基于支持向量机的美元指数预测研究
发布时间：2019-05-15 14:24
【摘要】：美元自量化宽松后走势模糊,一直在多空之间震荡；我国的外汇储备逐年上升,对外贸易、对外投资与吸引外资投资量处于上升周期,经济发展和美元强弱联系密切。因此,研究美元指数的预测方法对于国家、金融机构和个人都具有重要的现实意义。 本文主要通过支持向量机模型对美元指数进行预测研究。本文以汇率决定理论为基础,筛选出影响美元指数的合适指标,建立指标体系。本文分别用三种不同的变量降维方法对数据进行预处理,并选择出最好的变量降维方法；再用三种不同的参数优化方法对模型进行优化,并选择出最好的优化方法,将两种方法结合,构建出一个优化后的支持向量机模型,完成对短期内美元指数的预测。 在实证部分,本文分别使用粒子群优化算法(简称PSO)、遗传算法(简称GA)和网格遍历法(简称GRID)进行了参数优化,并比较了预测结果,发现遗传算法的优化效果最佳,平均误差率只有0.393556%,最低误差率达到了0.004165%,均方差也只有0.21761；之后分别用因子分析(FA)、粗糙集方法(RS)和主成分分析(PCA)对变量进行降维,得出在该特定问题上因子分析的变量降维效果最好的结论。最后,为了证明本文所建立的FA-GA支持向量机模型的有效性,本文还用BP神经网络模型和未进行变量降维的普通支持向量机模型对相同的数据进行了预测,预测结果发现还是优化后的支持向量机的预测结果最好,这一方面证明了本文建立指标的可行性和模型的优越性,另一方面证明了对数据进行变量降维能大大提高支持向量机的运算速度和准确率。
[Abstract]:After quantitative easing, the trend of the US dollar is vague and has been fluctuating between many short periods. China's foreign exchange reserves are rising year by year, foreign trade, foreign investment and attracting foreign investment are in an upward cycle, and economic development is closely related to the strength of the US dollar. Therefore, it is of great practical significance for countries, financial institutions and individuals to study the prediction method of dollar index. In this paper, the support vector machine model is used to predict the dollar index. Based on the theory of exchange rate determination, this paper selects the appropriate indexes that affect the dollar index and establishes the index system. In this paper, three different variable dimension reduction methods are used to preprocess the data, and the best variable dimension reduction method is selected. Then three different parameter optimization methods are used to optimize the model, and the best optimization method is selected. Combining the two methods, an optimized support vector machine model is constructed to predict the dollar index in the short term. In the empirical part, the particle swarm optimization algorithm (PSO), genetic algorithm) and grid ergodicity method (GRID) are used to optimize the parameters, and the prediction results are compared, and it is found that the genetic algorithm has the best optimization effect. The average error rate is only 0.393556%, the minimum error rate is 0.004165%, and the mean variance is only 0.21761. Then the factor analysis (FA), rough set method (RS) and principal component analysis (PCA) are used to reduce the dimension of variables respectively, and it is concluded that the variable dimension reduction effect of factor analysis is the best on this particular problem. Finally, in order to prove the effectiveness of the FA-GA support vector machine model established in this paper, the BP neural network model and the general support vector machine model without variable dimension reduction are also used to predict the same data. The prediction results show that the optimized support vector machine has the best prediction results, which proves the feasibility of establishing the index and the superiority of the model in this paper. On the other hand, it is proved that variable dimension reduction can greatly improve the operation speed and accuracy of support vector machine.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F827.12;F832.6
[Abstract]:After quantitative easing, the trend of the US dollar is vague and has been fluctuating between many short periods. China's foreign exchange reserves are rising year by year, foreign trade, foreign investment and attracting foreign investment are in an upward cycle, and economic development is closely related to the strength of the US dollar. Therefore, it is of great practical significance for countries, financial institutions and individuals to study the prediction method of dollar index. In this paper, the support vector machine model is used to predict the dollar index. Based on the theory of exchange rate determination, this paper selects the appropriate indexes that affect the dollar index and establishes the index system. In this paper, three different variable dimension reduction methods are used to preprocess the data, and the best variable dimension reduction method is selected. Then three different parameter optimization methods are used to optimize the model, and the best optimization method is selected. Combining the two methods, an optimized support vector machine model is constructed to predict the dollar index in the short term. In the empirical part, the particle swarm optimization algorithm (PSO), genetic algorithm) and grid ergodicity method (GRID) are used to optimize the parameters, and the prediction results are compared, and it is found that the genetic algorithm has the best optimization effect. The average error rate is only 0.393556%, the minimum error rate is 0.004165%, and the mean variance is only 0.21761. Then the factor analysis (FA), rough set method (RS) and principal component analysis (PCA) are used to reduce the dimension of variables respectively, and it is concluded that the variable dimension reduction effect of factor analysis is the best on this particular problem. Finally, in order to prove the effectiveness of the FA-GA support vector machine model established in this paper, the BP neural network model and the general support vector machine model without variable dimension reduction are also used to predict the same data. The prediction results show that the optimized support vector machine has the best prediction results, which proves the feasibility of establishing the index and the superiority of the model in this paper. On the other hand, it is proved that variable dimension reduction can greatly improve the operation speed and accuracy of support vector machine.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F827.12;F832.6
【参考文献】
相关期刊论文 前10条
1 喻胜华;肖雨峰;;基于信息粒化和支持向量机的股票价格预测[J];财经理论与实践;2011年06期
2 彭民;孙彦彬;李凤升;姚丽霞;李臣;;美元指数对国际原油价格影响的实证分析[J];大庆石油学院学报;2010年06期
3 彭望蜀;;基于BP神经网络与支持向量机的股票指数预测模型比较[J];南方金融;2013年01期
4 刘q，
本文编号：2477570
本文链接：https://www.wllwen.com/jingjilunwen/guojijinrong/2477570.html
