The academic literature is showing a growing interest in such trading rules as Moving Average. The majority of researches were made using simple moving average. Although semi-professional traders use the technical analysis methods to predict the future stock prices, to identify the stock trend changes, OMX Baltic Benchmark Index was never tested. Previous researches on the S&P 500 Index using the most widely used method of technical analysis -Moving Averages are more or less appellative. Technical analysis is opponent to classical economic theory but investors use it widely all over the world. Technical Analysis methods can be less or more effective than it was thought until nowadays. This paper compares 2 trading rules of technical analysis -exponential smoothing method and simple moving average rule. Both methods were applied to US index S&P 500 and OMX Baltic Benchmark Index and the results were compared using systematic error (mean square error, the mean absolute deviation, mean forecast error, the mean absolute percentage error) and tracking signal evaluation, bias distribution estimation and appropriate Constanta level finding for each market forecast: the case of Standard and Poor's 500 and OMX Baltic Benchmark Index.
Technical and fundamental analyses are the two investment making decisions widely spread all around the world. The financial crisis of 2008-2009 had a negative impact on the decisions of the Lithuanian investors to choose stock as the best investment option. However, national economics is cyclical and after recession recovery follows. Production volumes are anticipated to increase seeing that analysts forecast further GDP growth. Due to this reason, additional funding for the successful performance of enterprises will be required. Therefore, financial resources must be attracted by issuing new volumes of stocks. On the other hand, the successful performance of an issuer has a positive influence on the stock price in the market which is the subject of forecast made by the investors of Lithuania. Positive changes of stock prices in the market are partially influenced by the expectations of investors that stock prices will grow rapidly in the future. However, this feature is not known and can only be forecasted using different econometric models. At the theoretical level scientists disagree about the effectiveness of the methods used by the Lithuanian investors. Recently technical and fundamental analyses became popular among investors, though there is not much research done in order to test the effectiveness of the applicability of these methods in the Lithuanian stock market. With reference to the above mentioned information, this research is aimed to determine whether it is possible to forecast stock prices by estimating the financial ratios of a particular company. Due to this reason, a link between the return of a stock price and the financial ratios of the selected companies will be evaluated using correlation and covariance as the main analytical tools. Appropriate conclusions and suggestions are provided after obtaining reliable empirical results.
With increasing competitiveness of companies and business sectors in the domestic markets of Lithuania, economic units are frequently confronted with the lack of methods for more detailed analysis of external factors explaining the variation over time of corporate financial indicators. The analysis or forecasting of financial indicators is usually linked with the development of a stock market or undertaken to estimate the probability of bankruptcy. However, there is a lack of studies aimed at identifying links between macroeconomic factors and financial performance indicators and explaining their variation over time. To serve that purpose, the factors of the macroeconomic environment that are most significant for certain economic activities have been identified and analysed to enable explaining the variation over time patterns of corporate financial indicators. The analysis covers economic performance, i.e. financial performance indicators and their links with macroeconomic factors, in 89 business sectors of Lithuania at a three-digit level of NACE 2 ed. The findings of the research indicate that the unemployment level in the country, the volume of export and import and the GDP are the most important macroeconomic factors that can be used to forecast different profitability, financial leverage, liquidity and other financial performance indicators of individual business sectors or companies. The research has not unfolded any significant differences between business sectors therefore the above factors are considered generic macroeconomic factors enabling to explain financial performance indicators of the 89 business sectors. Hence, special attention has to be paid to identifying and analysing specific factors and assessing the causal link. When established, the set of such factors provides a framework for building of a model to forecast business sector financial indicators.
This study was driven by the dissimilar performance characteristics displayed by asset classes over the business cycle. The authors aim to explore assets classes on the grounds of a scientific literature review and a statistical analysis. Business cycles are divided into four stages to explore broad movements in returns of asset classes and a possible existence of asymmetrical effects of determinants within stages. Six main asset classes were analysed: US stocks, EAFE stocks, Bonds, Gold, Real Estate and Commodities. Monthly data from February 1976 to August 2011 were used for the study. The article combines business cycle and asset allocation theories by adding valuable information about performance of asset classes during different phases of the business cycle. Using the OECD Composite Leading Indicator as a business cycle measure, the authors demonstrate that different assets classes have different return/risk characteristics over the business cycle. The article demonstrates how to use the business cycle approach for investment decision-making. The OECD Composite Leading Indicator can provide significant information on market expectations and the future outlook; hence, results of this study can help every investor improve his/her performance and risk management.
A dissimilar performance characteristic displayed by asset classes over the economic business cycle has determined the purpose of this study -the integration of the business cycle approach into the construction of optimal investment portfolios. The paper combines business cycle, asset allocation and portfolio optimization theories by presenting a new model of the investment process and adding valuable information about the performance of asset classes in different phases of the business cycle. One of the best measures for the business cycle are leading indicators that can provide significant information on market expectations and future outlook; hence, every investor can improve his performance and risk management by adopting the results of this study. The use of the OECD Composite Leading Indicator as a business cycle measure assists in showing methods for constructing optimal portfolios and making investment decisions. The conducted analysis uses 6 asset classes: US stocks, EAFE stocks, Bonds, Gold, Real estate and Commodities. Monthly data on the performed research covers the period from February 1976 to December 2011.
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