Abstract
Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions.
Highlights
Stock investments may earn profits but are often associated with inherent risks
Benefiting the consideration of financial stock investment settled in the big data framework of the complicated stock market for interesting financial truths and options, accurately classifying the earnings per share (EPS) of listed companies is an interesting issue attracting investors; unscientific decisions are involved in the profit-making development, which may never be successful for the investing plan
This study proposes a map of advanced multicomponential discretization models for identifying financial diagnoses and has the purpose of using 2009–2014 financial statements to research the EPS of companies on Taiwan Stock Exchange (TWSE) from six different industry online financial databases to assess the componential performance of the models, with effective comparative studies for getting rich features
Summary
Stock investments may earn profits but are often associated with inherent risks. That is, risks in terms of a stock are a part of financial investments. This study is focused on financial applications with big data solutions that trigger advanced and intelligent componential models These have posed great opportunities and challenges for data researchers because applied large-scale data are collected from diversified database services in a mixed-industry setting. Benefiting the consideration of financial stock investment settled in the big data framework of the complicated stock market for interesting financial truths and options, accurately classifying the EPS of listed companies is an interesting issue attracting investors; unscientific decisions are involved in the profit-making development, which may never be successful for the investing plan. To keep making the right investor decisions, this study uses the advantages of past reviews of literature and the managerial experience of experts on financial ratios to propose a hybrid multicomponential discretization model with ML techniques to develop effective early-warning rules for the identification of positive/negative EPS. There are nine components (stages), with 11 detailed steps for raising the advantages and rationalities of this study. (1) Data-preprocessing: This component is used to build a tangible benefit from the reviews of literature and experts from a specific database
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