Neural network forecasts of Canadian stock returns using accounting ratios

Olson, Dennis; Mossman, Charles E.

doi:10.1016/s0169-2070(02)00058-4

Cited by 153 publications

(84 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hill et al (1994) noticed that ANNs are likely to work best for high frequency financial data and Balkin & Ord (2000) also stressed the importance of a long time series to ensure optimal results from training ANNs. 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).…”

Section: Neural Netsmentioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

1,295

632

View full text Add to dashboard Cite

During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

show abstract

Section: Neural Netsmentioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

1,295

632

View full text Add to dashboard Cite

show abstract

“…Kuan and Liu (1995) [18] used a feed-forward ANN model as is used in this study in forecasting financial time series data. Olson and Mossman (2003) [19] found that ANN networks performed better forecasts with Canadian stock returns than logistic and ordinary least squares (OLS) regression. Lastly, Ghiassi, Saidane, and Zimbra(2005) [20] found that the ANN was more accurate in forecasting time series data than autoregressive integrated moving average (ARIMA) models.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets

French

2016

IJFS

View full text Add to dashboard Cite

Abstract:The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study.

show abstract

“…Their results showed that BPN model is capable of excellent prediction. Olson and Mossman compared error back-propagation neural network method with Logit model and Ordinary Least Square methods [4]. The results show that the neural network model is more accurate and possesses lower error.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary hybrid method to predict pistachio price

Heydari

Keynia

Shahsavari-Pour

et al. 2017

Complex Intell. Syst.

View full text Add to dashboard Cite

Pistachio produce is of great importance in the world and considered as a valuable agricultural produce. This product is important for the economy of the producing countries as well as other importer countries. In this regard, countries which are capable of getting control over this product's market would obtain considerable benefits; one way to do so is predicting market trend or the price of this product and the affecting factors. In this survey, initially, the affecting parameters on pistachio price are identified. Then, using a novel combined intelligent method, the price of this product is predicted. In addition, using analysis of variance method, the results of the proposed method are compared to other intelligent combined methods, such as Feed Forward-Particle Swarm Optimization, Elman-Imperialist Competitive Algorithm, Feed Forward-Imperialist Competitive Algorithm, Elman-Genetic Algorithm, and Feed Forward-Genetic Algorithm. The results of the mentioned comparison indicate that the proposed method owns an excellent performance.

show abstract

Neural network forecasts of Canadian stock returns using accounting ratios

Cited by 153 publications

References 20 publications

25 years of time series forecasting

25 years of time series forecasting

Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets

An evolutionary hybrid method to predict pistachio price

Contact Info

Product

Resources

About