Forecasting Macroeconomic Variables Using Artificial Neural Network and Traditional Smoothing Techniques

Önder, Emrah; Bayir, Firat; Hepşen, Ali

doi:10.2139/ssrn.2264379

Cited by 16 publications

(13 citation statements)

References 7 publications

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“…In contrast, Kiani and co-workers (Kiani, 2005;Kiani, Bidarkota, & Kastens, 2005) employed shallow neural networks with macroeconomic time series and proved that shallow neural networks perform better than the linear model. Önder, Bayır, and Hepsen (2013)…”

Section: Heuristic Methods For Exchange Rate Prediction: Neural Netmentioning

confidence: 99%

“…Önder, Bayır, and Hepsen () made the comparison between neural networks implementation and the other time series techniques for prediction of financial markets. Based on empirical analysis, the authors find that neural networks are universal functions approximation and they can model any continuous and nonlinear function to a desired accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Heuristic Methods For Exchange Rate Prediction: Neural Netmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Technological bias at the exchange rate market

Galeshchuk

2017

Intell Sys Acc Fin Mgmt

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“…However, in the real-world scenario the less specialized and less complex ones, i.e., the generalized ones, provided better results and more profit. Another example of the use of ANN for economic issues is discussed in [21]. The study in [21] compares the performance of an ANN with other time-series-based techniques.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

et al. 2019

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Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

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“…Recently, artificial neural networks (ANNs) have been successfully applied to various areas, including forecasting (Perez, 2006, andÖnder et al, 2013), data mining (Pal, 2002;Craven & Shavlik, 1997) and pattern recognition (Bishop, 1995) and smoothing the data or parameters (Moon & Janowski, 1995;Hill & Goring, 1998;Ferrari & Stengel, 2005;Yang & Wu, 2012). The good results of the ANNs in all of above-mentioned areas are based on the unique properties and features of the method, including the following: firstly, ANNs are universal functional approximators, and can approximate any continuous function to any desired accuracy.…”

Section: Introductionmentioning

confidence: 99%

Forecasting financial variables using artificial neural networks - dynamic factor model

Babikir

Mwambi

2017

J. econ. financ. sci.

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In this paper we introduce a new model that uses the dynamic factor model (DFM) framework combined with artificial neural network (ANN) analysis, which accommodates a large cross-section of financial and macroeconomic time series for forecasting. In our new ANN-DF model we use the factor model to extract factors from ANNs in sample forecasts for each single series of the dataset, which contains 228 monthly series. These factors are then used as explanatory variables in order to produce more accurate forecasts. We apply this new model to forecast three South African variables, namely, Rate on threemonth trade financing, Lending rate and Short-term interest rate in the period 1992:1 to 2011:12. The model comparison results, based on the root mean square errors of three, six and twelve months ahead out-of-sample forecasts over the period 2007:1 to 2011:12 indicate that, in all of the cases, the ANN-DFM and the DFM statistically outperform the autoregressive (AR) models. In the majority of cases the ANN-DFM outperforms the DFM. The results indicate the usefulness of the factors in forecasting performance. The RMSE results are confirmed by the test of equality of forecast accuracy proposed by Diebold-Mariano. KeywordsArtificial neural network; Dynamic factor model; Forecast accuracy; Root mean square error.

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Forecasting Macroeconomic Variables Using Artificial Neural Network and Traditional Smoothing Techniques

Cited by 16 publications

References 7 publications

Technological bias at the exchange rate market

Technological bias at the exchange rate market

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

Forecasting financial variables using artificial neural networks - dynamic factor model

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