DIGITAL IMPERATIVES FOR THE DEVELOPMENT OF THE FINANCIAL MARKET OF THE NATIONAL AND WORLD VECTOR

Аbstract. Trends in the modern financial market are characterized by new challenges of the financial environment and the impact of digital transformation, which form abnormal areas of development. Defining trends, digital models, determinants and technologies of the financial market has become an urgent necessity. The object of the study is the trends in the Ukrainian financial market and global markets of financial derivatives, Forex, ОТС markets group under digital transformation. For this purpose, we have applied the methods of nonlinear dynamics, time series, technical and fundamental analysis, modeling, forecasting, market divergence, fluctuations and behaviorism. The study of the financial market has identified: the dominant trend in digital imperatives and digital technologies of the financial space; the cyclic and behavioristic development of the Ukrainian financial market; the growth in actualization of activities of Ukrainian financial institutions, which is associated with the alternative parabanking services; higher growth rates (3,3 units) of exchange transactions with precious metals, the dependence of the dynamics of currency on the impact of the global digital imperatives; the growth in OTC derivatives trades (the rate of growth of 1.1 units). Determinants of trends financial market are: financial and economic policy, digitalization of society, force majeure. The study of forecast patterns of changes in trends has enabled to determine the economic and mathematical model for calculating the index of forecasting the price of a digital financial asset and income from a transaction with cryptocurrency. A close correlation-regressive relationship (linear R2 = 0.6361, exponential R2 = 0.6948, power R2 = 0.7142) of the cryptocurrency exchange rate from the trading interest is determined (the elasticity coefficient is 2.7 units). Keywords: financial indicators, determinants, financial assets, derivatives, patterns, digital financial technologies, forecasting, Forex, OTC. JEL Classification G12, G13, G17 Formulas: 3; fig.: 7; tabl.: 3; bibl.: 16.

Synthesis of stable coalitions in the economy based on fundamental criteria of the portfolio theory

Subject. The article outlines basic principles of a mathematical model for formation of stable coalitions in the economy at the interstate level. Objectives. We focus on developing a mathematical model to build such coalitions. Methods. The study employs the portfolio theory, risk-return model, correlation and regression analysis, technical and fundamental analysis. The proposed model rests on fundamental provisions of creating a multicomponent, widely diversified investment portfolio. The model uses the key concepts, like expected profitability, risk level, industry diversification and hedging, in combination with the synthesis of a diversified group of leading commodity indices. Results. We show possibilities of using internal (based on the country’s indices) and external (based on other countries’ indices) correlation analysis, according to data on trends in economic indices, to ensure sectoral diversification within the country and maximize the international level of sectoral diversification, respectively. We performed a fundamental analysis of the condition of economies of the countries included in the coalition, and of the countries, which are considered to be included in the coalition as appropriate. The paper assesses positive and negative factors of joint functioning of the economies of the coalition countries, from the point of view of their geographic location. Conclusions. The model makes it possible to build new optimal coalitions in the economy, to analyze the practicability of further existence of previously formed coalitions, and to update the composition of coalitions, according to trends in the world economy development.

Comparison of Technical and Fundamental Analysis Trading Disciplines on Portfoilo Performance: Short and Long Term Backtest Analysis on Borsa İstanbul National Stock Indices

The aim of this study is to comparatively analyze the backtest performances of trading disciplines applied in various portfolio baskets (Bist 30, 50 and 100) for different investment periods (short term – ytd and long term). According to the results of the analysis, it has been determined that in all trading disciplines, the investor has a higher return than the benchmark indicator in a 5-year term, that is, they can earn abnormal returns. Also, the return in the 5-year term is much higher than the 1-year and YTD returns. In the P / E & MA model, the Bist - 50 index in the 5-year period and the Bist - 100 index in the 1-year period provide the maximum return, while according to the P / E model, the Bist-30 and Bist -50 indices provide optimum returns in all maturity options. Based on these findings, it can be expected that if the trading disciplines used in this study are applied in a long term such as 5 years and on the portfolio basket consisting of Bist-30 and Bist-50 industrial stocks, it will maximize returns. In terms of risk and return, in YTD period, the sharpe and treynor ratios of the model portfolio formed in all trading disciplines except M /B trading discipline were lower than in 1 year in the 5-year investment period. This situation arose due to the increased risk of the portfolio as a result of the extended maturity and is in line with our expectations.

SCIENTIFIC BASES FOR STOCK MARKET FIASCO FORECASTING TECHNOLOGY WITH USE OF INFORMATION SPACE ENTROPY

The article contains a theoretical study and description of general algorithm for predicting a stock market fiasco caused by non-financial and other factors. Market fiasco is considered as non-periodical, sudden and random event which can arise due to the many latent reasons. Methods of technical and fundamental analysis are useless to solve this problem, therefore, the use of systems analysis methods is proposed. The author’s idea is the numerical calculation of search queries entropy as a part of global information space. Decrease in the Renyi’s entropy, associated with rapid grow search queries, containing key terms from the subject area, indicates the possible stock market fiasco in the near future. This article presents an algorithm for the dynamic calculation of Renyi’s entropy, allowing predict rare events which are not reflected in statistical data (or frequency of their realizations is too small). The method and algorithm can be realized in trade information systems and decision-making systems in economic sphere.

