Download pdf
Stock Return Prediction under US Federal Funds Rate Hike Based on Supervised Machine Learning
- 1 School of Accounting, Zhejiang University of Finance and Economics, Hangzhou, China
* Author to whom correspondence should be addressed.
Abstract
As US Federal Reverse Systems has continued to raise federal funds in the past few years in order to curb inflation, this has had a huge impact on chinese capital market. Investors and financial institude are actively engaged in predicting stock returns to maximize their investment income. This study constructs a model to analyze and predict the financial factor of stock income based on supervised machine learning methods and evaluate the results. The datasample is extracted from wind, for the period half a year before and after the recent federal funds rate hike. Utilizing machine learning packages in R, this paper construct models based on linear discriminant analysis, decision tree and random forest models. After constructing estimation models, this paper visualizes the prediction result by utilizing drawing package in R. It has been found that the random forest algorithm predicting model generates very successful results for the financial factor prediction as the model accuracy reaches 99%. According to the analysis, relevant model to predict financial factors and non-financial factors based on non-linear supervised machine learning algorithm can be built by investors or hedge fund agency to estimate stock performance in the future. This study also investigates the economic result of the Federal Funds Rate hikes, which is instructive for investors to make well-informed decisions.
Keywords
Supervised machine learning, stock market index, federal funds rate hikes, public policy, fintech
[1]. Narayan, S. and Narayan, P.K. (2012). Do US macroeconomic conditions affect Asian stock markets. Journal of Asian Economics, 23(6), 669-679.
[2]. Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of financial economics, 108(1), 1-28.
[3]. Easton, P.D. (2004). PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. The accounting review, 79(1), 73-95.
[4]. Bousoño-Calzón, C., Bustarviejo-Muñoz, J., Aceituno-Aceituno, P. and Escudero-Garzás, J.J. (2019). On the economic significance of stock market prediction and the no free lunch theorem. IEEE Access, 7, 75177-75188.
[5]. Kim, J. (2023). Stock market reaction to US interest rate hike: evidence from an emerging market. Heliyon, 9(5).
[6]. Hsu, M.W., Lessmann, S., Sung, M.C., Ma, T. and Johnson, J.E. (2016). Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert systems with Applications, 61, 215-234.
[7]. Khaidem, L., Saha, S. and Dey, S.R. (2016). Predicting the direction of stock market prices using random forest. arxiv preprint arxiv:1605.00003.
[8]. Meher, B.K., Singh, M., Birau, R. and Anand, A. (2024). Forecasting stock prices of fintech companies of India using random forest with high-frequency data. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100180.
[9]. Gertler, M., and Hubbard, R. G. (1988). Financial factors in business fluctuations. National Bureau of Economic Research 13.
[10]. Xanthopoulos, P., Pardalos, P. M., Trafalis, T.B., Xanthopoulos, P., Pardalos, P.M. and Trafalis, T.B. (2013). Linear discriminant analysis. Robust data mining, 27-33.
Cite this article
Fan,Y. (2024).Stock Return Prediction under US Federal Funds Rate Hike Based on Supervised Machine Learning.Advances in Economics, Management and Political Sciences,111,1-9.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content
About volume
Volume title: Proceedings of Finance's Role in the Just Transition - ICFTBA 2024
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).