Abstract
This chapter proposes an algorithmic trading (AT) strategy based on a newly developed investment indicator called the “Balanced Investment Indicator” (BII), which has been shown to be able to balance risk and profitability accurately. This indicator is crucial for develo** an AT strategy that allows algorithmic traders to use big data to analyze portfolios and seek the BII algorithm's highest value. The chapter reviews and analyzes current AT strategies and compares them with the proposed strategy of the chapter. The results of this comparison show that the indicator performs strongly, as its investment recommendations coincide in some cases with relevant institutions, such as the Bank of America. For investors, this chapter provides decision-making tools for selecting different portfolios that balance profitability with default risk.
All the Tables and Figures are author originated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Data obtained January 13, 2022.
References
Adrian, T., Crump, R. K., & Vogt, E. (2019). Nonlinearity and flight-to-safety in the risk-return trade-off for stocks and bonds. The Journal of Finance, 74(4), 1931–1973. https://doi.org/10.1111/jofi.12776
Aggarwal, R. K., & Samwick, A. A. (1999). The other side of the trade-off: The impact of risk on executive compensation. Journal of Political Economy, 107(1), 65–105. https://doi.org/10.1086/250051
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Bloomberg. (2022, January 13). https://www.bloomberg.com/markets/stocks/futures
BME. (2022, January 14). https://www.bolsasymercados.es/esp/Estudios-Publicaciones/Estadisticas
Dunis, C. L., Laws, J., & Naïm, P. (2004). Applied quantitative methods for trading and investment. Wiley.
Expansión. (2022, January 13). https://www.expansion.com/ahorro/2022/01/13/61dee6d0468aebc4578b4670.html
Expansión. (2022, January 14). https://www.expansion.com/mercados/2022/01/14/61e12c8ee5fdea4a128b4661.html
Feuerriegel, S., & Prendinger, H. (2016). News-based trading strategies. Decision Support Systems, 90, 65–74. https://doi.org/10.1016/j.dss.2016.06.020
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A Bat-Neural Network Multiagent System (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196–210. https://doi.org/10.1016/j.asoc.2014.12.028
Hansen, K. B. (2020). The virtue of simplicity: On machine learning models in algorithmic trading. Big Data & Society, 7(1), 1–14. https://doi.org/10.1177/2053951720926558
Hilbert, M., & Darmon, D. (2020). How complexity and uncertainty grew with algorithmic trading. Entropy, 22(5), 499. https://doi.org/10.3390/e22050499
Ho, T., & Saunders, A. (1981). The determinants of bank interest margins: Theory and practice. Journal of Financial and Quantitative Analysis, 16, 581–600. https://www.jstor.org/stable/2330377
Huang, B., Huan, Y., Xu, L. D., Zheng, L., & Zou, Z. (2019). Automated trading systems statistical and machine learning methods and hardware implementation: a survey. Enterprise Information Systems, 13(1), 132–144. https://doi.org/10.1080/17517575.2018.1493145
Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534–551. https://doi.org/10.1016/j.asoc.2015.07.008
Kaufman, P. J. (2005). The new trading systems and methods. Wiley Trading.
López-Laborda, J., & Peña, G. (2018). A new method for applying VAT to financial services. National Tax Journal, 71(1), 155–182. https://doi.org/10.17310/ntj.2018.1.05
Peña, G. (2020). A new trading algorithm with financial applications. Quantitative Finance and Economics, 4(4), 596–607. https://doi.org/10.3934/QFE.2020027
Peña, G. (2021). The key role of quoted spreads in financial services and transactions. Economics and Business Letters, 10(3), 208–216. https://doi.org/10.17811/ebl.10.3.2021.208-216
Pricope, T. V. (2021). Deep reinforcement learning in quantitative algorithmic trading: A review. ar**v preprint ar**v:2106.00123
Treleaven, P., Galas, M., & Lalchand, V. (2013). Algorithmic trading review. Communications of the ACM, 56(11), 76–85.
Virgilio, G. P. M. (2019). High-frequency trading: A literature review. Financial Markets and Portfolio Management, 33(2), 183–208. https://doi.org/10.1007/s11408-019-00331-6
World Bank Database. (2021, June 25). https://data.worldbank.org/indicator
Yan, H. (2019). The real effects of algorithmic trading (Doctoral dissertation). Duke University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Peña, G. (2022). An Algorithmic Trading Strategy to Balance Profitability and Risk. In: Walker, T., Davis, F., Schwartz, T. (eds) Big Data in Finance. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-12240-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-12240-8_3
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-031-12239-2
Online ISBN: 978-3-031-12240-8
eBook Packages: Economics and FinanceEconomics and Finance (R0)