Relevant bibliographies by topics / Stock Prediction / Dissertations / Theses
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Dissertations / Theses on the topic 'Stock Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Chatterjee, Arijit. "Stock Prediction Analyzing Investor Sentiments." Diss., North Dakota State University, 2017. http://hdl.handle.net/10365/26045.

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We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stock
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2

Fatah, Kiar, and Taariq Nazar. "Stock Market Prediction With Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293853.

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Due to the unpredictability of the stock market,forecasting stock prices is a challenging task. In this project,we will investigate the performance of the machine learningalgorithm LSTM for stock market prediction. The algorithmwill be based only on historical numerical data and technicalindicators for IBM and FORD. Furthermore, the denoising anddimension reduction algorithm, PCA, is applied to the stockdata, to examine if the performance of forecasting the stockprice is greater than the initial model. A second method, transferlearning, is applied by training the model on the IBM datasetand th
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Bahceci, Oktay, and Oscar Alsing. "Stock Market Prediction using Social Media Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166448.

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Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices. The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter. I
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Ghodsi, Boushehri Ali. "Applying fuzzy logic to stock price prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ54332.pdf.

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5

Marques, Inês Filipa Rodrigues. "Machine learning in finance : stock market prediction." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19517.

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Mestrado em Finanças<br>Esta dissertação traz novas ideias na utilização de Machine Learning nos mercados financeiros, tendo por base o índice S&P500 durante o período 2000-2019. A dificuldade subjacente à aplicação das técnicas de Machine Learning é superada através da implementação de métodos automáticos de Machine Learning. Com esta implementação, investidores com pouco ou nenhum know-how podem tirar vantagem do uso destas técnicas. Nós investigámos a performance das técnicas de Machine Learning e comparámos com a performance de técnicas tradicionais de previsão de séries temporais, como o
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HALLGREN, FREDRIK. "On Prediction and Filtering of Stock Index Returns :." Thesis, KTH, Matematik (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-35154.

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7

Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.

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Modarres, Najafabadi Sayed Reza. "Prediction of stock market indices using machine learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40795.

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Financial time series prediction is a very important economical problem but the data available is very noisy. In this thesis, we explain the use of statistical and machine learning methods for stock market prediction and we evaluate the performance of these methods on data from the S&P/TSX 60 stock index. We use both linear regression and support vector regression, a state-of-art machine learning method, which is usually robust to noise. The results are mixed, illustrating the difficulty of the problem. We discuss the utility of using different types of data pre-processing for this task as wel
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Holmqvist, Carl. "Opinion analysis of microblogs for stock market prediction." Thesis, KTH, Teoretisk datalogi, TCS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233197.

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This degree project investigates if a company’s stock price development can be predicted using the general opinion expressed in tweets about the company. The project starts off with the model from a previous project and then tries to improve the results using state-of-the-art neural network sentiment analysis and more tweet data. This project also attempts to perform hourly predictions along with daily predictions in order to investigate the method further. The results show a decrease in accuracy compared to the previous project. The results also indicate that the neural network sentiment anal
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10

Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This ap
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11

Wang, Zhuowen. "Prediction of stock market prices using neural network techniques." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26802.

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Issuing stocks is the key method to raise money for corporations. Today, stocks have become the most important financial instruments. Currently, there are several methods by which one can predict financial markets, but none of them is quite accurate. After introducing same basic concepts and the history of stocks, this work continues to introduce some typical fundamental and technical analysis methods already developed by economists, and then presents a relatively new system to forecast the stack market using revised Back Propagation (BP) algorithms. The system exploits BP neural networks to h
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12

FILHO, HERALDO PIMENTA BORGES. "STOCK MARKET BEHAVIOR PREDICTION USING FINANCIAL NEWS IN PORTUGUESE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25123@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Um conjunto de teorias financeiras, tais como a hipótese do mercado eficiente e a teoria do passeio aleatório, afirma ser impossível prever o futuro do mercado de ações baseado na informação atualmente disponível. Entretanto, pesquisas recentes têm provado o contrário ao constatar uma relação entre o conteúdo de uma notícia corrente e o comportamento de um ativo. Nosso objetivo é projetar e implementar um algoritmo de predição que utiliza not
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13

Liu, Clare H. "Applications of twitter emotion detection for stock market prediction." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113131.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-76).<br>Currently, most applications of sentiment analysis focus on detecting sentiment polarity, which is whether a piece of text can be classified as positive or negative. However, it can sometimes be important to be ab
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14

Kirchheimer, Franz David. "Analysis and prediction of intraday stock returns following news." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702905.

