灰色—加权马尔可夫链的研究及在股市预测中的应用
发布时间：2018-07-31 18:38
【摘要】：股市是一个部分信息已知、部分信息未知的灰色系统，股票价格作为其系统行为的主要特征量，是一个灰色量，并且由于受到公司自身经营状况、国内经济以及市场竞争程度等各种因素的影响，总是处于不停的波动变化之中。因而，本文尝试把灰色预测和马尔可夫链预测两种预测方法结合起来，取长补短，并将相关分析的思想融入其中，建立灰色加权马尔可夫链，用GM(1,1)模型来揭示经济现象发展变化的某种总趋势，而用加权马尔可夫模型来确定现象状态之规律，进而研究股票价格的运动趋势，最终来预测股价未来可能的走势。但是由于GM(1,1)模型预测的局限性，灰色加权马尔可夫链只适合于短期预测，为了综合分析股市，本文又给出了中长期预测效果比较好的加权马尔可夫链预测模型，以及预测股市灾变日期的灰色灾变预测模型，，并进行了实例分析，如对股价的运动特征（短期、中长期）、涨落的时间周期和灾变日期进行了定量分析，目的在于提供依据，使投资者在面对错综复杂的股市时把握时机，采取科学合理的投资策略，使收益最大化。
[Abstract]:The stock market is a grey system with known partial information and unknown part information. Stock price, as the main characteristic quantity of its system behavior, is a grey quantity, and because of the company's own operating condition, The influence of various factors such as domestic economy and market competition is always in constant fluctuation. Therefore, this paper attempts to combine the grey prediction and Markov chain prediction, learn from each other, and incorporate the thought of correlation analysis into it, and establish grey weighted Markov chain. The GM (1) model is used to reveal the general trend of the development and change of economic phenomena, while the weighted Markov model is used to determine the law of phenomenon state, and then to study the movement trend of stock price, and finally to predict the possible trend of stock price in the future. However, due to the limitation of GM (1K1) model, grey weighted Markov chain is only suitable for short-term forecasting. In order to analyze the stock market comprehensively, this paper gives a weighted Markov chain forecasting model with good effect in the medium and long term forecasting. The grey catastrophe forecasting model for predicting the date of stock market catastrophe is also given. Examples are analyzed, such as the characteristics of stock price movement (short, medium and long term), the time period of fluctuation and the date of catastrophe, in order to provide the basis. In the face of the complicated stock market, investors can seize the opportunity and adopt scientific and reasonable investment strategies to maximize the returns.
【学位授予单位】：河南科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F224;F832.51
本文编号：2156479
[Abstract]:The stock market is a grey system with known partial information and unknown part information. Stock price, as the main characteristic quantity of its system behavior, is a grey quantity, and because of the company's own operating condition, The influence of various factors such as domestic economy and market competition is always in constant fluctuation. Therefore, this paper attempts to combine the grey prediction and Markov chain prediction, learn from each other, and incorporate the thought of correlation analysis into it, and establish grey weighted Markov chain. The GM (1) model is used to reveal the general trend of the development and change of economic phenomena, while the weighted Markov model is used to determine the law of phenomenon state, and then to study the movement trend of stock price, and finally to predict the possible trend of stock price in the future. However, due to the limitation of GM (1K1) model, grey weighted Markov chain is only suitable for short-term forecasting. In order to analyze the stock market comprehensively, this paper gives a weighted Markov chain forecasting model with good effect in the medium and long term forecasting. The grey catastrophe forecasting model for predicting the date of stock market catastrophe is also given. Examples are analyzed, such as the characteristics of stock price movement (short, medium and long term), the time period of fluctuation and the date of catastrophe, in order to provide the basis. In the face of the complicated stock market, investors can seize the opportunity and adopt scientific and reasonable investment strategies to maximize the returns.
【学位授予单位】：河南科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F224;F832.51
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