Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system

Omega - Tập 115 - Trang 102787 - 2023
Abtin Ijadi Maghsoodi1,2
1Department of Information Systems and Operations Management, Faculty of Business and Economics, Business School, University of Auckland, Auckland 1010, New Zealand
2Department of Intelligence & Insights, Te Whatu Ora Health New Zealand Waikato District, Hamilton 3240, New Zealand

Tài liệu tham khảo

Haugen, 1988, Bankruptcy and agency costs: their significance to the theory of optimal capital structure, J Financ Quant Anal, 23, 27, 10.2307/2331022 Jiang, 2017, Cryptocurrency portfolio management with deep reinforcement learning, 2017 Intell Syst Conf, 905 Li, 2014, Online portfolio selection: A survey, ACM Computing Surveys (CSUR), 46, 1 Nakamoto, 2008, Bitcoin: a peer-to-peer electronic cash system, Decentralized, Bus Rev, 21260 Narayanan, 2016 Pintelas, 2020, Investigating the problem of cryptocurrency price prediction, A Deep Learning Approach, 99 Dixon Jr, 2019, Cryptocurrency: the Next Step in the Noncash Era?, Judges J, 58, 37 Rubinstein, 2002, Markowitz's “portfolio selection”: a fifty-year retrospective, J Finance, 57, 1041, 10.1111/1540-6261.00453 Ehrgott, 2004, An MCDM approach to portfolio optimization, Eur J Oper Res, 155, 752, 10.1016/S0377-2217(02)00881-0 Li, 2015, A fuzzy portfolio selection model with background risk, Appl Math Comput, 256, 505 Markowitz, 1968 Ho, 2011, Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM, Expert Syst Appl, 38, 16, 10.1016/j.eswa.2010.05.058 Rahiminezhad Galankashi, 2020, Portfolio selection: a fuzzy-ANP approach, Financ Innov, 6, 17, 10.1186/s40854-020-00175-4 Aouni, 2018, On the increasing importance of multiple criteria decision aid methods for portfolio selection, J Oper Res Soc, 69, 1525, 10.1080/01605682.2018.1475118 Aljinović, 2021, Cryptocurrency portfolio selection—a multicriteria approach, Math, 9 Van Heerden, 2021, Evaluation of the importance of criteria for the selection of cryptocurrencies, ArXiv Prepr Sebastião, 2021, Forecasting and trading cryptocurrencies with machine learning under changing market conditions, Financ Innov, 7 Olvera-Juarez, 2019, Forecasting bitcoin pricing with hybrid models: a review of the literature, Int J Adv Eng Res Sci, 6, 161, 10.22161/ijaers.69.18 Ijadi Maghsoodi, 2018, CLUS-MCDA: a novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem, Comput Ind Eng, 118, 409, 10.1016/j.cie.2018.03.011 Ijadi Maghsoodi, 2020, An integrated parallel big data decision support tool using the W-CLUS-MCDA: a multi-scenario personnel assessment, Knowl-Base Syst, 195 Opricovic, 2007, Extended VIKOR method in comparison with outranking methods, Eur J Oper Res, 178, 514, 10.1016/j.ejor.2006.01.020 Bontempi, G, Ben Taieb, S, Le Borgne, YA. Machine Learning Strategies for Time Series Forecasting. In: Aufaure, MA., Zim..nyi, E. (eds) Business Intelligence. eBISS 2012. Lecture Notes in Business Information Processing, vol 138. Springer, Berlin, Heidelberg; 2013. doi:10.1007/978-3-642-36318-4_3. Athiyarath, 2020, A comparative study and analysis of time series forecasting techniques, SN Comput Sci, 1, 175, 10.1007/s42979-020-00180-5 Palit, 2005 De Gooijer, 2006, 25 years of time series forecasting, Int J Forecast, 22, 443, 10.1016/j.ijforecast.2006.01.001 Ahmed, 2010, An empirical comparison of machine learning models for time series forecasting, Econom Rev, 29, 594, 10.1080/07474938.2010.481556 Taylor, 2018, Forecasting at scale, Am Stat, 72, 37, 10.1080/00031305.2017.1380080 Shen, 2020, Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea, PeerJ, 8, 10.7717/peerj.9961 Papacharalampous, 2018, Evaluation of random forests and Prophet for daily streamflow forecasting, Adv Geosci, 45, 201, 10.5194/adgeo-45-201-2018 Yenidogan, 2018, Bitcoin forecasting using ARIMA and PROPHET, 621 Hastie, 1987, Generalized additive models: some applications, J Am Stat Assoc, 82, 371, 10.1080/01621459.1987.10478440 Rezaei, 2015, Best-worst multi-criteria decision-making method, Omega, 53, 49, 10.1016/j.omega.2014.11.009 Maghsoodi, AI, Rasoulipanah, H, L..pez, LM, Liao, H, Zavadskas, EK. Integrating interval-valued multi-granular 2-tuple linguistic BWM-CODAS approach with target-based attributes: Site selection for a construction project. Computers & Industrial Engineering, 2020;139:106–147. doi:10.1016/j.cie.2019.106147. Ijadi Maghsoodi, 2020, Integrating interval-valued multi-granular 2-tuple linguistic BWM-CODAS approach with target-based attributes: site selection for a construction project, Comput Ind Eng, 139, 10.1016/j.cie.2019.106147 Ijadi Maghsoodi, 2019, Service quality measurement model integrating an extended SERVQUAL model and a hybrid decision support