Journal articles: 'Stock price forecasting – Computer simulation'

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Agung, Ignatius Wiseto Prasetyo. "Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (July 21, 2022): 82–90. http://dx.doi.org/10.31289/jite.v6i1.7158.

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The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.

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Rath, Smita, Binod Kumar Sahu, and Manoj Ranjan Nayak. "Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction." International Journal of Intelligent Computing and Cybernetics 12, no. 2 (June 10, 2019): 175–93. http://dx.doi.org/10.1108/ijicc-10-2018-0145.

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Purpose Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices. Design/methodology/approach A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms. Findings Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study. Originality/value The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

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Wang, Xiang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, and Daqing Wu. "A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN." Complexity 2022 (February 27, 2022): 1–15. http://dx.doi.org/10.1155/2022/3216036.

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An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.

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Panella, Massimo, Francesco Barcellona, and Rita L. D'Ecclesia. "Forecasting Energy Commodity Prices Using Neural Networks." Advances in Decision Sciences 2012 (December 31, 2012): 1–26. http://dx.doi.org/10.1155/2012/289810.

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A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics.

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Zhang, Daxing, and Erguan Cai. "Improving Stock Price Forecasting Using a Large Volume of News Headline Text." Computers, Materials & Continua 69, no. 3 (2021): 3931–43. http://dx.doi.org/10.32604/cmc.2021.012302.

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Göçken, Mustafa, Aslı Boru �°pek, Mehmet Özçalıcı, and Ayşe Tuğba Dosdoğru. "Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting." International Journal of Data Mining, Modelling and Management 14, no. 4 (2022): 335. http://dx.doi.org/10.1504/ijdmmm.2022.10051603.

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Özçalıcı, Mehmet, Ayşe Tuğba Dosdoğru, Aslı Boru �°, N. A. pek, and Mustafa Göçken. "Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting." International Journal of Data Mining, Modelling and Management 14, no. 4 (2022): 335. http://dx.doi.org/10.1504/ijdmmm.2022.126664.

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Ying, Jun, Lynn Kuo, and Gim S. Seow. "Forecasting stock prices using a hierarchical Bayesian approach." Journal of Forecasting 24, no. 1 (January 2005): 39–59. http://dx.doi.org/10.1002/for.933.

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arman, Sup, Yahya Hairun, Idrus Alhaddad, Tedy Machmud, Hery Suharna, and Mohd Saifullah Rusiman. "Forecasting Software Using Laplacian AR Model based on Bootstrap-Reversible Jump MCMC: Application on Stock Price Data." Webology 18, Special Issue 04 (September 30, 2021): 1045–55. http://dx.doi.org/10.14704/web/v18si04/web18180.

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The application of the Bootstrap-Metropolis-Hastings algorithm is limited to fixed dimension models. In various fields, data often has a variable dimension model. The Laplacian autoregressive (AR) model includes a variable dimension model so that the Bootstrap-Metropolis-Hasting algorithm cannot be applied. This article aims to develop a Bootstrap reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the Laplacian AR model. The parameters of the Laplacian AR model were estimated using a Bayesian approach. The posterior distribution has a complex structure so that the Bayesian estimator cannot be calculated analytically. The Bootstrap-reversible jump MCMC algorithm was applied to calculate the Bayes estimator. This study provides a procedure for estimating the parameters of the Laplacian AR model. Algorithm performance was tested using simulation studies. Furthermore, the algorithm is applied to the finance sector to predict stock price on the stock market. In general, this study can be useful for decision makers in predicting future events. The novelty of this study is the triangulation between the bootstrap algorithm and the reversible jump MCMC algorithm. The Bootstrap-reversible jump MCMC algorithm is useful especially when the data is large and the data has a variable dimension model. The study can be extended to the Laplacian Autoregressive Moving Average (ARMA) model.

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Cheng, Ching-Hsue, Ming-Chi Tsai, and Chin Chang. "A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits." Systems 10, no. 6 (December 3, 2022): 243. http://dx.doi.org/10.3390/systems10060243.

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A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders.

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Obot, Okure Udo, Uduak David George, and Victoria Sunday Umana. "A Decision Support Tool (DST) for Inventory Management." International Journal of Decision Support System Technology 11, no. 2 (April 2019): 27–47. http://dx.doi.org/10.4018/ijdsst.2019040103.

