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Статті в журналах з теми "Stock price forecasting – Computer simulation"
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.
Повний текст джерелаRath, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаPanella, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаGöçken, 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.
Повний текст джерелаÖzçalıcı, 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.
Повний текст джерелаYing, 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.
Повний текст джерелаarman, 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.
Повний текст джерелаCheng, 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.
Повний текст джерелаДисертації з теми "Stock price forecasting – Computer simulation"
Li, Qi. "Application of Improved Feature Selection Algorithm in SVM Based Market Trend Prediction Model." Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10979352.
Повний текст джерелаIn this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (
GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of
GSPC, and SupportVector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.
Caley, Jeffrey Allan. "A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/2001.
Повний текст джерелаDappiti, Ramana Reddy, and Mohan Krishna Thalluri. "Brownian Dynamic Simulation to Predict the Stock Market Price." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2627.
Повний текст джерелаPrediction of stock markets has been the research interest of many scientists around the world. Speculators who wish to make a “quick buck” as well as economists who wish to predict crashes, anyone in the financial industry has an interest in predicting what stock prices are likely to be. Clearly, there is no model which can accurately predict stock prices; else markets would be absolutely perfect! However, the problem is pertinent and any improvement in the accuracy of prediction improves the state of financial markets today. This forms the broad motivation of our study.
Huo, Shiyin. "Detecting Self-Correlation of Nonlinear, Lognormal, Time-Series Data via DBSCAN Clustering Method, Using Stock Price Data as Example." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1321989426.
Повний текст джерелаSeshadri, Mukund. "Comprehensibility, overfitting and co-evolution in genetic programming for technical trading rules." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518.
Повний текст джерелаKeywords: comprehensiblity; technical analysis; genetic programming; overfitting; cooperative coevolution. Includes bibliographical references (p. 82-87).
Zhai, Yuzheng. "Improving scalability and accuracy of text mining in grid environment." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/5927.
Повний текст джерелаThe emerging Grid technology shows promising results in solving the problem of scalability by splitting the works from text clustering algorithms into a number of jobs, each to be executed separately and simultaneously on different computing resources. That allows for a substantial decrease in the processing time and maintaining the similar level of quality at the same time.
To improve the quality of the text clustering results, a new document encoding method is introduced that takes into consideration of the semantic similarities of the words. In this way, documents that are similar in content will be more likely to be group together.
One of the ultimate goals of text mining is to help us to gain insights to the problem and to assist in the decision making process together with other source of information. Hence we tested the effectiveness of incorporating text mining method in the context of stock market prediction. This is achieved by integrating the outcomes obtained from text mining with the ones from data mining, which results in a more accurate forecast than using any single method.
De, Villiers J. "The use of neural networks to predict share prices." Thesis, 2012. http://hdl.handle.net/10210/6001.
Повний текст джерелаThe availability of large amounts of information and increases in computing power have facilitated the use of more sophisticated and effective technologies to analyse financial markets. The use of neural networks for financial time series forecasting has recently received increased attention. Neural networks are good at pattern recognition, generalisation and trend prediction. They can learn to predict next week's Dow Jones or flaws in concrete. Traditional methods used to analyse financial markets include technical and fundamental analysis. These methods have inherent shortcomings, which include bad timing of trading signals generated, and non-continuous data on which analysis is based. The purpose of the study was to create a tool with which to forecast financial time series on the Johannesburg Stock Exchange (JSE). The forecasted time series information was used to generate trading signals. A study of the building blocks of neural networks was done before the neural network was designed. The design of the neural network included data choice, data collection, calculations, data pre-processing and the determination of neural network parameters. The neural network was trained and tested with information from the financial sector of the JSE. The neural network was trained to predict share prices 4 days in advance with a Multiple Layer Feedforward Network (MLFN). The mean square error on the test set was 0.000930, with all test data values scaled between 0.1 - 0.9 and a sample size of 160. The prediction results were tested with a trading system, which generated a trade yielding 20 % return in 22 days. The neural network generated excellent results by predicting prices in advance. This enables better timing of trades and efficient use of capital. However, it was found that the price movement on the test set within the 4-day prediction period seldom exceeded the cost of trades, resulting in only one trade over a 5-month period for one security. This should not be a problem if all securities on the JSE are analysed for profitable trades. An additional neural network could also be designed to predict price movements further ahead, say 8 days, to assist the 4-day prediction
"Price discovery of stock index with informationally-linked markets using artificial neural network." 1999. http://library.cuhk.edu.hk/record=b5889930.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 83-87).
