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Dissertations / Theses on the topic 'Financial Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Fletcher, T. S. B. "Machine learning for financial market prediction." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1338146/.

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The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exoge

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Alhnaity, Bashar. "Financial engineering modelling using computational intelligent techniques : financial time series prediction." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13652.

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Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial

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Stulpinienė, Vaida. "Financial distress prediction model of family farms." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140123_133545-56537.

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Designed financial distress prediction model is intended directly for the farmer (decision-maker) in order to diagnose the farm’s financial condition and predict the likelihood of financial distress, by using financial information of his farm. There are identified family farm characteristics in which family farms have higher risks to run in financial distress and are guidelines for the family farms that intend to more carefully monitor and control their financial condition. The aim of the research: after analysing the conception of financial distress and identifying the factors determining the

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4

Albanis, George T. "Financial prediction using non linear classification techniques." Thesis, City University London, 2001. http://openaccess.city.ac.uk/8289/.

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In this thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural Network (PNN), Learning Vector Quanization (LVQ), Oblique Classifer (OCI), and Ripper Rule Induction (RRI).

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5

Avaritsioti, Eleni. "Financial time series prediction in the wavelet domain." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502386.

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Salina, Aigul Pazenovna. "Financial soundness of Kazakhstan banks : analysis and prediction." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3128.

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Purpose – The financial systems in many emerging countries are still impacted by the devastating effect of the 2008 financial crisis which created a massive disaster in the global economy. The banking sector needs appropriate quantitative techniques to assess its financial soundness, strengths and weaknesses. This research aims to explore, empirically assess and analyze the financial soundness of the banking sector in Kazakhstan. It also examines the prediction of financial unsoundness at an individual bank level using PCA, cluster, MDA, logit and probit analyses. Design/Methodology/Approach –

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Gottschling, Andreas Peter. "Three essays in neural networks and financial prediction /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9728773.

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8

Cutugno, Carmen. "Statistical models for the corporate financial distress prediction." Thesis, Università degli Studi di Catania, 2011. http://hdl.handle.net/10761/283.

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Chan, Ho-cheong, and 陳浩昌. "Financial ratios, discriminant analysis and the prediction of corporate financial distress in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1985. http://hub.hku.hk/bib/B31263100.

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10

Pun, Pou Kao. "Some applications of support vector machines in financial prediction." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1943000.

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11

Punsalan, Romeleo N. "Bankruptcy prediction in the construction industry: financial ratio analysis." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25711.

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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

Ghazali, Rozaida. "Higher order neural networks for financial time series prediction." Thesis, Liverpool John Moores University, 2007. http://researchonline.ljmu.ac.uk/5879/.

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Neural networks have been shown to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive to financial analysts and traders is the fact that governments and companies benefit from it to make decisions on investment and trading. However, when the number of inputs to the model and the number of training examples becomes extremely large, the training procedure

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14

da, Costa Joel. "Online Non-linear Prediction of Financial Time Series Patterns." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32221.

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We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learnin

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Prasad, Jayan Ganesh Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Financial forecasting using artificial neural networks." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2008. http://handle.unsw.edu.au/1959.4/38700.

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Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network predict

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Campbell, Alyce. "An empirical study of a financial signalling model." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26969.

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Brennan and Kraus (1982,1986) developed a costless signalling model which can explain why managers issue hybrid securities—convertibles(CB's) or bond-warrant packages(BW's). The model predicts that when the true standard deviation (σ) of the distribution of future firm value is unknown to the market, the firm's managers will issue a hybrid with specific characteristics such that the security's full information value is at a minimum at the firm's true σ. In this fully revealing equilibrium market price is equal to this minimum value. In this study, first the mathematical properties of the hypo

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Wen, Tsou Hui, and 鄒惠雯. "Financial Distress Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/13836405838104904375.

