摘要翻译：
本文将弦理论的一种新方法应用于实际金融市场。它是文献[1]在价格预测中的直接推广和应用。该模型采用基于字符串不变量的预测模型(PMBSI)的思想构造。在人工时间序列和金融时间序列上，将PMBSI与支持向量机(SVM)和人工神经网络(ANN)的性能进行了比较。给出了结果和分析的简要概述。第一个模型是基于相关函数作为不变量，第二个模型是基于对闭合字符串/模式形式(PMBCS)的偏差的应用。我们发现了这两种方法之间的区别。与第二个模型相比，第一个模型不能以较好的效率预测外汇市场的行为，并且能够每年获得相应的利润。
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英文标题：
《The string prediction models as an invariants of time series in forex
  market》
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作者：
Richard Pincak and Marian Repasan
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最新提交年份：
2012
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year.
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PDF链接：
https://arxiv.org/pdf/1109.0435