Uczenie maszynowe w budowie portfela inwestycyjnego

The chapter describes the characteristics of machine learning methods in their possible application in investment portfolio optimization. With the use of the SWOT analysis, the features of the algorithms responsible for their increasing popularization in the formulation of investment strategies and their limitations in this regard were discussed. The prospects for further development of machine learning were described in the context of the market and technological environment. In addition, based on the review of the research, the possibilities of using machine learning algorithms in managing the investment portfolio and the use of modern research methods, which can be a creative development of the needs and solution to the problems faced by researchers of financial science and financial market practitioners, have been presented.

A Personalized Mean-CVaR Portfolio Optimization Model for Individual Investment

Investment as an important issue in daily life is accompanied by the occurrence of various financial assets, such as stocks, bonds, and mutual funds. However, risk tolerances vary across individuals. Individual investors have to select corresponding personalized investment portfolios to satisfy their own needs. Moreover, it is difficult for ordinary people to select a personalized investment portfolio by themselves, and it is too expensive and inefficient to look for professional consultation. Therefore, the objective of this research is to propose a personalized portfolio recommendation model, which can build the personalized portfolio based on investors’ risk tolerances. In this research, investors’ risk tolerance is determined by the fuzzy comprehensive evaluation method based on investors’ demographic characteristics. The CVaR is used as the risk measurement of financial assets. The dynamics of the distribution of returns are described in the combined Copula-GARCH model, and the future scenarios of returns are generated by the Monte Carlo simulation based on the combined Copula-GARCH model to estimate CVaR. The mean-CVaR portfolio optimization model is used to find out the best personalized portfolio. Finally, experiments are conducted to validate the applicability and feasibility of the personalized investment portfolio optimization model. Results show that the proposed investment portfolio optimization model can recommend personalized investment portfolio according to investor’s risk tolerance.

Essence and Concept of Capitalization of Enterprises its Types and Methods of Evaluation

The historical aspect of the development of scientists' views on the company's capitalization is studied in the work, the stages of development of the concept of cost-oriented management are determined. The role and place of market capitalization in the system of indicators of cost-oriented management are determined, modern methodical approaches to the assessment of the value of the enterprise taking into account the indicator of market capitalization are given. The fundamental differences between the areas of application of indicators of market value of the enterprise and market capitalization are established. The current problems of the Ukrainian economy that limit the widespread use of market capitalization in the valuation of companies are outlined, and proposals are made on the feasibility of valuing Ukrainian companies in terms of real capitalization. The importance of enterprise capitalization in the development of a market economy is emphasized. The evolution of the views of foreign scientists on the capitalization of enterprises has been studied. It is established that the economic essence of the capitalization of enterprises, as well as any asset, is the present value of future income generated by this asset. Bringing future income to the present is a process of discounting. The theory of capitalization of the company in its modern context began to be formed by neoclassical economists from the end of the XIX century. and gained popularity in the second part XX century. The theory of capitalization has given impetus to well-known neoclassical theories of corporate finance, such as the irrelevance theorem, risk theory, investment portfolio optimization theory, arbitrage pricing theory, and so on. It is established that according to the theory of capitalization, the capitalization of the enterprise and the market value of the enterprise are identical concepts, although the methods of their calculation differ. The theory of capitalization has given impetus to the development of many modern financial theories, which are currently under development and improvement.

Markowitz Model Investment Portfolio Optimization: a Review Theory

In the face of investment risk, investors generally diversify and form an investment portfolio consisting of several assets. The problem is the fiery proportion of funds that must be allocated to each asset in the formation of investment portfolios. This paper aims to study the optimization of the Markowitz investment portfolio. In this study, the Markowitz model discussed is that which considers risk tolerance. Optimization is done by using the Lagrangean Multiplier method. From the study, an equation is obtained to determine the proportion (weight) of fund allocation for each asset in the formation of investment portfolios. So by using these equations, the determination of investment portfolio weights can be determined by capital.

