Market-neutral trading with fuzzy inference, a new method for the pairs trading strategy

Bayram, Mehmet; Akat, Muzaffer

doi:10.5755/j01.ee.30.4.14350

Cited by 4 publications

(4 citation statements)

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“…Therefore, this research requires the use of appropriate mathematical apparatus. To solve these problems, scientists have actively used statistical methods (Adeyemo et al, 2020), regression models (Safarov et al, 2022), lag econometric models (Tiutiunyk et al, 2022b), vector error correction (Lyulyov et al, 2021), mar-spline approach (Bozhenko et al, 2022a), DEA-model and its extension (Wang et al, 2022), Fuzzy Logic (Bayram & Akat, 2019), game theory (Stehel et al, 2019), Artificial Intelligence (Gupta & Mishra, 2022) and Data Mining tools (Kuzmenko et al, 2020). In this study, Data Mining tools will be the most effective, allowing you to use different types and arrays of data, conduct analysis according to various parameters, and easily interpret the obtained results.…”

Section: Literature Reviewmentioning

confidence: 99%

Socio-economic profiles of countries - cybercrime victims

Yarovenko

Łopatka

Vasilyeva

et al. 2023

Economics &Amp; Sociology

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Adeyemo, K. A., Isiavwe, D., Adetula, D., Olamide, O., & Folashade, O. (2020). Mandatory adoption of the Central Bank of Nigeria’s cashless and e-payment policy: implications for bank customers. Banks and Bank Systems, 15(2), 243-253. https://doi.org/10.21511/bbs.15(2).2020.21 Barabashev, A., Makarov, I., & Zarochintcev, S. (2022). How to shape government policies on high-technology development using the indicative evaluation of risks? Administratie si Management Public, 38, 70-89. https://doi.org/10.24818/amp/2022.38-04 Bayram, M., & Akat, M. (2019). Market-Neutral Trading with Fuzzy Inference, a New Method for the Pairs Trading Strategy. Engineering Economics, 30(4), 411-421. https://doi.org/10.5755/j01.ee.30.4.14350 Bing, C., & Schectman, J. (2019). Inside the UAE’s secret hacking team of American mercenaries. Retrieved from: https://www.reuters.com/investigates/special-report/usa-spying-raven/ (31.01.2023). Bozhenko, V. (2022). Tackling corruption in the health sector. Health Economics and Management Review, 3(3), 32-39. https://doi.org/10.21272/hem.2022.3-03 Bozhenko, V. V., Lyeonov, S. V., Polishchuk, E. A., Boyko, A. O., & Artyukhova, N. O. (2022). Identification of determinants of corruption in government: a mar-spline approach. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 6, 176-180. https://doi.org/10.33271/nvngu/2022-6/176 Bozhenko, V., Mynenko, S., & Shtefan, A. (2022b). Financial Fraud Detection on Social Networks Based on a Data Mining Approach. Financial Markets, Institutions and Risks, 6(4), 119-124. https://doi.org/10.21272/fmir.6(4).119-124.2022 Caballero-Morales, S.-O., Cordero Guridi, J. de J., Alvarez-Tamayo, R. I., & Cuautle-Gutiérrez, L. (2020). EDUCATION 4.0 to support entrepreneurship, social development and education in emerging economies. International Journal of Entrepreneurial Knowledge, 8(2), 89-100. https://doi.org/10.37335/ijek.v8i2.119 Chen, Y., Xu, S., Lyulyov, O., & Pimonenko, T. (2023). China’s digital economy development: incentives and challenges. Technological and Economic Development of Economy, 29(2), 518-538. https://doi.org/10.3846/tede.2022.18018 Ćwiklicki, M., & Wojnarowska, M. (2020). Circular Economy and Industry 4.0: One-Way or Two-way Relationships? Engineering Economics, 