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数据分析与知识发现  2019, Vol. 3 Issue (8): 94-104    DOI: 10.11925/infotech.2096-3467.2018.1137
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于RBR和CBR的金融事件本体推理研究 *
强韶华1(),罗云鹿2,李玉鹏1,吴鹏3
1南京工业大学经济与管理学院 南京 211800
2西南财经大学经济信息工程学院 成都 611130
3南京理工大学经济管理学院 南京 210094
Ontology Reasoning for Financial Affairs with RBR and CBR
Shaohua Qiang1(),Yunlu Luo2,Yupeng Li1,Peng Wu3
1School of Economics and Management, Nanjing Tech University, Nanjing 211800, China
2School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
3School of Economics and Management, Nanjing University of Science &Technology, Nanjing 210094, China;
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摘要 

【目的】综合企业财务、非财务和舆情等因素预测金融事件对企业股价的影响, 支持基于特定行业、特定金融事件主题之间的推理。【方法】基于本体的规则推理技术和案例推理技术, 构建金融事件本体, 设计基于本体的SWRL推理规则, 采用Dloors引擎进行规则推理(RBR)。然后利用本体表示案例结构, 建立基于本体的主题事件案例库, 设计案例推理(CBR)表示、检索、重用、修正与保存模型。【结果】基于具体企业实例对规则推理和案例推理的结果进行验证, 证明了本文所提推理模型的可靠性。【局限】本文重点在于金融本体及其推理模型的构建, 股价预测是一种推理结果, 故没有和其他股价预测方法进行定量比较。【结论】融合企业的舆情、财务和非财务指标, 基于金融事件主题的案例推理和基于关联规则的规则推理模型, 可以对大数据环境下企业股价进行预测。

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强韶华
罗云鹿
李玉鹏
吴鹏
关键词 金融事件本体规则推理案例推理股价预测    
Abstract

[Objective] This paper tries to predict the impacts of financial events/news on stock price with financial, non-financial and public opinion factors. [Methods] We designed a financial affairs ontology based on the Rule-Based Reasoning (RBR) and Case-Based Reasoning (CBR). Then, we created a SWRL rule-based reasoning model, which pursued the rule-based reasoning using the Dloors engine. Thirdly, we designed a topic case database to describe the structure of the financial cases. Finally, we used the model to describe, retrieve, reuse, correct and preserve the data. [Results] We conducted an empirical study to examine the reliability of rule-based reasoning and case-based reasoning with enterprise data. [Limitations] We did not compare our model with the existing methods. [Conclusions] The proposed method could predict the stock price in big data environment.

Key wordsFinancial Affairs    Ontology    Rule-Based Reasoning    Case-Based Reasoning    Stock Price Forecast
收稿日期: 2018-10-16     
中图分类号:  TP393 G35  
基金资助:*本文系国家自然科学基金项目“突发事件网民负面情感的模型检测研究”(71774084);国家自然科学基金项目“基于时间感知模型的学术主题检索与演化挖掘研究”的研究成果之一(71503124)
通讯作者: 强韶华     E-mail: shaohua3900@163.com
引用本文:   
强韶华,罗云鹿,李玉鹏,吴鹏. 基于RBR和CBR的金融事件本体推理研究 *[J]. 数据分析与知识发现, 2019, 3(8): 94-104.
Shaohua Qiang,Yunlu Luo,Yupeng Li,Peng Wu. Ontology Reasoning for Financial Affairs with RBR and CBR. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.1137.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1137
图1  金融事件本体推理模型构建框架
评测指标 备注
所处行业
舆情 股市情绪 网民情感
财务指标 发展能力 净利润增长率
现金流水平 全部现金回收率
盈利能力 净资产收益率、成本费用利润率、营业利润率、资产报酬率
经营能力 固定资产周转率、应收账款周转率、总资产周转率、流动资产周转率
风险能力 经营杠杆、综合杠杆、财务杠杆
非财务指标 专利数量
信息披露
社会责任
子公司数
媒体和机构关注度
金融事件 政府政策
自身危机
行业创新
股价 事件发生前5天涨跌
事件发生前10天涨跌
事件发生后股价涨跌
表1  金融领域本体类详细结构
规则 说明
规则1 发展能力=0.074×净利润增长率
规则2 现金流水平=0.074×全部现金回收率
规则3 盈利能力=0.083×净资产收益率+0.074×成本费用利润率+0.083×营业利润率+0.078×资产报酬率
规则4 经营能力=0.088×固定资产周转率+0.078×应收账款周转率+0.088×总资产周转率+0.088×流动资产周转率
规则5 风险水平=0.069×经营杠杆+0.0698×综合杠杆+0.064×财务杠杆
规则6 财务指标=发展能力+现金流水平+盈利能力+经营能力+风险水平
规则7 非财务指标=0.17×专利数量+0.392×社会责任+0.545×媒体和分析机构关注度+0.352×信息披露+0.441×子公司数
规则8 舆情=1×正面评论数+0×中性评论数+(-1)×负面评论数
规则9 股价后续发展得分=0.65×财务指标+0.17×非财务指标+0.18×舆情
规则10 股价得分≥90 →股价极有可能上涨, 61<股价得分<89→股价可能上涨, 31<股价得分<60→股价持平, 股价得分<30→股价下跌
表2  规则列表
图2  金融领域概念层次结构
图3  案例推理流程
概念类 相似度
Sim (产品危机, 人事危机) 4/5
Sim (公共政策, 产品创新) 3/5
表3  相似度计算结果
图4  金融领域本体的类
图5  乐视企业本体实例
图6  金融事件本体
图7  Dloors推理机
图8  金融事件发生后股价变化
属性名称 乐视案例 贵州茅台案例 相似度 属性权重 加权相似度
所处行业 传播文化 食品饮料 0 0.05 0
金融舆情 -0.285 -0.263 0.978 0.20 0.196
财务状况 65.62 150.34 0.511 0.36 0.184
非财务状况 153.44 44.13 0.221 0.09 0.020
金融事件 经营危机、前5天股价下跌、前10天股价下跌、后5天股价下跌 产品危机、前5天股价上涨、前10天股价下跌、后5天股价下跌 0.690 0.30 0.207
相似度总和 0.607
表4  案例间相似度计算结果
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