에너지 상품 시장에서 인터넷 검색 자료의 활용 방안
An Application of Web Search Data for a Energy Commodity Market
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
거시경제, 금융 그리고 경영 등 다양한 분야에서 인터넷 검색 자료를 활용한 연구가 활발히 이루어지고 있으나 에너지 상품 시장에서의 활용에 관한 연구는 미미한 실정이다. 이에 본 연구는 k-평균 군집분석과 기댓값 최대화 군집분석을 통해인터넷 검색 자료와 WTI 원유 선물 가격의 변동성 관계를 분석하였다. 그 결과 원유 선물 가격 변동성이 커짐에 따라 인터넷검색 자료 빈도가 증가하는 경향을 확인하였고, 추가적으로 인터넷 검색 자료를 활용한 포트폴리오 투자 전략을 제안하였다. 인터넷 검색자료를 활용한 포트폴리오는 기준 포트폴리오에 비해 표준편차가 0.032% 감소하여 위험도가 낮아지는 것으로확인되었으며 주간 평균 수익률은 0.08% 증가 하였다. 해당 결과는 인터넷 검색 자료가 위험관리 지표로 활용 가능함을 시사한다.
Abstract
Web search data has actively been utilized in various fields such as finance, macroeconomics, and business. There are only a few studies, however, related to web search data in energy commodity market. We thus investigate the relationship between web search data and the volatility of WTI futures prices using k-means and expectationmaximization cluster analysis. The result shows that the volatility of WTI futures price has a tendency to cause the increase in web search data. In addition, we propose a portfolio investment strategy using web search data. The performance of the proposed portfolio provides that the standard deviation decreases by 0.032% and weekly average return increases by 0.08% comparing a benchmark portfolio. This result means that web search data can be used for a risk management.
참고문헌 (15)
- Bollen, J, "Twitter mood predicts the stock market", Journal of Computational Science, 2(1), pp.1~8 (2011)
- Bordino, I, "Web Search Queries Can Predict Stock Market Volumes", PLoS ONE, 7(7), pp.e40014~ (2012)
- Carrière-swallow, Y, "Nowcasting with Google Trends in an Emerging Market", Journal of forecasting, 32(4), pp.289~298 (2013)
- Choi, H, "Predicting the present with Google Trends", Economic Record, 88(Issue 6), pp.2~9 (2012)
- Ettredge, R, "Using webbased search data to predict macroeconomic statistics", Communications of the ACM, 48(11), pp.87~92 (2005)
- Goel, S, "Predicting consumer behavior with web search", Proceedings of the National Academy of Sciences of the United States of America, 107(41), pp.17486~17490 (2010)
- Ji, Q, "Oil price volatility and oil-related events: An Internet concern study perspective", Applied Energy, 137, pp.256~264 (2015)
- Can Google Trends search queries contribute to risk diversification?, "Kristoufek, L", Scientific Reports, ~, pp.Scientific Reports, Vol. 3, Article number: 2713 (2013)
- Predictability of stock market activity using Google search queries, "Latoerio, P", Universidad Carlos III de Madrid, ~, pp.Statistics and Econometrics Series 05 (2013)
- Mclaren, L, "Using internet search data as economic indicator", Bank of England Quarterly Bulletin, 2(Q2), pp.134~140 (2011)
- Moat, H.S, "Quantifying Wikipedia usage patterns before stock market moves", Scientific Reports, 3 (2013)
- Pang-Ning, T, "Introduction to Data Mining", pp.488~568 (2014), Pearson Education
- Preis, T, "Quantifying Trading Behavior in Financial Markets Using Google Trends", Scientific Reports (2013)
- Ranco, G, "The Effects of Twitter Sentiment on Stock Price Returns", PLoS ONE, 10(9), pp.e0138441~ (2015)
- Vosen, S, "Forecasting private consumption:survey-based indicators vs. Google trends", Journal of forecasting, 30(6), pp.565~578 (2011)