@inproceedings{979bdda0c6c74e2188e1b1e06ae3f535,
title = "Neural network prediction and decision making system for investment assets",
abstract = "The problem is to test the weak hypothesis of efficient markets through three neural networks that can predict the trends of investment assets such as: The Dow Jones, gold and Euro dollar, according to theories of technical analysis to automate positions of both long and short investment in the Spot market. With regard to forecasting time series, multiple approaches have been tested, through statistical models such as [1–3], where forecasts are made from different information sources with characteristics differentiated (sasonality, tendency, periodicity), however, other actors have begun to gain strength by getting the first places in international competitions, this is the case of Neural Networks, in works published as [4–6] the results have shown that this type of model offers a real opportunity to work with time series of different characteristics.",
keywords = "Assests, Decision making, Invesment, Neural network, Prediction",
author = "{Valencia N}, Cesar and Alfredo Sanabria and {Gonz{\'a}lez A}, Hernando and {Arizmendi P}, Carlos and {Orjuela C}, David",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2021.; null ; Conference date: 06-11-2019 Through 08-11-2019",
year = "2021",
doi = "10.1007/978-3-030-53021-1_27",
language = "Ingl{\'e}s",
isbn = "9783030530204",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "262--272",
editor = "{Cortes Tobar}, {Dario Fernando} and {Hoang Duy}, Vo and {Trong Dao}, Tran",
booktitle = "AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences",
}