Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of si...Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.展开更多
In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-...In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.展开更多
针对现有均值反转类策略存在的预测模型参数无法动态更新和未充分考虑动量效应的问题,提出一种策略M-ODMAR。使用简单移动平均模型对股票价格进行预测,并通过在线牛顿步(Online Newton Step,ONS)算法对模型参数进行动态更新;利用在线被...针对现有均值反转类策略存在的预测模型参数无法动态更新和未充分考虑动量效应的问题,提出一种策略M-ODMAR。使用简单移动平均模型对股票价格进行预测,并通过在线牛顿步(Online Newton Step,ONS)算法对模型参数进行动态更新;利用在线被动攻击(Passive Aggressive,PA)算法选取投资组合;使用L1中位数来提取价格动量信息并对投资组合进行调整。实验结果显示,在四个数据集上该策略的累积收益高于所对比的其他策略,说明了参数的动态更新和动量效应的加入对于均值反转类策略的累积收益提高具有促进作用。展开更多
Online portfolio selection and simulation are some of the most important problems in several research communities,including finance,engineering,statistics,artificial intelligence,machine learning,etc.The primary aim o...Online portfolio selection and simulation are some of the most important problems in several research communities,including finance,engineering,statistics,artificial intelligence,machine learning,etc.The primary aim of online portfolio selection is to determine portfolio weights in every investment period(i.e.,daily,weekly,monthly,etc.)to maximize the investor’s final wealth after the end of investment period(e.g.,1 year or longer).In this paper,we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks.Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets(i.e.,NYSE,S&P500,DJIA,and TSX),the proposed strategy has been shown to outperform both Anticor and OLMAR—the two most prominent portfolio selection strategies in contemporary literature.展开更多
在被动进攻算法(passive aggressive mean reversion, PAMR)的基础上引入了递推最小二乘,使用其预测值代替原先的相对价格,同时实证分析了国内外5个股票数据集.结果证明,递推最小二乘被动进攻算法均取得了更好的累计收益,证实了其更加...在被动进攻算法(passive aggressive mean reversion, PAMR)的基础上引入了递推最小二乘,使用其预测值代替原先的相对价格,同时实证分析了国内外5个股票数据集.结果证明,递推最小二乘被动进攻算法均取得了更好的累计收益,证实了其更加优异的收益能力.展开更多
文摘Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.
文摘In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.
文摘针对现有均值反转类策略存在的预测模型参数无法动态更新和未充分考虑动量效应的问题,提出一种策略M-ODMAR。使用简单移动平均模型对股票价格进行预测,并通过在线牛顿步(Online Newton Step,ONS)算法对模型参数进行动态更新;利用在线被动攻击(Passive Aggressive,PA)算法选取投资组合;使用L1中位数来提取价格动量信息并对投资组合进行调整。实验结果显示,在四个数据集上该策略的累积收益高于所对比的其他策略,说明了参数的动态更新和动量效应的加入对于均值反转类策略的累积收益提高具有促进作用。
文摘Online portfolio selection and simulation are some of the most important problems in several research communities,including finance,engineering,statistics,artificial intelligence,machine learning,etc.The primary aim of online portfolio selection is to determine portfolio weights in every investment period(i.e.,daily,weekly,monthly,etc.)to maximize the investor’s final wealth after the end of investment period(e.g.,1 year or longer).In this paper,we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks.Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets(i.e.,NYSE,S&P500,DJIA,and TSX),the proposed strategy has been shown to outperform both Anticor and OLMAR—the two most prominent portfolio selection strategies in contemporary literature.
