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Dissertations / Theses on the topic 'Market price prediction opportunities'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Li, Heng S. M. Massachusetts Institute of Technology. "A price prediction method In real estate market." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103843.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-75).<br>Current housing price prediction usually employs hedonic or repeat-sales models. The objective is to build a statistical model which is more focused on statistic methods. Neither ordinary nor regularized regression model haven been applied to the field of real estate, even though they are rather well-known statistical procedures. This thesis concludes lots of ordinary and regularized regre

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2

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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3

Le, Minxian. "Prediction of large price changes in the energy market using extreme value statistics." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14146.

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In this project we have first and foremost been comparing the performance of the ACER method with the POT method in the prediction of extreme values from the heavy tailed distributions; especially for data from the energy markets. The energy market is an exciting dynamic market where small singularities can make large differences in the price. Therefore it is very important and challenging to analyse and make predictions in this market. We have also analysed a dataset which is not from the energy market, to compare and see the main differences between the two markets. We have also taken in con

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4

Wilkens, Carl. "Auri sacra fames : interest rates : prediction, jumps and the market price of risk /." Stockholm : Department of Economics, Stockholm University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-710.

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5

Forman, Marcus R., and John M. Wear. "Putting a Price on Strategy: Implementing a Prediction Market in a Modern Military Unit." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/7341.

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Prediction markets are speculative markets created for aggregating relevant information on some measurable future event. Simply put, prediction markets ask participants to trade ideas as stocks. The “market price” of a particular idea or contract can then be interpreted as the probability that an event will occur, or as a feedback mechanism regarding how well some course of action is working. The application and utility of prediction markets to military strategy and decision-making has yet to be adequately tested in any real or empirical way. This thesis seeks to understand the conditions unde

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6

Burgard, Andrew. "Can Business News Provide Insight into a Stock’s Future Price Performance?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1673.

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Mutual funds and money managers have recently come under fire for their inability to beat market level returns since the Great Recession. With the recent trend towards passive money management through ETFs and other market-based securities, many investors have come to doubt whether above market returns are realizable in today’s economic climate. This paper examines whether business news has any predictable impact on stock price. Specifically, the paper explores the impact of analyst reports, mergers & acquisition news, legal affairs, insider buying and selling and changes to executive leadersh

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7

Candela, Garza Eduardo. "Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113433.

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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 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 53-55).<br>We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. Firs

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8

Yeoh, Daniel Ghee Chong, and danielyeoh@cimb com my. "An Empirical Examination of Physical Asset Expenditure Announcements in Australia: Growth Opportunities, Free Cash Flow and Capital Market Monitoring." The Australian National University. Commerce, 2001. http://thesis.anu.edu.au./public/adt-ANU20010702.160428.

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This thesis examines the stock market price variations associated with physical asset expenditure announcements in Australia. With the exception of the study of Chen and Ho (1997) in Singapore, most capital expenditure studies in other markets investigate the announcement effects associated with changes in budgeted capital expenditures. The fact that there is almost never any firm level capital budget announcement in Australia presents a unique opportunity to examine individual physical asset expenditure announcements. ¶ Three primary hypotheses pertaining to growth opportunities, free cash

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9

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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<p> In this study, a <b>Prediction Accuracy Based Hill Climbing Feature Selection Algorithm</b> <b>(AHCFS)</b> is created and compared with an <b>Error Rate Based Sequential Feature Selection Algorithm</b> <b> (ERFS)</b> 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 (</p><p>GSPC) prices under certain circumstances. The twoalgorithms are tested based on historical data of </p><p>GSPC, and <b>SupportVector Ma

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10

Rosenius, Niklas, and Gustav Sjöholm. "Arbitrage opportunities on the OMXS : How to capitalize on the ex-dividend effect." Thesis, Umeå universitet, Företagsekonomi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-81173.

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Investors are continuously looking to increase the return on their investments. In an ideal world investors want to increase there return and outperform the market. Theory states that it is impossible to do so without increasing your risk. Arbitrage is a concept where investors are able to generate risk-free returns exceeding the market. Dividend is a common tool for publicly listed firms when rewarding their shareholders. On ex- dividend day, the day after the dividend payout, the stock price should according to theory decrease in order for the valuation of the stock to be held constant. In o

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11

Li, Jiasen. "Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's Market." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663082026476.

