Surveying stock market forecasting techniques – Part II: Soft computing methods

Atsalakis, George S.; Valavanis, Kimon P.

doi:10.1016/j.eswa.2008.07.006

Cited by 700 publications

(378 citation statements)

References 69 publications

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“…The survey by Atsalakis et al [6] identified various studies on forecasting for more than 20 stock markets. Although our approach has been demonstrated through some of the selected stocks from Hong Kong stock market, it can be applied to any other markets.…”

Section: Related Workmentioning

confidence: 99%

Strategy Modeling and Classifier Training for Share Trading

Lei

Chiu

2010

Database Theory and Application, Bio-Science and Bio-Technology

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Abstract. In technical analysis, each trading strategy can trigger a buy or sell action whenever the specified conditions are satisfied. When a set of strategies are applied to a particular stock, a trader often receives conflicting recommendations from each strategy. In this paper, we propose a unified data mining approach in which the outcomes of each strategy are taken into consideration for decision making. First, we develop a framework for composing complex trading strategies. Next, we show how to perform simulation analysis on constructed strategies using extracted historical prices. The result of the simulation analysis is then used for training classifiers which can be used for recommending stock trading actions. Experiments conducted with the price data from Hong Kong Stock Market show promising results.

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Section: Related Workmentioning

confidence: 99%

Strategy Modeling and Classifier Training for Share Trading

Lei

Chiu

2010

Database Theory and Application, Bio-Science and Bio-Technology

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“…Their approach is based on MLPs and historical time series data about the electricity demand and price. For a comprehensive review about neural network technologies their application on forecasting stock markets, it is referred to 80 .…”

Section: Financial Market Forecastingmentioning

confidence: 99%

An overview of the use of neural networks for data mining tasks

Stahl

Jordanov

2012

WIREs Data Min & Knowl

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In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks.

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“…Japan and China are examples of two different stock markets. Japan is a showcase of a mature financial market with lots of research being conducted on [54][55][56][57][58]. On the other hand, China is an archetypical model of a young emerging stock market with fewer studies conducted on [54] and [59].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Quite a bit of efficacious applications has revealed that neural networks are a promising technique to predict stock prices and indexes. The nonlinear mapping function of neural networks does not require a presupposition of the data properties [54,[59][60][61]. In order to construct a forecasting model with neural networks, the original observed data is used exactly [60][61].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Effects of Technical Market Indicators on Stock Market Index Direction Forecasting

Şenyurt¹,

Subaşı²

2015

Modeling of Artificial Intelligence

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In this study, we examine and compare the performances of data mining techniques based on the daily indexes of five different markets, namely DAX, FTSE 100, NASDAQ Composite, NIKKEI 225 and NYSE Composite. The A/D oscillator, chaikin oscillator, moving average convergence/divergence, stochastic oscillator, acceleration, momentum, chaikin volatility, fast stochastics, slow stochastics, williams %R, negative volume index, positive volume index, relative strength index, accumulation/distribution line, bollinger band, highest high, lowest low, median price, on balance volume, price rate of change, price-volume trend, typical price, volume rate of change, weighted close and williams accumulation/distribution make up the 28 technical indicator attributes used in this study. The data mining techniques utilized in this study comprise of random forest model, artificial neural network (ANN) and support vector machine (SVM). Initially, the input features are sorted with WEKA's Info Ranker which reveals the best attributes based on relative weight. Afterwards, ten attributes having the highest relative weight values are fed into random forest, ANN and SVM. Then different attribute combinations based on their relative weights are tested in turns for more precision.

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Surveying stock market forecasting techniques – Part II: Soft computing methods

Cited by 700 publications

References 69 publications

Strategy Modeling and Classifier Training for Share Trading

Strategy Modeling and Classifier Training for Share Trading

An overview of the use of neural networks for data mining tasks

Effects of Technical Market Indicators on Stock Market Index Direction Forecasting

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