The master's thesis is focused on Stock portfolio optimalization on Czech capital market. The analysis of each stock, estimation and portfolio optimalization proposal are included. In the practical part the Fundamental analysis is applied. The portfolio optimalization is estemated by portfolio theory which is consist in the relationship between stock price and market trends represents by PX Index and expressing correlation of them by beta coefficient.

There are multiple ways to analyze stock companies. One way is using fundamental analysis, which means one is analyzing the company’s business key figures, such as revenue, net income and more. The key figures that were chosen to be analyzed in this report were: revenue, net income, free cash f low, return on invested capital and debt-to-equity ratio. In this thesis, machine learning was implemented to evaluate if it is possible to automate fundamental research of companies and to be able to produce a portfolio that would outperform the Swedish stock index. The data set used for both training and testing the classifiers contained the company’s basic information, 10 years of fundamental history and stock price history from the past 10 years. The companies examined were every stock listed on Nasdaq Stockholm, Nasdaq First North, Spotlight Market, Nordic Growth Market and PepMarket. The data that was gathered stretches from 2012 to 2021 which were split up into f ive-year periods and made up the training and testing period. The training data contained fundamental history from every company from these five-year periods. The classifier’s results from the testing period were used to create the portfolios during the holding period 2021-2022 to benchmark against the Swedish stock index. The results indicate that it is indeed possible to create portfolios using machine learning that will outperform the market over a year of holding the stocks. / Det finns många olika sätt att analysera aktieföretag. Ett sätt är att använda fundamental analys, vilket innebär att man analyserar företagets nyckeltal, såsom omsättning, årets resultat, etc. Nyckeltalen som har valts att analyseras i denna studie var: omsättning, årets resultat, fritt kassaflöde, avkastning på investerat kapital samt skuldsättningsgrad. I denna avhandling har maskininlärning implementerats för att undersöka om det är möjligt att automatisera fundamental forskning av företag och skapa en portfölj som ger bättre avkastning än svenska aktieindex. Data som används för både träning och testning av klassificerare innehöll företagens grundläggande information, 10 år av fundamental historik samt aktiekurs historik för dem senaste 10 åren. Företagen som undersöktes var varje aktie listad på Nasdaq Stockholm, Nasdaq First North, Spotlight Market, Nordic Growth Market och PepMarket. Data som har samlats sträcker sig från 2012 till 2021 och var uppdelade i femårsperioder till träning och testning. Träningsdata innehöll fundamental historik från varje företag från dessa femårsperioder. Resultaten från klassificerare från testning perioden användes för att skapa portföljer under 20212022 som jämfördes med svenska aktieindex. Resultaten från detta indikerar att det är möjligt att skapa portföljer med hjälp av maskininlärningsmetoder som kan ge bättre avkastning än svenska aktieindex över en innehavsperiod på ett år.

The thesis deals with the assessing of investment opportunity in the field of renewable natural resources. Fundamental analysis is carried out on First Solar which is a company producing solar modules. Thus, the work is focused on the area of photovoltaics. To determine the company's value, the method of discounted cash flow is used. The overall situation of the company is analyzed by the implementation of global, industry and company analysis.

First chapter describes International Financial Markets. Second chapter describes market fundamental analysis. Third chapter describes market technical analysis and efficient market hypothesis testing. Fourth chapter discusses market psychological analysis. Fifth chapter encompasses other theoretical background to be used in application. Sixth chapter deals with application of fundamental and technical analysis on a tennis betting market having found parallels between the sports betting markets and financial markets.

