The Cyclic Dynamics of the Venture Capital
Venture capital (VC) provides financial and managerial support for new innovative ideas at the initial stages of commercialization. It has helped to find the market for many radical innovations of 20th century, including personal computer, Internet and genetic engineering.
As a part of market economy venture business was not stable from the very beginning. The periods of rapid growth alternated with deep recessions. However each time VC revived anew as the Phoenix due to its very important function in modern knowledge-based economy.
This report presents an analysis of statistical data that prove the existence of several cycles in VC dynamics in the USA and the Great Britain. The main factors of these cycles formation are discussed. The author proposes two possible scenarios of development of VC market for the first 30 years of the new 21st century. A hypothesis is put forward about the relation between VC cycle's amplitude and a phase of Kondratieff's cycle.
The article discusses the new organizational form of the activity of a regional industrial complex in the form of a system and structure that provide for the interaction of innovation-active enterprises and venture investors on the market.
The paper represents the review of contemporary approaches to the analysis of financial market imperfections and financial crises and their impact on fluctuations of the key macroeconomic variables during the business cycle as well as the transmission mechanism of financial shocks on the real economy in the framework of New Keynesian dynamic stochastic general equilibrium models. These models are widely used for the evaluation of monetary policy effects on macroeconomy and constitute the theoretical base for elaboration the optimal monetary policy not only during the crisis but for the further perspective. The construction of such models types for different economies including the Russian economy requires considering the institutional features and specific development and functioning characteristics of the of the national financial sector and economy as a whole.
Financial markets have always been attractive as a means of increasing one's wealth, and those who make accurate predictions take the prize. Forecasting models such as linear ones are simple to compute, however, they give rough approximations of the underlying relationships in the data, thus, producing poor forecasts. The solution to this issue could be the nonlinear models which try to fit the data and display the relationships with higher accuracy. Previous research seems to prove this statement from the statistician's point of view which might be of little use for an investor. Therefore, the focus of this paper is on the comparison of three types of models (nonlinear: ANN, STAR, and linear: AR) in terms of financial performance. Our research is based on the initial code for GAUSS and papers by Dick van Dijk. The data used is the monthly S&P 500 Index values from 1970 to 2012 provided by the Robert Shiller's website. Forecasting index changes begins at 1995 and ends in 2012 providing up-to-date results for 14 model specifications. The best model proves to be the flexible ANN, beating the linear AR in the majority of cases, leaving the underperforming heavy-parameterized STAR model behind. Thus, it is evident that the more flexible nonlinear models outperform the heavily parameterized ones as well as linear models for the S&P 500 Index. The introduced type of performance evaluation has a more comprehensible application to the financial market analysis.
Materials for the International Workshop on “Networked embedded and control system technologies: European and Russian R&D cooperation”
In this article we describe a system allowing companies to organize an efficient inventory management with 40 suppliers of different products. The system consists of four modules, each of which can be improved: demand planning, inventory management, procurement planning and KPI reporting. Described system was implemented in a real company, specializing on perishable products totaling over 600 SKUs. The system helped the company to increase its turnover by 7% while keeping the same level of services.