Neural network forecasts of Canadian stock returns using accounting ratios

By Dennis Olson, Charles E. Mossman. 2003

182 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, background): “For example, Olson and Mossman [8] attempted to predict one-year-ahead stock returns for 2,352 Canadian companies using 61 accounting ratios as input values and reported that neural networks outperform traditional regression techniques.”
An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange (2010, unknown): “…Kim & Chun, 1998; Kim & Han, 2000; Kimoto, Asakawa, Yoda, & Takeoka, 1990; Leigh, Purvis , & Ragusa, 2002; Manish & Thenmozhi, 2006; Oh & Kim, 2002; Olson & Mossman, 2003; Pai & Lin, 2005; Saad, Prokhorov, & Wunsch, 1998; Takahashi, Tamada, & Nagasaka, 1998; Tan, Quek, & See Ng, 2007; Tay & Cao,…”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, methodology): “Hence, a fundamental analysis is the process of analyzing information contained in financial statements, such as the company's annual report, balance sheets, and income statements [41].”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Qi (2001) pointed out that ANNs are more likely to out-perform other methods when the input data is kept as current as possible, using recursive modelling (see also Olson & Mossman, 2003).”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, background): “On the contrary, Olson and Mossman (2003), Zorin and Borisov (2002) use 59 and 61input variables, respectively.”
25 years of time series forecasting (2006, methodology): “Qi (2001) pointed out that ANNs are more likely to outperform other methods when the input data is kept as current as possible, using recursive modelling (see also Olson & Mossman, 2003).”
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, unknown): “…the stock price return (Avci, 2007; Egeli, Ozturan, & Badur, 2003; Karaatli, Gungor, Demir, & Kalayci, 2005; Kimoto, Asakawa, Yoda, & Takeoka, 1990; Olson & Mossman, 2003; White, 1988; Yoon & Swales, 1991).. Leung et al. (2000) examined various prediction models based on multivariate…”
Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015, methodology): “The backpropagation algorithm has emerged as one of the most widely used learning procedures for multilayer networks [27] [28] [29].”