股票市场交易策略及其可适应规则挖掘研究

发布时间：2018-06-16 05:19

本文选题：中国股票市场性质 + 概念漂移　；参考：《广东外语外贸大学》2014年硕士论文

【摘要】：自从Fama和French发表了经典的论著，阐述了证券市场有效性的讨论，近些年来，该论题一直是学者关注的焦点。本文从基本面和技术面两个角度检验中国股票市场的有效性，都否认了有效市场假说。尤其是本文构建的技术面分析模型，相比之前的研究，我们的模型在扣除了交易费用之后能够在不同的市场阶段内攫取稳定的超额回报。在基本面分析部分，使用横截面回归模型论证上市公司披露的财务数据与股票期望回报之间的相关性，发现账面市值比，市值等财务因素对股票预期回报存在较强的解释力。但是中国市场与美国市场在特定指标的解释力上有些不同。更为有趣的是，本文的得出了与之前美国股票市场的研究类似的结论，中国股票市场也发生了时间上的概念漂移现象，即在不同的测试时间段内账面市值比指标与预期回报的相关性发生了反转。股票市场中存在“异象”。在技术面分析部分，考虑到显性知识和概念漂移是构建金融数据模型过程中不可忽视的两个重要的因素。本文使用eXtended Classifier Systems（XCS）算法来构建智能交易模型—eTrend。模型基于当前的股票价格行情数据特征以及趋势跟踪投资规则进行股票买卖决策，在交易过程中，eTrend不断的自动适应股票市场的变化并记忆显性交易规则，实时的提供最优的投资决策支持。文章选取上海证券市场的三只指数作为测试样例，在12年的测试周期内，扣除了交易费用的收益序列与大盘的收益序列相比，eTrend能够在牛/熊市不同的市场阶段内获得稳定的超额收益，并保持较低的下行风险，反映在统计指标上是接近于1.0的索提诺比率。同时从对比的实验中可以看出，无论从总收益还是收益的稳定性方面基于XCS的eTrend模型预测效果明显要好于Decision Tree（DT）和Artificial Neural Network （ANN）。本文主要的贡献体现在：(1)在学术层面，基本面分析部分的结果较为有趣，在测试样本上发现的地理位置，时间序列上的反转异象，使得在金融研究中概念漂移问题的解决显得尤为重要；相应的技术面分析部分，eTrend自适应智能交易模型的提出，对于有效解决股票数据的概念漂移问题和显性知识挖掘的论题提供了一定的帮助。(2)从实践层面，为投资者、学者更好的认识股票市场提供了一定的依据；尤其是eTrend模型，，使用实践中的投资规则与人工智能算法融合进行约束学习，在长期的测试周期内都取得的较好的效果，对于股票市场中的投资策略的研发者而言极具参考价值。
[Abstract]:Since Fama and French published their classic treatises on the validity of the securities market, this topic has been the focus of scholars' attention in recent years. In this paper, the validity of Chinese stock market is tested in terms of fundamental and technical aspects, and the efficient market hypothesis is denied. In particular, the technical surface analysis model is constructed in this paper. Compared with previous studies, our model can capture stable excess returns in different market stages after deducting transaction costs. In the part of fundamental analysis, the cross-section regression model is used to prove the correlation between the financial data disclosed by listed companies and the expected return of stock. It is found that the financial factors such as book market value ratio, market value and other financial factors have strong explanatory power to the expected return of stocks. But the Chinese market and the U. S. market in the interpretation of specific indicators are somewhat different. What is more interesting is that this paper draws a conclusion similar to the previous research on the American stock market. The Chinese stock market also has a phenomenon of conceptual drift in time. That is, in different test periods, the correlation between book market value ratio and expected return is reversed. There are anomalies in the stock market. In the part of technical surface analysis, it is considered that explicit knowledge and conceptual drift are two important factors which can not be ignored in the process of constructing financial data model. In this paper, we use the extended Classifier Systems (XCSS) algorithm to construct the intelligent trading model-e trend. Based on the characteristics of the current stock price data and the trend tracking investment rules, the model makes stock trading decisions. During the trading process, trend constantly adapts to the changes of the stock market and memorizes the dominant trading rules. Provide optimal investment decision support in real time. In this paper, three indexes of Shanghai stock market are selected as test samples, and the test period is 12 years. After deducting the transaction cost income series compared with the larger market earnings series, trend can obtain stable excess returns at different market stages of the bull / bear market and maintain lower downside risk. Reflected in the statistical indicators is close to 1.0 of the Sodino ratio. At the same time, it can be seen from the comparative experiments that the prediction effect of the eTrend model based on XCS-based model is better than that of decision Tree DTT and Artificial Neural Network in terms of the stability of total income and income. The main contribution of this paper is that at the academic level, the results of the fundamental analysis are more interesting, the geographical location found in the test samples, the inversion anomalies in the time series, Therefore, it is very important to solve the problem of concept drift in financial research. It provides some help to solve the problem of concept drift of stock data and the topic of explicit knowledge mining. It provides some basis for investors and scholars to better understand the stock market from the practical level, especially the eTrend model. Using the combination of investment rules in practice and artificial intelligence algorithm for constraint learning, good results have been achieved in the long test cycle, which is of great reference value to the R & D of investment strategy in the stock market.
【学位授予单位】：广东外语外贸大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F832.51;F224

【参考文献】