Artificial neural networks in business: Two decades of research

By Michal Tkác, Robert Verner. 2016

313 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research, ML Systems & Hardware

How researchers see this paper

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

In Accounting

1. Frank Xing
2. Weiwei Jiang
3. Benaceur Outtaj
4. A. Ozbayoglu
5. Longbing Cao
6. Roy E. Welsch
7. Omer Berat Sezer
8. M. Ugur Gudelek

In Artificial Intelligence

1. Frank Xing
2. Roy E. Welsch
3. Omer Berat Sezer
4. M. Ugur Gudelek
5. Erik Cambria
6. Weiwei Jiang
7. Benaceur Outtaj
8. A. Ozbayoglu

In Management Science and Operations Research

1. Weiwei Jiang
2. M. Ugur Gudelek
3. Omer Berat Sezer
4. A. Ozbayoglu
5. Longbing Cao
6. Frank Xing
7. Benaceur Outtaj
8. Roy E. Welsch

In ML Systems & Hardware

1. Weiwei Jiang
2. A. Ozbayoglu
3. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “The authors of [16] surveyed different implementations of ANN in financial applications including stock price forecasting.”
• Natural language based financial forecasting: a survey (2017, background): “It is quite interesting that, though financial forecasting covers a wide range of ideas from inflation rate prediction to credit scoring [116,101], a large proportion of the studies that employed textual data focus on stock market and foreign exchange rate (FOREX) prediction.”
• AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “(15) Neural computing methods model the relations, structures, sequential movements, transitions and influence between EcoFin variables by neural networks, in particular, deep neural networks (DNN) nowadays [151].”
• Artificial neural networks applications in economics and management research: An exploratory literature review (2018, background): “Tkáč and Verner [7] presented a systematic review of 412 research papers that had been published during the period 1994-2015.”
• Applications of deep learning in stock market prediction: recent progress (2020, background): “Tkáč & Verner (2016) provides a systematic overview of neural network applications in business between 1994 and 2015 and reveals that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to…”