Este trabalho apresenta um método de predição não linear de séries temporais econômicas. O método baseia-se na análise técnica e fundamentalista de cotação de ações, filtragem wavelet, seleção de padrões e redes neurais artificiais. No modelo técnico emprega-se a transformada wavelet para filtrar a série temporal econômica de comportamentos aleatórios ou não econômicos. Após a filtragem dos dados o algoritmo de projeções sucessivas é utilizado para a seleção de padrões de treinamento para a rede neural artificial, com o objetivo de selecionar os padrões de comportamento mais importantes na série. No modelo fundamentalista utiliza-se variáveis econômicas que podem estar correlacionadas com a série, com o objetivo de aprimorar a predição da série na rede neural artificial. Para avaliação do método são utilizados dados de séries temporais econômicas referentes à cotação de preços de ações negociadas na bolsa de valores de São Paulo, onde os resultados da predição do comportamento futuro são comparados com modelos matemáticos clássicos e com o modelo convencional, que se baseia somente na análise técnica. Apresenta-se uma comparação dos resultados entre modelos técnicos, modelos matemáticos e o método proposto. O modelo matemático utilizado (ARIMA) apresentou seu melhor desempenho em séries com pouca variância, porém com desempenho inferior quando comparado com o modelo técnico e com o método proposto. A avaliação do erro de predição em termos de RMSEP evidenciou que o método proposto apresenta os melhores resultados em relação aos demais métodos. / This work presents a method for predicting nonlinear economic time series. The method is based on fundamental and technical analysis of script quotation, a multiscale wavelet filtering, pattern selection and artificial neural networks. In the technical model is used the wavelet transform in order to filter the economic time series from random or not economic behaviors. After the data filtering, the successive projections algorithm was used for the training pattern selection to the artificial neural network. In the fundamentalist model is used financial and macroeconomics variables that is correlated with the time serie in order to improve the network forecasting. For the evaluation of the proposed method are used temporal series data related to scrips prices quotation of São Paulo stock market. It presents a comparison of the results between technical model, mathematical model and proposed method. The mathematical model (ARIMA) presented better results in series with few variance, however have low performance when compared with the technical model and with the proposed method. The prediction error evaluation shows that the proposed method has better results than the other methods.

Este trabalho apresenta um método de predição não linear de séries temporais econômicas. O método baseia-se na análise técnica e fundamentalista de cotação de ações, filtragem wavelet, seleção de padrões e redes neurais artificiais. No modelo técnico emprega-se a transformada wavelet para filtrar a série temporal econômica de comportamentos aleatórios ou não econômicos. Após a filtragem dos dados o algoritmo de projeções sucessivas é utilizado para a seleção de padrões de treinamento para a rede neural artificial, com o objetivo de selecionar os padrões de comportamento mais importantes na série. No modelo fundamentalista utiliza-se variáveis econômicas que podem estar correlacionadas com a série, com o objetivo de aprimorar a predição da série na rede neural artificial. Para avaliação do método são utilizados dados de séries temporais econômicas referentes à cotação de preços de ações negociadas na bolsa de valores de São Paulo, onde os resultados da predição do comportamento futuro são comparados com modelos matemáticos clássicos e com o modelo convencional, que se baseia somente na análise técnica. Apresenta-se uma comparação dos resultados entre modelos técnicos, modelos matemáticos e o método proposto. O modelo matemático utilizado (ARIMA) apresentou seu melhor desempenho em séries com pouca variância, porém com desempenho inferior quando comparado com o modelo técnico e com o método proposto. A avaliação do erro de predição em termos de RMSEP evidenciou que o método proposto apresenta os melhores resultados em relação aos demais métodos. / This work presents a method for predicting nonlinear economic time series. The method is based on fundamental and technical analysis of script quotation, a multiscale wavelet filtering, pattern selection and artificial neural networks. In the technical model is used the wavelet transform in order to filter the economic time series from random or not economic behaviors. After the data filtering, the successive projections algorithm was used for the training pattern selection to the artificial neural network. In the fundamentalist model is used financial and macroeconomics variables that is correlated with the time serie in order to improve the network forecasting. For the evaluation of the proposed method are used temporal series data related to scrips prices quotation of São Paulo stock market. It presents a comparison of the results between technical model, mathematical model and proposed method. The mathematical model (ARIMA) presented better results in series with few variance, however have low performance when compared with the technical model and with the proposed method. The prediction error evaluation shows that the proposed method has better results than the other methods.