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碩士<br>健行科技大學<br>國際企業管理研究所<br>101<br>In this study, logical construct financial distress logistic regression model for the study period from 2007 to 2011, the Hong Kong enterprises as the research object, assess Hong Kong''s corporate financial variables on the early warning model predictive ability; empirical results show that the financial ratio variables debt and total asset turnover ratio greater impact on the enterprise; insufficient if the company''s profitability, debt ratio is higher, but will cause cash flow problems of the situation, the enterprise is the higher th

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Qian, Bo. "Intelligent financial market prediction." 2006. http://purl.galileo.usg.edu/uga%5Fetd/qian%5Fbo%5F200608%5Fphd.

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Lin, Sheng-Hui, and 林昇暉. "Financial Product Tendency Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/b3jy2u.

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碩士<br>國立中央大學<br>資訊工程學系在職專班<br>107<br>In Capitalism, investment is a method to earn more money. There are lots of financial products to let humans invest. By financial theory, people create couples strategy to make invest decision. Including technical analysis, fundamental analysis, chip analysis and news analysis. The technical analysis is based on daily price and volume to predict future tendency to figure out appropriate buy or sell timing. There are a large amount of financial products. People who are not an expert can’t analyze all products in a short time. This paper is focus on training

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Kao, Wei-Bo, and 高偉柏. "Prediction of Corporate Financial Distress." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/70643773063389329545.

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Chnag, Yu-Hsin, and 張玉欣. "Fair Value Accounting and the Predictive Ability of Financial Crisis- Two Different Financial Crisis Prediction Models." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/55601661361531336728.

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碩士<br>國立高雄應用科技大學<br>商務經營研究所<br>99<br>The purpose of this study is to explore the effect of financial statement data on the predictive ability of financial crisis after implementing the Statement Financial Accounting Standard (SFAS) No.34-Fair Value Accounting. Using hazard model and Z-score from 2004 to 2009, this paper examines the relationship between financial statement data and the predictive ability of financial crisis during three periods, that is, historical cost accounting, fair value accounting, and revised fair value accounting. I define 2004 to 2005 which SFAS No. 27 is implemented

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Hsiao, Ching-Han, and 蕭清漢. "Financial Distress Prediction Using Data Mining." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/79449758562654212379.

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碩士<br>中國文化大學<br>會計學系<br>104<br>To predict the financial crisis of enterprise in the past using traditional regression models, many scholars have been using data mining method of forecasting enterprises financial crisis. The accuracy has improved, but it is incomplete in the literature. This study uses data mining to forecast a business's financial crisis. The data is from TEJ during year 2004 to 2014. In the first stage, we using neural network, stepwise regres-sion and decision tree C5.0. In the second stage we combine with two artificial intelli-gence algorithms “ decision tree CHAID ”, and

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Jan, Yitzung, and 詹益宗. "Comparison Between Financial Distress Prediction Models." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/86423538258426182202.

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碩士<br>國立交通大學<br>財務金融研究所<br>94<br>Based on the data of Taiwan corporations trading in TSE and OTC, this study used financial accounting variables and market variables to construct financial distress prediction models, such as Logit model, MDA model and discrete-time hazard model. With such methodology, I examined whether the added-in market variables could enhance the model’s discrimination ability and predicting capability or not, furthermore, I compared the accuracy of three statistical models. This study classified the variables into four categories, which are financial accounting variable g

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Sera, Roxana. "Financial distress prediction for portuguese SMEs." Master's thesis, 2020. http://hdl.handle.net/1822/69982.

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Dissertação de mestrado em Finance<br>In Portugal, small and medium-sized enterprises (SMEs) represent 99.9% of the total number of companies and are key generators of employment and contributors to the country`s economy. Given their key role and the fact that their main source of funding comes from financial institutions, it is vital that they have easy access to diversified financing instruments as well as the capacity of presenting their activity and results in an efficient way in order to gain access to them. In this context, a way of interpreting the information available about a com

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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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Lu, i.-shan, and 呂宜珊. "Wavelet-based prediction of financial data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/69675921817104331020.

