Nikolay Lomakin - Academia.edu (original) (raw)

Books by Nikolay Lomakin

Предложены подходы в формировании прибыльной, безрисковой стратегии торговли фьючерсом РТС на рос... more Предложены подходы в формировании прибыльной, безрисковой стратегии торговли фьючерсом РТС на российском рынке FORTS. В доступной форме изложена последовательность работы с использованием программы QUIK: от настройки интерфейса, до выставления заявки с установкой функций стоп-лос и тейк-профит. Рассматриваются методы использования и настройки средств технического анализа рынка фьючерсов: скользящих средних 200 дневных и коротких 50 дневних, индикатора боллинджера, индикатора относительной силы RSI, волны Эллиотта и другие. Исследуются виды, принципы работы и классификация торговых роботов, выявляются перспективы их использования в биржевой торговле. Книга рассчитана на широкий круг читателей и адресована студентам, преподавателям ВУЗов, предпринимателям, инвесторам, представителям среднего класса и всем желающим освоить секреты биржевой торговли.

Papers by Nikolay Lomakin

Krasnoyarsk Science

The article analyzes current trends in the use of artificial intelligence, cognitive technologies... more The article analyzes current trends in the use of artificial intelligence, cognitive technologies, Big Data and other innovations in order to predict the stability of the Russian banking system. The current trends in the development of banking innovations in the context of digitalization of the economy are summarized. Among the research methods used, the Random Forest artificial intelligence system, formed in the Google Collab cloud service in Python, should be noted. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF – random forest) machine learning model is proposed. The results of the study indicate that, with the average value of the profit of banks for the period 2010-2021. 1126.0416 billion rubles, the mean absolute error (Mean Absolute Error) amounted to 129.96666 billion rubles, or 11.5%, which can be regarded as a good result in conditions of market uncertainty. The analysis showed that current trends are global in ...

Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy

The relevance of the research study is due to the fact that the article attempts to prove or fals... more The relevance of the research study is due to the fact that the article attempts to prove or falsify the hypothesis that the "AI-Decision Tree" neural network model makes it possible to obtain a forecast of Russia's GDP for various scenarios. Various aspects of the AI application in the field of big data processing, deep learning and forecasting have been investigated in the article. However, experience has proven that, certain issues of using artificial intelligence require further scientific research in order to achieve a balanced and sustainable growth of the financial and economic system. Theoretical foundations of sustainable economic growth in the country have been studied. The authors have reviewed modern domestic and foreign literature on the topic and paid special attention to the issues of balanced financial and economic system and sustainable economic growth in modern conditions. We demonstrate the factors increasing risk and market uncertainty and other in order to achieve a balanced and sustainable growth of the financial system based on the AI-system "Decision Tree" developed. The trends in functioning of the financial and economic system have been determined; the dynamics of the balanced profit volumes in real sector organizations has been traced quarterly for the period of 2015-2018. Live data of the Federal State Statistics Service showed that the balanced financial result (profit except for loss) of organizations (apart from small business entities, banks, insurance organizations and state and municipal institutions) in current prices decreased by 8.5% in 2017. In order to visualize the dynamics of the effective factor - GDP a neural network model "AI-quantization of data" has been developed. In order to achieve a balanced and sustainable growth of the financial system based on the AI-system "Decision Tree" developed.

International Journal of Technology

The study's relevance lies in the fact that the important problem is ensuring the sustainability ... more The study's relevance lies in the fact that the important problem is ensuring the sustainability of the development of the Russian economy. Its financial system is influenced by many factors, among which are increased risks and market uncertainty, aggravation of the global military-political confrontation between the largest world powers, and technological innovations associated with the emergence of a new technological order "Industry 4.0". The purpose of the work is to study the financial stability of the state as a complex system based on a cognitive model that involves the use of an artificial intelligence system and a VaR model. The novelty of the study lies in the proposed approach, which involves the formation of a cognitive model of economic processes, which includes the Decision Tree artificial intelligence system and the VaR model, both generate GDP forecasts, then their forecast values are compared. A hypothesis has been put forward and proved that with the help of a cognitive model that includes an A.I. system and a VaR model, it is possible to obtain a forecast of the volume of Russia's GDP, the dynamics of which allows us to assess the sustainability of the development of the country's economy.

