Forecasting Stock Exchange Movements Using Neural Networks: A Case Study

By Hanif Tahersima, Mohammad H. Tahersima, M. Fesharaki, Navid Hamedi. 2011

21 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

In Economics and Econometrics

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

In Management Science and Operations Research

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

Citations in the corpus

• Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, background): “MLP neural networks are mostly used by the researchers for its inherent capabilities to approximate any non-linear function to a high degree of accuracy (Lin and Feng, 2010; Tahersima et al., 2011).”