Using Entropy to Forecast Bitcoin’s Daily Conditional Value at Risk

Conditional value at risk (CVaR), or expected shortfall, is a risk measure for investments according to Rockafellar and Uryasev. Yamai and Yoshiba define CVaR as the conditional expectation of loss given that the loss is beyond the value at risk (VaR) level. The VaR is a risk measure that represents how much an investment might lose during usual market conditions with a given probability in a time interval. In particular, Rockafellar and Uryasev show that CVaR is superior to VaR in applications related to investment portfolio optimization. On the other hand, the Shannon entropy has been used as an uncertainty measure in investments and, in particular, to forecast the Bitcoin’s daily VaR. In this paper, we estimate the entropy of intraday distribution of Bitcoin’s logreturns through the symbolic time series analysis (STSA) and we forecast Bitcoin’s daily CVaR using the estimated entropy. We find that the entropy is positively correlated to the likelihood of extreme values of Bitcoin’s daily logreturns using a logistic regression model based on CVaR and the use of entropy to forecast the Bitcoin’s daily CVaR of the next day performs better than the naive use of the historical CVaR.

Investment Portfolio Optimization on Russian Stock Market in Context of behavioral theory

The paper investigates possible investment portfolio optimization considering behavioral errors. The research rationale is due to the adaption of the investment recommendations for unqualified investors on the Russian stock market. In economic literature, the consequences of behavioral effects are not detailed enough when making a portfolio of Russian securities. The aim of the article is to make the most optimal portfolio based on the risk/reward ratio. The author made a hypothesis on applying various periods of profitability analysis to improve profitability indicators and increase the subjective probability of its achievement. To build a portfolio model, the behavioral portfolio theory and its optimization through linear programming were used. The study was based on modeling the investment portfolio of the most liquid stocks on the Russian stock market. Modified elements of the cumulative prospect theory with behavioral coefficients were used as indicators of profitability and probability. Based on the analysis results, the model of semi-annual portfolio analysis was proposed as a tool for portfolio optimization. The investor review of the portfolio semi-annual rate of profitability led to its best final index of effectiveness. In the medium-term assessment of portfolio profitability, the influence of behavioral factors decreases while maximizing returns with medium high risk. The research result is consistent with the basics of behavioral economics as the prospect theory regarding risk and loss aversion. Moreover, the factor of frequency of access to information and the degree of naive portfolio diversification with high profitability are promising areas for the development of research in behavioral finance. However, determining by the investor the objective probability to achieve the expected return level by using specific benchmarks is controversial.

Game Risk Management Methods for Investment Portfolio Optimization

The world is on the verge of technological revolution, which will fundamentally change the way of life, work and attitude towards each other [1]. We live in the era of the Third Industrial (or Digital) Revolution, but modern world leaders are already actively preparing for a new period - the Fourth Industrial Revolution or Industry 4.0. New forms of interaction open up opportunities in the organization of work and change requirements for employees. Business becomes digital, but the person stays. Employees will continue to remain crucial factors in innovation, continually developing products and services. The lack of talented and skilled personnel will be the main problem of the Fourth Industrial Revolution. Flexible production through FIR will require much more skills in all workflows for all employees. Skilled workers will be more in demand in the future for making decisions that can not be replaced by any algorithm. At the same time, employees must be trained and qualified for new jobs, so it is essential to review standards, for example, in education and training, and adapt them to new requirements. The implementation of interactive technologies in learning has become a necessity. One of the effective methods is business games: both independently and in combination with theoretical training. Precisely this combination (practice) is optimal for students to learn lecture material and acquire competencies. The article is devoted to the description of the technique of conducting risk management training. We described the technology of the training, dividing it into two parts. In the first part, the students are reminded of the risk management theory; in the second part, a business game is held - "Investment portfolio". In the article, we showed how it could be carried out with and without the use of gadgets/computers. As an example, we considered the most frequent mistake when conducting this game and gave a correct model. Besides, in the article, we gave formulas for automating the game of Excel