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Loss of customer goodwill is one of the greatest losses a business organization can incur. One reason for such a loss is stock outage. In an attempt to solve this problem, an overstock could result. Overstock comes with an increase in the holding and carrying cost. It is an attempt to solve these twin problems that an economic order quantity (EOQ) model was developed. Information on fifteen items comprised of 10 non-seasonal and 5 seasonal items was collected from a supermarket in Ikot Ekpene town, Nigeria. The information includes the quantity of daily sales, the unit price, the lead time and the number of times an item is ordered in a month. Based on this information, a simple moving average and y-trend method of forecasting were used to forecast the sales quantity for the following month for the non-seasonal and seasonal items. The forecast value was used to compute the EOQ for each of the items. Different scenarios were created to simulate the fuzzy logic EOQ after which the result of the conventional method, EOQ method, and fuzzy EOQ methods were obtained and compared. It was revealed that if the EOQ method is adopted, savings of 43% of holding and carrying cost would be made. From the scenarios of a fuzzy EOQ, a savings of 35.65% was recorded. It was however observed that in a real-life situation, the savings on a fuzzy EOQ is likely to be higher than that of an EOQ considering the incessant public power outages and the increase in transportation fares due to the high cost of fuel and the bad state of roads in Nigeria. To this end, a Decision Support Tool (DST) was developed to help the supermarket manage its inventory based on daily predictions. The DST incorporates a filter engine to take care of some emotional and cognitive incidences within the environment.

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Reddy, Dinesh, and Abhinav Karthik. "Forecasting Stock Price using LSTM-CNN Method." International Journal of Engineering and Advanced Technology 11, no. 1 (October 30, 2021): 1–8. http://dx.doi.org/10.35940/ijeat.a3117.1011121.

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Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.

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Hyup Roh, Tae. "Forecasting the volatility of stock price index." Expert Systems with Applications 33, no. 4 (November 2007): 916–22. http://dx.doi.org/10.1016/j.eswa.2006.08.001.

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Hoang Vuong, Pham, Trinh Tan Dat, Tieu Khoi Mai, Pham Hoang Uyen, and Pham The Bao. "Stock-Price Forecasting Based on XGBoost and LSTM." Computer Systems Science and Engineering 40, no. 1 (2022): 237–46. http://dx.doi.org/10.32604/csse.2022.017685.

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Sun, Guang, Jingjing Lin, Chen Yang, Xiangyang Yin, Ziyu Li, Peng Guo, Junqi Sun, Xiaoping Fan, and Bin Pan. "Stock Price Forecasting: An Echo State Network Approach." Computer Systems Science and Engineering 36, no. 3 (2021): 509–20. http://dx.doi.org/10.32604/csse.2021.014189.

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He, Wu, Lin Guo, Jiancheng Shen, and Vasudeva Akula. "Social Media-Based Forecasting." Journal of Organizational and End User Computing 28, no. 2 (April 2016): 74–91. http://dx.doi.org/10.4018/joeuc.2016040105.

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Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors' findings indicate, in the financial services context, negative sentiments predict firms' future stock prices. However, the number of and the positive sentiment of tweets are not correlated with stock prices. The findings of this paper suggest the possible predictive value of social media data on stock prices at the company level.

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Amiens, Ernest Oseghale, and Ifuero Osad Osamwonyi. "Stock price forecasting using hidden Markov model." International Journal of Information and Decision Sciences 14, no. 1 (2022): 39. http://dx.doi.org/10.1504/ijids.2022.122721.

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Amiens, Ernest Oseghale, and Ifuero Osad Osamwonyi. "Stock price forecasting using hidden Markov model." International Journal of Information and Decision Sciences 14, no. 1 (2022): 39. http://dx.doi.org/10.1504/ijids.2022.10047265.

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STÁDNÍK, Bohumil, Jurgita RAUDELIŪNIENĖ, and Vida DAVIDAVIČIENĖ. "FOURIER ANALYSIS FOR STOCK PRICE FORECASTING: ASSUMPTION AND EVIDENCE." Journal of Business Economics and Management 17, no. 3 (June 7, 2016): 365–80. http://dx.doi.org/10.3846/16111699.2016.1184180.

