Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics

This paper documents a systematic investigation on the predictability of short-term trends of crude oil prices on a daily basis. In stark contrast with longer-term predictions of crude oil prices, short-term prediction with time horizons of 1–3 days posits an important problem that is quite different from what has been studied in the literature. The problem of such short-term predicability is tackled through two aspects. The first is to examine the existence of linear or nonlinear dynamic processes in crude oil prices. This sub-problem is addressed with statistical analysis involving the Brock-Dechert-Scheinkman test for nonlinearity. The second aspect is to test the capability of artificial neural networks (ANN) for modeling the implicit nonlinearity for prediction. Four experimental models are designed and tested with historical data: (1) using only the lagged returns of filtered crude oil prices as input to predict the returns of the next days; this is used as the benchmark, (2) using only the information set of filtered crude oil futures price as input, (3) combining the inputs from the benchmark and second models, and (4) combing the inputs from the benchmark model and the intermarket information. In order to filter out the noise in the original price data, the moving averages of prices are used for all the experiments. The results provided sufficient evidence to the predictability of crude oil prices using ANN with an out-of-sample hit rate of 80%, 70%, and 61% for each of the next three days’ trends.     

基于TN-LP-LSTM-SVM混合模型的原油期货次日价格变化方向和波动率大小预测

原油价格受国际政治、经济、军事、外交以及其他复杂因素的影响,这些因素的频繁变化使油价表现出随机波动,给原油投资及交易决策带来困难,准确预测油价已成为能源领域学术界的研究热点.但是,现有关于原油价格预测的文献大多数是预测原油价格的数值而不是变化方向,而且不是同时预测原油价格和波动率,因此无法给投资者充分的决策指导信息.为了填补这一研究空白,提出一种结合转移网络(TN)、链接预测(LP)、长短期记忆模型(LSTM)和支持向量机(SVM)的新的混合模型TN-LP-LSTM-SVM来更精确地预测WTI期货次日价格变化方向和波动率大小,为投资者、能源相关企业和参与政策决策的政府人员提供有益的建议.在不同的时间窗口下($h\in [1,50]$且$h\in {\bm Z     

基于GA-BP的个股超短线预测应用研究

本文将遗传算法(GA)与BP算法相结合的人工神经网络模型学习算法,通过对海信电信(600060)的股票收盘价进行超短线预测研究,该模型通过matlab编程仿真,通过实验证明了股价超短线预测模型的可行性。     

区间计量方法及其在油价预测中的应用研究

经典的计量经济学建模与预测方法是基于点数据的,忽略了区间内价格波动的大量信息,因而预测效果欠佳。引入区间计算与区间计量方法,应用于国际原油期货价格的预测,研究结果表明：相对于经典AR-GARCH模型的置信区间预测结果,区间计量方法的预测结果具有更高的准确度与更小的预测误差。研究证实了区间计算与区间计量方法的优越性,并揭示了在经济领域的重要应用价值。     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

基于EMD改进的Elman神经网络对股票的短期预测模型

Elman神经网络在短期预测股市收盘价时存在预测趋势良好但准确度较低的问题。在Elman神经网络的思想上提出以经验模态分解EMD为基础的Elman新组合模型。应用EMD将各交易日的收盘价序列分解成不同时间尺度上的本征模函数IMF分量和剩余分量,进而利用偏自相关函数PACF计算每一个分量的滞后期,以确定各分量在Elman神经网络中的输入和输出变量,从而得到各分量的预测值,相加得到最终的预测结果。与EMD单一网络、EMD-Elman模型、BP网络及EMD-BP模型进行实验对比,结果表明:该短期预测模型的预测值均方误差、平均绝对误差和平均绝对百分比误差都得到较大的改善;新组合模型可有效实现对股票收盘价的短期预测,且能降低非平稳性对预测结果的影响。该研究为进一步预测股市的走向提供了有效依据,也为投资者提供了更充分的决策参考。     

A Bayesian regularized artificial neural network for stock market forecasting

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. The accurate prediction of stock price movements could play an important role in helping investors improve stock returns. The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. Experiments were performed with Microsoft Corp. and Goldman Sachs Group Inc. stock to determine the effectiveness of the model. The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

