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Classification and time series forecasting: Applications in the stock market.

机译：分类和时间序列预测：在股票市场中的应用。

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摘要

In this thesis, we evaluate the effectiveness of time series analysis methods and classification methods in terms of their ability to forecast future stock market values. The differences between the use of time series and classification tools, and the resulting differences in their respective models, are explained. Three specific models from each field are examined in terms of their conceptual and mathematical bases. Three econometric models examined are the Classical Linear Regression Model, the Autoregressive Moving Average model, and the Vector Autoregression model. The three classification models are the Support Vector Machine, Random Forest, and Artificial Neural Network.;After describing the differences between the modeling methods, a model of each type is implemented and used to evaluate financial time series data for 10 publicly traded companies. The models predict the sign of the returns in the next period, and each model is evaluated based on the output it provides. Fitted values from the models are evaluated based on whether the predicted return is realized within the next few trading sessions with 2-day, 3-day, and 5-day periods used for the evaluation. Where appropriate, models of data sets that are better suited to the given model are also demonstrated.;The models are compared according to a number of criteria, most critically according to the kappa statistics that they achieve. Kappa indicates the superiority of the prediction accuracy over a random predictor --- one that simply predicts the most common outcome from the data set. Additionally, the models are evaluated according to how well they predict large returns, how well they predict in periods of high volatility, and how much profit a trader could attain using the recommendations of the model. The results show that, on average, the random forest models achieve the highest kappa for each of the three period lengths, and the ARMA models would to achieve the highest profits if trades of equal value are placed for each recommendation. Across all stocks modeled and evaluated over 3 day periods, the different methods all produce kappa values near 30%. The random forests, on average, achieve a kappa of 36%, and the ARMA's achieve an average kappa of 31%. As a method for building models, the ARMA proves much more consistent, whit a standard deviation of kappa rates of 0.05, compared to 0.10 for random forests. The best method to use depends on the data used as inputs, and for the stocks analyzed in this thesis, ARMA models are best for low volatility stocks; the opposite is demonstrated for random forests.
机译：本文根据时间序列分析方法和分类方法预测未来股票市场价值的能力来评估其有效性。解释了时间序列和分类工具的使用之间的差异，以及它们各自模型中所产生的差异。根据概念和数学基础，研究了每个领域的三个特定模型。研究的三个计量经济学模型是古典线性回归模型，自回归移动平均模型和向量自回归模型。这三种分类模型分别是支持向量机，随机森林和人工神经网络。在描述了建模方法之间的差异之后，每种类型的模型都被实现并用于评估10家上市公司的财务时间序列数据。这些模型预测下一个时期的收益率迹象，并根据其提供的输出评估每个模型。基于是否在接下来的两个交易日（用于评估的2天，3天和5天）内实现了预期收益，来评估模型的拟合值。在适当的情况下，还将展示更适合给定模型的数据集模型。根据各种标准对模型进行比较，最关键的是根据它们实现的kapp统计量进行比较。 Kappa表示​​预测精度优于随机预测器-一种简单地从数据集中预测最常见结果的预测器。此外，根据模型对大笔收益的预测能力，在高波动时期的预测能力以及交易者使用模型的建议可以获得多少利润来对模型进行评估。结果表明，平均而言，随机森林模型在三个期间长度中的每个期间都实现了最高的kappa值，而ARMA模型在为每个建议进行同等价值的交易时将获得最高的利润。在为期3天的建模和评估的所有股票中，不同的方法都产生接近30％的kappa值。随机森林的平均Kappa值为36％，而ARMA的平均Kappa值为31％。作为构建模型的方法，ARMA被证明更加一致，卡伯​​率的标准偏差为0.05，而随机森林的标准偏差为0.10。最佳的使用方法取决于输入的数据，对于本文分析的股票，ARMA模型最适合于低波动率股票。随机森林则相反。

著录项

  • 作者

    O'Connor, William B.;

  • 作者单位

    Southern Methodist University.;

  • 授予单位 Southern Methodist University.;
  • 学科 Computer science.;Finance.;Economics.
  • 学位 M.S.
  • 年度 2016
  • 页码 94 p.
  • 总页数 94
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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