A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

By Qing Cao, Karyl B. Leggio, M. Schniederjans. 2005

255 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, 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 Economics and Econometrics

1. Melek Acar Boyacioglu
2. George S. Atsalakis
3. Stephen J. Roberts
4. Y. Kara
5. Zihao Zhang
6. Seungwoo Jeon
7. Kimon P. Valavanis
8. Victor I. Chang

In Electrical and Electronic Engineering

1. Stephen J. Roberts
2. Y. Kara
3. Seungwoo Jeon
4. Kimon P. Valavanis
5. George S. Atsalakis
6. Victor I. Chang
7. Bonghee Hong
8. Ö. K. Baykan

In Management Science and Operations Research

1. Melek Acar Boyacioglu
2. Stefan Zohren
3. George S. Atsalakis
4. Stephen J. Roberts
5. Y. Kara
6. Zihao Zhang
7. Seungwoo Jeon
8. Kimon P. Valavanis

Citations in the corpus

• Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, neutral): “As an application to emerging market, Cao et al. [9] predicted stock returns in the Chinese stock market.”
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “There is, however, agreement that stock returns behave in more complex ways, typically highly nonlinearly [19, 20].”
• Forecasting stock market short-term trends using a neuro-fuzzy based methodology (2009, unknown): “, 2005 ; Bouq - ata , 2000 ; Cao , Karyl , & Schniederjans , 2005 ; Doesken , Abraham , Thomas , & Paprzycki , 2005 ; Kosaka , Mizuno , Sasaki , Someya , & Hamada , 1991 ; Kuo , 1998 ; Kuo & Xue , 1998 ; Lin , Khan , & Huang , 2002 ; Lin & Lee , 1991 ; Nayak , Sudheer , Rangan , & Ramasastri , 2004…”
• Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, background): “Cao, Leggio, and Schniederjans (2005) aimed to demonstrate the accuracy of ANN in predicting stock price movement for firms traded on the Shanghai Stock Exchange (SHSE).”
• Artificial neural networks in business: Two decades of research (2016, unknown): “[73] compared neural network to Fama and French ́s model, whereas Quah [322] focused on neuro-fuzzy inference systems and general growing and pruning radial basis function.”
• Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, methodology): “Shanghais stock market Cao et al. (2005), Yiwen et al.”
• Pattern graph tracking-based stock price prediction using big data (2018, methodology): “After selecting the historical dataset, to generate the predicted stock data, we use an artificial neural network algorithm because it is the most widely used in stock price forecasts ([9, 28, 33]) and has a high predictive power as an advantage over learning by iterative adjustment.”