用pso辅助量子非谐振子模型模拟股票价格动态:以雅加达综合指数为例

T. Sumaryada, Anisah Rahajeng Kartika Sari, A. Kartono
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引用次数: 0

摘要

采用量子非谐振子和粒子群优化算法对雅加达综合指数(JCI)股票价格动态进行了建模。影响收益概率密度的一些常数是做市商控制市场的能力(γ),逆向投资者和趋势追随者对价格回报的行为(c),以及投资者对感知波动的行为(k)。模拟结果产生的JCI在机会密度和股票价格回报方面的误差最小,分别为8.36%和3.6%。采用指数平滑法对未来3个月进行前向预测,股价收益的机会密度误差为17.77%,股价收益误差为10.6%。基于这些结果,可以得出结论,股票价格动态可以使用非调和量子振荡器建模,其中前一时期的流动性和波动性值影响投资者和下一时期的股票价格回报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulating the Dynamics of Stock Price via PSO-Assisted Quantum Anharmonic Oscillator Model: Case of Jakarta Composite Index
The Jakarta Composite Index (JCI) stock price dynamics have been modeled by a quantum anharmonic oscillator and the PSO (Particle Swarm Optimization) Algorithm. Some of the constants that affect the probability density of return are the ability of the market makers to control the market (γ), the behaviour of contrarians and the trend followers to the price return (c), and the investor behaviour towards perceived volatility (k). The simulation results have produced the slightest error of the JCI at 8.36% for the opportunity density and 3.6% for the stock price returns. Forward prediction for the next three months using the exponential smoothing method resulted in a 17.77% error in the opportunity density of the stock price return and a 10.6% error in the stock price return. Based on those results, it can be concluded that the stock price dynamics can be modelled using an anharmonic quantum oscillator where the value of liquidity and volatility in the previous period affects the investor and the stock's price return in the next period.
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