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15

Fallahi, Faraz. "MACHINE LEARNING ON BIG DATA FOR STOCK MARKET PREDICTION." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2178.

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In recent decades, the rapid development of information technology in the big data field has introduced new opportunities to explore a large amount of data available online. The Global Database of Events, Location (Language), and Tone (GDELT) is the largest, most comprehensive, and highest resolution open source database of human society that includes more than 440 million entries capturing information about events that have been covered by local, national, and international news sources since 1979 in over 100 languages. GDELT constructs a catalog of human societal-scale behavior and beliefs a
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Pokhrel, Abhishek <1996&gt. "Stock Returns Prediction using Recurrent Neural Networks with LSTM." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/22038.

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Research in asset pricing has, until recently, side-stepped the high dimensionality problem by focusing on low-dimensional models. Work on cross-sectional stock return prediction, for example, has focused on regressions with a small number of characteristics. Given the background of an enormously large number of variables that could potentially be relevant for predicting returns, focusing on such a small number of factors effectively means that the researchers are imposing a very high degree of sparsity on these models. This research studies the use of the recurrent neural network (RNN) method
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17

Bakshi, Nikhil. "Stock market prediction using online data fundamental and technical approaches /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Institut fur Computational Science, 2008. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=392.

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18

Schwerk, Thomas. "NELION: a non-linear stock prediction and portfolio management system." [S.l. : s.n.], 2001. http://www.diss.fu-berlin.de/2001/85/index.html.

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19

Martinssson, Filip, and Ivan Liljeqvist. "Short-Term Stock Market Prediction Based on Candlestick Pattern Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209820.

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This study performs a comparative analysis and evaluates the impact of different Relative Strenght Index (RSI) and stop loss configurations on a trading algorithm based on candlesticks patterns. It is tested on both the Swedish OMXS30 market and the UK FTSE100 market. By tweaking the configurations, RSI and stop loss was found to have a substantial impact on the performance of the algorithm. On both OMXS30 and FTSE100 markets the difference between configurations was shown to be significant<br>Denna studie gör en jämförelse och analyserar olika Relative Strenght Index (RSI) och stop loss-konfi
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20

Chen, Zexun. "Gaussian process regression methods and extensions for stock market prediction." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/40502.

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Gaussian process regression (GPR) is a kernel-based nonparametric method that has been proved to be effective and powerful in many areas, including time series prediction. In this thesis, we focus on GPR and its extensions and then apply them to financial time series prediction. We first review GPR, followed by a detailed discussion about model structure, mean functions, kernels and hyper-parameter estimations. After that, we study the sensitivity of hyper-parameter and performance of GPR to the prior distribution for the initial values, and find that the initial hyper-parameters’ estimates de
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Bergström, Carl, and Oscar Hjelm. "Impact of Time Steps on Stock Market Prediction with LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262221.

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Machine learning models as tools for predicting time series have in recent years proven to perform exceptionally well. With financial time series in the form of stock indices being inherently complex and subject to noise and volatility, the prediction of stock market movements has proven to be especially difficult throughout extensive research. The objective of this study is to thoroughly analyze the LSTM architecture for neural networks and its performance when applied to the S&amp;P 500 stock index. The main research question revolves around quantifying the impact of varying the number of ti
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22

Saha, Suman. "Stock market movement prediction using machine learning techniques and graph-based approaches." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30018.

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Machine learning techniques are preferred now than the statistical methods for stock movement prediction due to their efficiency and effectiveness. Stock market movement prediction is impacted significantly by choice of input features and prediction algorithms. We focus on a specific event of ex-dividend day and use event-specific input features of cum-dividend period for predicting price movement on the ex-dividend day. Performance improves significantly when these event-specific optimum input features are used along with machine learning models. The relative order or ranking of stocks is mo
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23

Ferreira, de Melo Filho Alberto. "Predicting the unpredictable - Can Artificial Neural Network replace ARIMA for prediction of the Swedish Stock Market (OMXS30)?" Thesis, Mittuniversitetet, Institutionen för ekonomi, geografi, juridik och turism, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36908.

