英文标题：
《The Evolution of Security Prices Is Not Stochastic but Governed by a
  Physicomathematical Law》
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作者：
Wally Tzara
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最新提交年份：
2019
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英文摘要：
  Since Bachelier\'s thesis in 1900 (laying the foundation of the stochastic process, or Brownian motion, as a model of stock price changes), attempts at understanding the nature of prices and at predicting them have failed. Statistical methods have only found minor regularities/anomalies, and other mathematical and physical approaches do not work. This leads researchers to consider that the evolution of security prices is basically random, and, thus, inherently not predictable. We show that the evolution of security prices is not a stochastic process but largely deterministic and governed by a physical law. The law takes the form of a physicomathematical theory centered around a purely mathematical function, unrelated to models and statistical methods. It can be described as an \"isodense\" network of moving regression curves of an order greater than or equal to 1. The salient aspect of the function is that, when inputting a time series of any security into the function, new mathematical objects emerge spontaneously, and these objects exhibit the unique property of attracting and repelling the quantity. The graphical representation of the function is called a \"topological network\" due to the preeminence of shapes over metrics, and the emergent objects are called \"characteristic figures\" (mainly \"cords\"). The attraction and repulsion of the price by the cords results in the price bouncing from cord to cord. Thus, the price has to be considered as driven by the cords in a semi-deterministic manner (leaning towards deterministic). With a function that describes the evolution of the price, we now understand the reason behind each price movement and can also predict prices both qualitatively and quantitatively. The function is universal, does not rely on any fitting, and, due to its extreme sensitivity, reveals the hidden order in financial time series data that existing research never uncovered.
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中文摘要：
自1900年Bachelier的论文（奠定了随机过程或布朗运动作为股价变化模型的基础）以来，试图理解价格本质和预测价格的尝试都失败了。统计方法只发现了微小的规律/异常，其他数学和物理方法不起作用。这使得研究人员认为，证券价格的演变基本上是随机的，因此本质上是不可预测的。我们表明，证券价格的演化不是一个随机过程，而是一个很大程度上具有确定性的过程，并受一个物理定律的支配。该定律采用以纯数学函数为中心的物理数学理论的形式，与模型和统计方法无关。它可以描述为大于或等于1阶的移动回归曲线的“等密度”网络。该函数的一个突出方面是，当向函数中输入任何安全性的时间序列时，新的数学对象会自动出现，这些对象表现出吸引和排斥数量的独特特性。由于形状优于度量，函数的图形表示被称为“拓扑网络”，出现的对象被称为“特征图形”（主要是“线”）。电线对价格的吸引和排斥导致电线之间的价格反弹。因此，必须将价格视为由跳线以半确定性方式驱动（倾向于确定性）。通过一个描述价格演变的函数，我们现在了解了每次价格变动背后的原因，也可以定性和定量预测价格。该函数具有普遍性，不依赖任何拟合，并且由于其极端敏感性，揭示了现有研究从未揭示的金融时间序列数据中的隐藏顺序。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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