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Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity

机译：使用基于属性粒度的粗糙集分类器预测金融行业的PGR

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摘要

In the financial industry, continually changing economic conditions and characteristics involving uncertainty and risk have made financial forecasts even more difficult, increasing the need for more reliable ways to forecast a bank’s operating performance. However, early related studies of performance analysis for using statistical methods usually become more complex when relationships in input/output data are nonlinear. Furthermore, strict data assumptions, such as linearity, normality, and independence, limit real-world applications often. Additionally, a drawback of traditional rough sets is that data must be discretized first for improving classification accuracy. To remedy the existing shortcomings above, the study proposes a hybrid procedure, which mixes professional knowledge, an attribute granularity, and a rough sets classifier, for automatically classifying profit growth rate (PGR) to solve real problems faced by investors. The proposed procedure is illustrated by examining a practical dataset for publicly traded financial holding stocks in Taiwan‘s stock markets. The experimental results reveal that the proposed procedure outperforms listing methods in terms of accuracy, and they provide useful insights in responsiveness to rapidly changing stock market conditions. Importantly, the output created by the rough sets LEM2 (Learning from Examples Module, version 2) algorithm is a set of comprehensible rules applied in a knowledge-based investment system for investors.
机译：在金融业中，不断变化的经济状况和特征（包括不确定性和风险）使财务预测变得更加困难，从而需要更加可靠的方法来预测银行的运营绩效。但是，当输入/输出数据中的关系为非线性时，使用统计方法进行性能分析的早期相关研究通常会变得更加复杂。此外，严格的数据假设（例如线性，正态性和独立性）经常会限制实际应用。另外，传统粗糙集的缺点是必须首先离散化数据以提高分类精度。为了弥补上述不足，该研究提出了一种混合程序，该程序将专业知识，属性粒度和粗糙集分类器相结合，用于自动对利润增长率（PGR）进行分类，以解决投资者面临的实际问题。通过检查台湾股票市场公开交易的金融控股股票的实用数据集来说明建议的程序。实验结果表明，所提出的程序在准确性方面优于列表方法，它们为快速变化的股票市场状况提供了有用的见解。重要的是，由粗糙集LEM2（从示例模块中学习，版本2）算法创建的输出是应用于投资者的基于知识的投资系统中的一组可理解规则。

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