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During several decades the stock market has been an area of interest forresearchers due to its complexity, noise, uncertainty and nonlinearity of thedata. Most of the studies regarding this area use a classical stochastics method,an example of this is ARIMA which is a standard approach for time seriesprediction. There is however another method for prediction of the stock marketthat is gaining traction in the recent years; Artificial Neural Network (ANN).This method has mostly been used in research on the American and Asian stockmarkets so far. Therefore, the purpose of this essay was to explor
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Karlemstrand, Roderick, and Ebba Leckström. "Using Twitter Attribute Information to Predict Stock Prices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299835.

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Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of Long Short-Term Memory (LSTM) and fully connected layers. It is trained wit
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Tekel, Onur. "Business Failure Predictions In Istanbul Stock Exchange." Thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610621/index.pdf.

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This study aims to develop business failure prediction models using the data of selected firms from ISE markets. The sample data comprise ten selected financial ratios for 27 non-going concerns (failed businesses) and paired 27 going concerns. Two non-parametric classification methods are used in the study: Artificial Neural Networks (ANN) and Decision Trees. The classification results show that there is equilibrium in the classification of the training samples by the models, but ANN model outperform the decision tree model in the classification of the testing samples. Further, the potential u
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Steiner, Michael Steiner Michael. "Risk factors, fund performance, and prediction in the Swiss stock market /." [S.l.] : [s.n.], 2009. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000292655.

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Bergman, August, and Sonja Ericsson. "Applying investor sentiment to a prediction model of the stock market." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208663.

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Using data-driven methods to predict the movements of the stock market is a growing field of research. Recently, large amounts of data sourced from online news and social media have been utilized to predict movements in financial markets. With the emergence of social media platforms, data can be gathered and used to quantify the sentiment of the market. This study investigates whether investor sentiment can be used to improve the precision of a prediction model of the stock market, specifically to explore whether the precision of a model which predicts intraday price change in direction of cer
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Raahemi, Mohammad. "Intelligent Prediction of Stock Market Using Text and Data Mining Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40934.

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The stock market undergoes many fluctuations on a daily basis. These changes can be challenging to anticipate. Understanding such volatility are beneficial to investors as it empowers them to make inform decisions to avoid losses and invest when opportunities are predicted to earn funds. The objective of this research is to use text mining and data mining techniques to discover the relationship between news articles and stock prices fluctuations. There are a variety of sources for news articles, including Bloomberg, Google Finance, Yahoo Finance, Factiva, Thompson Routers, and Twitter. In our
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Switlyk, Victoria Switlyk. "Model Comparison for the Prediction of Stock Prices in the NYSE." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530869448495865.

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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictio
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Steiner, Michael. "Risk factors, fund performance, and prediction in the Swiss stock market /." lizenzfrei, 2009. http://www.gbv.de/dms/zbw/610285734.pdf.

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Fan, Yu-Ping, and 范宇蘋. "Stock Prediction and Implementation of Stock Management Platform." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qckqmt.

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碩士<br>國立臺北科技大學<br>電機工程系<br>106<br>In this thesis, we propose a stock management platform for users to manage stock in a more convenient way, decide buy/sell strategy for stock with stock indicators, and control the return on investment efficiently by changing the stock strategy. User can register for the platform to use all functions, including select stock with indicators, recommended stock, store management system and customize own strategies. The user can select stocks which meet the customize indicator strategies. The server will crawl stock data daily, and use the preprocessed data to p
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Kuan, Mei-Lan, and 官美蘭. "Comprehensive and Personalized Stock Performance Prediction." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/27211154926656315250.

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碩士<br>輔仁大學<br>資訊管理學系<br>87<br>This thesis aims to find new value-added services for traditional stock systems based on neural networks. For example, they are capable of soliciting valuable information related to why the stock prediction is made, and furthermore offer investors personalized stock prediction. That is, we attempt to build a comprehensive and personalized stock prediction system, which can become a personalized financial consultant for investors. There are two major components in this system. The first component is a comprehensive component, which is built on the rule e
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WANG, HSIAO-YU, and 王筱瑜. "Financial Distress Prediction and Stock Returns." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/02812360762111558985.

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碩士<br>國立中正大學<br>財務金融系研究所<br>104<br>This study explores the relationship between default risk and stock returns. There are many kinds of financial distress prediction models and I choose financial distress prediction models constructed by univariate analysis, discriminant analysis and regression analysis. Using bankruptcy prediction models as signals, I buy high-default risk stocks and sell low-default-risk stocks at the same time and compare their returns. The results show that financial distress prediction models shown as function forms can’t be signals, only using financial ratios can gener
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Tsai, Po-Yu, and 蔡伯煜. "Mining candlestick charts for stock prediction." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/33453794056293566302.