Mezhdunarodnaja jekonomika (The World Economics)

The article examines the theoretical foundations for the emergence of overdue debts on loans and ... more The article examines the theoretical foundations for the emergence of overdue debts on loans and forecasting financial risk in Russian banks in modern conditions. The relevance of the study is that the growth of bad debts of commercial banks on loans is currently one of the most acute problems. The collected material made it possible to analyze the dynamics of the volume of overdue debt on loans in commercial banks of the Russian Federation for the period 2013–2021. In the course of the study, it was revealed that many factors influence the volume of bad debts. In order to study the influence of factorial signs on the effective sign — the amount of overdue debt, an attempt was made to use such models as: correlation-regression, AI and VaR. A hypothesis has been put forward and proved that with the help of models: correlation-regression, AI and VaR, it is possible to obtain a forecast of the volume of overdue loans in the portfolio of commercial banks of the Russian Federation. The c...

Mezhdunarodnaja jekonomika (The World Economics)

The article discusses the theoretical foundations of the analysis and forecasting of financial ri... more The article discusses the theoretical foundations of the analysis and forecasting of financial risk in the banking sector in conditions of market uncertainty. The relevance of the study lies in the fact that the growth of problem debts of commercial banks on loans to legal entities, individual entrepreneurs and individuals is currently the most relevant and debated issue in the banking community. The analysis of the dynamics of assets and the share of overdue loans in 2010–2021 has been carried out, trends in portfolio changes have been identified. The authors considered the advantages of using the VaR indicator as a measure of risk, noting that its weak side is the inability to assess extreme losses (in the tails) if the risk is realized in the range above the confi dence interval. The Perseptron program has been developed for forecasting the dynamics of the share of overdue loans in the portfolio of a commercial bank, which is formed on the Deductor platform. Quantization (groupin...

Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy, 2019

The article represents theoretical foundations investigated for application of artificial intelli... more The article represents theoretical foundations investigated for application of artificial intelligence systems in Big Data processing. The most comprehensive list of tools for data analysis and machine learning has been considered. A comparative Hadoop framework and Deductor analytical platform opportunity analysis has been performed. An AI-system has been proposed for predicting the cost of innovative products in the context of digitalization of the Russian economy. A hypothesis that a neural network makes it possible to obtain a forecast for the cost of innovative products in the Russian Federation has been put forward and proved. The neural network model included such parameters as GDP (billion rubles), key rate (%), RTS index, output of innovative products (billion rubles), costs of innovative products (billion rubles), dollar exchange rate (rubles), balanced profit (billion rubles), risk (σ), loans originated (billion rubles), VIX-Index and forecast for the volume of innovative products (billion rubles). The list of parameters presented reflects the development of both the economic sphere and Russia's financial sector quarterly for the period of from 2015 to 2018. Based on quantization and subsequent visualization of big data and using a multidimensional diagram, the artificial intelligence system developed allows revealing the GDP trend in Russia depending on the cost of innovative products and the VIX option stock-exchange quotation in the global economic landscape. The AI-system that enables prediction for the cost of innovative products using the "what-if" function in the Deductor platform has been developed.

Proceedings of the Volgograd State University International Scientific Conference "Competitive, Sustainable and Safe Development of the Regional Economy" (CSSDRE 2019), 2019

The paper presents a developed neural network model of interaction between entrepreneurship enter... more The paper presents a developed neural network model of interaction between entrepreneurship enterprises and financial system. A hypothesis that a neural network makes it possible to simulate the interaction between complex systems and develop a profit forecast for enterprises in the real sector of the economy under risk has been put forward and proved.