31(4), 387-397. https://doi.org/10.5755/j01.ee.31.4.24565 DavidPur, N. (2022). Which Countries are Most Dangerous? Cyber Attack Origin – by Country. Retrieved from: https://blog.cyberproof.com/blog/which-countries-are-most-dangerous (31.01.2023). Dečman, M., Stare, J., & Klun, M. (2022). The impact of the COVID-19 crisis on the development of the information society in Slovenia. Administratie si Management Public, 39, 77-96. https://doi.org/10.24818/amp/2022.39-05 Deutsche Welle (2022). Ukrainian websites hacked in 'global attack'. Retrieved from: https://www.dw.com/en/ukraine-government-websites-hacked-in-global-attack/a-60421475 (31.01.2023). Dluhopolskyi, O., Pakhnenko, O., Lyeonov, S., Semenog, A., Artyukhova, N., Cholewa-Wiktor, M., & Jastrzębski, W. (2023). Digital financial inclusion: COVID-19 impacts and opportunities. Sustainability (Switzerland), 15(3), 2383. https://doi.org/10.3390/su15032383 Economist Intelligence (2023). Democracy Index. Retrieved from: https://www.eiu.com/n/campaigns/democracy-index-2022/?utm_source=google&utm_medium=paid-search&utm_campaign=democracy-index-2022&gclid=CjwKCAjwgqejBhBAEiwAuWHioAEruOQA25JyHg-61MBEiYNJp9hvu3Pf91E_tWO2W0nauZ6on003ORoC6UsQAvD_BwE (31.01.2023). E-Governance Academy (2023). National Cyber Security Index. Retrieved from: https://ncsi.ega.ee/ncsi-index/ (31.01.2023). Fobel, P., & Kuzior, A. (2019). The future (Industry 4.0) is closer than we think. Will it also be ethical? Paper presented at the AIP Conference Proceedings, 2186. https://doi.org/10.1063/1.5137987 Glova, J., Bernatik, W., & Tulai, O. (2020). Determinant Effects of Political and Economic Factors on Country Risk: An Evidence from the EU Countries. Montenegrin Journal of Economics, 16(1), 37-53. https://doi.org/10.14254/1800-5845/2020.16-1.3 Gontareva, I., Babenko, V., Kuchmacz, B., & Arefiev, S. (2020). Valuation of information resources in the analysis of cybersecurity entrepreneurship. Estudios De Economia Aplicada, 38(4), https://doi.org/10.25115/EEA.V38I4.3984 Gupta, A., & Mishra, M. (2022). Ethical Concerns While Using Artificial Intelligence in Recruitment of Employees. Business Ethics and Leadership, 6(2), 6-11. https://doi.org/10.21272/bel.6(2).6-11.2022 Gurbanov, N., Yagublu, N., Akbarli, N., & Niftiyev, I. (2022). Digitalization and the Covid-19-led public crisis management: an evaluation of financial sustainability in the Azerbaijan business sector. SocioEconomic Challenges, 6(3), 23-38. https://doi.org/10.21272/sec.6(3).23-38.2022 Institute for Economics and Peace (2022). Global Terrorism Index 2022. Retrieved from: https://reliefweb.int/report/world/global-terrorism-index-2022 (31.01.2023). Kaspersky (2023). Cyberthreat real-time map. Retrieved from: https://cybermap.kaspersky.com/ (31.01.2023). Krebs, B. (2021). At Least 30,000 U.S. Organizations Newly Hacked Via Holes in Microsoft’s Email Software. Retrieved from: https://krebsonsecurity.com/2021/03/at-least-30000-u-s-organizations-newly-hacked-via-holes-in-microsofts-email-software/ (31.01.2023). Kumar, N., & Kumar, J. (2019). Efficiency 4.0 for Industry 4.0. Human Technology, 15(1), 55-78. https://doi.org/10.17011/ht/urn.201902201608 Kurniawati, E., Kohar, U.H.A., & Pirzada, K. (2022). Change or destroy: the digital transformation of Indonesian MSMES to achieve sustainable economy. Polish Journal of Management Studies, 26(2), 248-264. https://doi.org/10.17512/pjms.2022.26.2.15 Kuzior, A., & Kwilinski, A. (2022). Cognitive technologies and artificial intelligence in social perception. Management Systems in Production Engineering, 30(2), 109-115. https://doi.org/10.2478/mspe-2022-0014 Kuzmenko, O., Šuleř, P., Lyeonov, S., Judrupa, I., & Boiko, A. (2020). Data mining and bifurcation analysis of the risk of money laundering with the involvement of financial institutions. Journal of International Studies, 13(3), 332-339. https://doi.org/10.14254/2071-8330.2020/13-3/22 Lăzăroiu, G., Androniceanu, A., Grecu, I., Grecu, G., & Neguriță, O. (2022). