Stock Volatility Prediction by Hybrid Neural Network

Wang, Yujie; Liu, Hui; Guo, Qiang; Xie, Shenxiang; Zhang, Xiaofeng

doi:10.1109/access.2019.2949074

Cited by 30 publications

(12 citation statements)

References 30 publications

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“…Combining techniques (ensemble) using several learning methods [46], [47], [48], [49], [31], [11], [4], [5]. Using boost algorithm (Boosting Method) [50], [51], [13], [34], [52]. Added a feature selection method [53], [54], [55], [36], [56].…”

Section: Proposed Methods Improvements and Modification For Stock Predictionsmentioning

confidence: 99%

“…Several studies have proven that optimized parameters and hyper-parameters can improve model performance than models without optimization. Research [36] in hyper-parameter XGBoost algorithm optimized using Genetic Algorithm, [51] LSTM neural network parameters optimized using Random Search, [5] hyperparameter Random Forest algorithm optimized using the Grid Search method, [52] experimented with four algorithms namely ANN, SVM, RF, and LR which were optimized using the Grid Search method, as well as [43] the Deep Naural Network that was built optimized using the grid Search method.…”

Section: Parameter and Hyper-parameter Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

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Section: Proposed Methods Improvements and Modification For Stock Predictionsmentioning

confidence: 99%

Section: Parameter and Hyper-parameter Optimizationmentioning

confidence: 99%

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

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“…Moreover, there have been various studies on text mining applications to financial markets due to considerable influence of text news on the markets. Wang et al [25] propose a hybrid time-series predicted neural network for forecasting stock volatility. Crone and Koeppel [26] explore efficacy of using sentiment indicators as a predictor for foreign exchange rates.…”

Section: A Level Factor Representingmentioning

confidence: 99%

Interest Rate Model With Investor Attitude and Text Mining

et al. 2020

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This paper develops and estimates an interest rate model with investor attitude factors, which are extracted by a text mining method. First, we consider two contrastive attitudes (optimistic versus conservative) towards uncertainties about Brownian motions driving economy, develop an interest rate model, and obtain an empirical framework of the economy consisting of permanent and transitory factors. Second, we apply the framework to a bond market under extremely low interest rate environment in recent years, and show that our three-factor model with level, steepening and flattening factors based on different investor attitudes is capable of explaining the yield curve in the Japanese government bond (JGB) markets.Third, text mining of a large text base of daily financial news reports enables us to distinguish between steepening and flattening factors, and from these textual data we can identify events and economic conditions that are associated with the steepening and flattening factors. We then estimate the yield curve and three factors with frequencies of relevant word groups chosen from textual data in addition to observed interest rates. Finally, we show that the estimated three factors, extracted only from the bond market data, are able to explain the movement in stock markets, in particular Nikkei 225 index.INDEX TERMS Interest rate model, text mining, filtering.

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“…In addition, NN does not require assumptions regarding the distribution of data while it can learn from the data. The NN models have recently been applied to predict volatility series in many fields [11], [38], [45], [52]. In the case of the exchange rate volatility forecasting, Panda and Narasimhan [6] found that Neural Network (NN) has a better exchange rate forecasting performance for not only in-sample but also outof-sample period, compared to the linear regression and random walk models.…”

Section: Related Workmentioning

confidence: 99%

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

2020

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The motivation of this study is built from the previous research to find a way to enhance the forecast of advanced and emerging market currency volatilities. Given the exchange rate's nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional Heteroscedasticity (MS-Betat-EGARCH) model. Our hybrid model synthesizes these two approaches' advantages to predict exchange rate volatility. We validate the performance of our proposed model by comparing it with various traditional volatility forecasting models. In-sample and out-of-sample volatility forecasts are considered to achieve our comparison. The empirical results suggest that our hybrid NN-MS Beta-t-EGARCH outperforms the other models for both emerging and advanced market currencies. INDEX TERMS Exchange rate volatility, neural networks, Markov-switching Beta-t-EGARCH. I. INTRODUCTION Volatility represents the degree to which variable changes over time, and it is an essential facet in risk evaluation of many essential economic tasks such as value at risk, financial asset pricing, and exchange rate [52]. Volatility has three significant characteristics in the financial area, namely, volatility clustering property [37], asymmetric property [10], and nonlinearity property [19]. These properties lead to uncertainty in financial time series. In the context of economic globalization, international transactions, and capital flow across the borders have increased. There is an indisputable fact that the foreign exchange market is a crucial factor affecting the transactions and capital flows. Thus, policymakers must understand the exchange rate volatility before making fiscal and monetary policy decisions, especially in those import-led and export-led countries. In this respect, our study aims to forecast the exchange rate volatility of the three emerging markets' currencies, namely the Brazilian Real, Chinese Yuan, and Indian Rupee, and three advanced markets' currencies, namely, Euro, The associate editor coordinating the review of this manuscript and approving it for publication was Francesco Benedetto .

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Stock Volatility Prediction by Hybrid Neural Network

Cited by 30 publications

References 30 publications

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Interest Rate Model With Investor Attitude and Text Mining

Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

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