European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

Deep learning with long short-term memory for stock market predictions and portfolio optimization

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An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants

In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data-Snooping Bias

In this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameter

Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Arti ficial Bee Colony Algorithm, Journal of Telecommunications and Information Technology, 2016, nr 2

Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions

Machine Learning based Models for Fresh Produce Yield and Price Forecasting for Strawberry Fruit

Building market price forecasting models of Fresh Produce (FP) is crucial to protect retailers and consumers from highly priced FP. However, the task of forecasting FP prices is highly complex due to the very short shelf life of FP, inability to store for long term and external factors like weather and climate change. This forecasting problem has been traditionally modelled as a time series problem. Models for grain yield forecasting and other non-agricultural prices forecasting are common. However, forecasting of FP prices is recent and has not been fully explored. In this thesis, the forecasting models built to fill this void are solely machine learning based which is also a novelty. The growth and success of deep learning, a type of machine learning algorithm, has largely been attributed to the availability of big data and high end computational power. In this thesis, work is done on building several machine learning models (both conventional and deep learning based) to predict future yield and prices of FP (price forecast of strawberries are said to be more difficult than other FP and hence is used here as the main product). The data used in building these prediction models comprises of California weather data, California strawberry yield, California strawberry farm-gate prices and a retailer purchase price data. A comparison of the various prediction models is done based on a new aggregated error measure (AGM) proposed in this thesis which combines mean absolute error, mean squared error and R^2 coefficient of determination. The best two models are found to be an Attention CNN-LSTM (AC-LSTM) and an Attention ConvLSTM (ACV-LSTM). Different stacking ensemble techniques such as voting regressor and stacking with Support vector Regression (SVR) are then utilized to come up with the best prediction. The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-LSTM using a linear SVR is the best performing based on the proposed aggregated error measure. To show the robustness of the proposed model, it was used also tested for predicting WTI and Brent crude oil prices and the results proved consistent with that of the FP price prediction

Forecasting oil & gas etfs´ price movements using convolutional neural networks

Thanks to advances in processing power, we have seen the revival of artificial intelligence after the 1980s, and algorithmic trading has become quite popular in the last two decades. In this paper, a convolutional neural network for image recognition was constructed. The CNN recognises patterns in 2D images generated from financial data and classifies them as BUY, SELL or HOLD. The analysed ETF, XLE, is from the Oil & Gas sector. The results are evaluated computationally and financially and compared to other industries. Overall, the CNN approach seems promising but generally, it was not possible to outperform the Buy&Hold strategy