基于均线预测的股票市场投资策略构建及其实证

发布时间：2018-06-29 03:13

本文选题：两水平自回归 + 非参数AC算法　；参考：《电子科技大学》2012年硕士论文

【摘要】：长久以来，对股票的投资策略构建的重点主要集中于两个方面，一是简单的利用移动平均规则进行交易，其最终目的往往不是为了进行投资，而是为了检验市场的有效性，二是构建投资组合以求投入到实际应用当中。而关于股票的投资策略最近的研究则转向了以对股市进行预测为基础的策略构建。本文以均线交易规则为基础，构建了一套具有较强操作性的投资策略，该策略的关键就是通过预测模型得到均线的预测值进而得到股价的预测值，然后通过策略设定的买卖条件进行交易。对于短期投资者来说，提前预测到均线的走势进而判断股价的走势和拐点，意义就尤为重大。本文首先使用了两水平自回归模型进行预测，首先使用均线数据建立上水平模型，再同时使用均线和股价数据建立下水平模型，利用上水平模型得到的均线预测值对下水平模型的预测结果进行调控，使其在延长预测区间的同时尽量减少预测的偏差。接着，本文使用了非参数的AC预测模型。AC算法是一种图形拟合的非参数预测方法，有利于寻找股价的拐点。通过在历史时期寻找与当前时期状态相近的数据，对历史数据的延拓进行变换组合，得到当前的预测值。由于股价的变动比较剧烈，图形缺乏规律性，故单纯使用AC算法时得到的结果还不能令人满意，于是我们将EMD引入，通过将原始数据构成的曲线分解为若干有一定规律性的曲线，极大的提高了AC算法的预测精度和准确度。在三个预测模型的预测结果基础上，，我们使用构建的股票投资策略，任选30只股票进行实证。结果表明，在三种均线预测模型中，基于EMD分解的AC模型具有最高的预测准确性，在相同的交易策略下其虚拟操作的结果也是最好的和唯一具备现实投资价值的。同时通过对三种模型的实证分析，小盘股的收益率及其波动均大于同等情况下的大盘股。因此，基于EMD分解的AC预测模型的这个交易策略具备良好的现实可操作性和盈利性，可作为广大股票投资者的重要参考。
[Abstract]:For a long time, the focus of investment strategy construction on stock is mainly focused on two aspects. First, it is simple to use mobile average rules to trade. The ultimate goal is not to invest, but to test the effectiveness of the market. The two is to build an investment portfolio in order to put into practical application. Recent research on strategy has shifted to a strategy based on stock market prediction.
On the basis of the average trading rules, this paper constructs a set of investment strategy with strong operability. The key of this strategy is to get the forecast value of the average line through the prediction model and then get the forecast value of the stock price, and then deal with the trading conditions set by the strategy. For short term investors, the trend of the average line is predicted in advance. Judging the trend and inflection point of stock price is of great significance.
First, the two level autoregressive model is used to predict. First, the level model is built using the average line data, and then the horizontal model is established by using the average line and the stock price data. The prediction results of the lower level model are adjusted by the average forecast value obtained by the upper level model, so that it can be reduced as much as possible while prolonging the prediction interval. Less predicted deviations.
Then, this paper uses the non parametric AC prediction model.AC algorithm to be a non parametric prediction method of graphic fitting, which is helpful to find the turning point of the stock price. By searching for the data similar to the current state in the historical period, the continuation of the historical data is transformed and combined to get the current prediction value.
Because the change of the stock price is more violent and the graphics are not regular, the results obtained by using the AC algorithm are not satisfactory. So we introduce the EMD into the curve which is made up of the original data into some regular curves, which greatly improves the accuracy and accuracy of the AC algorithm.
On the basis of the prediction results of the three prediction models, we use the proposed stock investment strategy and choose 30 stocks to carry out an empirical study. The results show that in the three average forecasting models, the AC model based on EMD decomposition has the highest prediction accuracy. Under the same transaction strategy, the result of its virtual operation is also the best and the only one. Real investment value. At the same time, through the empirical analysis of the three models, the yield and volatility of the small stocks are greater than those in the same situation. Therefore, the AC prediction model based on EMD decomposition has good practical maneuverability and profitability, which can be used as an important reference for the stock investors.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F830.91;F224

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