Growth and Value Investment Strategy Applied on Chosen Stocks of the New York Stock Exchange

Purpose of the article: Nowadays, investors use many investment strategies. They can realize different profits using different strategies because of different principles of strategies. Author assesses two investment strategies. Calculating different financial indicators leads to comparison of yield and risk of indexes related to these strategies. Presented results could be important for investors within their investment decision in order to create investment portfolio. Methodology/methods: Growth and value investment strategies are theoretically described and applied in real data within 2008–2012. Using the CAPM model, author divides stocks of the NYSE included in the base of the DJIA into growth stocks and value stocks. Author compares index trends in charts, interpolates individual index charts by trend line and calculates financial indicators. Scientific aim: The aim of the article is to assess growth and value investment strategies which are applied to chosen stocks traded on the NYSE within 2008–2012. Author compares yield and risk of growth index with yield and risk of value index and DJIA index. Findings: Author applies growth and value investment strategies to chosen stocks. Sharpe ratio of indexes within whole period 2008–2012 is negative, whereas Jensen’s alpha is positive. Information ratio is positive only within 2008. Value investment strategy has lead to higher yield than growth investment strategy since October 2010, whereas before this month values had not been so different, but rather similar. Risk of value index is higher than risk of growth index and smaller than risk of DJIA index within each year of whole period 2008–2012. Conclusions: Similarly to results of empirical studies cited in this article, results of author indicate that value stock yields are higher than growth stock yields. Based on assessment of these investment strategies, investors could use results when they are making investment decision

The Structure of Consumer Basket in the Czech Republic and Slovak Republic

Purpose of the article: Nowadays, consumer behaviour is still at the centre of attention of many researchers. The consumer basket belongs among important economic characteristics, which determine the economic level of each country. The contribution of the article consists in identification of changes in the structure of consumer basket related to two chosen countries. Methodology/methods: The development of the structure of consumer basket of average household in two countries during the period from 2004 to 2014 is analysed. The structure is expressed by means of expenditure shares, while the development by means of trend models. Coefficient of determination and correlation coefficient are used within choosing the appropriate trend function. Scientific aim: The aim of this article is to assess the development of the structure of consumer basket of average household in the Czech Republic and Slovak Republic during the period from 2004 to 2014 by each country including mutual comparison. In detail, the development of consumer expenditure groups of consumer basket is assessed. Findings: Expenditure shares are found to be enough volatile in time. The highest coefficients of determination are found just in case of quadratic function. Mostly, the found correlation in trend model is strong. Incomes and expenditures mostly increase in time. Based on the 2004 and 2012 comparison, the percentage rates expressing, how much incomes exceed expenditures increase about 11% by the household in the Slovak Republic, however the ones increase only about 3% by the household in the Czech Republic. Conclusions: The structure of consumer basket of household and the structure of household expenditures in the Slovak Republic differs from the ones in the Czech Republic. Both differences and common features are found within the development of indicators. Common features could be caused by the fact, that these two countries constituted only one country till 1. 1. 1993

The Use of Indicators in Modified Historical Model to Estimate the Intrinsic Value of a Stock

The article mentions several methods of a fundamental analysis used to value stocks. It primarily focuses on the historical model. This model enables undervalued, correctly valued and overvalued stocks to be identified. The model is further modified in the article, using selected accounting indicators. The modified model versions are applied to selected stocks in the SPAD segment, Prague Stock Exchange, within the 2005-2010 period. Empirical analysis is applied to a comparison of accuracy of the accounting indicator value estimates and accuracy of stock intrinsic value estimates, both separately for each stock and accounting indicator. The comparisons of accuracy of the accounting indicator value estimates and the accuracy of the stock intrinsic value estimates are also done based on the length of applied time period. With respect to the obvious fierce competition between stock issuers within the financial markets, the model enables potential investors, who are to select from an extensive offer of stocks, to make better informed investment decisions

Stock prices prediction using the value at risk model

Value at Risk model is often used for risk analyses mostly in the banking and insurance industries. Following the characteristics of the model principle, the Value at Risk is interpreted in the economic sense. Attention is paid to three sub-methods, concretely historical simulation method, variance covariance method and Monte Carlo method. A number of empirical studies focused on the application of these submethods in practice is presented. Various risk factors are used by these studies. Value at Risk model is applied to selected stocks from SPAD segment of Prague Stock Exchange within the 2011, in the paper. This unique application is the aim of the paper. The reliability interval, hold period, historical period and other important parameters related to the sub-methods are selected within the application. Using covariance matrices, correlation matrices as well as other types of matrices and statistical indicators, the Value at Risk are calculated. The comparison of calculated diversified and non-diversified Value at Risk by sub-methods is realized. Mentioned are also back testing, stress testing, the essence of the relative and marginal Value at Risk and other options of practical application of this model