system, Eur Res Manag Bus Econ, 25, 151, 10.1016/j.iedeen.2019.04.004 Ijadi Maghsoodi, 2020, A phase change material selection using the interval-valued target-based BWM-CoCoMULTIMOORA approach: a case-study on interior building applications, Appl Soft Comput, 95, 10.1016/j.asoc.2020.106508 Mi, X, Tang, M, Liao, H, Shen, W, Lev, B. The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?. Omega 2019;87:205–225. doi:10.1016/j.omega.2019.01.009. Ijadi Maghsoodi, 2019, Hybrid hierarchical fuzzy group decision-making based on information axioms and BWM: prototype design selection, Comput Ind Eng, 127, 788, 10.1016/j.cie.2018.11.018 Jahan, 2011, A comprehensive VIKOR method for material selection, Mater Des, 32, 1215, 10.1016/j.matdes.2010.10.015 Ijadi Maghsoodi, 2018, Selection of waste lubricant oil regenerative technology using entropy-weighted risk-based fuzzy axiomatic design approach, Inform, 29, 41 Jahan, 2013, Weighting of dependent and target-based criteria for optimal decision-making in materials selection process: biomedical applications, Mater Des, 49, 1000, 10.1016/j.matdes.2013.02.064 Hafezalkotob, 2015, Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications, Mater Des, 87, 949, 10.1016/j.matdes.2015.08.087 Ijadi Maghsoodi, 2019, Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes, Arch Civ Mech Eng, 19, 1194, 10.1016/j.acme.2019.06.010 Peng, 2012, A multicriteria decision making approach for estimating the number of clusters in a data set, PLoS One, 7 Friedman, 2001 Syakur, 2018, Integration K-means clustering method and elbow method for identification of the best customer profile cluster, {IOP} Conf Ser Mater Sci Eng, 336, 12017, 10.1088/1757-899X/336/1/012017 Chévez, 2017, Application of the k-means clustering method for the detection and analysis of areas of homogeneous residential electricity consumption at the Great La Plata region, Buenos Aires, Argentina, Sustain Cities Soc, 32, 115, 10.1016/j.scs.2017.03.019 Bock, 2007, Clustering methods: a History of k-means algorithms, 161 Ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd, 226 Wang, 2015, Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to data, 445 Sharma, 2016, Improved density based spatial clustering of applications of noise clustering algorithm for knowledge discovery in spatial data, Math Probl Eng, 1, 10.1155/2016/1564516 Hahsler, 2019, dbscan: fast density-based clustering with R, J Stat Softw, 91, 1, 10.18637/jss.v091.i01 Zoraghi, 2013, A fuzzy MCDM model with objective and subjective weights for evaluating service quality in hotel industries, J Ind Eng Int, 9, 38, 10.1186/2251-712X-9-38 Brauers, 2006, The MOORA method and its application to privatization in a transition economy by A new method : the MOORA method, Control Cybern, 35, 445 Hafezalkotob, 2018, An overview of MULTIMOORA for multi-criteria decision-making: theory, developments, applications, and challenges, Inf Fusion Ijadi Maghsoodi, 2019, Evaluation of the influencing factors on job satisfaction based on combination of PLS-SEM and F-MULTIMOORA approach, Symmetry (Basel), 11 Hafezalkotob, 2016, Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree: biomaterials selection, J Ind Eng Int Ijadi Maghsoodi, 2018, Renewable energy technology selection problem using integrated H-SWARA-MULTIMOORA approach, Sustainability, 10, 4481, 10.3390/su10124481 Chen, 2021, A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment, Appl Soft Comput, 105, 10.1016/j.asoc.2021.107222 Zelany, 1974, A concept of compromise solutions and the method of the displaced ideal, Comput Oper Res, 1, 479, 10.1016/0305-0548(74)90064-1 Opricovic, 2004, Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, Eur J Oper Res, 156, 445, 10.1016/S0377-2217(03)00020-1 Tian, 2021, A sustainability evaluation framework for WET-PPP projects based on a picture fuzzy similarity-based VIKOR method, J Clean Prod, 289, 10.1016/j.jclepro.2020.125130 Büyüközkan, 2021, A decision-making framework for evaluating appropriate business blockchain platforms using multiple preference formats and VIKOR, Inf Sci (Ny), 571, 337, 10.1016/j.ins.2021.04.044 Demir, 2018, A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT, Expert Syst Appl, 114, 479, 10.1016/j.eswa.2018.07.071 Shekhovtsov, 2020, A comparative case study of the VIKOR and TOPSIS rankings similarity, Procedia Comput Sci, 176, 3730, 10.1016/j.procs.2020.09.014 Hafezalkotob, 2017, Interval target-based VIKOR method supported on interval distance and preference degree for machine selection, Eng Appl Artif Intell, 57, 184, 10.1016/j.engappai.2016.10.018 Ijadi Maghsoodi, A. Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm, Mendeley Data, V2 (2022). doi:10.17632/37nb83jwtd.2.