본문 바로가기
KERD
논문KCI 등재

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

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

초록

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.

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.

참고문헌 (18)

  1. Tobin, J., "World economy and financial markets", Japan and the World Economy, 10(3), pp.377~379 (1998)[DOI]
  2. "S&P 500(SPX)" (2020)[URL]
  3. "Buffett Indicator: Where Are We with Market Valuations?" (2020)[URL]
  4. "Fed to release 3600 trillion won" (2020)[URL]
  5. "Fed's policy to increase the amount of money in the U.S. is good for stock prices for more than two years" (2020)[URL]
  6. "Fed freezes'zero interest rate'... Retention by 2023 Preview" (2020)[URL]
  7. "Monthly Supply of Houses in the United States" (2020)[URL]
  8. "Initial Jobless Claims" (2020)[URL]
  9. "Unemployment Rate" (2020)[URL]
  10. "Existing home sales" (2020)[URL]
  11. Amal, M., "Determinants of Foreign Direct Investment: Theoretical Approaches", Foreign Direct Investment in Brazil, pp.9~62 (2016)[DOI]
  12. 홍성혁, "A study on stock price prediction system based on text mining method using LSTM and stock market news", 디지털융복합연구, 18(7), pp.223~228 (2020), 한국디지털정책학회[DOI]
  13. 황희수, "심층 신경회로망 모델을 이용한 일별 주가 예측", 한국융합학회논문지, 9(6), pp.39~44 (2018), 한국융합학회[DOI]
  14. 홍성혁, "Research on Stock price prediction system based on BLSTM", 한국융합학회논문지, 11(10), pp.19~24 (2020), 한국융합학회[DOI]
  15. 홍성혁, "거시지표와 딥러닝 알고리즘을 이용한 자동화된 주식 매매 연구", 디지털융복합연구, 18(11), pp.267~272 (2020), 한국디지털정책학회[DOI]
  16. Hong, S., "Research on Stock Prediction Technology using RNN and YText Miner", Test Engineering and Management, 83, pp.4315~4321 (2020)
  17. Ding, X, "Deep learning for event-driven stock prediction", Twenty-fourth international joint conference on artificial intelligence (2015)
  18. Akita, R, "Deep learning for stock prediction using numerical and textual information", 2016 IEEE, 2016, 1~6[DOI]

관련 논문AI추천

이 간행물의 다른 논문 (29건)