財務報表記錄可說是企業經營績效良窳的反映指標，而其中所衍生出來的財務比率，向 來均是管理者、投資者進行企業診斷或未來經營績效預測的重要資訊來源。然而，相關 的研究發現，由於產業間經濟環境與市場結構特性的不同，所呈現出來的財務報表資訊 內涵亦將有所差別。因此，若進一步運用個別產業之報表資訊預測公司未來盈餘時，將 能夠提供產業間結果進行分析與比較的基礎。 如何自報表中獲取與公司經營績效相關之會計資訊，進而建構出優良的盈餘預測模式， 是近幾年來學者感興趣的研究課題之一。鑑於人工智慧之類神經網路系統擁有多項的特點，因此，對於盈餘預測會計資訊萃取的應用上，無非是提供了我們一個新的選擇途徑。 本研究即根據此項概念，以民國70年第一季至民國82年第三季為止共十五項大小產業之 股票上市公司財務報表以及股價報酬等資料作為研究樣本，進行盈餘預測模式的建構以 及投資超額報酬的計算。 進一步地說，本研究的內容可以分成三個部份，第一部份是以整體市場樣本為例，對類 神經網路主要參數如輸入變數組合、隱藏層節點數等進行調整及測試，以從中選取出盈 餘預測效果較佳之模式設定；在第二部份則是運用此一盈餘預測模式，分別對整體市場 以及紡織、電子電機 兩項產業樣本進行網路的訓練與測試，並根據模式所獲得之區別及 預測能力評估指標，探討不同產業特性樣本所建構的模式之間，其預測結果上的差異性 ；而第三部份則是利用各類產業模式預測結果的資訊，從利潤與風險兩種角度，定義"總 體"、"高利潤"、"低風險"、 "高利潤低風險"等四種不同類型投資策略，並以事件研究 法計算各項策略所能獲取之累積超額報酬，最後，則根據各策略之獲利績效，進行產業 間的分析比較，以找出本研究各類特定產業之最適投資策略。 本研究根據前述方式所進行的實驗研究中，獲得了以下三點結論： 一、類神經網路盈餘預測模式之建構 (一)以整體市場樣本為對象所進行之網路的測試中，發現模式整體區別能力大致介於五 到七成之間；而整體預測能力則介於四到六成之間。 (二)本研究所找出盈餘預測效果較佳之網路模式設定如下：1.輸入變數組合：單因子多變量變異數分析之22項顯著性財務比率 2.網路架構(輸入層-隱藏層-輸出層)：22-22-1 3.連結權數初始值設定範圍：-0.1~0.1 二、產業盈餘預測結果之分析 (一)整體而言，產業間模式測試結果的差異並不大，其中以紡織產業的模式區別及預測 能力最好(70%以上)，電子電機產業次之，而整體市場模式的結果均不及兩項單一性產業。 (二)模式預測能力穩定性方面，各產業於五個年度間預測率的波動大致還算穩定，其中 就紡織產業而言，其年度之間模式預測能力的差別不大，但電子電機產業年度間的變化 則要比前者來得明顯。 三、產業投資策略績效之分析 (一)各類型投資策略的整體結果中，紡織與電子電機兩項產業的獲利績效相當，且均要 比整體市場來得好，其中，紡織產業之"高利潤低風險"策略所獲得的累積超額報酬(43.28%) 更居全體之冠。 (二)本研究所找出之個別產業最適投資策略分別為： 1.整體市場：總體策略、低風險策略 2.紡織產業：高利潤低風險策略、高利潤策略 3.電子電機產業：高利潤低風險策略、低風險策略 / Financial Statements are very important information indicating performance of corporations. Managers and investors use financial ratios as vital indexes to evaluate and predict operating results of corporations, and make their decisions. ategy, and compute CAR for each investment strategies. At last, I analyze the investing results of the four strategies for individual industry. ANN ( Artificial Nerual Network) shoot a new direction on researching application of abstracting accounting information which can efficiently predict earnings. According to results of relative researches, financial statements from different industries present and implicate different accounting information. If we further apply ANN on financial statement information to predict earnings of corporations, we can use the results as bases of analyses and comparisons among industries. Because ANN model has many advantages, in this research, I use financial statements and return on stocks from corporations as researching samples to construct prediction models and compute CAR(Cumulative Abcdrmal Return) on investments. These samples are chosen from 15 different industries and covered from the first quarter of 1981 to the third quarter of 1993. This research consists of three parts: 22 financial ratios selected by MANOVA First, I use the general market samples to adjust and predict the vital parameters of ANN models, such as the selection of input variable, the number of hidden node, and finally pick better setups for the prediction model. Second, I use this model to train and test samples from the general market, the textile, and the electron industry, and research the variation of predicting results by different models made up different industries by means of evaluation indexes . Third, I use the results predicted by the three different industry models, inspect of risk and return, to define four types of investment strategies -- "the general", "the high return", "the low risk", and "the high return - low risk" strategy, and compute CAR for each investment strategies. At last, I analyze the investing results of the four strategies for individual industry. After researching, I find:s of the textile and electron industry are better than the general markets'. 1.The better setups of ANN predition models are :industries are: (1)the selection of input variable:the 22 financial ratios selected by MANOVA (2)the ANN model topology(input node - hidden node - output node):22-22-1 rategy (3)the range of initial connection weights:-0.1~0.1 return - low risk strategy 2.The analyses of results predicted by the three different industry models are: (1)the predicting abilities of the textile and electron industry are better than the general markets'. 3.The proper investment strategies of individual industries are: (1)the general market:the general and the low risk strategy (2)the textile industry:the high return and the high return - low risk strategy (3)the electron industry:the low risk and the high return - low risk strategy

