Journal ArticleDOI
Forecasting stock market movement direction with support vector machine
TLDR
This paper investigates the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index and proposes a combining model by integrating SVM with the other classification methods.About:
This article is published in Computers & Operations Research.The article was published on 2005-10-01. It has received 984 citations till now. The article focuses on the topics: Ranking SVM & Support vector machine.read more
Citations
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Journal ArticleDOI
Time Series Prediction Using Support Vector Machines: A Survey
TL;DR: A survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM).
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Surveying stock market forecasting techniques - Part II: Soft computing methods
TL;DR: This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets to show that soft computing techniques are widely accepted to studying and evaluating stock market behavior.
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Predicting direction of stock price index movement using artificial neural networks and support vector machines
TL;DR: This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index, finding that average performance of ANN model was found significantly better than that of SVM model.
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Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
TL;DR: Experimental results show that the performance of all the prediction models improve when these technical parameters are represented as trend deterministic data, and random forest outperforms other three prediction models on overall performance.
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A deep learning framework for financial time series using stacked autoencoders and long-short term memory
TL;DR: A novel deep learning framework where wavelet transforms, stacked autoencoders and long-short term memory are combined for stock price forecasting and shows that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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Multivariate data analysis
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
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A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
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The Cross‐Section of Expected Stock Returns
Eugene F. Fama,Kenneth R. French +1 more
TL;DR: In this paper, Bhandari et al. found that the relationship between market/3 and average return is flat, even when 3 is the only explanatory variable, and when the tests allow for variation in 3 that is unrelated to size.