基于债券信息发现的知识服务

发布时间：2018-09-17 09:01

【摘要】：随着金融大数据技术的发展和投资者个性化、多样化的需求,金融知识服务面临着更大的挑战。投资者如何获取及时准确的实时数据,如何估计某一只债券的未来价格和未来收益,如何在收益相当的债券中选择风险最低的债券,如何在最短的时间内使得收益最大化等等,都是投资者关心的热点问题。以上问题亟需异构信息处理技术、数据挖掘方法的技术支持,因此,本文主要研究从海量金融信息中发现、挖掘出更有价值信息的方法和策略,应用于企业债券知识服务。债券信息的获取是知识服务的基础。为了保证知识服务的准确性和高效性,首先需要获取全面而准确的数据,其次,通过进一步去噪、优化等处理,将金融数据处理为结构化数据,为整个服务过程中的推荐策略和趋势预测提供准确的数据保障。为了使投资者投入更少的精力而获得相对较高的收益,提出具有针对性的同类益高债券推荐策略。从债券投资者的角度出发,深入分析、研究了投资过程中影响债券收益率的关键特征组合,从而为用户提供更高效的、个性化的投资策略。债券趋势预测为投资者提供了债券价格和收益变化趋势的参考。利用机器学习的方法基于债券价格时间序列、行业、公司新闻等信息对债券未来趋势进行预测,综合多个影响债券价格走势的特征因素及多种特征形式,提高了预测的准确性。综上所述,本文利用自然语言处理技术、数据挖掘方法从海量金融数据中获取数据信息,并处理为结构化数据,进一步发现、挖掘更有价值的信息,利用债券推荐策略和趋势预测方法为用户提供个性化、多样化且高效的金融知识服务。
[Abstract]:With the development of financial big data technology and the individualized and diversified demand of investors, financial knowledge service is facing more challenges. How to obtain timely and accurate real-time data, how to estimate the future price and future income of a certain bond, how to choose the lowest risk bond in the equivalent bond, how to maximize the return in the shortest time, etc. Investors are concerned about hot issues. These problems need the technical support of heterogeneous information processing technology and data mining method. Therefore, this paper mainly studies the methods and strategies of mining more valuable information from the massive financial information, and applies them to corporate bond knowledge services. The acquisition of bond information is the basis of knowledge service. In order to ensure the accuracy and efficiency of knowledge service, first of all, we need to obtain comprehensive and accurate data. Secondly, through further de-noising, optimization and other processing, the financial data is processed into structured data. Provides the accurate data guarantee for the recommendation strategy and the trend forecast in the whole service process. In order to make investors invest less energy and obtain relatively high returns, the paper puts forward the recommendation strategy of the same kind of higher interest bond. From the point of view of bond investors, this paper analyzes the key characteristics of bond yield in the process of investment, and provides users with more efficient and personalized investment strategies. Bond trend forecast provides investors with reference to bond price and yield trends. The method of machine learning is used to predict the future trend of bond based on the time series of bond price, industry, company news and so on. The accuracy of prediction is improved by synthesizing many characteristic factors and various characteristic forms that affect the trend of bond price. To sum up, this paper uses natural language processing technology, data mining method from massive financial data to obtain data information, and processing as structured data, and further discover, mining more valuable information, Using bond recommendation strategies and trend forecasting methods to provide users with personalized, diversified and efficient financial knowledge services.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F830.91;TP311.13

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