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碩士<br>國立中央大學<br>資訊管理研究所<br>100<br>Stock prediction is an interesting and important issue for many investors. However, the factors that affect stock price are very complicated and difficult to analyze. Therefore, it is very hard to effectively predict stock price. In general, both fundamental analysis and technical analysis have been used for stock prediction. The analysis of candlestick chart (also called K chart) is one of the technical analysis methods since such figures usually contain lots of trading information which allow the investors to analyze the stock trend. However, previous studie
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Boushehri, Ali Ghodsi. "Applying fuzzy logic to stock price prediction." Thesis, 2000. http://spectrum.library.concordia.ca/1116/1/MQ54332.pdf.

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The major concern of this study is to develop a system that can predict future prices in the stock markets by taking samples of past prices. Stock markets are complex. Their dramatic movements, and unexpected booms and crashes, dull all traditional tools. This study attempts to resolve such complexity using the subtractive clustering based fuzzy system identification method, the Sugeno type reasoning mechanism, and candlestick chart analysis. Candlestick chart analysis shows that if a certain pattern of prices occurs in the market, then the stock price will increase or decrease. Inspired by
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ZHI-HAO, YE, and 葉志豪. "Applications of data cleaning in stock prediction." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/02957833773987607843.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>92<br>Stock market likes the black box. It includes a lot of information and phenomenon，But it includes a few uncertainly factors、noises 、、、and so on. it has many researches of application in stock prediction since 1995 ；example：1、 intelligence algorithm (fuzzy、neural network、Genetic algorithm、decision tree、、、and so on)2、economy model(regression model 、statistics model、、、and so on)。 But both of them have a many drawbacks influence the researches of the prediction in stock market。Common prediction model’s input is the technique indices 。the prediction’s out
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Kao, Rung-Tai, and 高榮泰. "Stock Market Prediction Using the Representative Features." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/65437544748162968985.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>97<br>With disclosure regulation, a large amount of financial reports is available for investment purposes and research analysis. In this thesis, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports, where each financial report is represented by a feature vector. The proposed method consists of three phases. First, we use the hierarchical agglomerative clustering (HAC) algorithm to divide the feature vectors into several clu
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"Sentiment Analysis for Long-Term Stock Prediction." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.39401.

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abstract: There have been extensive research in how news and twitter feeds can affect the outcome of a given stock. However, a majority of this research has studied the short term effects of sentiment with a given stock price. Within this research, I studied the long-term effects of a given stock price using fundamental analysis techniques. Within this research, I collected both sentiment data and fundamental data for Apple Inc., Microsoft Corp., and Peabody Energy Corp. Using a neural network algorithm, I found that sentiment does have an effect on the annual growth of these companies but
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Kuo, Chen-Yu, and 郭鎮宇. "LSTM-Based Taiwan Stock Index Futures Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/xam4d3.

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碩士<br>國立臺灣大學<br>經濟學研究所<br>107<br>This study uses the price data of Taiwan stock index futures and several common technical indicators to create RNN and LSTM model to predict futures’ price data. LSTM model is a RNN model for processing time series data. Due to its special structural design, LSTM models are more suitable for data with long time periods or long delays, and are mostly used in image recognition and speech recognition. The price data in the financial market also has similar characteristics. This study attempts to apply deep learning models on futures price market to predict future
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Guan-YILee and 李冠毅. "Toward stock price prediction using Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/52kzef.

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Li, Lan-Xuan, and 李蘭萱. "Institutional Herding and the Prediction of Stock Returns: Evidence from Taiwan Stock Market." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/49bfry.

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碩士<br>國立政治大學<br>財務管理學系<br>107<br>The study analyzes portfolios which are composed with institutional investors’ herding behavior in Taiwan stock market, and is intended to find the key factors and the price impact of herding. Using the shareholding data from April 2000 to June 2018, the study finds higher levels of herding by dealers in large-cap and low dividend yield stocks. On the other hands, there is some evidence for higher herding levels by fund managers in high Price-Earning ratio and low dividend yield stocks. Although stocks that foreign investors buy and funds sell outperform the ma
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Juchelka, Tomáš. "Prediction of Stock Return Volatility Using Internet Data." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-367646.