Este trabalho investiga a utilidade e as limitações de estratégias de investimento em ações baseadas na análise de demonstrações contábeis. Inicialmente a avaliação é realizada para o conjunto total de empresas listadas na Bovespa. Na seqüência, restringe-se a investigação para os subconjuntos de empresas com alto índice PL/P (proxy de risco) ou/e com baixo BCGI (proxy de governança). De acordo com evidências apresentadas na literatura de contabilidade e finanças conjectura-se que estes grupos possuem características para que as estratégias de investimento baseadas na análise de demonstrações contábeis sejam mais úteis para a discriminação de boas e más oportunidades de investimento. As evidências encontradas apontam para uma maior utilidade da análise de balanços para a seleção de uma carteira de investimento em ações no grupo de empresas com alto PL/P e/ou baixo BCGI. Adicionalmente, incorporam-se nas análises econométricas os fatores de risco que poderiam ter relação com os resultados encontrados. Demonstra-se que a implementação da estratégia é mais realista (em termos de volume financeiro negociado das ações) para o grupo de empresas com baixo BCGI. Entretanto, há uma redução dos retornos obtidos com essa estratégia - selecionar empresas fortes financeiramente dentro do grupo de empresas com baixo BCGI - em relação à estratégia de seleção de empresas com alto PL/P e com bons indicadores financeiros. Esse resultado é consistente com a relação teórica negativa esperada entre liquidez e retorno e contribuí com a literatura para a explicação da obtenção de retornos anormais com estratégias de investimento baseadas na utilização de análise de demonstrações contábeis. / This thesis investigates the usefulness and limitations of investment strategies based on financial statement analysis. Initially I assess the usefulness of the strategy for the full sample of Brazilian public-held firms. An additional analysis considers the partition of high book-to-market (HBM) or/and poor corporate governance (CG) firms. Capital markets research in accounting and finance show evidences that permit one to posit that firms within these groups (HBM and/or poor CG) present specific features that should enhance the usefulness of financial statement analysis as an investment tool. I find evidences that the analyzed strategies significantly differentiate between winners and losers for both groups (HBM and poor CG) but not for the full sample of firms. These results confirm and expand Piotroski\'s (2000) evidences. Further I consider the possible omitted risk-factors that could explain the results obtained. I show that the practical implementation of the strategy is more realistic (regarding stock\'s trading volume) if applied for firms with poor corporate governance arrangements when compared to the HBM ones. However, the strategy returns are lower when applied to the subset of poor corporate governance firms. This evidence is consistent with the negative expected relation between liquidity and returns (Bekaert et al, 2006) and contributes to previous research (Piotroski, 2000; Mohanran, 2005) on abnormal returns obtained with financial statements analysis.

The diploma thesis is focused on testing of effectiveness of selected methods of fundamental analysis on a sample of Russian stocks. The introductory part lists briefly different ways of describing changes of stock prices. The second chapter explains key principles of fundamental analysis with focus on models which are used for determination of intrinsic value of stocks, and the necessary inputs. The following chapter describes development of Russian stock market in last two decades. The last chapter includes stock evaluation of nine Russian companies at the end of 2007 via DCF equity model and dividend discount models. Effectiveness of this analysis is then evaluated by comparison of particular stock return with return of the market portfolio represented by the MICEX index during the three-year time.