Proceedings of the International Scientific Conference "Competitive, Sustainable and Secure Development of the Regional Economy: Response to Global Challenges" (CSSDRE 2018), 2018

The article focuses on solving the problem of sustainable development of regions based on the app... more The article focuses on solving the problem of sustainable development of regions based on the application of artificial intelligence (AI). In the condition of transition to digital economy, the use of the innovation vector is of special significance, e.g. the sustainable development of regions based on the application of big data processing for the purpose of achieving profitable operation of enterprises. Although forecasting corporate profit in the situation of market uncertainty is of great importance, research shows that this area has not been studied sufficiently in scientific literature. A hypothesis has been proposed and validated that it is possible to develop a Rosneft profit forecast with a margin of error of no more than 5% by using the Perceptron neural network. The developed neural network used is a two-layer perceptron with one hidden layer intended for Rosneft's next year profit forecast. The input layer of the neural network structure includes a number of parameters, such as the American S&P500 Index, USD rate, BRENT oil price, Rosneft share price-Pros, Gazprom share price-Pgasp, and Rosneft profit-PROFITros. The output layer of the model has only one parameter-Rosneft forecast profit PROFITros(prog).

IV International Scientific and Practical Conference, 2021

The article analyzes the state of the Russian insurance market and considers its development tren... more The article analyzes the state of the Russian insurance market and considers its development trends. An algorithm for forecasting the net profit of insurance companies using the AI system “Kohonen map” has been proposed. A hypothesis that an AI system enables forecasting of the net profit of a Russian insurance company was proposed and proved. This determines the relevance and practical significance of this study in the context of the digitalization of the economy. In developing a neural network model, performance indicators of the domestic “Top-10 insurance companies” were applied. The analysis showed that the growth was weaker than a year earlier, when the non-life market grew by 8.4%. One of the leaders 2019 was the voluntary health insurance (VHI) that showed an increase of +28.8 billion RUB due to the de-mand for full packaged products, new VHI options in consumer life insurance policies, annual inflation of medical services, and accident and illness insurance that ensured an increase of +17.8 billion RUB that year. The current state of the Russian insurance market and its development trends in the context of digitalization of business processes has been analyzed. There has been developed a mathematical model based on the Kohonen map neural network that makes it possible to forecast the company's net profit based on Data Mining processing. The study of the theoretical foundations of the ap-plication of artificial learning systems in order to solve the problems of the in-surance market in the context of the digitalization of the economy, market un-certainty is very important.

Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy: SPBPU IDE-2020, 2020

The paper considers the theoretical foundations for the use of artificial intelligence systems fo... more The paper considers the theoretical foundations for the use of artificial intelligence systems for managing bank capital. By growth rates of assets of a commercial bank, one can judge about its success and competitiveness. However, any activity of a bank is associated with the risk of growth, and, depending on a size of the bank, the parameters under consideration will differ. The aim of the study is to develop a neural network model to ensure the capital management of a commercial bank. The results obtained represent an increment in scientific knowledge, since the developed neural network algorithm provides a contribution to the scientific field regarding the use of artificial intelligence systems in solving problems in the banking sector. The hypothesis was advanced and proved that the neural network model "Kohonen Map" can be used to forecast the asset growth. Thus, for example, with the input parameters of the model: asset in 2018 - 26972302745 thousand rubles and asse...

Исследованы теоретические основы формирования финансового результата российскими нефтяными компан... more Исследованы теоретические основы формирования финансового результата российскими нефтяными компаниями. В работе анализируются финансовые результаты поквартальной работы нефтяной компании ПАО «НК РОСНЕФТЬ» (далее Роснефть). Выдвинута и доказана гипотеза, что на основе применения искусственного интеллекта – «персептрона», можно получать точный прогноз чистой прибыли нефтяной компании поквартально. На основе исходных данных была сформирована и обучена система искусственного интеллекта. Этот факт определяет актуальность и практическую значимость результатов представленного исследования. Это особенно важно в свете майских указов Президента РФ в современных условиях формирования цифровой экономики и развития нового технологического уклада «Индустрия 4.0».

