Moving average rules, volume and the predictability of security returns with feedforward networks

Article Abstract:

The linear prediction of current returns in linear conditional mean specifications with past buy-sell signals does not bring any forecast improvements in comparison with linear models which use past returns as regressors. This was gleaned from an examination of the linear and nonlinear predictability of stock market returns with some simple technical trading rules, using the daily Dow Jones Industrial Average Index from 1963 to 1988. Results showed that models with past returns offer an average of 2.5% forecast improvement in non-linear conditional mean specifications over the benchmark linear model with past returns.

Securities and Commodity Exchanges, Security and commodity exchanges, Securities Exchanges, Stock-exchange, Stock exchanges, Exchanges, Return on investment, Rate of return

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Weight space analysis and forecast uncertainty

Article Abstract:

A study of the symmetries of feedforward networks according to their corresponding groups has found that these groups naturally act on and partition weight space into disjunct domains. An algorithm to generate representative weight vectors in a fundamental domain was obtained. When the metric structure of the fundamental domain was analyzed, a clustering method that uses the natural metric of the fundamental domain was derived, which obtains several clusters of weight vectors, even for large networks and large datasets.

Methods, Cluster analysis

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Non-linear prediction of security returns with moving average rules

Article Abstract:

The use of buy-sell signals elicited from moving average rules with a band between the short and long moving averages as inputs in models for forecasting linear and nonlinear stock market returns was investigated. Specifically, the prediction performance of autoregressive, generalized autoregressive conditionally heteroscedastic-in-mean and feedforward network models were evaluated. Daily returns between Jan. 2, 1963 and Jun. 30, 1988 from the Dow Jones Industrial Average index were used as the data set.

Case studies, Econometrics, Stock price forecasting, Regression analysis, Prediction theory, Dow Jones Industrial Average (Index)

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