Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 7th International Conference on Economic Management and Green Development

Series Vol. 42 , 10 November 2023


Open Access | Article

Analysis of Short-term Entry and Exit Strategies Based on ADX and APO Indicators, and Long-term Holding Strategies for Various Household Investors: A Comparative Study

Erjun Lou * 1
1 Southwestern University of Finance and Economics

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 42, 130-138
Published 10 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Erjun Lou. Analysis of Short-term Entry and Exit Strategies Based on ADX and APO Indicators, and Long-term Holding Strategies for Various Household Investors: A Comparative Study. AEMPS (2023) Vol. 42: 130-138. DOI: 10.54254/2754-1169/42/20232095.

Abstract

This research delves into the different types of household investors, based on a comparison of short-term and long-term investment strategies. The study uses the ADX and APO indicators to simulate the active short-term trading strategy of the investment portfolio of ['AGG', 'GLD', 'SHY', 'SPY'] and SSE as the treatment group. Meanwhile, the long-term fixed holding strategy of 10 years from 2013 to 2022 and 5 years from 2018 to 2022 serves as the control group. The evaluation indicators include cumulative annualized return, standard deviation, Sharpe ratio, and Sortino ratio. The research discovered that for conservative investors, the active short-term trading strategy based on the ADX indicator may be more appropriate for them who have an investment portfolio of ['AGG', 'GLD', 'SHY', 'SPY']. At the same time, the active short-term trading strategy based on the APO indicator may be more suitable for those investing in the SSE. For household investors pursuing high returns, the long-term holding strategy may be more advantageous for investment portfolios of ['AGG', 'GLD', 'SHY', 'SPY'] and the SSE. And for those seeking a high benefit-risk ratio, the active short-term trading strategy may be more fitting. This research places emphasis on considering the risk tolerance of various investors, providing a reference for household investors to choose appropriate investment strategies for different investment targets. In terms of policy recommendations, household investors should select an investment strategy that suits their risk tolerance and investment goals by drawing comparisons with this research.

Keywords

household investors, active short-term trading strategy, long-term fixed holding strategy, ADX and APO

References

1. Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.

2. Lv, T., Huang, G., Yang, Y., Wang, F., & Huang, J. (2019). A survey of stock market prediction with machine learning. IEEE Access, 7, 207307-207327.

3. Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2017). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1), 1-122.

4. Gerlein, E. A., McGinnity, M., Belatreche, A., & Coleman, S. (2016). Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications, 54, 193-207.

5. Dash, R., & Dash, P. K. (2016). A hybrid stock trading framework integrating technical analysis with machine learning techniques. Journal of Banking & Finance, 62, 215-243.

6. Huang, Y., Nakamori, Y., & Wang, S. Y. (2019). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.

7. Deng, S., Zhang, Y., & Chang, Y. (2016). A study on application of machine learning based trading strategy. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 100-105.

8. Xiong, T., Liu, Y., & Zhang, A. (2018). A novel machine learning approach for stock market prediction. In Proceedings of the 2018 International Joint Conference on Neural Networks, 1-6.

9. Li, Z., Zhang, L., & Xu, W. (2019). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2759-2768.

10. Zhang, Q., Huang, G. B., Huang, G., & Song, S. (2020). High-level feature based stock market prediction with deep learning. Neural Computing and Applications, 32(11), 7809-7819.

11. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.

12. Chen, Y., Da, Z., & Huang, D. (2019). Arbitrage trading: The long and the short of it. The Review of Financial Studies, 32(4), 1608-1646.

13. Browne, H. (2001). Fail-Safe Investing: Lifelong Financial Security in 30 Minutes. St. Martin's Press.

14. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.

15. Stock Charts School. (n.d.). Average Directional Index (ADX). StockCharts.com. Retrieved June 27, 2023, from https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx.

16. The Forex Geek. (n.d.). Absolute Price Oscillator. The Forex Geek. Retrieved June 27, 2023, from https://theforexgeek.com/absolute-price-oscillator/.

17. Fidelity. (n.d.). Absolute Price Oscillator. Fidelity. Retrieved June 27, 2023, from https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/apo.

18. Elder, A. (1993). Trading for a living: Psychology, trading tactics, money management. John Wiley & Sons.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International 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 Instruction).

Volume Title
Proceedings of the 7th International Conference on Economic Management and Green Development
ISBN (Print)
978-1-83558-105-6
ISBN (Online)
978-1-83558-106-3
Published Date
10 November 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/42/20232095
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated