explaining general health condition. A functional probit regression model is introduced to investigate how the income profiles of middle-aged people can affect the health condition of the last period of the given period using the Panel Study of Income Dynamics data. We also perform the p
The near-field scanning micro scope (NSOM) technique is in the spotlight as the next generation storage device. Many different types of read/write mechanism for NSOM have been introduced in the literature. In order for a near-field probe to be successfully implemented in the system, a suitable slider and suspension are needed to be properly designed. The optical slider is designed considering near-filed optics and probe array. The suspension generally supports slider performance, and tracking se
AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models’ accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assump
This paper provides a novel approach to extract the trend and seasonal components from panel data consisting of individual entries showing both strong trend and seasonality. For such a data set, the usual principal component analysis generally fails to disentangle them. In the paper, we suggest a methodology to separately identify them using the Hodrick-Prescott filter that is commonly and widely used to remove trends in various economic data. We apply our methodology to a food product sales pan
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