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustain-able cyber-physical management systems in big data-driven cognitive manufacturing. Oeconomia Copernicana, 13(4), 1047-1080. https://doi.org/10.24136/oc.2022.030 Lucas, G. (2016). Ethics and Cyber Warfare: The Quest for Responsible Security in the Age of Digital Warfare. Oxford University Press. Lyulyov, O., Lyeonov, S., Tiutiunyk, I., & Podgórska, J. (2021). The impact of tax gap on macroeconomic stability: Assessment using panel VEC approach. Journal of International Studies, 14(1), 139-152. https://doi.org/10.14254/2071-8330.2021/14-1/10 Mačiulytė-Šniukienė, A., Butkus, M., & Davidavičienė, V. (2022). Development of the model to examine the impact of infrastructure on economic growth and convergence. Journal of Business Economics and Management, 23(3), 731-753. https://doi.org/10.3846/jbem.2022.17140 Melnyk, L., Derykolenko, O., Kubatko, O., & Matsenko, O. (2019). Business models of reproduction cycles for digital economy. Paper presented at the CEUR Workshop Proceedings, 2393, 269-276. Retrieved from https://www.scopus.com/record/display.uri?eid=2-s2.0-85069504652&origin=resultslist Melnyk, L., Kubatko, O., Piven, V., Klymenko, K., & Rybina, L. (2021). Digital and economic transformations for sustainable development promotion: A case of OECD countries. Environmental Economics, 12(1), 140-148. https://doi.org/10.21511/EE.12(1).2021.12 Millia, H., Adam, P, Muhatlib, A. A., & Tajuddin and Pasrun, Y. P. (2022). The Effect of Inward Foreign Direct Investment and Information and Communication Technology on Economic Growth in Indonesia. AGRIS on-line Papers in Economics and Informatics, 14(1), 69-79. https://doi.org/10.7160/aol.2022.140106 Mnohoghitnei, I., Horobeț, A., & Belașcu, L. (2022). Bitcoin is so Last Decade-How Decentralized Finance (DeFi) could Shape the Digital Economy. European Journal of Interdisciplinary Studies, 14(1), 87-99. https://doi.org/10.24818/ejis.2022.01 Numbeo (2023). Crime Index by Country 2022. Retrieved from: https://www.numbeo.com/crime/rankings_by_country.jsp?title=2022 (31.01.2023). Orlov, V., Bukhtiarova, A., Marczuk, M., & Heyenko, M. (2021). International economic and social determinants of the state economic security: A causal analysis. Problems and Perspectives in Management, 19(4), 301-310. https://doi.org/10.21511/ppm.19(4).2021.24 Pakhnenko, O., & Kuan, Z. (2023). Ethics of Digital Innovation in Public Administration. Business Ethics and Leadership, 7(1), 113-121. https://doi.org/10.21272/bel.7(1).113-121.2023 Pakhnenko, O., Rubanov, P., Girzheva, O., Ivashko, L., Britchenko, I., & Kozachenko, L. (2022). Cryptocurrency: Value formation factors and investment risks. Journal of Information Technology Management, 14, 179-200. https://doi.org/10.22059/JITM.2022.88896 Perlroth, N., Scott, M, & Frenkel, S. (2017). Cyberattack Hits Ukraine Then Spreads Internationally. Retrieved from: https://www.nytimes.com/2017/06/27/technology/ransomware-hackers.html (31.01.2023). Remeikienė, R., Ligita, G., Fedajev, A., Raistenskis, E., & Krivins, A. (2022). Links between crime and economic development: EU classification. