The Influence Of Financial Constraints And Attitude Towards Risk In Corporate Investment Decisions
The paper contains empirical estimates of how behavioral factor (an attitude towards risk), rationality and uncertainty influence on investment decisions (capital investment) of Russian companies. The research is guided by the models of Sandmo (1971), Bo and Sterken (2007). We have tested a hypothesis, that risk preferring companies tend to grow investment, while risk averse companies are more likely to decrease the number of investment projects under uncertainty. The following rational variables, explaining investment policy, are included into the model: sales growth, market power, return on equity, debt to equity ratio, current liquidity. Since the time span of the research includes both the crisis period (years 2008, 2009) and the period before the crisis (2004-2007) we have also estimated the time effect on the companies’ investments.
The following estimators have been used to get the results: ordinary least squares; fixed effects model; random effects model; panel data models with binary variables controlling time effects; Hausman-Teylor’s model, generalized method of moments.
The article presents the key aspects of successful investment process modeling for investment projects. A successful investment process means a series of investment decisions on timing, risk and investment objects and activities for their implementation, aimed to generating positive indicator "alpha", which is the maximum possible total return of specific investment project. In practice, there is no unique investment process, applicable to any investment decision, however, there are basic requirements that must be met to ensure a successful outcome of investment process. These requirements include availability of investment opportunities, forecasting skills and mechanism for investment project implementation.
The purpose of this article is to conduct a comprehensive analysis of the Russian forestry sector to identify the prospects for its further development. It is the presence of non-system (specific) risks inherent in the logging industry which complicate the decision making process for investing in the most promising projects. The practical significance of this work is to develop the tools to assess the non-systematic risks when implementing projects in the Russian forestry sector, making it possible to assess the risks to logging enterprises and sawmilling and woodworking enterprises.
Article is devoted to problem solving of a lack of capital resources for realization of project portfolio on basis of revealed laws in financing, budgeting and capital rationing system in the company.
The instability of the stock market spurs investors to seek alternative ways of allocating financial resources. In this case, art assets could be considered as an attractive investment. Due to the uniqueness, specific costs, and risks inherent to the artworks, the fine art market is very heterogeneous and needs special treatment. In this article, we investigate attractiveness of the fine art market for investors in several ways. First, we construct hedonic art price indexes using the time dummy variable method based on the quantile regression. Secondly, we assess the art assets risk through CAPM model. Data include 536660 observations about oil paintings on auctions around the world during 2005–2015. According to the estimation results, the postwar paintings sold in the high price sector could be considered as an attractive sector for the investors but its acquisition is accompanied by a relatively high risk compared to the operation on the stock market.
The paper studies a problem of optimal insurer’s choice of a risk-sharing policy in a dynamic risk model, so-called Cramer-Lundberg process, over infinite time interval. Additional constraints are imposed on residual risks of insureds: on mean value or with probability one. An optimal control problem of minimizing a functional of the form of variation coefficient is solved. We show that: in the first case the optimum is achieved at stop loss insurance policies, in the second case the optimal insurance is a combination of stop loss and deductible policies. It is proved that the obtained results can be easily applied to problems with other optimization criteria: maximization of long-run utility and minimization of probability of a deviation from mean trajectory.