Abstracts in English and Chinese.
Chapter I. --- INTRODUCTION --- p.1
Chapter II. --- LITERATURE REVIEW --- p.5
Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6
Chapter 2.2 --- Importance of Index Forecasting --- p.6
Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9
Chapter 2.4 --- Importance of the lead-lag relationship --- p.10
Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10
Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12
Chapter 2.7 --- Artificial Neural Network Architecture --- p.14
Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20
Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25
Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28
Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29
Chapter IV. --- METHODOLOGY --- p.37
Chapter 4.1 --- ADF Test for Unit Root --- p.38
Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38
Chapter 4.3 --- Comparison Criteria of Different Models --- p.39
Chapter 4.4 --- Data Analysis --- p.39
Chapter 4.5 --- Data Manipulations --- p.41
Chapter V. --- RESULTS --- p.42
Chapter 5.1 --- The Resulting Models --- p.42
Chapter 5.2 --- The Prediction Power among the Models --- p.45
Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46
Chapter VI. --- CAUSALITY ANALYSIS --- p.54
Chapter 6.1 --- Granger Casuality Analysis --- p.55
Chapter 6.2 --- Results Interpretation --- p.56
Chapter VII --- CONSISTENCE VALIDATION --- p.61
Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67
Chapter 7.1 --- Trading System Architecture --- p.68
Chapter 7.2 --- Simulation Runs using the Trading System --- p.77
Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79
"Application of neural network to study share price volatility." 1999. http://library.cuhk.edu.hk/record=b5896263.
Повний текст джерелаThesis (M.B.A.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 72-73).
ABSTRACT --- p.ii.
TABLE OF CONTENTS --- p.iv.
Section
Chapter I. --- OBJECTIVE --- p.1
Chapter II. --- INTRODUCTION --- p.3
The principal investment risk --- p.3
Effect of risk on investment --- p.4
Investors' concern for investment risk --- p.6
Chapter III. --- THE INPUT PARAMETERS --- p.9
Chapter IV. --- LITERATURE REVIEW --- p.15
What is an artificial neural network? --- p.15
What is a neuron? --- p.16
Biological versus artificial neuron --- p.16
Operation of a neural network --- p.17
Neural network paradigm --- p.20
Feedforward as the most suitable form of neural network --- p.22
Capability of neural network --- p.23
The learning process --- p.25
Testing the network --- p.29
Neural network computing --- p.29
Neural network versus conventional computer --- p.30
Neural network versus a knowledge based system --- p.32
Strength of neural network --- p.34
Weaknesses of neural network --- p.35
Chapter V. --- NEURAL NETWORK AS A TOOL FOR INVESTMENT ANALYSIS --- p.38
Neural network in financial applications --- p.38
Trading in the stock market --- p.41
Why neural network could outperform in the stock market? --- p.43
Applications of neural network --- p.45
Chapter VI. --- BUILDING THE NEURAL NETWORK MODEL --- p.47
Implementation process --- p.48
Step 1´ؤ Problem specification --- p.49
Step 2 ´ؤ Data collection --- p.51
Step 3 ´ؤ Data analysis and transformation --- p.55
Step 4 ´ؤ Training data set extraction --- p.58
Step 5 ´ؤ Selection of network architecture --- p.60
Step 6 ´ؤ Selection of training algorithm --- p.62
Step 7 ´ؤ Training the network --- p.64
Step 8 ´ؤ Model deployment --- p.65
Chapter 7 --- RESULT AND CONCLUSION --- p.67
Result --- p.67
Conclusion --- p.69
BIBLIOGRAPHY --- p.72
Книги з теми "Stock price forecasting – Computer simulation"
Kovalerchuk, Boris, and Evgenii Vityaev. Data Mining in Finance: Advances in Relational and Hybrid Methods (The International Series in Engineering and Computer Science). Springer, 2000.
Знайти повний текст джерелаInstitute, SAS, ed. Stock market analysis using the SAS system: Technical analysis. Cary, NC: SAS Institute, 1995.
Знайти повний текст джерелаЧастини книг з теми "Stock price forecasting – Computer simulation"
Vieira, R., L. Maciel, R. Ballini, and Fernando Gomide. "Stock Market Price Forecasting Using a Kernel Participatory Learning Fuzzy Model." In Communications in Computer and Information Science, 361–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95312-0_31.