The aim of this paper is to test whether an investment strategy originally created by Piotroski (2000), can be refined by combining it with the price-to-earnings-anomaly. In detail, we accomplish this by implementing Piotroskis F_SCORE-model to identify and consequently separate financially weak- and strong firms. Furthermore, we create an investment portfolio based on a combination of the highest rated companies according to the F_SCORE-model, and the most undervalued companies from the price-to-earnings-anomaly, to create a joint investment strategy (M_STRAT). This is carried out during the time-period 1999-2009, while reconstructing the portfolio annually. The results of our study show that, by combining the two models, we are able to achieve a market-adjusted return of 44,1%, hence amplifying the original F_SCORE-model by 17%.

Previous research has confirmed the existence of a value premium in a wide array of markets and using this value stock anomaly has yielded superior performance. This thesis investigates if one could take advantage of the existence of a value premium to deploy a dynamic investment strategy on the Swedish stock market (OMXS30) with focus on minimizing risk to achieve higher risk adjusted performance than the stock market index. The investment strategy implemented use Market-to-Book-Value to screen for both entry and exit signals and Modern Portfolio Theory, using the minimum-variance portfolio with short-selling constraints, to allocate assets within the portfolio. The investment strategy is evaluated using the Modigliani-Modigliani Risk Adjusted Performance measure. Conclusions from the thesis are that the strategy does outperform the Swedish stock market index, both in terms of nominal return and risk-adjusted performance. The suboptimal behaviour of investors where they overreact  to signals and unconsciously rely on heuristics is used to explain why this is possible. Market-to-Book-Value, using the first quartile as entry signal and third quartile as exit signal, is considered to be a successful key ratio to screen for value stocks.

Problemformulering: Går det att uppnå en signifikant återkommande överavkastning samt riskjusterad överavkastning i förhållande till den svenska aktiemarknaden genom systematiskt följande av investeringsstrategin “Dogs of the Dow”? Syfte: Syftet är att undersöka huruvida teorin ”Dogs of the Dow” är applicerbar på den svenska aktiemarknaden i sökandet efter en signifikant återkommande överavkastning i förhållande till marknaden. Med detta hoppas det finnas en positiv differens av den riskjusterade överavkastningen gentemot index. Metod: Studien samlar in primärdata för empirin via SIX Trust, SIX Edge samt från Riksbankens hemsida. Sekundärdata härstammar från vetenskapliga artiklar uthämtade från främst JStor och EBSCO Host. Även studentlitteratur, tidigare studier utgör sekundärdata. Studien tillämpar befintliga teorier för att via modeller studera studiens syfte. Resultat: Resultatet anses av författarna vara imponerande. Portföljerna sammansatta i enlighet med investeringsstrategin ”Dogs of the Dow” presterar till de skådade faktorerna i genomsnitt över lag bättre än jämförelseindexen SIXRX och SIX30RX. Resultaten kan dock inte fastställas statistiskt men författarna önskar skilja på statistisk och praktisk signifikans då en möjlig kumulativ effekt genererar enorm förmögenhetsutveckling. / Problem: Is it possible to receive a reoccurring significant abnormal return as well as risk adjusted abnormal return against the Swedish stock market through systematic appliance of the investment strategy ”Dogs of the Dow”? Objective: The objective is to study whether the theory ”Dogs of the Dow” is applicable on the Swedish stock market in the search of a significant reoccurring abnormal return against the market. The hopes are to find a positive difference between the risk adjusted abnormal return and index. Method: The study collects the primary empirical data through SIX Trust, SIX Edge as well as from the Swedish central bank. The secondary data is derived from scientific articles, student literature, and previous studies. Models are used to study the objective. Results: The authors find the results to be impressive. The portfolios structured through the investment strategy “Dogs of the Dow” outperform the comparison indices SIXRX and SIX30RX in general on all the observed accounts. The results can although not be stated as statistically significant within any reasonable confidence levels, but the authors would like to emphasize the difference between the terms statistically and practically significant. This since cumulative gains could contribute to a massive gain of wealth which could be practically significant for the long-term investor.

