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Please refer to this blog post for more information. The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions.

Stock Forecast Based On a Predictive Algorithm | I Know First |2 Stock Forecasting Methods You Should Use

This paper surveys more than related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used.

Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.

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Surveying stock market forecasting techniques - Part II

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Please enable JavaScript to use all stock market forecasting techniques features on this page. Expert Systems with Applications Volume 36, Issue 3, Part 2AprilPages Surveying stock market forecasting techniques — Part II: Author links open the author workspace.

How to Forecast the Stock Market | Finance - Zacks

Opens the author workspace Opens the author workspace a. Numbers and letters correspond to the affiliation list.

Click to expose these in author workspace Kimon P. Click to expose these in author workspace a Department of Production Engineering and Management, Technical University of Crete, University Campus, Kounoupidiana, ChaniaGreece b Department of Computer Science and Engineering University of South Florida, Tampa, FLUSA.

Abstract The key to successful stock market forecasting is achieving best results with minimum required input data.

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