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The research addressed the relevant question whether the Fourier analysis really provides practical value for investors forecasting stock market price. To answer this question, the significant cycles were discovered using the Fourier analysis inside the price series of US stocks; then, the simulation of an agent buying and selling on minima and maxima of these cycles was made. The results were then compared to those of an agent operating chaotically. Moreover, the existing significant cycles were found using more precise methods, suggested in the research, and based on the results of an agent buying and selling on all possible periods and phases. It has been analysed whether these really existing cycles were in accordance with the significant cycles resulting from the Fourier analysis. It has been concluded that the Fourier analysis basically failed. Suchlike failures are expected on similar data series. In addition, momentum and level trading backtests have been used in a similar way. It has been found that the level trading does provide a certain practical value in comparison to the momentum trading method. The research also simplifies the complicated theoretical background for practitioners.

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Zhang, Qunhui, Mengzhe Lu, and Liang Dai. "On Mixed Model for Improvement in Stock Price Forecasting." Computer Systems Science and Engineering 41, no. 2 (2022): 795–809. http://dx.doi.org/10.32604/csse.2022.019987.

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Rammurthy, Shruthi Komarla, and Sagar B. Patil. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–12. http://dx.doi.org/10.4018/ijcini.20211001.oa3.

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A stock market is an aggregation of buyers and sellers where issuance, buying, and selling of stocks happen. Predicting stock price is a significant concern due to volatility. Historical stock price and historical price data reveal the effect of such factors. Since stock data is time series and prediction can be made accurately with time series forecasting model. LSTM (Long Short Term Memory) model, a particular kind of RNN (Recurrent Neural Network), based on time series forecasting used to predict stock price. LSTM doesn’t have long term dependencies because of its distinctive structure. The study focuses on major IT firms considering the company’s low and high prices. But, mid-price, which is a mean of the low and close price, is considered for the prediction. LSTM based methodology employing mid-price is effective in predicting values compared to other attributes and accuracy of prediction using the LSTM model. We conclude with the present model is more efficient in stock price prediction with a decrease in mean square error.

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Babirath, Julia, Karel Malec, Rainer Schmitl, Kamil Maitah, and Mansoor Maitah. "Forecasting based on spectral time series analysis: prediction of the Aurubis stock price." Investment Management and Financial Innovations 17, no. 4 (December 4, 2020): 215–27. http://dx.doi.org/10.21511/imfi.17(4).2020.20.

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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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Tang, Xiaobin, Nuo Lei, Manru Dong, and Dan Ma. "Stock Price Prediction Based on Natural Language Processing1." Complexity 2022 (May 6, 2022): 1–15. http://dx.doi.org/10.1155/2022/9031900.

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The keywords used in traditional stock price prediction are mainly based on literature and experience. This study designs a new text mining method for keywords augmentation based on natural language processing models including Bidirectional Encoder Representation from Transformers (BERT) and Neural Contextualized Representation for Chinese Language Understanding (NEZHA) natural language processing models. The BERT vectorization and the NEZHA keyword discrimination models extend the seed keywords from two dimensions of similarity and importance, respectively, thus constructing the keyword thesaurus for stock price prediction. Furthermore, the predictive ability of seed words and our generated words are compared by the LSTM model, taking the CSI 300 as an example. The result shows that, compared with seed keywords, the search indexes of extracted words have higher correlations with CSI 300 and can improve its forecasting performance. Therefore, the keywords augmentation model designed in this study is helpful to provide references for other variable expansion in financial time series forecasting.

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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.

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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>

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Guo, Yanhui, Siming Han, Chuanhe Shen, Ying Li, Xijie Yin, and Yu Bai. "An Adaptive SVR for High-Frequency Stock Price Forecasting." IEEE Access 6 (2018): 11397–404. http://dx.doi.org/10.1109/access.2018.2806180.

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Lv, Jiehua, Chao Wang, Wei Gao, and Qiumin Zhao. "An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model." Computational Intelligence and Neuroscience 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8128879.

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Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.

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Li, Hui, Jinjin Hua, Jinqiu Li, and Geng Li. "Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–7. http://dx.doi.org/10.1155/2020/7681209.

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This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things (IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training complexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature selection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future potential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the feature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is decided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing price of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the accuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction.

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Gao, Ya, Rong Wang, and Enmin Zhou. "Stock Prediction Based on Optimized LSTM and GRU Models." Scientific Programming 2021 (September 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/4055281.