<p><strong>Datum</strong>: 2010-06-03</p><p><strong>Kurs</strong>: Kandidatuppsats i Företagsekonomi</p><p><strong>Lärosäte</strong>: Mälardalens högskola, Västerås</p><p><strong>Institution</strong>: Akademin för hållbar samhälls- och teknikutveckling</p><p><strong>Titel</strong>: Företagsvärdering med Fundamental analys</p><p><strong>Författare</strong>: Sebastian Järvinen och Slavisa Stupic</p><p><strong>Handledare</strong>: Riitta Lehtisalo</p><p><strong>Examinator</strong>: Cecilia Lindh</p><p><strong>Frågeställningar</strong>: Vilka värderingsmetoder och modeller är mest lämpliga för en värdering av Eolus Vind AB?</p><p>Är Eolus aktie över- eller undervärderad?</p><p>Vilken påverkan har avvikelser gentemot prognos i värderingen av Eolus?</p><p><strong>Syfte</strong>: Syftet med studien är att utföra en företagsvärdering av Eolus för att fastställa om dess aktie är över- eller undervärderad samt behandla osäkerheten kring en värdering.</p><p><strong>Metod</strong>: I studien har en deduktiv metod tillämpats då vi valt att utgå från redan befintlig teori. Vidare har kvalitativa och kvantitativa metoder tillämpats för att få en djupare förståelse för hur värderingsmetoderna och modellerna används i praktiken som sedan ligger till grund för värderingen av Eolus.</p><p><strong>Slutsats</strong>: Lämpligaste metoden för en värdering av Eolus är en så kallad DCF analys, där en fundamental analys ligger till grund för de prognostiserade antaganden. Det innebär att bolaget, marknaden och dess bransch studeras för att sedan ligga till grund för de prognoser och antaganden som krävs i en DCF analys. En multipelvärdering är att föredra då den anses göra DCF analysen komplett. Enligt våra prognoser, antaganden och beräkningar anser vi Eolus aktie vara undervärderad gentemot dagens kurs.</p><p><strong>Nyckeltal</strong>: Företagsvärdering, Fundamental analys, Eolus Vind AB, DCF, CAPM, WACC, multipelvärdering</p> / <p><strong>Date</strong>: 2010-06-03</p><p><strong>Subject</strong>: Bachelor Thesis in Business Administration</p><p><strong>University</strong>: Mälardalen University, Vasteras</p><p><strong>Institute</strong>: School of Sustainable Development of Society and Technology</p><p><strong>Title</strong>: Business valuation with Fundamental analysis</p><p><strong>Authors</strong>: Sebastian Järvinen and Slavisa Stupic</p><p><strong>Tutor</strong>: Riitta Lehtisalo</p><p><strong>Examiner</strong>: Cecilia Lindh</p><p><strong>Questions</strong>: What valuation methods and models are most appropriate for the valuation of Eolus Vind AB?</p><p>Is Eolus share over- or undervalued?</p><p>What influence do the deviations in the prognosis have against the forecasted valuation of Eolus?</p><p><strong>Purpose</strong>: The purpose of this study is to perform a Business valuation of Eolus and determine it its share is over- or undervalued. The study will also deal with the uncertainty in the valuation.</p><p><strong>Method</strong>: The study has a deductive approach, which says that the starting point lies in a existing theory. Furthermore, qualitative and quantitative methods are used to obtain a deeper understanding of how valuation methods and models are used in the practice, which t hen forms the basis of a valuation of Eolus.</p><p><strong>Conclusion</strong>: The most appropriate method for a valuation of Eolus is a DCF analysis, where the first step is to do a fundamental analysis, which involves a study of the company, its industry and the market to form a basis for the forecasts, and assumptions that are required in a DCF analysis. A relative valuation is a preferable combination with the DCF analysis. According to our projections, assumptions and estimates we believe that Eolus share is undervalued in relation to today’s share price.</p><p><strong>Keywords</strong>: Business Valuation, Fundamental analysis, Eolus Vind AB, DCF, CAPM, WACC, relative valuation</p>

På vilket sätt är redovisningsmått som publiceras i företagens årsredovisningar relevanta för att bestämma aktievärden? Kan redovisningsmått användas för att utforma lönsamma placeringsstrategier i aktier? Frågor som dessa är av intresse för såväl akademiker som professionellt verksamma placerare.  I denna avhandling utreds huruvida publicerade redovisningsmått är värderelevanta, i betydelsen att de kan användas för prognoser av företagens framtida räntabilitet på eget kapital. Statistiska modeller för prognos av räntabilitet på eget kapital med hjälp av redovisningsbaserade nyckeltal har estimerats och utvärderats. Det empiriska datamaterialet har utgjorts av årsredovisningar för svenska rörelsedrivande företag under perioden 1970-1985.  Vidare studeras om placeringsstrategier baserade på prognoser av framtida räntabilitet på eget kapital kan ge en aktieavkastning utöver vad som motiveras av placeringens risk. I denna del av studien prövas huruvida den svenska aktiemarknaden är informationseffektiv med avseende på offentligt tillgänglig årsredovisningsinformation. Placeringstrategier har utvärderats på aktier som fanns noterade på Stockholms fondbörs under perioden 1983-1994.  Den empiriska kartläggningen indikerar att redovisningsmått kan användas för att prognostisera framtida räntabilitet på eget kapital och att placeringsstrategier baserade på offentligt tillgänglig årsredovisningsinformation har genererat en avkastning utöver vad som motiveras av olika mått på placeringsrisk. I studien observeras dock betydande tidsmässiga instabiliteter beträffande såväl möjligheterna att prognostisera framtida räntabilitet på eget kapital, som förekomsten av avkastning utöver vad som motiveras av placeringsrisk. / <p>Diss. Stockholm : Handelshögskolan, 2002</p>