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碩士<br>國立臺北大學<br>統計學系<br>98<br>Regarding to the model building of financial products, many researchers applied regression method to build time series model for analysis and prediction. The regression model of time series data developed from the earliest Autoregressive model (AR model) to present the GARCH family. The ARIMA/GARCH family has been developed very large and complex. The regression models of time series data have to be built under the data being stationary. Since financial time series data are usually non-stationary, the original data need to be transformed to meet the stationary

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賴士詮. "Prediction of financial distress with text mining and financial indicators." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r37695.

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碩士<br>國立政治大學<br>資訊管理學系<br>106<br>The financial crisis of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss, and that could also lead to the chaos of financial environment. It is important to establish an effective early warning system for prediction of financial crisis. The early warning system can detect the financial deterioration of the company earlier and find the company which have potential crisis. It also can prevent and decrease the harm in the international financial markets. Financial annua

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Min-HsienTsou and 鄒明憲. "The Prediction of Financial Distress with Macroeconomic Variables, Financial and Non-financial Factors." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/25234937270291369652.

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碩士<br>國立成功大學<br>財務金融研究所碩士在職專班<br>103<br>Using sample data of TSEC-listed and OTC-listed companies between 2001 and 2010, this study develops a financial distress forecasting model on a logit base. Different from most previous studies, besides six financial variables, this study adds in seven non-financial variables that are more difficult to be quantified and four macroeconomic variables for improving the forecasting capacity of the model. Moreover, in contrast to the conventional use of 0.5 as the financial distress critical value, this study chooses the critical probability based on the pr

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Lu, Shu-Hui, and 盧淑惠. "A Study on Financial Crisis Prediction Model Using Non-Financial Factors." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/a2a5fs.

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碩士<br>國立臺北科技大學<br>高階管理碩士雙聯學位學程<br>106<br>This paper mainly uses public information of non-financial factors to assess the companys financial crisis. It attempts to utilize readily available public information so that the general public can use it to predict the companys situation. In view of the fact that most of the traditional financial crisis prediction models are based on financial data, it is not always easy for the general public to fully understand the data and contents of the financial statements. This paper selects the defaulters defined by Taiwan Corporate Credit Risk Index (TCRI) in

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Chang, Chia-Ting, and 張嘉婷. "FINANCIAL DISTRESS PREDICTION MODELBASED ON GREYART NETWORK." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/13956687491860418786.

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碩士<br>大同大學<br>事業經營學系(所)<br>93<br>This study attempts to use the GreyART network to construct a financial distress prediction model. The inputs applied to the GreyART network are the historical data containing 18 different financial ratios. They are collected from the Taiwan Economic Journal Data Bank covering the period 2000-03. In order to determine the best result the proposed method can attain, a new performance index defined as the product of the partition quality and the classification accuracy is proposed to solve this problem. The best results achieve the classification hit rates of

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Wu, Chao-hui, and 吳昭慧. "On Multiple Classifiers to Financial Distress Prediction." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/49529988765091549433.

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碩士<br>國立中央大學<br>軟體工程研究所<br>99<br>How to effectively predict financial distress is an important issue in corporate financial management. We use data mining and machine learning methodology to analysis financial statement or financial ratio. Traditional approaches usually formalize financial prediction problem as two-class problem, attempting to differentiate the financially distressed companies (the distressed class) from the normal companies (the non-distressed class). However, there are many factors contributing to a company’s financial crisis. Taiwan Stock Exchange Corporation (TWSE) defines

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Tsai, Mei-Ling, and 蔡美玲. "Exploring Financial Distress Prediction of Steel Industry." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/49554844947632606716.

