The stock market is a complex and challenging field of research that has attracted researchers from several fields, such as for instance engineering. This dissertation approaches stock market analysis from an engineering point of view. It is empirically shown that techniques, such as neural networks, can be applied in many stock markets, as a...
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August 5, 2021 (v1)PublicationUploaded on: March 25, 2023
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October 19, 2020 (v1)Publication
In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen...
Uploaded on: December 4, 2022 -
October 19, 2020 (v1)Publication
Narrow markets are typically considered those that due to limited liquidity or peculiarities in its investor base, such as a particularly high concentration of retail investors, make the stock market less e cient and arguably less predictable. We show in this article that neural networks, applied to narrow markets, can provide relatively...
Uploaded on: March 26, 2023