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Stock market prediction has always been an important research topic in the financial field. In the past, inventors used traditional analysis methods such as K-line diagrams to predict stock trends, but with the progress of science and technology and the development of market economy, the price trend of a stock is disturbed by various factors. The traditional analysis method is far from being able to resolve the stock price fluctuations in the hidden important information. So, the prediction accuracy is greatly reduced. In this paper, we design a new model for optimizing stock forecasting. We incorporate a range of technical indicators, including investor sentiment indicators and financial data, and perform dimension reduction on the many influencing factors of the retrieved stock price using depth learning LASSO and PCA approaches. In addition, a comparison of the performances of LSTM and GRU for stock market forecasting under various parameters was performed. Our experiments show that (1) both LSTM and GRU models can predict stock prices efficiently, not one better than the other, and (2) for the two different dimension reduction methods, both the two neural models using LASSO reflect better prediction ability than the models using PCA.

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Chandar, S. Kumar. "Forecasting intraday stock price using ANFIS and bio-inspired algorithms." International Journal of Networking and Virtual Organisations 25, no. 1 (2021): 29. http://dx.doi.org/10.1504/ijnvo.2021.117754.

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Chandar, S. Kumar. "Forecasting intraday stock price using ANFIS and bio-inspired algorithms." International Journal of Networking and Virtual Organisations 25, no. 1 (2021): 29. http://dx.doi.org/10.1504/ijnvo.2021.10041248.

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WANG, HONG-YONG, HONG LI, and JIN-YE SHEN. "A NOVEL HYBRID FRACTAL INTERPOLATION-SVM MODEL FOR FORECASTING STOCK PRICE INDEXES." Fractals 27, no. 04 (June 2019): 1950055. http://dx.doi.org/10.1142/s0218348x19500555.

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Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model are integrated to predict the every 5-min high frequency index data of Shanghai Composite Index. The experimental results show that the hybrid model is suitable for forecasting the stock index time series with fractal characteristics. In addition, a comparison of the prediction accuracy is carried out among the hybrid model and other three commonly used models. The results show that the prediction performance of the hybrid model is superior to that of other three models.

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Mumini, Omisore Olatunji, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward, and Adeniyi Shukurat Abidemi. "Simulation of Stock Prediction System using Artificial Neural Networks." International Journal of Business Analytics 3, no. 3 (July 2016): 25–44. http://dx.doi.org/10.4018/ijban.2016070102.

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Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

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Hossain, Mohammad Raquibul, and Mohd Tahir Ismail. "EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE." Journal of Information and Communication Technology 19, Number 4 (August 20, 2020): 533–58. http://dx.doi.org/10.32890/jict2020.19.4.4.

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Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue. Then, the Theta method forecasted each decomposed subseries. Considering different forecast horizons, the effectiveness of this hybridisation was evaluated through values of conventional error measures found for test data and forecast data, which were obtained by adding forecast results for all component counterparts extracted from the EMD process. This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.

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Syukur, Abdul, and Aris Marjuni. "Stock Price Forecasting Using Univariate Singular Spectral Analysis through Hadamard Transform." International Journal of Intelligent Engineering and Systems 13, no. 2 (April 30, 2020): 96–107. http://dx.doi.org/10.22266/ijies2020.0430.10.

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HUANG, WEI, KIN KEUNG LAI, YOSHITERU NAKAMORI, SHOUYANG WANG, and LEAN YU. "NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING." International Journal of Information Technology & Decision Making 06, no. 01 (March 2007): 113–40. http://dx.doi.org/10.1142/s021962200700237x.

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Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

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Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, Abeer D. Algarni, Jawaid Iqbal, Sami Bourouis, and Syed Sajid Ullah. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model." Mathematics 11, no. 3 (January 22, 2023): 590. http://dx.doi.org/10.3390/math11030590.

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Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.

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Dong, Jichang, Wei Dai, Ying Liu, Lean Yu, and Jie Wang. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method." International Journal of Information Technology & Decision Making 18, no. 05 (September 2019): 1605–29. http://dx.doi.org/10.1142/s0219622019500287.

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In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine ( Baidu.com ) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy.

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Ma, Guifen, Ping Chen, Zhaoshan Liu, and Jia Liu. "The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model." Computational Intelligence and Neuroscience 2022 (August 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/9193055.