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碩士<br>國立臺灣師範大學<br>高階經理人企業管理碩士在職專班(EMBA)<br>104<br>In recent years, Taiwan’s iron and steel industry has gradually lost its competitive advantage in production technology and prices because of the significant growth of technology and price-cutting strategy in China’s steel industry. In addition, the steel market has been declining over the past few years as a result of the large oversupply of steel that pressured prices downwards. The global recession that hit the commodities sectors hard also contributed to the shrinking growth in steel industry. Thus, the profits of the steel industry in Taiw

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Hung, Min-Yu, and 洪旻郁. "When Will Financial Distress Prediction Models Fail?" Thesis, 2009. http://ndltd.ncl.edu.tw/handle/60003156094196645699.

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碩士<br>臺灣大學<br>財務金融學研究所<br>98<br>he thesis investigates reasons behind failed distressed prediction models. Since variables of models reflect the current status of a company’s operating and financing situation. The quality of inputted information is quite influential for an effective distressed model. We compare our defined best accounting and market distressed forecasting models under five hypotheses to see the relative performance of both models. Our defined best accounting and market distressed prediction models have reached statistical significant in forecasting default. Particularly, the m

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KUO, CHIEN-CHIH, and 郭建志. "Bivariate Density Prediction for Financial Asset Prices." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/02716982279725150029.

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碩士<br>國立中央大學<br>財務金融研究所<br>95<br>Option prices provide a rich source of information for estimating risk-neutral world densities. This paper exploits lognormal mixture distribution and generalized beta distribution to forecast asset price risk-neutral probability distribution when options expire. The power utility function is used to estimate the risk aversion parameter, and transform the risk-neutral world density into the real world density. After completing transformation, four kinds of copula functions, including Gassian copula, Frank copula, Gumbel copula and Clayton copula are used to com

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Lin, Pei-Chien, and 林珮阡. "Corporate Governance and Financial Distress Prediction Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/50528742452250301843.

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碩士<br>國立高雄大學<br>金融管理學系碩士班<br>98<br>To financial institutions, measuring and managing credit risk has always been an important issue. Credit risk has been not only a major factor of making financial decision for financial institutions, but also for in the management of firms. Measuring the credit risk of a firm could detect the financial problems earlier and it is helpful to prevent from financial distress in advance. Previous literatures focused on the financial ratio analysis, but the effect of “window dressing” makes the analysis unreliable. Furthermore, Taiwanese government is promoting the

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Yang, Hia-Hwa, and 楊嘉華. "The research of corporate financial distress prediction." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/08932967134883967346.

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碩士<br>國立高雄第一科技大學<br>財務管理研究所<br>100<br>A series of financial distress have broken out in our country since 1998. The reason was partly a continuously expansive effect of Asian Financial Crisis.Many firm stocks had damaged by opaque finance and loose internal control.As a result, some companies are still fighting for survival by extending loans again and again, consequently. The overdue loan ratio of financial institutes keeps rising. Affected by the shocking economic cycle, company credit deteriorates, and banks do not approve loans easily even with idle money. This kind of vicious cycle only m

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YANG, SHUN-HSIUNG, and 楊順雄. "Financial distress prediction based on multiple Classifiers." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/63937393583473674577.

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碩士<br>國立中央大學<br>資訊工程學系<br>101<br>In recent years, many economic entities have suffered great loss or even become bankrupt due to the breakout of global financial crisis. Financial distress prediction (FDP) has been widely studding. In the field of corporate finance, to predict financial distress effectively is an important issue in corporate financial management. Recently, some studies which take advantages of multiple classifiers combination to solve FDP problem have been noticed. This paper proposed a FDP method with increased efficiency based on parallel combination of multiple classifiers.

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Chou, Tsu-Yueh, and 周祖玥. "A Multi-Stage Financial Crisis Prediction Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/53433238739307210752.