A hybrid method for crude oil price direction forecasting using multiple timeframes dynamic time wrapping and genetic algorithm

This study proposes a hybrid method based on similarity measurement of time series from multiple timeframes to predict direction changes of crude oil price, as well as executing simulated trading. Except daily timeframe data, it is essential for utilizing the information from various representations of the same data source; hence weekly data are also used. For the proposed method, firstly, it uses the Multiple Dynamic Time Wrapping (MDTW) to collect similar time series from daily and weekly data, and direction changes and returns of them one week later. Next, it calculates a comprehensive expected return based on the expected return results of two timeframes and their weights. Then, the proposed method predicts the direction change of current time series for one week later, and executes simulation trading upon the prediction results. Lastly, the proposed method adopted the genetic algorithms to optimize several model parameters for trading strategy. Experimental results showed that the proposed method achieved excellent performances in terms of hit ratio, accumulated return and Sharpe ratio, and the results are significantly superior to that of benchmark methods. The proposed method can provide beneficial advises for investors, energy-related enterprises, and government officers engaged in policy decisions.     

基于隐马尔可夫模型的股票价格预测组合模型

提出了一种用于股票价格预测的人工神经网络(ANN),隐马尔可夫模型(HMM)和粒子群优化算法(PSO)的组合模型-APHMM模型.在APHMM模型中,ANN算法将股票的每日开盘价、最高价、最低价与收盘价转换为相互独立的量并作为HMM的输入.然后,利用PSO算法对HMM的参数初始值进行优化,并用Baum-Welch算法进行参数训练.经过训练后的HMM在历史数据中找出一组与今天股票的上述4个指标模式最相似数据,加权平均计算每个数据与它后一天的收盘价格差,则今天的股票收盘价加上这个加权平均价格差便为预测的股票收盘价.实验结果表明,APHMM模型具有良好的预测性能.     

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits.     

Forecasting stock market short-term trends using a neuro-fuzzy based methodology

A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive neuro-fuzzy technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the Efficient Market Hypothesis (EMH) by demonstrating much improved and better predictions, compared to other approaches, of short-term stock market trends, and in particular the next day’s trend of chosen stocks. The ANFIS controller and the stock market process model inputs are chosen based on a comparative study of fifteen different combinations of past stock prices performed to determine the stock market process model inputs that return the best stock trend prediction for the next day in terms of the minimum Root Mean Square Error (RMSE). Gaussian-2 shaped membership functions are chosen over bell shaped Gaussian and triangular ones to fuzzify the system inputs due to the lowest RMSE. Real case studies using data from emerging and well developed stock markets – the Athens and the New York Stock Exchange (NYSE) – to train and evaluate the proposed system illustrate that compared to the “buy and hold” strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far superior.     

Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting

This paper proposes a decomposition based method in fusion with the non-iterative approach for crude oil price forecasting. In this approach, the robust random vector functional link network (RVFLN), a non-iterative approach in fusion with the most efficient decomposition technique called variational mode decomposition (VMD) is proposed which is executed with two links — fixed assigned random weights and direct link from input to output, and the iterative learning process is not involved in its functioning which makes it faster in execution as compared to many existing techniques proposed for forecasting. The fusion of VMD and robust RVFLN called VMD-RVFLN is implemented for crude oil price forecasting where the crude oil price series is decomposed using VMD into a linear smoother series by extracting useful information and the decomposed modes pass through the robust RVFLN model which produces the final forecasting values. The analysis performed in the study approves its efficiency and reports improvement in forecasting accuracy and execution time as compared to some of the traditional iterative techniques like BPNN (back propagation neural network), ARIMA (auto-regressive integrated moving average), LSSVR (least squares support vector regression), ANFIS (adaptive neuro-fuzzy inference system), IT2FNN (interval type-2 fuzzy neural network) and RNN (recurrent neural network), etc. However, both ELM and RVFLN without modes decomposition fusion exhibit less execution time at the cost of reduction in prediction accuracy.     

Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis

In this paper, we examine the weak-form efficient market hypothesis of crude oil futures markets by testing for the random walk behavior of prices. Using a method borrowed from statistical physics, we find that crude oil price display weak persistent behavior for time scales smaller than a year. For time scales larger than a year, strong mean-reversion behaviors can be found. That is, crude oil futures markets are not efficient in the short-term or in the long-term. By quantifying the market inefficiency using a “multifractality degree”, we find that the futures markets are more inefficient in the long-term than in the short-term. Furthermore, we investigate the “stylized fact” of volatility dynamics on market efficiency. The simulating and empirical results indicate that volatility clustering, volatility memory and extreme volatility have adverse effects on market efficiency, especially in the long-term.     

Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems

Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development.This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables.     

原油期货价格预测模型CEEMDAN-PSO-ELM

为了进一步提升原油期货价格预测的精准性,本文基于CEEMDAN分解算法和ELM极限学习机模型,利用PSO粒子群优化算法对机器学习模型进行参数寻优,进而构建了CEEMDAN-PSO-ELM模型用于原油期货价格预测.先基于CEEMDAN算法对原始价格序列进行分解,然后利用Lempel-Ziv复杂度指数对分量进行重构,得到高频、中频和低频重构分量,再采用PSO-ELM模型对每个重构分量进行预测,利用PACF系数选取模型输入变量,最终加总集成各分量预测结果.实证结果表明,与其他15种基准模型相比,CEEMDAN-PSO-ELM模型的预测性能最佳,MCS检验和DM检验也进一步证实了该模型的稳健性.     

Predicting the direction,maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques

Bitcoin is the most accepted cryptocurrency in the world, which makes it attractive for investors and traders. However, the challenge in predicting the Bitcoin exchange rate is its high volatility. Therefore, the prediction of its behavior is of great importance for financial markets. In this way, recent studies have been carried out on what internal and/or external Bitcoin information is relevant to its prediction. The increased use of machine learning techniques to predict time series and the acceptance of cryptocurrencies as financial instruments motivated the present study to seek more accurate predictions for the Bitcoin exchange rate. In this way, in a first stage of the proposed methodology, different feature selection techniques were evaluated in order to obtain the most relevant attributes for the predictions. In the sequence, it was analyzed the behavior of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble algorithms (based on Recurrent Neural Networks and the k-Means clustering method) for price direction predictions. Likewise, the ANN and SVM were employed for regression of the maximum, minimum and closing prices of the Bitcoin. Moreover, the regression results were also used as inputs to try to improve the price direction predictions. The results showed that the selected attributes and the best machine learning model achieved an improvement of more than 10%, in accuracy, for the price direction predictions, with respect to the state-of-the-art papers, using the same period of information. In relation to the maximum, minimum and closing Bitcoin prices regressions, it was possible to obtain Mean Absolute Percentage Errors between 1% and 2%. Based on these results, it was possible to demonstrate the efficacy of the proposed methodology when compared to other studies.     

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

This article presents an intelligent stock trading system that can generate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks(dual net). Retrospective technical indicators extracted from raw price and volume time series data gathered from the market are used as independent variables for neural modeling. Both neural network modules of thedual net learn the correlation between the trends of price movement and the retrospective technical indicators by use of a modified back-propagation learning algorithm. Reinforcing the temporary correlation between the neural weights and the training patterns, dual modules of neural networks are respectively trained on a short-term and a long-term moving-window of training patterns. An adaptive reversal recognition mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our system. It is shown that the proposeddual net architecture generalizes better than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading suggestions shown in the performance evaluation, an intelligent stock trading system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.     

ANNSTLF-a neural-network-based electric load forecasting system

A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.e., the prediction of hourly loads (demand) for the next hour to several days out. The accuracy of such forecasts has significant economic impact for the utility. This paper describes a load forecasting system known as ANNSTLF (artificial neural-network short-term load forecaster) which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada. ANNSTLF can consider the effect of temperature and relative humidity on the load. Besides its load forecasting engine, ANNSTLF contains forecasters that can generate the hourly temperature and relative humidity forecasts needed by the system. ANNSTLF is based on a multiple ANN strategy that captures various trends in the data. Both the first and the second generation of the load forecasting engine are discussed and compared. The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. The results of testing the system on data from ten different utilities are reported.