This can also be expressed in the form:
Financial Applications Listed in order of citations per year, highest at the top. Last updated September Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines.
However, the three machine learning methods we employed Naive Bayes, maximum entropy classification, and support vector machines do not perform as well on sentiment classification as on traditional topic-based categorization.
We conclude by examining factors that make the sentiment classification problem more challenging. However, they also found that the three machine learning methods they employed Naive Bayes, maximum entropy classification, and support vector machines did not perform as well on sentiment classification as on traditional topic-based categorization.
On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference.
The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs.
The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem.
The one step ahead prediction performances obtained on the Svm in financial time series forecasting of the weekly day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic" applied the Bayesian evidence framework to least squares support vector machine LS-SVM regression to predict the weekly day T-bill rate and the daily DAX30 closing prices.
Application of support vector machines in financial time series forecastingOmega: The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation BP neural network.
Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series.
Modified support vector machines in financial time series forecastingNeurocomputing, Volume 48, IssuesOctoberPages This procedure is based on the prior knowledge that in the non-stationary financial time series the dependency between input variables and output variable gradually changes over the time, specifically, the recent past data could provide more important information than the distant past data.
In the experiment, C-ascending support vector machines are tested using three real futures collected from the Chicago Mercantile Market. It is shown that the C-ascending support vector machines with the actually ordered sample data consistently forecast better than the standard support vector machines, with the worst performance when the reversely ordered sample data are used.
Furthermore, the C-ascending support vector machines use fewer support vectors than those of the standard support vector machines, resulting in a sparser representation of solution. Credit rating analysis with support vector machines and neural networks: Recent studies have shown that Artificial Intelligence AI methods achieved better performance than traditional statistical methods.
This article introduces a relatively new machine learning technique, support vector machines SVMto the problem in attempt to provide a model with better explanatory power.
However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models.
Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets. Support vector machines experts for time series forecastingNeurocomputing, Volume 51, AprilPages The generalized SVMs experts have a two-stage neural network architecture.
In the first stage, self-organizing feature map SOM is used as a clustering algorithm to partition the whole input space into several disjointed regions.
A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM experts, that best fit partitioned regions are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs.
The sunspot data, Santa Fe data sets A, C and D, and the two building data sets are evaluated in the experiment. The simulation shows that the SVMs experts achieve significant improvement in the generalization performance in comparison with the single SVMs models.
In addition, the SVMs experts also converge faster and use fewer support vectors. Financial time series forecasting using support vector machinesNeurocomputing, Volume 55, Issues SeptemberPages This study applies SVM to predicting the stock price index.
In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction. An application of support vector machines in bankruptcy prediction modelExpert Systems with Applications, Volume 28, Issue 1, JanuaryPages Using Support Vector Machines in Financial Time Series Forecasting Financial Forecasting Using Support Vector Machines I really recommend that you go through the existent literature, but just for fun I will describe an easy way (probably not the best) to do it.
You're currently subscribed to some eWEEK features and just need to create a username and password. the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation.
Vol.7, No.3, May, Mathematical and Natural Sciences.
Study on Bilinear Scheme and Application to Three-dimensional Convective Equation (Itaru Hataue and Yosuke Matsuda). Type or paste a DOI name into the text box. Click Go. Your browser will take you to a Web page (URL) associated with that DOI name. Send questions or comments to [email protected] Full-Text Paper (PDF): Using Support Vector Machines in Financial Time Series Forecasting.