Exploring Quantum Harmonic Oscillator as an
Indicator to Analyse Stock Price Volatility

Atman Bhatt; Ravi Gor

doi:https://doi.org/10.14445/22315373/IJMTT-V69I7P501

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 69 | Issue 7 | Year 2023 | Article Id. IJMTT-V69I7P501 | DOI : https://doi.org/10.14445/22315373/IJMTT-V69I7P501

Exploring Quantum Harmonic Oscillator as an Indicator to Analyse Stock Price Volatility

Atman Bhatt, Ravi Gor

Received	Revised	Accepted	Published
18 May 2023	20 Jun 2023	06 Jul 2023	22 Jul 2022

Citation :

Atman Bhatt, Ravi Gor, "Exploring Quantum Harmonic Oscillator as an Indicator to Analyse Stock Price Volatility," International Journal of Mathematics Trends and Technology (IJMTT), vol. 69, no. 7, pp. 1-7, 2023. Crossref, https://doi.org/10.14445/22315373/IJMTT-V69I7P501

Abstract

This paper explores using the Quantum Harmonic Oscillator (QHO) indicator for predicting stock price movements in financial markets. Quantum Harmonic Oscillator is a technical analysis tool commonly used in physics that has recently gained attention in finance. By analysing the mean percentage change in price, it determines the range of energy levels that correspond to smaller price changes, suggesting stability in the price movement. The QHO indicator can help traders and investors identify potential breakouts or continuations of existing trends, leading to improved decision-making and profitability. This study presents a comprehensive analysis of the Quantum Harmonic Oscillator's performance and its potential application in financial markets.

Keywords

Quantum harmonic oscillator, Mean percentage change, Energy levels, Technical analysis.

References

[1] Belal E. Baaquie, Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates, Cambridge University Press, 2013.
[Google Scholar] [Publisher Link]
[2] M. Bologna, and P. Grigolini, “A Simple Approach to the Evaluation of the Quantum Harmonic Oscillator Propagator,” Journal of Mathematical Physics, vol. 31, no. 1, pp. 52-55, 1990.
[3] X. Chen, J. Wu, and X. Zhou, “Quantum Mechanics Inspired Financial Derivative Pricing,” Applied Mathematics and Computation, vol. 321, pp. 124-134, 2018.
[4] Y. Gao, and X. Cheng, “A Quantum Approach to Financial Risk Measurement,” Physica A: Statistical Mechanics and its Applications, vol. 371, no. 2, pp. 495-502, 2006.
[5] Dale W. Jorgenson, and Peter J. Wilcoxen, “Environmental Regulation and U.S. Economic Growth,” The RAND Journal of Economics, vol. 21, no. 2, pp. 314-340, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[6] R. Laskar, and R. Rebonato, “Quantum Finance: A New Paradigm,” Applied Mathematics and Computation, vol. 215, no. 5, pp. 1807-1819, 2009.
[7] S. Liu, and X. Zhou, “Time-dependent Quantum Harmonic Oscillator and Financial Analysis,” Physica A: Statistical Mechanics and its Applications, vol. 449, pp. 352-360, 2016.
[8] S. Makridakis, “Artificial Intelligence and Deep Learning for Time Series Forecasting: A Case Study on Stock Prices,” Journal of Forecasting, vol. 40, no. 1, pp. 72-93, 2021.
[9] B. Mandelbrot, The Fractal Geometry of Nature, Macmillan, 2013.
[10] Y. Zhang, S. Li, and X. Chen, “Quantum Harmonic Oscillator and Stock Volatility,” Physica A: Statistical Mechanics and its Applications, vol. 439, pp. 179-184, 2015.