This thesis is focused on services within unit-linked life insurance and strategic decisions in their selection. We characterize Czech Republic's insurance market and European Union market as a whole. The purpose of thesis is to analyse specific conditions on unit-linked life insurance market in Czech Republic. Unit-linked life insurance products of selected insurance companies are compared within this work.

The main objective of this thesis is to analyze selected sectors of the European capital market by means of methods of technical and fundamental analysis. Based on the results obtained for each frame exchanges, industry sectors and the best investment strategy. The first part deals with the theoretical description of securities to capital markets, investment strategies, methods of assessment of the securities in the capital markets, the theory of efficient markets, market testing and evidence of their effectiveness. On the European stock market index, including the German DAX30 randomly selected ninety nine companies with data for the period 2006 {-} the 2011th The work on the basis of a confirmed capital market inefficiencies can be found active investment strategy to achieve above average returns.

The aim of this work was to analyze selected branch from the stock market through technical and fundamental analysis. The target is to formulate the most appropriate investment strategy for each sector. The starting point for selecting appropriate investment strategy is inefficient market hypothesis. Selection of the investment strategy, depend on the current economic situation. Based on given results, it is not recommended to use any of the strategies. However, it can propose a suitable investment portfolio. The selected investment portfolio is certainly dependent on many factors. Among these factors belongs the current economic situation and investor´s attitude to risk. Choosing an investment strategy is also influenced by the investor's own attitude to the theory of efficient markets. Investors will opt for active or passive investment strategy on the basis of their opinion.

The aim of this diploma work was to analyze a selected segment of the stock exchange market using the theory of market efficiency and the methods of technical and fundamental analysis, to form an optimal investment strategy on the basis of the findings. The American stock exchange market was analyzed. Six different segments of the capital market were selected ? gold, oil and gas pipelines, steel and iron, car parts, food and telecommunication services. Each segment was represented by eight companies. The general characteristics of the companies were compared according to their profits, degree of risk, alpha and beta coefficients. Fundamental analysis was used to monitor the correlation between future profits for 2012 and alpha coefficients for the period 2007 ? 2011. The theory was proven ? at low levels of future profits, high levels of alpha coefficients were measured and vice versa - at high levels of future profits, low levels of alpha coefficients were measured. During efficiency tests, runs tests and correlation tests were monitored. During runs tests, the number of turns of a real file was compared with the number of runs of a simulated file; no distinctive variances were identified in the monitored stock titles. Forms of market efficiency were proven during the correlation tests and runs tests. The methods of technical analysis used were sliding averages, RSI indicators and Momentum. Trading on the basis of technical analysis is not completely possible because we did not succeed in finding an existing optimal strategy. If an optimal strategy works out it is regardless of the segment?s characteristics. I recommend a passive strategy with regards to the fundamental analysis.

The principal objective of this work is to test the efficiency of the U.S. capital market and to specify the degree of this effectiveness and then to find out the optimal strategy to evaluate the money invested into selected companies. At first there is theory description - the basic classification of securities, explication of the notion of efficiency of capital market, the methods of test the efficiency, several statistic indicators of the capital market like return average, standard deviation or coefficients of the capital market. Following this theoretical base there is create an analyse of one of the most popular capital markets in American index S&P 500 and of its sectors and some securities. Historical dates of years 2003 - 2008 are analysed and on the basis of results there are propositions which strategy to choose. There wasn't directly confirmed effectiveness of U.S. capital market in this work. So there was a possibility to choose an investment strategy to get an above-average return. The results were very influenced by the crisis since 2007.

The master thesis deals with proposal of automated trading system and its implementation in the Foreign exchange market environment. This system will be developed as investment model based on the analyzes performed and then tested on real data to achieve maximum stability and profit.