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The thesis investigates relationship between daily stock return volatility of Dow Jones Industrial Average stocks and data obtained on Twitter, the social media network. The Twitter data set contains a number of tweets, categorized according to their polarity, i.e. positive, negative and neutral sentiment of tweets. We construct two classes of models, GARCH and ARFIMA, where for either of them we research basic model setting and setting with additional Twitter variables. Our goal is to compare, which of them predicts the one day ahead volatility most precisely. Besides, we provide commentary r
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Hsiao, Yu-Chieh, and 蕭鈺潔. "Stock Prediction by Combining Multiple Feature Selection Methods." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/00304036090036697532.

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碩士<br>國立中正大學<br>會計與資訊科技研究所<br>97<br>Stock investment has become a popular investment activity in Taiwan. To effectively predict stock price for investors, it is a very important research problem and challenging. In literature, data mining techniques have been applied to stock price prediction. As feature selection is an important pre-processing step to select more representative variables for effective prediction, previous studies do not take all relevant variables into consideration seriously. In addition, they do not attempt to further combine multiple feature selection methods to filtering
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Chen, Ya-wen, and 陳雅雯. "Taiwan Stock Market Prediction using Support Vector Machines." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/50255345006129885588.

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碩士<br>南華大學<br>資訊管理學研究所<br>91<br>To make money, an appealing method is to invest companies’ stocks. However, it is risky as there are many factors that effect prices of the stocks. Many theories and methods are proposed for predicting the trend of stock markets. It is hoped that with the help of modern Information Technologies, especially AI, correct buying and selling can be achieved. 　 　　Our research used Support Vector Machines for predicting Taiwan stock price fluctuation. There are nine companies sampled in the study. 　 　　Principal Component Analysis (PCA) and Stepwise Regression are used
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Liu, Zai-Feng, and 劉再峰. "A MULTI-FACTOR MODEL FOR STOCK MARKET PREDICTION." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/16640064871913258687.

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Chen, Chang-Chieh, and 陳昌捷. "Stock Index Prediction Using Back Propagation Neural Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47651467208536124110.

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碩士<br>國立宜蘭大學<br>多媒體網路通訊數位學習碩士在職專班<br>103<br>Abstract The main purpose of this study is to construct a Back Propagation Neural Network ( BPNN) model on MATLAB for predicting the Taiwan stock exchange capitalization weighted stock (TAIEX). The data ranging from 2014.01.02 to 2014.07.31 is selected. The duration is 7 months and there are total 140 recorders. The weighted indexes and technical analysis indicators are screened as the input parameters by using Pearson correlation coefficient. Specifically, the indicators, that the r values are more than 0.7, are selected as the input parameters, an
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Chen, Yan-Ming, and 陳彥銘. "Development of Multiple Classifiers for Stock Price Prediction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/46868911887476918328.

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碩士<br>國立中正大學<br>會計所<br>95<br>The technique of predicting of stock investment has been developed for many years. Advancement of computer technology allows many studies to use data mining techniques to predict stock price, like neural network, decision trees, etc. In order to build a better modal for stock price prediction, this study tries to construct different ‘homogenous’ multiple classifiers (for example, an ensemble of neural networks) and ‘heterogeneous’ multiple classifiers (for example, an ensemble of neural networks, decision trees and logistic regression) against single classifiers th
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Lee, Chun-Yi, and 李俊逸. "Applying Recurrent Neural Networks to Stock Price Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7srsve.

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碩士<br>國立政治大學<br>資訊管理學系<br>107<br>With the rapid growth of computing equipment in Moore's Law and the rapid development of computing devices, humans now have computers with faster speeds, which makes artificial intelligence rise again. The reason is that the branch of artificial intelligence — deep learning becomes dominant, and many people are working on how to implement deep learning skill on difficult questions and make contribution to human society. This study attempts to use one of the deep learning skills, RNN (Recurrent Neural Network) to make predictions about the stock market. The fact
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Tsung-LingHsu and 許琮苓. "Stock Market Trend Prediction Based on LSTM Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9zca24.

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碩士<br>國立成功大學<br>工程科學系<br>107<br>The stock market has always been an important economic indicator for the society. It is believed that the fluctuation of the stock market seems changed according to some cyclic regularity. For a stock investor, how to find the cyclic regularity of a stock market is a most important issue. However, due to many factors affecting the stock market, it is difficult to obtain accurate predictions. At present, many algorithms have been applied for predicting stock market trends. Due to both local/regional and global economic performance will affect the stock market. Th
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