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 909-938. https://doi.org/10.24136/eq.2022.031 Rousseeuw, P.J. (1987). Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7 Safarov, G., Sadiqova, S., Urazayeva, M., & Abbasova, N (2022). Theoretical and Methodological Aspects of Innovative-Industrial Cluster Development in the Era of Digitalization. Marketing and Management of Innovations, 4, 184-197. https://doi.org/10.21272/mmi.2022.4-17 Șavga, L. (2019). Implementing the Smart Specialization Concept in the Republic of Moldova: Challenges and Initiatives. Journal of Research on Trade, Management and Economic Development, 6(2), 6-17. Şavga, L., & Baran, T. (2022). Boosting the process of smart specialization in the Republic of Moldova. Paper presented in Contemporary Issues in Economy and Technology (pp. 187-196). Shao, X., Wang, D., Li, X., & Shao, H. (2022). Impact of Internet technology on spatial technology heterogeneity: openness or convergence - evidence from provincial data in China. Transformations in Business & Economics, 21(2), 193-213. Shkolnyk, I., Frolov, S., Orlov, V., Datsenko, V., & Kozmenko, Y. (2022). The impact of financial digitalization on ensuring the economic security of a country at war: New measurement vectors. Investment Management and Financial Innovations, 19(3), 119-138. https://doi.org/10.21511/imfi.19(3).2022.11 Smith, E.T. (2013). Cyber warfare: a misrepresentation of the true cyber threat. American Intelligence Journal, 31(1), 82-85. Sobczak, A. (2022). Analysis of the Conditions Influencing the Assimilation of the Robotic Process Automation by Enterprises. Human Technology, 18(2), 143-190. doi: 10.14254/1795-6889.2022.18-2.4 Statista (2023). Most commonly reported cyber crime categories worldwide in 2022, by number of individuals affected. Retrieved from: https://www.statista.com/statistics/184083/commonly-reported-types-of-cyber-crime-global/ (31.01.2023). Stehel, V., Vochozka, M., Kliestik, T., & Bakes, V. (2019). Economic analysis of implementing VMI model using game theory. Oeconomia Copernicana, 10(2), 253-272. https://doi.org/10.24136/oc.2019.013 Straková, J., Talíř, M., & Váchal, J. (2022). Opportunities and threats of digital transformation of business models in SMEs. Economics and Sociology, 15(3), 159-171. https://doi.org/10.14254/2071-789X.2022/15-3/9 The Heritage Foundation (2023). 2023 Index of Economic Freedom. Retrieved from: https://www.heritage.org/index/download (31.01.2023). The World Bank (2023). Life expectancy at birth, total (years). Retrieved from: https://data.worldbank.org/indicator/SP.DYN.LE00.IN (31.01.2023). Tiutiunyk, I. V., Zolkover, A. O., Lyeonov, S. V., & Ryabushka, L. B. (2022a). The impact of economic shadowing on social development: challenges for macroeconomic stability. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 1, 183-191. https://doi.org/10.33271/nvngu/2022-1/183 Tiutiunyk, I., Cieśliński, W., Zolkover, A., & Vasa, L. (2022b). Foreign direct investment and shadow economy: One-way effect or multiple-way causality? Journal of International Studies, 15(4), 196-212. https://doi.org/10.14254/2071-8330.2022/15-4/12 Tran, L. Q. T., Phan, D. T., Herdon, M., & Kovacs, L. (2022). Assessing the Digital Transformation in Two Banks: Case Study in Hungary. AGRIS on-line Papers in Economics and Informatics, 14(2), 121-134. https://doi.org/10.7160/aol.2022.140210 Transparency International (2023). Corruption_Perceptions_Index. Retrieved from: https://www.transparency.org/en/cpi/2021?gclid=CjwKCAjw67ajBhAVEiwA2g_jEPyd355cvDdhD7SdWVteYeer5WvV3BZFHMo-Ox6p3vXSGk9wKi4p4BoCRJgQAvD_BwE (31.01.2023). Tribune (2020). Major cyber attack by Indian intelligence identified: ISPR. Retrieved from: https://tribune.com.pk/story/2259193/major-cyber-attack-by-indian-intelligence-identified-ispr (31.01.2023). Tvaronaviciene, M., & Burinskas, A. (2020). Industry 4.0 significance to competition and the eu competition policy. Economics & Sociology, 13(3), 244-258. https://doi.org/10.14254/2071-789X.2020/13-3/15 U.S. Department of Homeland Security (2016). Joint Statement from the Department of Homeland Security and Office of the Director of National Intelligence on Election Security. Retrieved from: https://www.dhs.gov/news/2016/10/07/joint-statement-department-homeland-security-and-office-director-national (31.01.2023). Vasudevan, H. (2022). Management and Leadership in the Klang Valley IT Sector: Conceptual Approach. Marketing and Management of Innovations, 3, 56-65. https://doi.org/10.21272/mmi.2022.3-05 Vitvitskiy, S. S., Kurakin, O. N., Pokataev, P. S., Skriabin, O. M., & Sanakoiev, D. B. (2021). Peculiarities of cybercrime investigation in the banking sector of Ukraine: review and analysis. Banks and Bank Systems, 16(1), 69-80. https://doi.org/10.21511/bbs.16(1).2021.07 Voo, J., Hemani, I., & Cassidy, D. (2022). National Cyber Power Index 2022. Retrieved from: https://www.belfercenter.org/sites/default/files/files/publication/CyberProject_National%20Cyber%20Power%20Index%202022_v3_220922.pdf (31.01.2023). Voronenko, I., Nehrey, M., Laptieva, A., Babenko, V., & Rohoza, K. (2022). National cybersecurity: Assessment, risks and trends. International Journal of Embedded Systems, 15(3), 226-238. https://doi.org/10.1504/IJES.2022.124854 Wang, Q., Chen, Y., Guan, H., Lyulyov, O., & Pimonenko, T. (2022). Technological innovation efficiency in China: Dynamic evaluation and driving factors. Sustainability (Switzerland), 14(14). https://doi.org/10.3390/su14148321 Wisevoter (2023). Most Powerful Countries in the World. Retrieved from: https://wisevoter.com/country-rankings/most-powerful-countries-in-the-world/ (31.01.2023). World Happiness Report (2023). World Happiness Report 2022. Retrieved from: https://worldhappiness.report/ed/2022/ (31.01.2023). Yarovenko, H. (2020). Evaluating the threat to national information security. Problems and Perspectives in Management, 18(3), 195-210. https://doi.org/10.21511/ppm.18(3).2020.17 Yarovenko, H., & Rogkova, M. (2022). Dynamic and bibliometric analysis of terms identifying the combating financial and cyber fraud system. Financial Markets, Institutions and Risks, 6(3), 93-104. https://doi.org/10.21272/fmir.6(3).93-104.2022 Yoshimori, H., & Yoshimori, M. (2022). An Education Gift – Integrated Cognitive and Non-Cognitive Skills – for Future Generations to Grow the Economy in the Digital Phase. SocioEconomic Challenges, 6(2), 5-18. https://doi.org/10.21272/sec.6(2).5-18.2022 Yu, Y., Xinxin, W., Ruoxi, L., & Tingting, Y. (2023). The Mediating Role of Human Capital in the Relationship between Education Expenditure and Science and Technology Innovation: Evidence from China. SocioEconomic Challenges, 7(1), 129-138. https://doi.org/10.21272/sec.7(1).129-138.2023 Zimaitis, I., Urbonavičius, S., Degutis, M., & Kaduškevičiūtė, V. (2022). Influence of trust and conspiracy beliefs on the disclosure of personal data online. Journal of Business Economics and Management, 23(3), 551-568. https://doi.org/10.3846/jbem.2022.16119