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12

Troeman, Reamflar Elvio Estebano, and Lisa Fischer. "Politics, Artificial Intelligence, Twitter and Stock Return : An Interdisciplinary Test for Stock Price Prediction Based on Political Tweets." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48436.

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As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twit

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13

Badenhorst, Dirk Jakobus Pretorius. "Improving the accuracy of prediction using singular spectrum analysis by incorporating internet activity." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80056.

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Thesis (MComm)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Researchers and investors have been attempting to predict stock market activity for years. The possible financial gain that accurate predictions would offer lit a flame of greed and drive that would inspire all kinds of researchers. However, after many of these researchers have failed, they started to hypothesize that a goal such as this is not only improbable, but impossible. Previous predictions were based on historical data of the stock market activity itself and would often incorporate different types of auxiliary data

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14

HOSSEINIIMANI, SEYEDMAHMOOD. "Assessing the Electricity Market Performance Through Data Analytics Approaches: The Italian Case." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2971122.

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15

Kasimoglu, Ata. "The Impact Of Wind Energy Development On Swedish Elspot Day-Ahead Prices." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-339817.

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The rapid development of wind energy in Sweden created a volatile environment for the electricity market. Variance in the daily prices and the reductions of the average prices over the years due to the merit order effect of intermittent wind energy resulted in increased unpredictability in financial returns, which led to many wind projects being cancelled. In this thesis, in order to shed more light on the impact of wind energy development on spot prices, an artificial neural network (ANN) electricity price forecasting model is designed in order to predict Sweden’s four electricity regions Nor

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16

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 enorm

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17

Ruthberg, Richard, and Sebastian Wogenius. "Stochastic Modeling of Electricity Prices and the Impact on Balancing Power Investments." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192111.

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Introducing more intermittent renewable energy sources in the energy system makes the role of balancing power more important. Furthermore, an increased infeed from intermittent renewable energy sources also has the effect of creating lower and more volatile electricity prices. Hence, investing in balancing power is prone to high risks with respect to expected profits, which is why a good representation of electricity prices is vital in order to motivate future investments. We propose a stochastic multi-factor model to be used for simulating the long-run dynamics of electricity prices as input

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18

Maršová, Eliška. "Predikce hodnot v čase." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255333.

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This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.

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19

"Margin variations in support vector regression for the stock market prediction." 2003. http://library.cuhk.edu.hk/record=b5891624.

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Yang, Haiqin.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (leaves 98-109).<br>Abstracts in English and Chinese.<br>Abstract --- p.ii<br>Acknowledgement --- p.v<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Time Series Prediction and Its Problems --- p.1<br>Chapter 1.2 --- Major Contributions --- p.2<br>Chapter 1.3 --- Thesis Organization --- p.3<br>Chapter 1.4 --- Notation --- p.4<br>Chapter 2 --- Literature Review --- p.5<br>Chapter 2.1 --- Framework --- p.6<br>Chapter 2.1.1 --- Data Processing --- p.8<br>Chapter 2.1.

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20

Cheng, Hsiu-Fen, and 鄭秀芬. "The study of building the price prediction model for electronic component retailing market." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/58476245077015375573.

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碩士<br>輔仁大學<br>資訊管理學系<br>91<br>The Price trend of stock is always a critical factor when retailers need to replenish the stock of merchandise. It will influence how many products need to be replenished and what price should be made. Because there are price variations of electronic component in retailing market, making a price is used as a weapon in market competition. In lately years, prediction technology is getting mature and used in prediction of society phenomenon. In this research, we collect history retailing prices of three different electronic components：main board、graphic card and LCD

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21

"An applicability study of technical analysis techniques for prediction of price movement in foreign exchange market." Chinese University of Hong Kong, 1987. http://library.cuhk.edu.hk/record=b5885744.

