COMPARATIVE PROFITABILITY ANALYSIS ON THE GLOBAL INVESTMENT MARKETS

The article focuses on the comparative analysis of the dynamics of prices and profitability of the investment markets of precious metals (illustrated by the gold market), the Forex exchange market (the currency pair of EUR/USD), and the cryptocurrency Bitcoin. The purpose of the current study is making of the comparative analysis of the price dynamics and its profitability on the certain investment markets of the world, estimating the characteristics of the markets performance, developing of recommendations on the benefits of investing at different planning horizons, and building of the forecast of profitability, by usage of neural networks. It keeps the estimation of the statistical performance of the price dynamics of investment instruments, as well as the study of time series for presence of the deterministic chaos. As for the persistent time series, there was applied the method of sequential R/S analysis to determine the fuzzy memory depth. Taking into account that from the point of view of investment needs, not only the dynamics of the prices of the investment markets is quite significant for the analysis, but also its profitability, there were studied the time series of profitability. The above mentioned characteristics of the TS (statistical and fractal ones) can be used for comparison of the price dynamics both in the context of object-carriers (spacial comparison), and in the time section of comparing the changes in the nature of dynamics at different time intervals, in terms of determining the nature of the “response” to the impact of the environment (external and internal actions), etc. In terms of the stochastic nature of the profitability, it was proposed to study the family of time series of the “delayed” profitability. There was specified the time interval, required for the “delayed” profitability to receive the features of the time series of prices, as well as calculated the fuzzy memory depth. It became the basis for generation of the forecast model of the neural network in the software product Deductor.