Using Neural Networks to Support Early Warning System for Financial Crisis Forecasting Summary: This study deals with the construction process of a daily financial condition indicator (DFCI), which can be used as an early warning signal using neural networks and nonlinear programming. One of the cha...
Kohara, K.: Neural networks for economic forecasting problems. In: Cornelius T. Leondes (ed): Expert Systems -The Technology of Knowledge Management and Decision Making for the 21st Century-. Academic Press. San Diego, CA (2002)K. Kohara, Neural networks for economic forecasting problems, in...
networks as well as learning systems for financial risks, and summarize different applications of artificial intelligence technologies in the relevant domains of financial risks and their management. Moreover, this Special Issue is an opportunity to provide a forum where researchers will be able to sha...
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...
Forecasting with Computational Intelligence - An Evaluation of Support Vector Regression and Artificial Neural Networks for Time Series Prediction Recently, novel algorithms of support vector regression and neural networks have received increasing attention in time series prediction. While they offer ... SF...
The use of neural networks for financial applications is quite common. New approaches use advances in neural technology such as fuzzy neural networks to improve the results. In this paper, the application of fuzzy neural networks to a major financial application is presented: the forecasting of fin...
The aim of this work is to examine how neural networks can be used for solving the problem of the forecast of large financial crashes due to the presence of speculative bubbles. Some microeconomic theories have been developed for the explanation of a bubble due to a cooperation among the inves...
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...
Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics, and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection, andrisk ...
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...