Editing Technical analysis (section)

===Neural networks===
Since the early 1990s when the first practically usable types emerged, [[artificial neural network]]s (ANNs) have rapidly grown in popularity. They are [[artificial intelligence]] adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal [[Function approximation|function approximators]],<ref>K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks vol 2, 1989</ref><ref>K. Hornik, Multilayer feed-forward networks are universal approximators, Neural Networks, vol 2, 1989</ref> meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.<ref>R. Lawrence. [http://people.ok.ubc.ca/rlawrenc/research/Papers/nn.pdf Using Neural Networks to Forecast Stock Market Prices]</ref><ref>B.Egeli et al. [http://www.hicbusiness.org/biz2003proceedings/Birgul%20Egeli.pdf Stock Market Prediction Using Artificial Neural Networks] {{Webarchive|url=https://web.archive.org/web/20070620024840/http://www.hicbusiness.org/biz2003proceedings/Birgul%20Egeli.pdf |date=20 June 2007 }}</ref><ref>M. Zekić. [http://oliver.efos.hr/nastavnici/mzekic/radovi/mzekic_varazdin98.pdf Neural Network Applications in Stock Market Predictions – A Methodology Analysis] {{Webarchive|url=https://web.archive.org/web/20120424231150/http://oliver.efos.hr/nastavnici/mzekic/radovi/mzekic_varazdin98.pdf |date=24 April 2012 }}</ref>

While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly [[neural network software]] has made the technology more accessible to traders.{{Citation needed|date=November 2023}}