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We compare the effectiveness of Support Vector Machines (SVM) and Tree-based Genetic Programming (GP) to make accurate predictions on the movement of the Dow Jones Industrial Average (DJIA). The approach is facilitated though a novel representation of the data as a pseudo financial factor model, based on a linear factor model for representing correlations between the returns in different assets. To...
We propose a method for collective sentiment analysis for stock market prediction and analyse its ability to predict the change of a stock price for the next day. The proposed method is a two-stage process, based on the latest natural language processing and machine learning algorithms. Our evaluation shows best performance with the SVM approach in sentiment detection, with accuracy rates of 71.84/74...
We examine whether stock price effects can be automatically predicted analyzing unstructured textual information in financial news. Accordingly, we enhance existing text mining methods to evaluate the information content of financial news as an instrument for investment decisions. The main contribution of this paper is the usage of more expressive features to represent text and the employment of market...
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from...
Financial distress is the most synthetic form of business crisis and financial distress prediction (FDP) has been a widely and continually studied topic in the field of corporate finance. This paper attempts to put forward OR-CBR in K-nearest neighbors model, which can be the implementation of corresponding algorithm.
Making financial decision is difficult because the process contains complex unpredictable factors and specific statistical characteristics of data. In this paper, we investigate the decision making problems and approach them by considering different categories of financial ratios as input to the RS-LSSVM based on fuzzy integral model.
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