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22

LEE, KUO-YAO, and 李國瑤. "The Development of Construction Industry Failure Prediction from Integration of Accounting and Market Stock Price Information." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98905254083260112569.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>97<br>The traditional corporation financial failure model is based on accounting information, and it provides different methods, such as single variable analysis, multivariate analysis, logistical regression model, etc. In 1997, Merton develop a company failure prediction based on Black and Scholes’s option price theory, whose concept was to estimate the asset value and its variability by the information of the market stock price and compared with the debt of accounting sheet to obtain the company failure probability. Theoretically, the market stock price is the hyp

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23

Cheng, Chi-Wei, and 鄭志偉. "A Neural Network Approach for Prediction and Analysis in the Taiwan Stock Market --- Stock Price and TSEWPI." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/84882368526384217481.

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碩士<br>淡江大學<br>資訊管理學系<br>85<br>This study attempts to select the significant ones from various offinancial ratios and indicators about sale events from the viewpoint offundamental analysis . After interviewing with investment experts and referencing the results from related studies , some indicators withcorrelation were eliminated and a group of indicators was reserved . Back-Propagation Network (BPN) approach is used in this study. We collectretrospective data as BPN''s training samples and

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24

Hsueh, Hung Yeh, and 薛弘業. "A Study of Stock Price Prediction with Text Mining, Classification and Clustering Techniques in Taiwan Stock Market." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/98972323381665117777.

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碩士<br>國立政治大學<br>資訊管理研究所<br>101<br>This study investigated the relation that the stock news effect on Taiwan Stock Market. Through collected the historical transaction data and stock news from July, 2012 to May, 2013, and use text mining、kNN Classification and 2-Way kNN Clustering technique analyzing the stock news, build a forecast model to analyze the degree of news effect on the stock price, and find the relation between the cluster which has great degree and the reversal points of stock price. The result shows that using the change range and Technical Indicator rise classification’s accura

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25

Sousa, Filipa Susana Caseiro de. "From Brick-and-Mortar to Click-and-Mortar : challenges and opportunities in the stationery market." Master's thesis, 2019. http://hdl.handle.net/10400.14/26954.

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Nowadays, everything happens at a fast-pace and adaptation becomes crucial for a company to survive. This is why several retailers are becoming multichannel companies by opening online stores. Firms are not only trying to maintain their market share but they are also following the fierce competition. By opening a new channel, they face challenges and opportunities that should be addressed. That’s the situation of Americana, a stationery company that is planning on opening an online store. Thus, the focus of this dissertation is to understand when to homogenize or differentiate the offline and

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26

Alves, Ana Maria da Rocha de Sousa Guedes. "Is the Iberian electricity market chaotic? Characterization and prediction with nonlinear methods." Doctoral thesis, 2013. http://hdl.handle.net/10071/6927.

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Classificação: C01, C02, C63, G17, Q41 Q47<br>Com a alteração do paradigma relativo aos sistemas eléctricos, deixando de ser regulados e passando a ser liberalizados, o estudo e a previsão de preços e de potências de carga nos sistemas eléctricos tornaram-se num novo tema de interesse para os investi- gadores. Devido às particularidades da electricidade, um mercado de electricidade tem regras muito especí cas que têm que ser compreendidas antes de se iniciar o seu estudo. Este trabalho apresenta um estudo sobre o mercado Ibérico de Electricidade, repres- entado pelas séries de potências d

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27

Januário, João Filipe Ferreira. "Electricity price forecasting utilizing machine learning in MIBEL." Master's thesis, 2019. http://hdl.handle.net/10071/20235.

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Short term electricity price forecasts have become increasingly important in the last few decades due to the rise of more competitive electricity markets throughout the globe. Accurate forecasts are now essential for market players to maximize their profits and hedge against risk, hence various forecasting methodologies have been applied to electricity price forecasting in the last few decades. This dissertation explores the main methodologies and how accurately can three popular machine learning models, SVR LSTM and XGBoost, predict prices in the Iberian market of electricity. Additionally, a

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28

Fonseka, Cicil. "Predicting the stock market to maximise returns for the small investor : a data mining approach." Thesis, 2009. http://handle.uws.edu.au:8081/1959.7/499604.

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Investors in the stock market are always interested and looking for better methods of predicting the direction of stock price movements with a view to reduce the risk of investment and to improve on the capital gains. This research introduces two algorithms to achieve this objective and evaluate their power of prediction. These algorithms are then generalised to develop a new data mining methodology in the area of "pattern recognition" to identify extreme events in a time varying and real time data stream analysis. Further a software tool is specifically developed as part of this research to p

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