Prediction of Quantitative Easing Policy Effect on the U.S. Stock Market Using ARIMA Model

Authors

  • Zheheng Liu

DOI:

https://doi.org/10.54691/bcpbm.v34i.3165

Keywords:

Quantitative Easing, S&P500, ARIMA model.

Abstract

The rapid spread of the coronavirus impacted the global financial market dramatically. A “rush for cash” - the eager demand for liquidity only - that suspended financial markets and endangered to make an already dreadful situation much worse was spurred by the sudden contraction and intense concern about the future consequence of the virus. To minimize the economic loss due to the COVID-19 pandemic, the Federal Reserve has a bunch of policy tools to maintain the credit flow in the market. These included substantial mortgage-backed and government-backed securities purchases as well as lending to households, businesses, participants in the financial market, and governments at different level. The Fed also initiated purchase of debt securities on a wide scope, which is an effective resort to combat economic crisis. In this paper, effect of the increased money supply on financial assets in stock market will be evaluated, revealing the impact of the aggressive expansionary monetary policy on the stock market performance through applying the autoregressive integrated moving average (ARIMA) models. The process of using ARIMA model to predict the stock market will be illustrated. Published stock data are obtained from S&P500 and M2 data from Board of the Federal Reserve System. Results obtained from the selected model revealed that the un-disciplined monetary policy had a tremendous positive impact on the stock market.

Downloads

Download data is not yet available.

References

Eric Milstein, David Wessel, 2021 ,“What did the fed do in response to the COVID-19 crisis?”

L.C. Kyungjoo, Y. Sehwan and J. John, 2007 ,“Neural Network Model vs. SARIMA Model in Forecasting Korean Stock Price Index (KOSPI), Issues in Information System, vol. 8 no. 2, pp. 372-378.

N. Merh, V.P. Saxena, and K.R. Pardasani, , 2010 ,“A Comparison Between Hybrid Approaches of ANN and ARIMA For Indian Stock Trend Forecasting”, Journal of Business Intelligence, vol. 3, no.2, pp. 23-43.

C. Lee, C. Ho, 2011, “Short-term load forecasting using lifting scheme and ARIMA model”, Expert System with Applications, vol.38, no.5, pp.5902-5911.

C. Wang, 2011, “A comparison study of between fuzzy time series model and ARIMA model for forecasting Taiwan Export”, Expert System with Applications.

N. Rangan and N. Titida, 2006, “ARIMA Model for Forecasting Oil Palm Price”, Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Universiti Sains Malaysia.

J. Sterba and Hilovska, 2010 ,“The Implementation of Hybrid ARIMA Neural Network Prediction Model for Aggregate Water Consumption Prediction”, Aplimat- Journal of Applied Mathematics, vol.3, no.3, pp.123-131.

P. Pai and C. Lin, 2005, “A hybrid ARIMA and support vector machines model in stock price prediction”

A. Meyler, G. Kenny and T. Quinn, “Forecasting Irish Inflation using ARIMA Models”, Central Bank of Ireland Research Department, Technical Paper, 3/RT/1998.

B.G. Tabachnick and L.S. Fidell, 2001, “Using multivariate statistics”, 4th ed., Person Education Company, USA.

Downloads

Published

2022-12-14