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This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. LSTM prediction models are used to perform error analysis on company data training. The 20-day change trend of the company’s stock returns under different models is predicted and analyzed. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The average return price of the LSTM prediction model is 14.01. This figure is closest to the average real return price of 13.89. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. The prediction accuracy is better than other prediction models. In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy.

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Zhang, Xiaoyong, and Li Zhang. "Forecasting Method of Stock Market Volatility Based on Multidimensional Data Fusion." Wireless Communications and Mobile Computing 2022 (April 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/6344064.

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The volatility of the stock market is related to the vital interests of stockholders and is essential for maintaining a stable financial environment. Through the analysis of data changes, excellent professional traders can extract information about the direction of stock changes, whether it is worth investing, and long-term or short-term trading. This article aims to study the forecasting methods of stock market volatility, by integrating multiparty data, in-depth analysis of the direction of data changes, predicting the price changes of the stock market, and better guiding stockholders’ investment. This paper proposes a multisource data fusion method to analyze the stock market price changes and find the best risk prediction method. The experimental results in this paper show that multisource data fusion can better help the stock market predict stock changes and reduce financial investment risks by 20%. Comparing the obtained prediction results with the real data, the MSE predicted by the ARIMA model is calculated to be 2.35. It provides a new idea for effectively analyzing nonstationary time series data with complex trend fusion characteristics by rationally screening feature signals and trend signals and modeling probability distribution.

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Zhang, Xiaoyong, and Li Zhang. "Forecasting Method of Stock Market Volatility Based on Multidimensional Data Fusion." Wireless Communications and Mobile Computing 2022 (April 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/6344064.

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The volatility of the stock market is related to the vital interests of stockholders and is essential for maintaining a stable financial environment. Through the analysis of data changes, excellent professional traders can extract information about the direction of stock changes, whether it is worth investing, and long-term or short-term trading. This article aims to study the forecasting methods of stock market volatility, by integrating multiparty data, in-depth analysis of the direction of data changes, predicting the price changes of the stock market, and better guiding stockholders’ investment. This paper proposes a multisource data fusion method to analyze the stock market price changes and find the best risk prediction method. The experimental results in this paper show that multisource data fusion can better help the stock market predict stock changes and reduce financial investment risks by 20%. Comparing the obtained prediction results with the real data, the MSE predicted by the ARIMA model is calculated to be 2.35. It provides a new idea for effectively analyzing nonstationary time series data with complex trend fusion characteristics by rationally screening feature signals and trend signals and modeling probability distribution.

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Khashei, Mehdi, and Zahra Hajirahimi. "A comparative study of series arima/mlp hybrid models for stock price forecasting." Communications in Statistics - Simulation and Computation 48, no. 9 (May 8, 2018): 2625–40. http://dx.doi.org/10.1080/03610918.2018.1458138.

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Xiao, Daiyou, and Jinxia Su. "Research on Stock Price Time Series Prediction Based on Deep Learning and Autoregressive Integrated Moving Average." Scientific Programming 2022 (March 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/4758698.

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Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market.

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Chang, To-Han, Nientsu Wang, and Wen-Bin Chuang. "Stock Price Prediction Based on Data Mining Combination Model." Journal of Global Information Management 30, no. 7 (September 2022): 1–19. http://dx.doi.org/10.4018/jgim.296707.

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Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

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Yu, Xinpeng, and Dagang Li. "Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network." Applied Sciences 11, no. 9 (April 28, 2021): 3984. http://dx.doi.org/10.3390/app11093984.

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Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.

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Liu, Hui, Liangchen Qi, and Mingsong Sun. "Short-Term Stock Price Prediction Based on CAE-LSTM Method." Wireless Communications and Mobile Computing 2022 (June 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/4809632.

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Artificial intelligence methods are important tools for mining information for forecasting in the stock market. Most of the literature related to short-term stock price prediction focuses on the technical data, but in the real market, many individual investors make investment decisions more from stock price shape characteristics rather than specific stock price values. Compared with traditional measurement methods, deep neural networks perform better in processing high-dimensional complex data such as images. This paper proposes a model that combines CAE (convolutional autoencoder) and LSTM (long short-term memory) neural network, uses CAE to extract stock price image feature data, and combines technical data to predict short-term stock prices. The results show that the CAE-LSTM model, based on stock price image morphological feature data and technical data, performs well in short-term stock price prediction and has good generalization ability. The root mean square error of the CAE-LSTM model decreased by about 4% from that of LSTM. CAE-LSTM models have better predictive power than LSTM models that only use technical indicator data as valid inputs.