The theoretical foundations of the GARCH-model to be applied for calculating financial risk have ... more The theoretical foundations of the GARCH-model to be applied for calculating financial risk have been considered. The article discusses the AI neural network for assessing financial risk conditioned by the time series volatility using the GARH method in order to reduce it during exchange trading.

Proceedings of the International Scientific-Practical Conference “Business Cooperation as a Resource of Sustainable Economic Development and Investment Attraction” (ISPCBC 2019), 2019

The purpose of the study was to suggest and prove the hypothesis that a neural network, taking in... more The purpose of the study was to suggest and prove the hypothesis that a neural network, taking into account the dynamics of financial risk and VIX index, makes it possible to develop a profit forecast for organizations of the financial and economic system. For the purpose, the following tasks have been set and solved. On the basis of the OWL technology, the ontology of the financial risk concept in the financial and economic system has been created in the program Untitled-ontology-3. The essence of the "real options" concept, their types and cost estimation procedure using the Black-Scholes and Cox-Ross-Rubinstein models has been investigated. The theoretical foundations of risk management in the operation of the financial and economic system in terms of digitalization have been investigated. The main trends in the development of technologies of a new technological mode (NTM) have been revealed. The theoretical bases of artificial intelligence systems used in organizations of the real sector of the economy and other spheres have been investigated. Based on the Deductor platform, there has been created a neural network, Kohonen Map that takes into account the dynamics of financial risk and VIX option and allows developing a profit forecast for organizations of the financial and economic system. Based on the neural network, a profit forecast for a socioeconomic system has been made. The scientific study required monographic, designcalculation and artificial intelligence methods applied. The main factors that determine the level of financial risk in releasing innovative products in the context of market uncertainty have been identified. The main conclusions that reflect the study results have been formulated and consist in the fact that in modern conditions of market uncertainty and risk, it is important to study the problems associated with the further development of entrepreneurship and innovation not only in the financial sector, but also in the real economy.

International Journal of Advanced Studies, 2021

В ходе проведенного исследования выявлена нелинейная зависимость динамики ВВП от увеличения грузо... more В ходе проведенного исследования выявлена нелинейная зависимость динамики ВВП от увеличения грузооборота. Разработана регрессионная модель, определяющая величину ВВП в зависимости от динамики грузооборота. Следует констатировать, что в России сложился довольно низкий уровень применения цифровых инноваций, по сравнению с зарубежными компаниями развитых стран и продолжает оставаться на низком уровне. Цель: Выдвинута и доказана гипотеза, что с помощью математической модели, можно получить прогноз ВВП России на следующий год на основе использования как линейной регрессионной модели, отражающей зависимость величины ВВП от динамики грузооборота, так и при использовании AI-модели «персептрон», включающей в себя совокупность данных, отражающих развитие экономики. Метод, или методология проведения работы: В работе применялись такие методы исследования, как: монографический, аналитический, линейная регрессия и нелинейная математическая модель, а также анализ, изучение и обобщение. Результаты:...

Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020), 2020

The article considered the theoretical basis for the calculation of financial risks, their hedgin... more The article considered the theoretical basis for the calculation of financial risks, their hedging with the help of artificial intelligence. The article presents a neural network for the assessment of financial risk for atime sequence of VAR-methods with a view to mitigate it when engaging in stock exchange trade. A hypothesis was formulated and proved that with the help of the developed AI-system that had been taught on the basis of a combined data sample with digitilized "new fluctuations" information from web-sites and time sequence candlestick charts for SiU9 US Dollar futures contracts in a 15-minute timeframe, it is possible to increase the accuracy of a futures price forecast and ensure the assessment of financial risks using the VAR-method for hedging an uncovered position with a PUT option. As is shown by scientific research, today the majority of stock operations are conducted using trade systems, or trade robots, and their number is continuously increasing. Among trade systems, we can single out the ones using artificial intelligence (AI). The novelty of the research results from the fact that in order to forecast the price of a futures contract, both candlestick charts parameters and size and digitilized "news fluctuations" received by Skraper programme from web-sites were used to teach the neural network. The created data set was used in the perceptron with 305 parameters in the input layer, 2 hidden layers by 100 10 parameters correspondingly and an output layer with one parameter, the target price. The perceptron was created and used on the Deductor platform, and the Python-based Skraper programme was put into Spark framework and was employed with the help of parallel calculations. The results yielded by the neural network were analysed concerning the accuracy of the forecast of the price of a financial instrument, a SiU9 futures contract at MoEx, a Russian stock exchange, when working in a 15-minute timeframe. The hedging mechanism for an uncovered long position of a SiU9 futures was activate by buying two PUT Si-9 options with an increasing risk parameter that had been calculated using the VaR-method. As a result of the conducted research, a neural network trade algorithm was created for speculative trading at the stock exchange with a SiU9 USD futures contract with high accuracy and minimal loss risks.

Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020), 2020

This paper presents the theory behind using AI for big data processing. It considers an artificia... more This paper presents the theory behind using AI for big data processing. It considers an artificial neural network designed to make decisions whether to open a position on a SiU9 futures contract; to that end, Spark-based big data processing is applied. It is hypothesized and proven herein that the futures closing price can be predicted by the developed AI trained on a dataset containing web-parsed digitized 'news-related' fluctuations as well as time-series Japanese candlesticks of a SiU9 futures contract on a 15-minute timeframe. Research has shown that in the modern world, the ever-larger bulk of stock transactions are done by using trading systems (trading robots). More and more of them use AI. The novelty hereof is that the futures contract price is predicted by an AI trained not only on quantities and Japanese candlestick parameters, but also on the digitized news-related fluctuations collected by Skraper from websites. The dataset generated that was was fed to a perceptron that had a 305-parameter input layer, two hidden layers having (100 parameters and 10 parameters), and an output layer that gave the predicted price. The perceptron was created and run on the Deductor platform, while Skraper was a Python program in the Spark framework. The research team further analyzed how accurately the ANN could predict the price of a financial instrument, the SiU9 futures contract, on a Russian stock exchange over a 15-minute timeframe. The study has effectively produced an ANN-based trading algorithm for opening long and short SiU9 positions in the fixed-term stock market; the algorithm has a good predictive accuracy.

Communications in Computer and Information Science, 2021

Alma mater. Vestnik Vysshey Shkoly, 2018

Krasnoyarsk Science

Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy

International Journal of Technology

Mezhdunarodnaja jekonomika (The World Economics)

Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy, 2019

Proceedings of the Volgograd State University International Scientific Conference "Competitive, Sustainable and Safe Development of the Regional Economy" (CSSDRE 2019), 2019

Proceedings of the International Scientific Conference "Competitive, Sustainable and Secure Development of the Regional Economy: Response to Global Challenges" (CSSDRE 2018), 2018

IV International Scientific and Practical Conference, 2021

Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy: SPBPU IDE-2020, 2020

Proceedings of the International Scientific-Practical Conference “Business Cooperation as a Resource of Sustainable Economic Development and Investment Attraction” (ISPCBC 2019), 2019

International Journal of Advanced Studies, 2021

Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020), 2020

Communications in Computer and Information Science, 2021

Alma mater. Vestnik Vysshey Shkoly, 2018

Digest Finance, 2018

Importance The article studies the evolution of credit portfolios of the Russian banks during the... more Importance The article studies the evolution of credit portfolios of the Russian banks during the analyzable using the self-organizing map (SOM). Objectives The article aims to prove or refute the hypothesis that by using a neural network, i.e. self-organizing map, it is possible to predict changes in the market share of bank's credit portfolio. Methods For the study, we used the self-organizing map. Results We have developed and now present a neural network model that helps predict the market share of a credit portfolio in a changing market under economic uncertainty environment. Conclusions and Relevance The application of the self-organizing map is important for obtaining some statistical information on commercial banks in the model clusters, as well as for forecasting the market share of the organization in a changing market environment. The findings can be used in bank marketing to predict the market share of the bank when the size of its portfolio changes.