이자율 기간구조가 주가지수에 미치는 영향 분석: ARDL과 기계학습 모형들 간의 분석 효율성 비교
The Effects of the Term Structure of Interest Rates on Stock Markets: Comparison of Analytic Efficiency between ARDL Model and Machine Learning Methods
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초록
채권시장에서 형성되는 이자율 기간구조는 다양한 경로를 통해 주식시장에 영향을 미칠 수 있다. 이에 대한 영향 분석은 전통적으로 통계적 회귀모형을 통해 이루어져 왔지만, 최근 들어 인공신경망을 포함하는 다양한 기계학습 모형들로도 왕성하게 이루어지고 있다. 이 연구는 이자율 기간구조의 구성요인들이 주가지수에 미치는 영향을 분석하기 위해 2013년에서 2022년까지의 일별 데이터로 이들 변수의 재무계량적 특징과 장기관계를 파악하는 ARDL 모형 회귀를 통해 분석의 효율성 기준을 확보한 후 이를 주요 기계학습 모형들의 학습결과와 비교하는 분석이다. 이를 위해 적분차수가 혼재해 있는 이 변수들 간의 장기관계 존재 여부를 Bounds 테스트로 확인하고 이들 간의 장기관계를 ARDL 모형으로 추정하여 얻어진 RMSE 및 MAE를 기계학습 모형들의 학습결과와의 비교 기준 통계량으로 삼았다. 결정 트리기반인 XGboost 모형과 SVR 모형의 학습결과는 NN기반인 LSTM 모형 학습결과보다는 우월했지만 ARDL회귀 추정보다는 분석 효율성이 떨어졌다. 이 같은 결과는 데이터 규모 한계나 혹은 데이터 간의 재무계량적 관계를 전제로 하는 경우에는 기계학습을 통한 분석이 반드시 전통적 재무계량적 분석에 비해 우월하다고 볼 수 없음을 보여준다. 다만 재무이론적으로 허용되는 분석 데이터들의 규모가 커지게 될 때 즉, 빅데이터로 분석되면 분석 효율성의 변화 가능성을 배제할 수는 없다.
Abstract
This study aims to analyze the impact of the term structure of interest rates on the stock market with the daily data from 2013 to 2022. The efficiency criteria based on the ARDL regression that captures the financial econometric characteristics is compared with the learning outcomes of major machine learning models. The relationship was estimated using the ARDL model, and the resulting RMSE and MAE were used as comparative statistics with the learning outcomes of machine learning models. The learning outcomes of the decision tree-based XGBoost model and the SVR model were superior to those of the NN-based LSTM model, but the analysis efficiency was lower than that of the ARDL regression estimates. This result suggests that when there are limitations in data size or when the specific econometric relationships between variables are assumed, machine learning analysis is not necessarily superior to traditional financial econometric analysis. However, it cannot be ruled out that the efficiency of analysis may change when the analysis is conducted based on the relevant big data.
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