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碩士<br>中國文化大學<br>會計學系<br>101<br>Financial crisis forecasting is an essential and widely researches domains since it can have considerable effect on inner and outside parts of companies. How to effectively predict financial crisis is an important task in accounting, finance and management. Though much attention has been paid to financial crisis forecasting approaches based on singular classifier, its limitation of uncertainty and advantage of hybrid mechanism for financial crisis forecasting has also been ignored. Inspired by ensemble learning, the study introduced an emerging hybrid mechanism w

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Dai, AN-jie, and 戴安傑. "Financial crisis prediction based on OR ensemble." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/06295521717539519268.

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碩士<br>國立中央大學<br>資訊工程學系<br>102<br>Financial crisis problem has been important and widely studied topic. Financial crisis prediction is receiving increasing attention of stakeholders and researchers in the worldwide. If you want to predict accurately, choose a good prediction model is very important, from the literature can be found , in the common data set, there is no consistant conclusion of the prediction model for us to solve the financial crisis prediction problem, and recent studies have shown that ensemble learning is good and stable than a single classifier, thus, this study base on ens

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Chen, Shin-ho, and 陳信何. "The research of corporate financial distress prediction." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/7j8au9.

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碩士<br>國立中山大學<br>高階經營碩士班<br>97<br>The research of corporate financial distress prediction model is always one of the important topics in financial management; and mostly people do the research and extract sample companies based on the definition for corporate default by Taiwan Economic Journal. However, we think the timing to observe the potential corporate financial distress is extremely vital; the actual benefit will not be good even with high accuracy if relevant counterparties recognize it too late to undertake certain action for mitigating loss. The main purpose of this study is trying to

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Po-HsunHsu and 許伯勳. "The Research of Financial Distress Prediction Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/a37sn7.

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碩士<br>國立成功大學<br>財務金融研究所<br>107<br>The impact of financial distress is quite extensive. In order to foresee the precursor of financial distress firms, this study is going to construct a model with different types of variables and is applicable for all industries. Investors and banks can avoid the firms with potential risk of financial distress with this prediction model. This study will discuss whether employing different types variables will increase the prediction power of the model and decrease the type II error. The variables employed by this study are the accounting-based, corporate govern

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Tsai, Chang-Yu, and 蔡長裕. "A Study on Measuring Corporate Financial Crisis Prediction Models by Financial Rations." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/50569141983313419480.

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碩士<br>國立臺北大學<br>國際財務金融碩士在職專班<br>98<br>In this study, 36 domestic publicly listed & OTC’s listed companies which suffered from financial crisis in 2003 to 2007 are selected. We do the matching comparison by the total assets of the companies with these randomized, 1:2, non-discrimination industries. A total of 108 companies are selected as the research sample, and 16 financial ratio variables, TCRI related literatures which bank’s credit practice common-used as research variables. With applying several variables discrimination analyses, variables are screened out for non-discriminated variab

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Li, Jenn Shyang, and 李振祥. "Establishing the Prediction Models Of Firms' Financial Crisis-A Financial Analysis Viewpoint." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/07346390314289302230.

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碩士<br>國立臺灣海洋大學<br>航運管理學系<br>93<br>Abstract A firm’s financial crisis affects not only employees’ job insecurity but also tremendous impacts on the stability of financial market and social system. Under such circumstance, the purpose of this research is to set up a prediction model to effectively detect a firm’s financial crisis in advance. By reviewing related papers and statistics, we concluded that financial analysis were useful tool to distinguish a financial crisis company from a good one. This research started from screening powerful financial indexes to form the prediction model, 36 fi

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Chang, Ching-Lan, and 張清蘭. "Corporate Governance and Financial Crisis Prediction Model - An Example of Financial Industry." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/33908777668843558929.

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碩士<br>國立屏東科技大學<br>企業管理系所<br>100<br>Previous studies always use financial ratios to construct financial distress warning models.However, the accounting numbers may be modified, which will reduce the accuracy of the information. In recent years, many enterprises face crises. The academics and practitioners are believed that strengthening corporate governance is the best solution. Therefore, the study incorporates the corporate governance variables to construct a better financial crisis prediction model. This study uses data from 2006 to 2010 drawn from the Taiwan Economic Journal (TEJ) database.

