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

Baek, Yujin; Kim, Ha Young

doi:10.1016/j.eswa.2018.07.019

Cited by 283 publications

(165 citation statements)

References 38 publications

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“…In the literature, different stock market index data were used for the experiments. [123,124,125,126,127,128,129,130,131,132,133,134,114] used S&P500 as their dataset. The authors of [123,124,135,136,137] used NIKKEI as their dataset.…”

Section: Index Forecastingmentioning

confidence: 99%

“…The authors of [123,124,135,136,137] used NIKKEI as their dataset. KOSPI was used in [135,131,132]. DJIA was used as the dataset in [123,136,137,138,139].…”

Section: Index Forecastingmentioning

confidence: 99%

“…The authors of [136] used LSTM to predict NIKKEI225, IXIC, HIS, GSPC and DJIA index data. In [147] and [132], LSTM was also used for the prediction of S&P500 and KOSPI200 index. The authors of [132] developed an LSTM based stock index forecasting model called ModAugNet.…”

Section: Index Forecastingmentioning

confidence: 99%

“…In [147] and [132], LSTM was also used for the prediction of S&P500 and KOSPI200 index. The authors of [132] developed an LSTM based stock index forecasting model called ModAugNet. The proposed method was able to beat Buy and Hold (B&H) in the long term with an overfitting prevention mechanism.…”

Section: Index Forecastingmentioning

confidence: 99%

See 3 more Smart Citations

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

802

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Index Forecastingmentioning

confidence: 99%

“…The authors of [123,124,135,136,137] used NIKKEI as their dataset. KOSPI was used in [135,131,132]. DJIA was used as the dataset in [123,136,137,138,139].…”

Section: Index Forecastingmentioning

confidence: 99%

Section: Index Forecastingmentioning

confidence: 99%

Section: Index Forecastingmentioning

confidence: 99%

See 2 more Smart Citations

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

802

294

View full text Add to dashboard Cite

show abstract

“…As a result, the method filtered noises effectively and outperformed prior models in dual sources stock prediction. Baek and Kim [ 32 ] proposed an approach for stock market index forecasting, which included a prediction LSTM module and an overfitting prevention LSTM module. The results confirmed that the proposed model had an excellent forecasting accuracy compared to a model without an overfitting prevention LSTM module.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Stock Market Prediction

Nabipour

Nayyeri

Jabani

et al. 2020

Entropy

259

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The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

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Bitcoin price prediction using optimized multiplicative long short term memory with attention mechanism using modified cuckoo search optimization

Ahamed¹,

Ravi²

2022

Concurrency and Computation

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For the past few years, Bitcoin plays a vital role in both the economical and financial industries. In order to gain a huge return on investment, the investors are eager to forecast the future value of Bitcoin. However, Bitcoin price variation is quite nonlinear and chaotic in nature, so it creates more difficulty in forecasting future value. Researchers found that the multiplicative long short term memory (LSTM) model will be more efficient for predicting those complex variations. So, target mission is about to develop an optimized multiplicative LSTM with an Attention mechanism using Technical Indicators derived from historical data. A modified cuckoo search optimization model is proposed to tune the hyperparameter of the Deep Learning model. This novel optimization algorithm eliminates the local optimum and slower convergence problem of the cuckoo search optimization algorithm. Deibold Mariano test is performed to statistically evaluate the proposed model and it is inferred that the recommended methodology is statistically fit. Regression metrics such as root mean square error, mean square error and mean absolute error has been used for comparative evaluation with related benchmark techniques such as genetic algorithm optimized LSTM (GA-LSTM), particle swarm optimized LSTM (PSO-LSTM) and cuckoo search optimized LSTM (CSO-LSTM). The empirical result shows that the recommended methodology outperforms the taken benchmark models and provides better accuracy.

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

Cited by 283 publications

References 38 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Deep Learning for Stock Market Prediction

Bitcoin price prediction using optimized multiplicative long short term memory with attention mechanism using modified cuckoo search optimization

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