Predicting the price of a stock using regression analysis

At the moment, there is a high tendency towards investing financial resources for additional earnings on stock exchanges, their speculations, etc. A large number of people work on trading platforms, buy or sell stocks, which causes their price to change in different periods. On the basis of their prices, charts are formed, according to which technical and fundamental analysis is performed to build a strategy for earning and forecast not even the price of a stock, but the direction of movement of its value in the direction of growth or decrease. To obtain an answer to this question, they resort to various methods of studying the historical data of stocks, studying their qualitative indicators, as well as mathematical forecasting methods. This article examines an example of how the algorithm for predicting the stock price at the close of the trading day on the market. The method, which will provide information about the stock price, is based on the use of linear regression analysis. This method is best suited for research, because stock price charts are linear. The method of regression analysis allows you to take into account such factors as the cyclical recurrence of trends and tendencies of increase or decrease in the value of a stock, the length of the trading period, during which historical data is obtained and studied, etc. The authors also studied the work of the forecasting algorithm based on regression analysis and studied factors affecting accuracy – the size of the training sample, the amount of historical data, the number of days for which the price will be predicted in the future. In the course of the study, information was obtained on the accuracy of forecasting stock prices, which is 96 % depending on the currency in which the stock is traded and the company that issued it. It should be noted that the accuracy of the forecast is also affected by the volume of historical data and the financial position of the corporation in the market. The materials are of practical importance for the development of decision support systems in economic sectors. The developed model can be used as a basis for helping to make decisions for trading on the stock exchange.

Stock Price Prediction

Stock trading is a very crucial activity in the world of Finance and is a supporting structure for many companies. Predicting the future value of a stock is the main goal of stock price prediction project. In this paper, we have used machine learning algorithms to predict future stock prices of a company. Stock prediction by the stock brokers is mainly done using the time series or the technical and fundamental analysis but as these techniques are very unreliable and limited, we propose making use of intelligent techniques such as machine learning. Python is a programming language which can be used to implement machine learning algorithms with its numerous inbuilt libraries. We propose an approach that uses machine learning algorithms and will be trained on the historical stock data that is available and gain intelligence, later it uses the knowledge acquired for predicting the stock prices accurately. Random Forest Regression is one of the machine learning technique that is used for stock price prediction for small and large capitalizations also in different markets employing both up-to-minute and daily frequencies.

Indicators of reserves and extraction of combustible minerals in Ukraine from 2013 to 2018 from the standpoint of technical analysis

Combustible minerals have a special position among others due to the fact that they are a source of substantial energy. This article outlines details of establishment in Ukraine of minerals nomenclature related to combustible minerals, and provides a list of corresponding State reserves of minerals. The article also defines trends of aposteriory changes of the outlined indicators during the period from 2013 through 2018 (and for extraction – from 2012 through 2017) in order to obtain in the future a possibility of considering the impact of other economic and organizational factors and to detect generalized regularities in the industry’s development in terms of prospects of combustible minerals extraction. This article describes the general basic features of the technical and fundamental analysis with references to works of founders of the American school of the technical and fundamental analysis. The article provides the analysis of dynamic ranks of data on reserves, the number of fields and extraction of combustible minerals during the period from 2013 to 2018 in Ukraine according to generalized information on condition of reserves of minerals laid out on the website “Mineral Resources of Ukraine”. The article also contains specific generalized data of results of analyses which are based on processing of tabular data and graphic charts. We created data tables based on processing of posteriori trends with application of standard tools and Excel calculation techniques. The article describes the main details of analysis tools and mechanisms based on Excel calculations, as well as corresponding applied dependencies, specific details of rows development for data about reserves of combustible mineral varieties and combustible mineral reserves being under exploitation. The article contains tables of source data that were applied for generalization and analysis. In this article we provide conclusions concerning dynamics of changes of indicators of reserves, the number of fields and extraction of combustible minerals.

The Impact of Algorithmic Trading in a Simulated Asset Market

In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage.

THE IMPLEMENTATION OF RISK MANAGEMENT OF FOREIGN EXCHANGE INVESTMENT AT PT. BEST PROFIT FUTURES MALANG

This study aimed (1) to determine the risk of forex investments and (2) to figure out the implementation of risk management on forex. This study used descriptive method to examine a brokerage firm located in Malang named PT Best Profit Future Malang. Based on the results, it was found that the risks in forex investment were a floating risk of market circumstances, price change, overnight, interest rates and political event. The analysis used to determine the volatility of the forex market were technical and fundamental analysis. Technical analysis used an accurate historical rates and calculations whereas fundamental analysis used information and market news. Forex investment had a high investment risk if market volatility was not favorable, so the risk management was urgently required. Therefore, there were five risk managements; hold, aver4age, limit loss/locking, switching, and cut loss.