A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.     

结构可调的支持向量回归估计

针对定义域各分区间内样本数据的噪声强度不同，以及在局部范围内数据变化急剧等复杂情况，提出了结构可调的支持向量回归估计(AS-SVR)方法，包括采用不同的损失函数，对各样本点自适应地选用不同的参数等。推导了求解公式，给出了调整算法。实例测试表明，AS-SVR方法的楚模效果优于常规方法。     

Visualization and clustering of categorical data with probabilistic self-organizing map

This paper introduces a self-organizing map dedicated to clustering, analysis and visualization of categorical data. Usually, when dealing with categorical data, topological maps use an encoding stage: categorical data are changed into numerical vectors and traditional numerical algorithms (SOM) are run. In the present paper, we propose a novel probabilistic formalism of Kohonen map dedicated to categorical data where neurons are represented by probability tables. We do not need to use any coding to encode variables. We evaluate the effectiveness of our model in four examples using real data. Our experiments show that our model provides a good quality of results when dealing with categorical data.     

Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond 1 year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.     

一种改进的再生核支持向量机回归模型

基于支持向量机核函数的条件,将Sobolev Hilbert空间的再生核函数进行改进,给出一种新的支持向量机核函数,并提出一种改进的最小二乘再生核支持向量机的回归模型,该回归模型的参数被减少,且仿真实验结果表明：最小二乘支持向量机的核函数采用改进的再生核函数是可行的,改进后的再生核函数不仅具有核函数的非线性映射特征,而且也继承了该再生核函数对非线性逐级精细逼近的特征,回归的效果比一般的核函数更为细腻。     

Pricing currency options with support vector regression and stochastic volatility model with jumps

This paper presents an efficient currency option pricing model based on support vector regression (SVR). This model focuses on selection of input variables of SVR. We apply stochastic volatility model with jumps to SVR in order to account for sudden big changes in exchange rate volatility. We use forward exchange rate as the input variable of SVR, since forward exchange rate takes interest rates of a basket of currencies into account. Therefore, the inputs of SVR will include moneyness (spot rate/strike price), forward exchange rate, volatility of the spot rate, domestic risk-free simple interest rate, and the time to maturity. Extensive experimental studies demonstrate the ability of new model to improve forecast accuracy.     

Classified self-organizing map with adaptive subcodebook for edge preserving vector quantization

This paper presents a novel classified self-organizing map method for edge preserving quantization of images using an adaptive subcodebook and weighted learning rate. The subcodebook sizes of two classes are automatically adjusted in training iterations based on modified partial distortions that can be estimated incrementally. The proposed weighted learning rate updates the neuron efficiently no matter of how large the weighting factor is. Experimental results show that the new method achieves better quality of reconstructed edge blocks and more spread out codebook and incurs a significantly less computational cost as compared to the competing methods.     

Regularized least squares fuzzy support vector regression for financial time series forecasting

In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on financial datasets available in the public domain.     

Efficient computation and model selection for the support vector regression

In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.     

A multi-fidelity surrogate model based on support vector regression

Structural and Multidisciplinary Optimization - Computational simulations with different fidelities have been widely used in engineering design and optimization. A high-fidelity (HF) model is...     

基于支持向量机的财务预警模型与应用研究

提出了利用支持向量机建立财务预警系统、进行财务风险监控的方法,给出了财务评价指标体系及其量化方法,利用支持向量机的分类能力建立了财务预警的模型.最后利用上市公司的财务数据进行训练和评估,证明了基于支持向量机的财务预警模型的可行性和实用性,实验表明支持向量机在小样本情况下具有良好的非线性建模能力和泛化能力.     

Linear support vector regression with linear constraints

Machine Learning - This paper studies the addition of linear constraints to the Support Vector Regression when the kernel is linear. Adding those constraints into the problem allows to add prior...     

Stochastic support vector regression with probabilistic constraints

Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, we introduce a novel model of SVR in which any training samples containing inputs and outputs are considered the random variables with known or unknown distribution functions. Constraints occurrence have a probability density function which helps to obtain maximum margin and achieve robustness. The optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several experiments including artificial data sets and real-world benchmark data sets.     

Grey relational grade in local support vector regression for financial time series prediction

Support vector regression (SVR) has often been applied in the prediction of financial time series with many characteristics. On account of much time consumption of global SVR, local machines are carried out to accelerate the computation. In this paper, we introduce local grey SVR (LG-SVR) integrated grey relational grade with local SVR for financial time series forecasting. Pattern search method and leave-one-out errors are adopted for model selection. Experimental results of three real financial time series prediction demonstrate that LG-SVR can speed up computing speed and improve prediction accuracy.     

基于MapReduce的最小二乘支持向量机回归模型

针对最小二乘支持向量机处理大规模数据集耗时长且受内存限制的特点,将局部多模型方法与MapReduce编程模式相结合,提出一种并行最小二乘支持向量机回归模型.模型由两组MapReduce过程组成,首先按照输入样本集对样本数据进行聚类操作,再对聚类后得到的子类按输出样本集进行二次聚类操作,分别得到局部模型数目和各局部模型综合加权输出计算结果.实验结果表明,并行最小二乘支持向量机回归模型具有较好的加速比和可扩展性.     

Robust linear and support vector regression

The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators. Previous models were significantly more complex or formulated in the dual space and most involved specialized numerical algorithms for solving the robust Huber linear estimator. Numerical test comparisons with these algorithms indicate the computational effectiveness of the new quadratic programming model for both linear and nonlinear support vector problems. Results are shown on problems with as many as 20000 data points, with considerably faster running times on larger problems     

参数生产前沿面分析的单边支持向量回归模型

为了解决了现有参数生产前沿面分析中先验生产函数难以选择的问题,提出参数生产前沿面分析的单边支持向量回归模型．该模型通过引入核方法,采用非线性映射将各生产决策单元的资源投入原始数据由数据空间映射到特征空间,然后在特征空间进行对应的线性操作。这样,则可以通过线性生产函数的非线性映射来解决生产函数的选择问题。最后,通过对珠三角各城市的经济发展效率进行评价,证明了该模型的有效性。     

具有多分段损失函数的多输出支持向量机回归

对多维输入、多维输出数据的回归,可以采用多输出支持向量机回归算法.本文介绍具有多分段损失函数的多输出支持向量机回归,其损失函数对落在不同区间的误差值采用不同的惩罚函数形式,并利用变权迭代算法,给出回归函数权系数和偏置的迭代公式.仿真实验表明,该算法的精确性和计算工作量都优于使用多个单输出的支持向量机回归算法.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.