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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Journal ArticleDOI
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
Comparing linear and nonlinear forecasts for stock returns
TL;DR: In this paper, the authors compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices, using a linear and a nonlinear artificial neural network (ANN) model.
Journal ArticleDOI
A simple neural network for ARMA(p,q) time series
H. Brian Hwarng,H.T Ang +1 more
TL;DR: The results affirm that it is unnecessary to use multi-layer feedforward networks for this special class of linear time series and that the two-layered network can be a useful forecasting alternative to the widely popular Box-Jenkins model.
Proceedings Article
Combined Neural Networks for Time Series Analysis
Iris Ginzburg,David Horn +1 more
TL;DR: It is found that the complex network is more difficult to train and performs worse than the two-step procedure of the combined system, and the small corrections of the secondary network can be regarded as resulting from a Taylor expansion of a complex network which includes the combinedSystem.
Journal Article
Stock Market Forecasting: Artificial Neural Network and Linear Regression Comparison in an Emerging Market
Erdinç Altay,M. Hakan Satman +1 more
TL;DR: In this article, the authors used ANNs to predict stock prices in emerging markets and found that ANN has better forecasting ability compared to ARIMA model and leads higher arbitrage profits compared to cost of cary model.
Journal ArticleDOI
Forecasting stock market prices in a thin security market
Ilkka Virtanen,Paavo Yli-Olli +1 more
TL;DR: In this article, it is shown that both the monthly and quarterly stock market prices (the general stock market index) can be adequately forecasted using either univariate time-series analysis or multivariate econometric modelling.
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