Forecast performance of neural networks and business cycle asymmetries

Kiani, Khurshid M.; Bidarkota, Prasad V.; Kastens, Terry L.

doi:10.1080/17446540500143848

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…The procedure for the AGS test is also described in Bradshaw and Orden (1990) and Kiani et al (2005). Figures 1-8 confirm the results described above.…”

supporting

confidence: 75%

“…Qi and Wu (2003), who employ an ANN model with monetary fundamentals, find that their model cannot supass the random walk model. Alternatively, Kiani (2005) and Kiani et al (2005) use ANN models with macroeconomic time series and find that these outperform the linear as well as other nonlinear models employed. O' Connor and Madden (2005) evaluate the effectiveness of using ANNs with external indicators, such as commodity prices and currency exchange rates, in predicting movements in the Dow Jones Industrial Average index.…”

Section: Introductionmentioning

confidence: 99%

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Neural Networks Applied to Stock Market Forecasting: An Empirical Analysis

Maciel¹,

Ballini²

2010

L&NLM

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Neural networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, artificial neural networks (ANNs) are among the most effective learning methods. During the last decade, they have been widely applied to the domain of financial time series prediction, and their importance in this field is growing. This paper aims to analyze neural networks for financial time series forecasting, specifically, their ability to predict future trends of North American, European, and Brazilian stock markets. Their accuracy is compared to that of a traditional forecasting method, generalized autoregressive conditional heteroskedasticity (GARCH). Furthermore, the best choice of network design is examined for each data sample. This paper concludes that ANNs do indeed have the capability to forecast the stock markets studied, and, if properly trained, robustness can be improved, depending on the network structure. In addition, the Ashley-Granger-Schmalancee and Morgan-Granger-Newbold tests indicate that ANNs outperform GARCH models in statistical terms.

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“…The procedure for the AGS test is also described in Bradshaw and Orden (1990) and Kiani et al (2005). Figures 1-8 confirm the results described above.…”

supporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Neural Networks Applied to Stock Market Forecasting: An Empirical Analysis

Maciel¹,

Ballini²

2010

L&NLM

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“…10. The procedure for the AGS test is also described in Bessler and Brandt (1992), Bradshaw and Orden (1990), Kastens and Brester (1996), and Kiani et al (2005).…”

Section: Ags Testmentioning

confidence: 99%

“…Qi and Wu (2003), who employ an ANN model with monetary fundamentals, find that their model cannot beat the random walk model. Alternatively, Kiani (2005) and Kiani et al (2005) used ANN models with macroeconomic time series and found that they outperformed the linear as well as other nonlinear models they employed. Lisi and Schiavo (1999) perform a detailed comparison of neural network and chaotic models for predicting monthly exchange rates.…”

Section: Introductionmentioning

confidence: 96%

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

Kiani

Kastens

2008

Comput Econ

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“…Qi and Wu () show that their model cannot beat the random walk model by implementing a shallow neural networks approach with monetary indicators. In contrast, Kiani and co‐workers (Kiani, ; Kiani, Bidarkota, & Kastens, ) employed shallow neural networks with macroeconomic time series and proved that shallow neural networks perform better than the linear model.…”

Section: Literature Reviewmentioning

confidence: 99%

Technological bias at the exchange rate market

Galeshchuk

2017

Intell Sys Acc Fin Mgmt

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Summary Prediction of exchange rates has been a topic for debate in economic literature since the late 1980s. The recent development of machine learning techniques has spurred a plethora of studies that further improves the prediction models for currency markets. This high‐tech progress may create challenges for market efficiency along with information asymmetry and irrationality of decision‐making. This technological bias emerges from the fact that recent innovative approaches have been used to solve trading tasks and to find the best trading strategies. This paper demonstrates that traders can leverage technological bias for financial market forecasting. Those traders who adapt faster to the changes in market innovations will get excess returns. To support this hypothesis we compare the performance of deep learning methods, shallow neural networks with baseline prediction methods and a random walk model using daily closing rate between three currency pairs: Euro and US Dollar (EUR/USD), British Pound and US Dollar (GBP/USD), and US Dollar and Japanese Yen (USD/JPY). The results demonstrate that deep learning achieves higher accuracy than alternate methods. The shallow neural network outperforms the random walk model, but cannot surpass ARIMA accuracy significantly. The paper discusses possible outcomes of the technological shift for financial market development and accounting conforming also to adaptive market hypothesis.

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Forecast performance of neural networks and business cycle asymmetries

Cited by 7 publications

References 30 publications

Neural Networks Applied to Stock Market Forecasting: An Empirical Analysis

Neural Networks Applied to Stock Market Forecasting: An Empirical Analysis

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

Technological bias at the exchange rate market

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