Stock market prediction using support vector machine

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Stock market prediction using evolutionary support vector machines: an application to the ASE20 index: The European Journal of Finance: Vol 22, No 12

Identification of main differentiating factors among the available solutions. The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses. That is why a number of researchers have turned their full attention to the different aspects of this problem lately.

However, there is no well-rounded theoretical and technical framework for approaching the problem to the best of our knowledge. We believe the existing lack of such clarity on the topic is due to its interdisciplinary nature that involves at its core both behavioral-economic topics as well as artificial intelligence.

We dive deeper into the interdisciplinary nature and contribute to the formation of a clear frame of discussion.

We review the related works that are about market prediction based on online-text-mining and produce a picture of the generic components that they all have. We, furthermore, compare each system with the rest and identify their main differentiating factors. Our comparative analysis of the systems expands onto the theoretical and technical foundations behind each.

This work should help the research community to structure this emerging field and identify the exact aspects which require stock market prediction using support vector machine research and are of special significance. Journals Books Register Sign in Sign in using your ScienceDirect credentials Username. Procedure of buying a binary options username or password? Sign in via your institution OpenAthens Other institution Recent Institutions.

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Expert Systems with Applications Volume 41, Issue 1615 NovemberPages Author links open the author workspace. Opens the author workspace Opens the author workspace a.

Predicting stock market price using support vector regression - IEEE Xplore Document

Numbers and letters correspond to the affiliation list. Click to expose these in author workspace Saeed Aghabozorgi. Click to expose these in author workspace Teh Ying Wah. Click to expose these in author workspace David Chek Ling Ngo.

Abstract The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses.

Keywords Online sentiment analysis. Social media text mining.

Stock prediction based on news. Check if you have access through your login credentials or your institution.

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