Artificial neural networks are universal and highly flexible function xpproximators first used in the fields of cognitive science and engineering. In recent years, neural network applications in finance for such
TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets 2023, Applied Energy Citation Excerpt : Moreover, many papers also concluded that hybrid models provide superior processing and results [39–41]. On the other hand...
Neural NetworksNon-parametric ModelsFinancial Time seriesIn the Italian financial market, stock fluctuations are highly dependent on political and economic events. For this reason, any realistic forecast should consider this kind of information. In this paper we show a way to include economic and ...
Neural networksbackpropagationcurrency exchangetraining set sizetime-seriesforecastingWhen data quantities are fixed, as they are for most financial modeling techniques at the time of model construction, a problematic issue in developing optimal models including neural network models is the selection of ...
Daniel 和 Moskowitz(2016),Momentum crashes, Journal of Financial Economics Ghoshal 和 Roberts(2018),Thresholded ConvNet ensembles: Neural networks for technical forecasting, Conference on Knowledge Discover and Data Mining (KDD) Gu等(2017),Empirical asset pricing via machine learning, Chicago Booth Re...
Neural networks are used increasingly in a variety of business applications, including forecasting and marketing research. In some areas, such as fraud detection orrisk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations...
Besides the comparison between versions of recurrent neural networks for financial time series forecasting, this work includes time series from stock market indexes and currency exchange rates from some of the most important economies from four continents, which allows us to make a wider analysis and...
Financial forecasting.Neural networks analyze vast amounts of financial data, from stock prices to global economic indicators, to forecast market movements and help investors make informed decisions. While neural networks are powerful, they are not a one-size-fits-all solution. Their strength lies in...
“predictors” which are based on the financial application of chaos and complexity theories, and presented in clear, trustworthy visualizations. These predictors are blended into forecasting models usinggenetically-enhanced neural networks. Our careful application of these mathematical modeling techniques ...
A commonly used application of this type of network is in pattern recognition in neural linguistics, in which handwritten letters of the alphabet are decoded or interpreted by networks for machine translation. However, in economic and financial applications, the combining of the input variables into ...