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전력소비량과 주식시장 활동 간의 관계 연구

The Relationship between Electricity Consumption and Stock Market Activity

박재홍|김현모
한국엔터프라이즈아키텍처학회 (Korea Institute of Enterprise Architecture)|2017.06|정보화연구 (The Journal of Information Technology and Architecture)|제14권 제2호|pp. 147-162

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참고문헌

키워드

벡터오차수정모형Business Cycle경기순환Vector Error Correction ModelKOSPI공적분 검정Cointegration TestUnit Root Test단위근 검정전력소비량종합주가지수Electricity consumption
주제분류과학기술학

초록

본 연구의 목적은 전력소비량 및 주식시장 활동 상호 간의 예측력을 실증적인 방법으로 확인하는 것이다. 지금까지 수행된 전력소비량 예측 연구는 예측변수로서 국내총생산, 국민총생산 등과 같은경제성장률 지표에 초점을 맞추었으나, 경제상황과 밀접한 관련성을 가진 국내 주식시장 활동을 고려하지 않았다. 뿐만 아니라, 경기순환의 대리변수 중 하나인 전력소비량이 주식시장 활동에 미치는 영향력을 확인하는 연구 또한 수행되지 않았다. 따라서 본 연구는 전력소비량 및 주식시장 활동 상호 간의 예측 가능성을 검토한다. 본 연구는 우리나라 산업용 전력소비량 중 많은 비중을 차지하는 제조업및 서비스업 전력소비량에 초점을 맞추었다. 또한 주식시장 활동을 나타내는 지표로서 국내 주식시장전체의 주가 움직임을 나타내는 종합주가지수(KOSPI)를 고려하였다. 본 연구는 실증분석을 위하여,1982년 1월부터 2014년 12월까지 발생된 우리나라 제조업 및 서비스업 전력소비량, 종합주가지수의월간 데이터를 수집하였고, 이를 기반으로 시계열 분석을 수행하였다. 가장 먼저, 단위근 및 공적분 검정, 적정시차 분석을 통해 벡터오차수정모형을 수립하였고, 이를 기반으로 제조업 및 서비스업 전력소비량과 종합주가지수 상호 간의 예측력을 확인하였다. 벡터오차수정모형 분석 결과, 서비스업 전력소비량과 제조업 전력소비량은 상호 간에 대한 예측력을 가지는 것으로 나타났으며, 서비스업 전력소비량은 종합주가지수(KOSPI)에 대한 예측력을 가지고 있는 것으로 나타났다. 본 연구는 에너지 관리및 재무경영학 문헌에 학술적인 시사점을 제공하며, 전력수요 관리 및 주식시장 활동 측면의 실무적시사점을 제공한다.

Abstract

The purpose of this study is investigate that relationship between electricity consumptionand KOSPI. Prior many studies in financial literature suggest that variables correlated withbusiness cycle can predict stock market activities. We considered industrial electricity consumptionto be a good proxy of a business cycle as higher electricity consumption reflects more intensivecapital utilization and implies more maintenance investment. Many researchers in energy fields suggest that energy consumption leads to economic growth. KOSPI is defined as the macroeconomicvariable that is a series of indexes that track the overall Korean Stock Exchanges. Thisindex represents a barometer of the overall macroscopic economic situation in Korea. Thus, weempirically showed that predictive power of electricity consumption on KOSPI, and predictivepower of KOSPI on electricity consumption. For the empirically analysis, we collected data of measuredelectricity consumption and KOSPI from 1982 January to 2014 December. We first performedunit root test and cointegration test. Based on the results, we employed Vector ErrorCorrection Model(VECM) model to estimate the predictive power between electricity consumptionand KOSPI. In VECM analysis results, we found that electricity consumption in service industrynegatively correlated with electricity consumption in manufacturing industry. Contrary, electricityconsumption of manufacturing industry has positively correlated with electricity consumption ofservice industry. On the other hand, electricity consumption of service industry has negatively correlatedwith KOSPI. The results of this study contribute to energy management and finance literature. We suggest implication of academics and practice for energy management and financeliterature.

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