G11 - Portfolio Choice; Investment DecisionsReturn

The paper investigates the households’ sentiment concerning financial decision making based on the micro data from the third wave of the Household Finance and Consumption Survey (HFCS) held in Slovakia in 2017. The aim of the research is to examine the sentiment of Slovak households using HFCS micro data and its impact on household’s financial decision making. According to previous research, we assume that positive sentiment increases stock market participation. For the purpose of the research, we created a Sentiment index based on the survey data concerning economic expectations and life satisfaction, as well as Portfolio index based on the households’ assets structure. The empirical results indicate a positive and statistically significant influence of sentiment on households’ financial portfolios applying Logit and OLS estimates.

The aim of the article is to determine and verify the key indicators of the assessment of the level of management of non-life insurance companies by applying multi-criteria decomposition methods. The specific indicators considered include standard financial analysis indicators (equity ratio, total debt ratio, return on equity indicator and return on assets), specific financial indicators of insurance companies (claims ratio, capital adequacy ratio, technical adequacy ratio, solvency ratio indicator and more) and indicators that are used to analyze the level of the insurance market (gross premiums written, net premiums earned and number of insurance contracts concluded). Firstly, multicriteria decomposition methods are described: analytical hierarchical process and analytical network process. Both approaches are described including the calculation procedure. Subsequently, they are applied in determining the preferences of indicators of the assessment of the level of management of insurance companies. Our results show the resulting preferences of individual indicators for assessing the level of performance of non-life insurance companies and key indicators. The most important indicators that can be described as the key indicators with a high preference are return on equity indicator, gross premiums written, return on assets, claims ratio and solvency ratio indicator.

Presented paper is dealing with using selected fundamental indicators by building a stock portfolio. Analysis is focused on using P/E and P/BV indicators by building a stock portfolio from stocks listed on Prague Stock Exchange. The aim of this paper is answer on question, if stocks with lower P/E and/or P/BV bring investor higher return than stocks with higher values of these indicators or not. The usability of indicators was confirmed by our findings. Another conclusion of this paper is finding that stocks with lower P/E and/or P/BV exceeded benchmark represented by index PX-TR. Observed time period was from 2004 till 2014. Selected period was also divided on 5 year slip periods. Results show that stock with lower P/E or P/BV bring higher profit than stock with higher P/E or P/BV. These stocks also exceeded the benchmark.

The importance of risk management is nowadays one of the most important activities of financial institutions. One of the most commonly used methods for measuring and managing market risk is the indicator of Value at Risk (the minimum predicted loss for given significant level and time horizon) and Conditional Value at Risk (the average of expected losses that exceed the value of the Value at Risk). The aim of submitted article is the estimation of Value at Risk and Conditional Value at Risk for given shares of stock's portfolio assuming elliptical distribution of probability. Significant level is determined for 15 %, 10 %, 5%, 1% and 0.5 % for time horizon one day. Firstly, fitting probability of time series will be estimated. It can be assumed that the least appropriate type of distribution for the time series will be the normal distribution. Next, VaR and CVaR will be calculated for all given probability distribution. Due to the fact that it is assumed that empirical time series and portfolio time series will correspond to either Student or Laplace distribution then the most appropriate model for estimating VaR and CVaR will be these two distributions.

This article is focused on the effect and implication of a change in the money supply for the US capital market. This market was chosen according to its part on the global market capitalization. Namely it is the Dow Jones Industrial Average (DJIA), which was chosen according to its long history, global sense and stabile construction. The money supply will be measured by the wider aggregate M2 and aggregate MZM (money with zero maturity). The goal of this paper is to detect, if the money supply influenced the stock indices in the years 1967 - 2011, if the impact of both money aggregates is nearly the same, and how the money supply influenced the bubble creation.

This article deals with the investment activity of insurance companies in the period of economic crisis. The principal aim of this article is to evaluate the impact of economic crises on investment activity of commercial insurance companies. Firstly, we briefly identify the term - investment activity of insurance companies. After that, we evaluate the development of investment activity of commercial insurance companies on Slovak, Czech, Austrian and Polish insurance market in the period of 2008 - 2011. Within the investment activity of commercial insurance companies we focus on the volume of investments and its structure. On the basis of discovered data, we state the possible impacts of economic crisis on the investment activity of commercial insurance companies.

Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle swarm optimization algorithm and quadratic programming method to find an efficient frontier in portfolio optimization problem. Two datasets are utilized. First dataset consists of the stocks incorporated in the Dow Jones Industrial Average, second dataset contains stocks from the Standard & Poor's 500. The comparison of found efficient frontiers for different limitation on the number of stock held is made at the close of the paper.