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Fu, Yizheng, Zhifang Su, Boyu Xu, and Yu Zhou. "Forecasting Stock Index Futures Intraday Returns: Functional Time Series Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 3 (May 20, 2020): 265–71. http://dx.doi.org/10.20965/jaciii.2020.p0265.

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It is of great significance to forecast the intraday returns of stock index futures. As the data sampling frequency increases, the functional characteristics of data become more obvious. Based on the functional principal component analysis, the functional principal component score was predicted by BM, OLS, RR, PLS, and other methods, and the dynamic forecasting curve was reconstructed by the predicted value. The traditional forecasting methods mainly focus on “point” prediction, while the functional time series forecasting method can avoid the point forecasting limitation, and realize “line” prediction and dynamic forecasting, which is superior to the traditional analysis method. In this paper, the empirical analysis uses the 5-minute closing price data of the stock index futures contract (IF1812). The results show that the BM prediction method performed the best. In this paper, data are considered as a functional time series analysis object, and the interference caused by overnight information is removed so that it can better explore the intraday volatility law, which is conducive to further understanding of market microstructure.

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Livieris, I. E., T. Kotsilieris, S. Stavroyiannis, and P. Pintelas. "Forecasting stock price index movement using a constrained deep neural network training algorithm." Intelligent Decision Technologies 14, no. 3 (September 29, 2020): 313–23. http://dx.doi.org/10.3233/idt-190035.

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The prediction of stock index movement is considered a rather significant objective in the financial world, since a reasonably accurate prediction has the possibility of gaining profit in stock exchange, yielding high financial benefits and hedging against market risks. Undoubtedly, the area of financial analysis has been dramatically changed from a rather qualitative science to a more quantitative science which is also based on knowledge extraction from databases. During the last years, deep learning constitutes a significant prediction tool in analyzing and exploiting the knowledge acquired from financial data. In this paper, we propose a new Deep Neural Network (DNN) prediction model for forecasting stock exchange index movement. The proposed DNN is characterized by the application of conditions on the weights in the form of box-constraints, during the training process. The motivation for placing these constraints is focused on defining the weights in the trained network in more uniform way, by restricting them from taking large values in order for all inputs and neurons of the DNN to be efficiently exploited and explored. The training of the new DNN model is performed by a Weight-Constrained Deep Neural Network (WCDNN) training algorithm which exploits the numerical efficiency and very low memory requirements of the L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) matrices together with a gradient-projection strategy for handling the bounds on the weights of the network. The performance evaluation carried out on three popular stock exchange indices, demonstrates the classification efficiency of the proposed algorithm.

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Gu, Wentao, Yongwei Yang, and Zhenshan Liu. "Forecasting Stock Returns Based on a Time-Varying Factor Weighted Density Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (October 20, 2018): 831–37. http://dx.doi.org/10.20965/jaciii.2018.p0831.

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Stock returns play an important role in the empirical study of asset pricing, and are often applied in portfolio allocation and performance evaluation. The effect of macroeconomic and financial variables on stock returns is a hot topic and many studies have utilized these variables in time series models to improve the forecasts of stock returns. This study imposes macroeconomic and financial variables as weighting factors on kernel density and establishes a new prediction model – the time-varying factor weighted density model. We apply this model to monthly price data of the Chinese stock index and employ the rolling window strategy for out-of-sample forecasting. The result shows that this method improves both statistical and economic measures of out-of-sample forecasting performance.

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Sun, Mei, Qingtao Li, and Peiguang Lin. "Short-Term Stock Price Forecasting Based on an SVD-LSTM Model." Intelligent Automation & Soft Computing 28, no. 2 (2021): 369–78. http://dx.doi.org/10.32604/iasc.2021.014962.

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Chen, Yu, Ruixin Fang, Ting Liang, Zongyu Sha, Shicheng Li, Yugen Yi, Wei Zhou, and Huilin Song. "Stock Price Forecast Based on CNN-BiLSTM-ECA Model." Scientific Programming 2021 (July 8, 2021): 1–20. http://dx.doi.org/10.1155/2021/2446543.

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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Journal articles on the topic 'Stock price forecasting – Computer simulation'

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