ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module

By Yujin Baek, Ha Young Kim. 2018

333 citations recorded by the source corpus.

Abstract Forecasting a financial asset's price is important as one can lower the risk of investment decision- making with accurate forecasts. Recently, the deep neural network is popularly applied in this area of research; however, it is prone to overfitting owing to limited availability of data points for training. We propose a novel data augmentation approach for stock market index forecasting through our ModAugNet framework, which consists of two modules: an overfitting prevention LSTM module and a prediction LSTM module. The performance of the proposed model is evaluated using two different representative stock market data (S&P500 and Korea Composite Stock Price Index 200 (KOSPI200)). The results confirm the excellent forecasting accuracy of the proposed model. ModAugNet-c yields a lower test error than the comparative model (SingleNet) in which an overfitting prevention LSTM module is absent. The test mean squared error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for S&P500 decreased to 54.1%, 35.5%, and 32.7%, respectively, of the corresponding S&P500 forecasting errors of SingleNet, while the same for KOSPI200 decreased to 48%, 23.9%, and 32.7%, respectively, of the corresponding KOSPI200 forecasting errors of SingleNet. Furthermore, through the analyses of the trained ModAugNet-c, we found that test performance is entirely dependent on the prediction LSTM module. The contribution of this study is its applicability in various instances where it is challenging to artificially augment data, such as medical data analysis and financial time-series modeling.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 114] used S&P500 as their dataset.”
Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020, methodology): “A forecasting LSTM model and an overﬁtting prevention LSTM module were suggested by Baek and Kim [21] to predict stock market.”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “Other options for feature selection include rough set attribute reduction (RSAR) (Lei, 2018), autocorrelation function (ACF) and partial correlation function (PCF) (Wu & Gao, 2018), the analysis of variance (ANOVA) (Niaki & Hoseinzade, 2013), and maximal information coefficient feature selection (MI”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “And paper [29] had the highest order of magnitude of MAPE.”
Deep Learning for Stock Market Prediction (2020, background): “Baek and Kim [32] proposed an approach for stock market index forecasting, which included a prediction LSTM module and an overfitting prevention LSTM module.”