DOI QR코드

DOI QR Code

Analysis of the Ripple Effect of the US Federal Reserve System's Quantitative Easing Policy on Stock Price Fluctuations

미국연방준비제도의 양적완화 정책이 주가 변동에 미치는 영향 분석

  • 홍성혁 (백석대학교 스마트IT공학부)
  • Received : 2020.12.28
  • Accepted : 2021.03.20
  • Published : 2021.03.28

Abstract

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.

거시경제는 한 나라의 경제 전반의 움직임을 나타내는 개념으로 경제주체인 기업, 정부, 가계경제 활동 전반에 영향을 미친다. 거시경제는 국민소득, 물가, 실업, 통화, 금리, 원자재 등의 변화를 살펴보면 경제 주체들의 행위와 상호작업이 제품과 서비스의 가격에 영향을 파악할 수 있다. 미국연방준비제도(FED)는 코로나 경제침체를 극복하기 위한 다양한 경기부양책을 내 놓으며, 세계경제를 이끌고 있다. 현재 코로나로 인한 주가가 2020년3월20일에 지속적으로 하락하였지만, FED의 강력한 경지부양책인 양적완화로 미국의 S&P500지수는 3월 23일이후 반등을 시작해 12월 15일 3,694.62까지 회복에 성공했다. 따라서 주가의 예측을 기업의 재무제표로 판단하는 것이 아니라 거시경제지표에 따른 FED의 경기부양책이 더 영향을 미치고 있는 실정이다. 따라서 본 연구는 FED의 경기부양책과 주가에 미치는 영향을 분석하여 주식투자에 손실을 줄이고 건전한 투자 정착을 위해 본 연구를 진행하였다.

Keywords

References

  1. Tobin, J. (1998). World economy and financial markets. Japan and the World Economy, 10(3), 377-379. doi:10.1016/s0922-1425(98)00038-3
  2. Investing.com. (2020.12.24.). S&P 500(SPX), https://www.investing.com/indices/us-spx-500
  3. Guru Focus. (2020.12.24). Buffett Indicator: Where Are We with Market Valuations?. https://www.gurufocus.com/stock-market-valuations.php
  4. S. H. Lee. (2020.12.24). Fed to release 3600 trillion won. https://biz.chosun.com/site/data/html_dir/2020/07/14/2020071401667.html
  5. K. H. Kim. (2020.12.24). Fed's policy to increase the amount of money in the U.S. is good for stock prices for more than two years. https://www.chosun.com/economy/stock-finance/2020/09/01/QJPQO4TUNZGM5KG337MDSZOPXM/
  6. J. B. Ryu. (2020.12.24). Fed freezes'zero interest rate'... Retention by 2023 Preview. https://www.yna.co.kr/view/AKR20200917005700071
  7. Fred Economic Data. (2020.12.24.) Monthly Supply of Houses in the United States. https://fred.stlouisfed.org/series/MSACSR
  8. Fred Economic Data. (2020.12.24.) Initial Jobless Claims. https://fred.stlouisfed.org/series/ICSA
  9. Fred Economic Data. (2020.12.24.) Unemployment Rate. https://fred.stlouisfed.org/series/UNRATE
  10. Fred Economic Data. (2020.12.24.) Existing home sales. https://fred.stlouisfed.org/series/EXHOSLUSM495S
  11. Amal, M. (2016). Determinants of Foreign Direct Investment: Theoretical Approaches. Foreign Direct Investment in Brazil, 9-62. doi:10.1016/b978-0-12-802067-8.00002-5
  12. Hong, S. (2020). A study on stock price prediction system based on text mining method using LSTM and stock market news. The Society of Digital Policy and Management, 18(7), 223-228. https://doi.org/10.14400/JDC.2020.18.7.223
  13. H. S. Hwang. (2018). Daily Stock Price Forecasting Using Deep Neural Network Model. Journal of the Korea Convergence Society, 9(6), 39-44. https://doi.org/10.15207/JKCS.2018.9.6.039
  14. Hong, S. (2020). Research on Stock price prediction system based on BLSTM. Journal of the Korea Convergence Society, 11(10), 19-24. https://doi.org/10.15207/JKCS.2020.11.10.019
  15. Hong, S. (2020). A Research on stock price prediction based on Deep Learning and Economic Indicators. Journal of Digital Convergence, 18(11), 267-272. https://doi.org/10.14400/JDC.2020.18.11.267
  16. Hong, S. and Han, J. (2020). Research on Stock Prediction Technology using RNN and YText Miner. Test Engineering and Management, 83, 4315-4321.
  17. Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015, June). Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence.
  18. Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016, June). Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.