Log-periodic power law and prediction of micex crashes
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.
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.
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.
1. Description of the problem. Instrumental analysis makes it possible to find the arguments of adjudication on the bounders and structure of corpus delicti, its correlation to criminal and filling-up legislation. 2. Initial theses. Corpus delicti is regarded as that expressed in criminal law doctrine result of reorganization of orders of criminal law into other practically necessary form. That happens in the process of theory and practical experience accumulation. The construction of corpus delicti is transformed for practical needs, textually expressed system of features, regulated by criminal law and characterizing deeds as a crime of a definite type. Correlation of construction of corpus delicti with law and doctrine. Corpus delicti, its algorithm. Transition from law regulations to corpus delicti can be done: 1) prog-nostically; 2) within constant analysis of law; 3) in the process of law application. 3. Stages of instrumental building of corpus delicti: prognostic, doctrinal, law applicatory. Instrumental approach to corpus delicti includes within each stage: 1) based on criminal law decision of classification of corpus delicti and its borders; 2) objective description of a factual model; 3) acception of meaning correlated with legal notions and constructions; 4) choice of the construction of the corpus delicti and disposal of characteristics; 5) verification of legitimacy, necessity and adequacy of foundation. 4. Instrumental analysis of disputable questions of understanding and application of constructions of corpus delicti. A. Functions and purposes of application of construction of corpus delicti. Functions of corpus delicti: a) modeling; b) communicative; c) identificatory; d) technological. B. Contents of corpus delicti. Contents of corpus delicti as it is traditionally regarded does not correspond to indications of crime, does not characterize features of social danger; sign of danger of penalty also does go into corpus delicti. Two variants are proposed for the discussion: widening of the borders of corpus delicti by means of introduction of signs of social danger and signs, defining individualization of penalty and to limitate corpus delicti by characteristic of criminally punished act, separating it from contents of guilt and contents of social danger. C. Structure of corpus delicti. There are two problems: division of elements of crime seems to be extremely harsh and inadequate - it is expedient to include signs of special and time limits of act, causal links, crossing signs of objective and subjective sides, first of all consequences and an object of crime, into the structure of corpus delicti. Forms of committing a criminally punished act is a crime commitment in complicity, ideal system, not finished crime.