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Chen, Sheng-Hsiang, and 陳生祥. "Exploit Data Mining Techniques to build Financial Crisis Prediction Models – Using Financial and Non Financial Indicators." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/38099095699463193881.

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碩士<br>中原大學<br>資訊管理研究所<br>93<br>Enterprise operating status will be disclosed periodically on financial statement and investors can get fully information once the formal financial statement is disclosed and published. If exectives of firms intentionally dress financial statements up , investors can not get real enterprise operating status from it. However, non-financial information was proved to predict financial distress by former researchers. But few studies exploit stock and credit ranking information to construct financial crisis prediction model. The study uses financial and non-financ

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Chen, Heng-Yu, and 陳恒裕. "Preference disaggregation for the prediction of financial distress." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/44579970396154610899.

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碩士<br>真理大學<br>管理科學研究所<br>89<br>The purpose of this paper is to establish the financial distress prediction system of publicly traded Taiwan firms by employing Zopounidis and Doumpos’s (1997) UTADIS method. The results show that current ratio and quick ratio, cash flow ratio, total asset turnover and return on assets are the most important variables to provide significantly discrimination ability. Besides, the debt ratio and long-term capital ratio in our prediction system provide the less discrimination ability. In addition, under the base sample the classified accuracy in last three years are

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Tsai, Yu-Keng, and 蔡毓耕. "Applying Classifier Systems in Financial Distress Prediction Modeling." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/34110774453482624829.

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碩士<br>國立交通大學<br>資訊管理研究所<br>92<br>The prediction of financial distress is an important and active topic since it is critical to all stakeholders both internal and external to the company. Earlier studies of financial distress prediction used statistical approaches such as multiple discriminant analysis, logistic regression and probit model. Recently, however, several studies have demonstrated that artificial intelligence methodology such as neural networks (NNs), has the superior abilities on classification problems. Even though some of the studies using NNs to the prediction of financial distr

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Lee, Ming-Jen, and 李明仁. "A Study on Corporate financial crisis prediction models." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/btzxc9.

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碩士<br>世新大學<br>財務金融學研究所(含碩專班)<br>96<br>After Beaver, Altman proposed corporate financial crisis prediction models, it was been study with human conern all the time, specially on parameter and method. This topic study on corporate financial crisis prediction models about forecast ability, stability, and usable value. We have three subjects in this research, （1）Using stock price target-EPS into discriminant analysis, logistic regression, cascaded logistic regression, back-propagation neural network, attempt to find out the models with better forecast ability.（2）Taking different proportion of samp

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Wang, Chuan-Ying, and 王傳英. "Financial Distress Prediction Model─ Constructed by Industry Samples." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/e93dxd.

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碩士<br>國立臺灣大學<br>國際企業學研究所<br>103<br>Company’s financial distress has been an important issue for decades because it is a huge cost for no matter corporations or nation’s economy. For solving this problem, previous scholars tried to find out a pattern to predict financial distress’s coming, for investors, to avoid the losses, for business owners, to avoid bankruptcy. In terms of prediction variables in financial distress prediction model, studies from accounting based variables, market based variables to macro and non-financial factors. As for methodology in modeling, researches form descriptive

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Yu, Chun-Yuan, and 游濬遠. "An Integrated Approach for Corporate Financial Failure Prediction." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/8tpec2.

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博士<br>國立交通大學<br>經營管理研究所<br>103<br>Predicting financial failure of a company is important for an investor or a creditor because financial failure without any symptom not only results in banks’ bad debts, but also causes huge losses to investors. Although there exist many models for predicting financial failure, their performance over a period of two or more years is poor. Therefore, the purpose of the present study is to propose an integrated approach by which a relatively higher accuracy rate, regardless of short term or long term prediction, can be obtained and the dynamic change of a company

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