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Section: Literature Reviewmentioning

confidence: 99%

Socio-economic profiles of countries - cybercrime victims

Yarovenko

Łopatka

Vasilyeva

et al. 2023

Economics &Amp; Sociology

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“…Fuzzy logic can also be used as part of a pair trading strategy. It can partially solve the problem of the difficult prediction of this market-neutral arbitrage strategy and increase its returns, which has been proven by comparing it with traditional technical analysis methods for trading within the spread [28].…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

2023

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The article presents the predictive capabilities of a fuzzy multi-criteria evaluation system that operates on the basis of a non-fuzzy neural approach, but also one that is capable of implementing a learning paradigm and working with vague concepts. Within this context, the necessary elements of fuzzy logic are identified and the algebraic formulation of the fuzzy system is presented. It is with the help of the aforementioned that the task of predicting the short-term trend and price of the Tesla share is solved. The functioning of a fuzzy system and fuzzy neural network in the field of time series value prediction is discussed. The authors are inclined to the opinion that, despite the fact that a fuzzy neural network reacts in terms of applicability and effectiveness when solving prediction problems in relation to input data with a faster output than a fuzzy system, and is more “user friendly”, a sufficiently knowledgeable and experienced solver/expert could, by using a fuzzy system, achieve a higher speed of convergence in the learning process than a fuzzy neural network using the minimum range of input data carrying the necessary information. A fuzzy system could therefore be a possible alternative to a fuzzy neural network from the point of view of prediction.

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“…Recently, a lot of attention has been paid to application of mathematical tools in the sphere of increase of purchases' quality. Thus, the results of studying the financial tools of pricing and forecasting of the stock market are given in Bayram and Akat (2019). The authors offer a mathematical tool of fuzzy inference for implementation of trade.…”

Section: Directions Of Research For Evaluating the Quality Of Budget Purchasesmentioning

confidence: 99%

Public procurement quality assessment of a region: regression analysis

Белокрылова

Белокрылов

et al. 2020

IJSSP

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PurposeThe paper aims to present the results of a study aimed at constructing economic and mathematical models used in decision-making in the management of budget procurement of a region, contributing to the efficient use of budget funds. One of the key problems in this area is the assessment of the quality of procurementDesign/methodology/approachOne of the key problems in this area is the assessment of the quality of procurement, the solution of which requires the application of economic and mathematical methods, models, tools and algorithms built into information systems for supporting decision-making. The introduction of these systems brings to a qualitatively new level the system of relations between subjects of budget procurement. The paper analyzes modern publications on the implementation of public procurement and related issues of efficiency (savings) of budgetary funds.FindingsThe indicators on the basis of which the evaluation of the quality of regional procurement is determined are proposed. The architecture of the information system for decision support in the evaluation of procurement quality has been developed. The system has the property of adapting to changes in the state of the environment through the constant accumulation of statistical data characterizing the procurement and adjustment of the parameters of the mathematical model in accordance with them. A mathematical model is built into the control loop that allows ranking the indicators of budget purchases by the degree of their influence on the effective attribute. The role of a functional feature is the quality of budget procurement. The mathematical model is based on the application of the mathematical apparatus of correlation and regression analysis. The impact of each of the factor attributes on the functional attribute has been assessed.Originality/valueThe mathematical model is based on the application of the mathematical apparatus of correlation and regression analysis. The impact of each of the factor attributes on the functional attribute has been assessed. Real statistics are given that characterize budget procurements carried out in retrospect. The coefficients of the linear regression model are determined based on the use of the STATISTICA application software package. The proposed decision support information system with a regression model built into it allows one to make scientifically and quantitatively sound decisions when assessing the quality of budget purchases of a region.

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Market-neutral trading with fuzzy inference, a new method for the pairs trading strategy

Cited by 4 publications

References 35 publications

Socio-economic profiles of countries - cybercrime victims

Socio-economic profiles of countries - cybercrime victims

A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study

Public procurement quality assessment of a region: regression analysis

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