Journal ArticleDOI
Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
Manish Kumar,M. Thenmozhi +1 more
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TLDR
The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.Abstract:
The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.read more
Citations
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Literature review: Machine learning techniques applied to financial market prediction
TL;DR: Bibliographic survey techniques are applied to the literature about machine learning for predicting financial market values, resulting in a bibliographical review of the most important studies about this topic, and it was concluded that the research theme is still relevant and that the use of data from developing markets is a research opportunity.
Journal ArticleDOI
Hybrid structures in time series modeling and forecasting: A review
Zahra Hajirahimi,Mehdi Khashei +1 more
TL;DR: It can be observed that combined methods are viable and accurate approaches for time series forecasting and also the parallel–series hybrid structure can obtain more accurate and promising results than other those hybrid structures.
Journal ArticleDOI
Variable Selection in Time Series Forecasting Using Random Forests
TL;DR: The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables, which could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.
Journal ArticleDOI
A deep learning framework for predicting cyber attacks rates
TL;DR: A deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM is developed, which achieves a significantly higher prediction accuracy when compared with the statistical approach.
Journal ArticleDOI
Can artificial intelligence enhance the Bitcoin bonanza
Matheus José Silva de Souza,Fahad Almudhaf,Bruno Miranda Henrique,Ana Beatriz Silveira Negredo,Danilo Guimarães Franco Ramos,Vinicius Amorim Sobreiro,Herbert Kimura +6 more
TL;DR: Findings indicate that traders are able to earn conservative returns on the risk adjusted basis, even accounting for transaction costs, when using SVM, and suggests that ANN can explore short run informational inefficiencies to generate abnormal profits.
References
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Journal ArticleDOI
Predicting customer retention and profitability by using random forests and regression forests techniques
Bart Larivière,Dirk Van den Poel +1 more
TL;DR: The research findings demonstrate that both random forests techniques provide better fit for the estimation and validation sample compared to ordinary linear regression and logistic regression models, and suggest that past customer behavior is more important to generate repeat purchasing and favorable profitability evolutions.
Journal ArticleDOI
Stock performance modeling using neural networks: a comparative study with regression models
TL;DR: It is shown that by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and can model their environment more convincingly than regression models.
Journal ArticleDOI
Forecasting stock indices: a comparison of classification and level estimation models
TL;DR: In this paper, the authors evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches and conduct time series comparisons between the two types of models on the basis of forecast performance and investment return.
Journal ArticleDOI
Combining neural network model with seasonal time series ARIMA model
TL;DR: A hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP, was used to forecast two seasonal timeseries data of total production value for Taiwan machinery industry and the soft drink time series.
Proceedings ArticleDOI
Support vector machine for regression and applications to financial forecasting
TL;DR: The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for financial forecasting applications.
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