Повний текст джерелаWu, Wenjun, Yue Wang, Jiaxiu Fu, Jiayi Yan, Zhiping Wang, Jiaqing Liu, Weifan Wang, and Xiuli Wang. "Preliminary Study on Interpreting Stock Price Forecasting Based on Tree Regularization of GRU." In Communications in Computer and Information Science, 476–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0121-0_37.
Повний текст джерелаQiu, Xueheng, Huilin Zhu, P. N. Suganthan, and Gehan A. J. Amaratunga. "Stock Price Forecasting with Empirical Mode Decomposition Based Ensemble $$\nu $$ -Support Vector Regression Model." In Communications in Computer and Information Science, 22–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6427-2_2.
Повний текст джерелаWu, Wenjun, Yuechen Zhao, Yue Wang, and Xiuli Wang. "Stock Price Forecasting and Rule Extraction Based on L1-Orthogonal Regularized GRU Decision Tree Interpretation Model." In Communications in Computer and Information Science, 309–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7984-4_23.
Повний текст джерелаMumini, Omisore Olatunji, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward, and Adeniyi Shukurat Abidemi. "Simulation of Stock Prediction System using Artificial Neural Networks." In Deep Learning and Neural Networks, 511–30. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch029.
Повний текст джерелаLahmiri, Salim. "Prediction of International Stock Markets Based on Hybrid Intelligent Systems." In Handbook of Research on Innovations in Information Retrieval, Analysis, and Management, 110–24. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8833-9.ch004.
Повний текст джерелаLahmiri, Salim. "On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 171–83. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8823-0.ch005.
Повний текст джерелаLahmiri, Salim. "Practical Machine Learning in Financial Market Trend Prediction." In Advances in Business Information Systems and Analytics, 206–17. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5958-2.ch010.
Повний текст джерелаBalmer, Michael, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, and Kai Nagel. "MATSim-T." In Multi-Agent Systems for Traffic and Transportation Engineering, 57–78. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-226-8.ch003.
Повний текст джерелаТези доповідей конференцій з теми "Stock price forecasting – Computer simulation"
Chi, Wan Le. "Stock price forecasting based on time series analysis." In 6TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5039106.
Повний текст джерелаToliyat Abolhassani, AmirMohsen, and Mahdi Yaghoobi. "Stock price forecasting using PSOSVM." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579738.
Повний текст джерелаKimiagari, A. M., M. Keyvanloo, M. Fallahnezhad, M. H. Moradi, and A. B. Farimani. "Stock Price Forecasting using Higher Order Neural Networks." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-107.
Повний текст джерелаWu, Jui-Yu, and Chi-Jie Lu. "Computational Intelligence Approaches for Stock Price Forecasting." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.23.
Повний текст джерелаCoelho, Joseph, Dawson D'almeida, Scott Coyne, Nathan Gilkerson, Katelyn Mills, and Praveen Madiraju. "Social Media and Forecasting Stock Price Change." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.10206.
Повний текст джерелаTao, Shasha, Yixuan Fan, and Runfeng Zhang. "A Hybrid Forecasting Model for Stock Price Prediction." In CSAE 2020: The 4th International Conference on Computer Science and Application Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3424978.3424985.
Повний текст джерелаFu-Yuan, Huang. "Forecasting Stock Price Using a Genetic Fuzzy Neural Network." In 2008 International Conference on Computer Science and Information Technology (ICCSIT). IEEE, 2008. http://dx.doi.org/10.1109/iccsit.2008.128.
Повний текст джерелаWenqing, Yao, Youwei Zhang, Sanfei Chang, Jing Li, Qingbo Zhao, and Fangli Ge. "Stock price analysis and forecasting based on machine learning." In International Conference on Computer Science and Communication Technology (ICCSCT 2022), edited by Yingfa Lu and Changbo Cheng. SPIE, 2022. http://dx.doi.org/10.1117/12.2662176.
Повний текст джерелаShi, Huaijin, Gao Yuan, Zhuoran Lu, and Qian Liang. "LSTM Based Model For Apple Inc Stock Price Forecasting." In 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT). IEEE, 2021. http://dx.doi.org/10.1109/iccsmt54525.2021.00017.
Повний текст джерелаWang, Guoxian, and Yingbing Fan. "Research on Stock Price Forecasting Model Based on Deep Learning." In ICISCAE 2021: 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3482632.3487545.
Повний текст джерела