Влияние наночастиц золота на сперматогенез мышей
The article is the historical and legal research of the evolution of monetary circulation in the Moscow state and the Russian Empire in the XVII – XVIII centuries. The main directions of the monetary policy and the notion «monetary regalia» are analyzed. The author makes conclusions about the existence of the negative trend throughout the period reviewed — use monetary regalia for fiscal purposes.
The article is the historical and legal research of the evolution of monetary circulation in the Russian Empire from the beginning of the 19th century until 1917. The author analyzes the problems of money circulation through significant historical events: the Great Patriotic War of 1812, the Russian-Turkish War, the Russian-Japanese War, the First World War. Paticular attention is paid to the historical experience of the introduction of platinum coins and formation of the gold standard in the reform of 1895—1897.
In global financial and economic crisis the theme of engineering of investment strategy is discussed with an especial sharpness. Along with other, it is caused by an inefficiency of previously created investment strategy owing to their obsolescence. It is necessary to work out actualization of key interrelations in the financial market, to check up reliability of the most important tools and to reveal the factors, influencing their dynamics. It’s not a secret that investment appeal of gold grows in a period of the expanded issue by the central banks worldwide. At the same time, the interrelation of dynamics of the given tool and dynamics of other key tools of the share market can be various. Given paper is devoted to consideration of this question.
The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.