Improving Deep Learning for Forecasting Accuracy in Financial Data

Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers. However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. Financial data is both nonlinear and nonstationary, with broadband frequency features. In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy. This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE). The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. The financial data for this study are from the Taiwan corporate social responsibility (CSR) index. First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF). The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain. In other words, the range of fluctuation is small. LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used. The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data.

[1]  K. V. Shihabudheen,et al.  Landslide displacement prediction technique using improved neuro-fuzzy system , 2017, Arabian Journal of Geosciences.

[2]  Erik Cambria,et al.  Natural language based financial forecasting: a survey , 2017, Artificial Intelligence Review.

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  L I OliveiraAdriano,et al.  Computational Intelligence and Financial Markets , 2016 .

[5]  Bart van Liebergen,et al.  Machine learning: A revolution in risk management and compliance? , 2017 .

[6]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[7]  Yi-Ping Phoebe Chen,et al.  Hybrid deep learning and empirical mode decomposition model for time series applications , 2019, Expert Syst. Appl..

[8]  Seyda Ertekin,et al.  Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition , 2018, Neurocomputing.

[9]  K. Lai,et al.  A new approach for crude oil price analysis based on Empirical Mode Decomposition , 2008 .

[10]  Adriano Lorena Inácio de Oliveira,et al.  Expert Systems With Applications , 2022 .

[11]  Nicholas G. Polson,et al.  Deep learning for finance: deep portfolios: J. B. HEATON, N. G. POLSON AND J. H. WITTE , 2017 .

[12]  Yong Fang.  A Study on the Correlations between Investor Sentiment and Stock Index and Macro Economy Based on EEMD Method , 2015 .

[13]  J. Stock.  Unit roots, structural breaks and trends , 1986 .

[14]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[15]  Chao Chen,et al.  A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks , 2012 .

[16]  Md. Khademul Islam Molla,et al.  Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition , 2012 .

[17]  Indranil Mukherjee,et al.  Empirical mode decomposition analysis of two different financial time series and their comparison , 2008 .

[18]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[19]  Mohd Tahir Ismail,et al.  Improving forecasting accuracy for stock market data using EMD-HW bagging , 2018, PloS one.

[20]  Ponnuthurai N. Suganthan,et al.  A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Wei-Chiang Hong,et al.  Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting , 2019, Energies.

[22]  Ramakant Bhardwaj Rasik M.Patel,et al.  Common Fixed Point Theorem for ψ-weakly commuting maps in L-Fuzzy Metric Spaces for integral type , 2015 .

[23]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[24]  G. N. Pillai,et al.  Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS) , 2017, Appl. Soft Comput..