Relevant bibliographies by topics / Stock price forecasting – Computer simulation

Academic literature on the topic 'Stock price forecasting – Computer simulation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Stock price forecasting – Computer simulation"

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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.

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In 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.

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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.

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In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.
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Dappiti, 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.

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Stock Prices have been modeled using a variety of techniques such as neural networks, simple regression based models and so on with limited accuracy. We attempt to use Random Walk method to model movements of stock prices with modifications to account for market sentiment. A simulator has been developed as part of the work to experiment with actual NASDAQ100 stock data and check how the actual stock values compare with the predictions. In cases of short and medium term prediction (1-3 months), the predicted prices are close to the actual values, while for longer term (1 year), the predictions begin to diverge. The Random Walk method has been compared with linear regression, average and last known value across four periods and has that the Random Walk method is no better that the conventional methods as at 95% confidence there is no significant difference between the conventional methods and Random Walk model.
Prediction 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.
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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.

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Seshadri, 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.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: comprehensiblity; technical analysis; genetic programming; overfitting; cooperative coevolution. Includes bibliographical references (p. 82-87).
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Zhai, Yuzheng. "Improving scalability and accuracy of text mining in grid environment." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/5927.

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The advance in technologies such as massive storage devices and high speed internet has led to an enormous increase in the volume of available documents in electronic form. These documents represent information in a complex and rich manner that cannot be analysed using conventional statistical data mining methods. Consequently, text mining is developed as a growing new technology for discovering knowledge from textual data and managing textual information. Processing and analysing textual information can potentially obtain valuable and important information, yet these tasks also requires enormous amount of computational resources due to the sheer size of the data available. Therefore, it is important to enhance the existing methodologies to achieve better scalability, efficiency and accuracy.
The 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.
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De, Villiers J. "The use of neural networks to predict share prices." Thesis, 2012. http://hdl.handle.net/10210/6001.

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M.Comm.
The 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
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"Price discovery of stock index with informationally-linked markets using artificial neural network." 1999. http://library.cuhk.edu.hk/record=b5889930.

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by Ng Wai-Leung Anthony.
Thesis (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
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"Application of neural network to study share price volatility." 1999. http://library.cuhk.edu.hk/record=b5896263.

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by Lam King Wan.
Thesis (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
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Books on the topic "Stock price forecasting – Computer simulation"

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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.

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Institute, SAS, ed. Stock market analysis using the SAS system: Technical analysis. Cary, NC: SAS Institute, 1995.

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Book chapters on the topic "Stock price forecasting – Computer simulation"

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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.

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Wu, 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.

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Qiu, 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.

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Wu, 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.

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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." In Deep Learning and Neural Networks, 511–30. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch029.

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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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Lahmiri, 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.

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This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.
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Lahmiri, 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.

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This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marquardt algorithm. Three synthetic signals are generated to conduct experiments. The simulation results showed that in general the NARX which is a dynamic system outperforms the popular BPNN. In addition, conjugate gradient algorithms provide better prediction accuracy than the Levenberg-Marquardt algorithm widely used in the literature in modeling exponential signal. However, the LM performed the best when used for forecasting the Moroccan and South African stock price indices under both the BPNN and NARX systems.
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Lahmiri, 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.

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Using the wavelet analysis for low-frequency time series extraction, the authors in this chapter conduct out-of-sample predictions of the S&P500 price index future trend (up and down) following two trading strategies. In particular, the goal is to separately predict an increase or decrease of stock market by 0.5%. Indeed, predicting market increases by 0.5% is suitable to active portfolio managers, whilst predicting its decreases by 0.5% is suitable to risk-averse portfolio managers to limit losses. The Support Vector Machine (SVM) with polynomial kernel is used as the baseline forecasting model. Its performance is respectively compared to that of the Probabilistic Neural Networks (PNN) and the well known k-Nearest Neighbour (k-NN) algorithm, which is a statistical classifier. The simulation results reveal that the predictive system based on the SVM with wavelet analysis coefficients as inputs outperforms all the other systems. The achieved accuracy is 98.13%. As a result, it is concluded that the wavelet transform and SVM as an integrated system are appropriate to capture the S&P500 price changes by more or less than 0.5%.
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Balmer, 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.

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Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time.
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Conference papers on the topic "Stock price forecasting – Computer simulation"

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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.

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Toliyat 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.

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Kimiagari, 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.

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Wu, 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.

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Coelho, 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.

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Tao, 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.

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Fu-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.

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Wenqing, 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.

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Shi, 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.

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Wang, 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.

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