Forecasting Cryptocurrency Investment Return Using Time Series and Monte Carlo Simulation

被引:0
|
作者
Zornic, Nikola [1 ]
Markovic, Aleksandar [1 ]
Cavoski, Sava [2 ]
机构
[1] Univ Belgrade, Fac Org Sci, Dept Business Syst Org, Jove Ilica 154, Belgrade 11000, Serbia
[2] Metropolitan Univ, FEFA, Bulevar Zorana Dindica 44, Belgrade 11000, Serbia
来源
CENTRAL EUROPEAN CONFERENCE ON INFORMATION AND INTELLIGENT SYSTEMS (CECIIS 2018) | 2018年
关键词
cryptocurrency; return on investment; time series; simulation; model; Monte Carlo simulation;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cryptocurrencies are attracting significant amount of attention. Everything started with Bitcoin and built up to the situation where we have over 1500 cryptocurrencies. One can say that cryptocurrency market is the new stock market. This market is still highly volatile, but decentralized, open, and widely accessible. In this paper we will use time series analysis and Monte Carlo simulation for forecasting cryptocurrencies' return for selected time period. With huge price oscillations present it is hard to provide precise return predictions, but any step towards analysing cryptocurrencies adds to understanding the market.
引用
收藏
页码:153 / 160
页数:8
相关论文
共 50 条
  • [31] Improving lead time of pharmaceutical production processes using Monte Carlo simulation
    Eberle, Lukas Gallus
    Sugiyama, Hirokazu
    Schmidt, Rainer
    COMPUTERS & CHEMICAL ENGINEERING, 2014, 68 : 255 - 263
  • [32] A study on magnetic Barkhausen emission using Monte Carlo simulation
    Sahai, M. K.
    Sasi, B.
    Rao, C. Babu
    Jayakumar, T.
    INSIGHT, 2012, 54 (05) : 262 - 266
  • [33] HYBRID CONTINUOUS TIME-MONTE CARLO SIMULATION OF DISPERSE SYSTEMS
    Lakatos, Bela G.
    Barkanyi, Agnes
    Nemeth, Sandor
    EUROPEAN SIMULATION AND MODELLING CONFERENCE 2013, 2013, : 13 - 20
  • [34] Forecasting of electricity consumption in Pakistan based on integrating machine learning algorithms and Monte Carlo simulation
    Nazir, Muhammad Umair
    Li, Jinchao
    ELECTRICAL ENGINEERING, 2025,
  • [35] On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation
    Costantini, Mauro
    Kunst, Robert M.
    INTERNATIONAL JOURNAL OF FORECASTING, 2021, 37 (02) : 445 - 460
  • [36] Monte Carlo simulation of induction time and metastable zone width; stochastic or deterministic?
    Kubota, Noriaki
    JOURNAL OF CRYSTAL GROWTH, 2018, 485 : 1 - 7
  • [37] Reliability Compliant Distribution System Planning Using Monte Carlo Simulation
    Battu, Neelakanteshwar Rao
    Abhyankar, Abhijit R.
    Senroy, Nilanjan
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2019, 47 (11-12) : 985 - 997
  • [38] Assessing the Reliability of the City Gate Station Using Monte Carlo Simulation
    Hokmabadi, Rajabali
    Zarei, Esmaeil
    Karimi, Ali
    JOURNAL OF HEALTH AND SAFETY AT WORK, 2023, 13 (02) : 252 - 268
  • [39] Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
    de Amorim, Fernando Rodrigues
    Guimaraes, Camila Carla
    Afonso, Paulo
    Tobias, Maisa Sales Gama
    APPLIED SCIENCES-BASEL, 2024, 14 (17):
  • [40] MONTE-CARLO SIMULATION METHOD FOR FORECASTING THE TIMING OF PEST INSECT ATTACKS
    PHELPS, K
    COLLIER, RH
    READER, RJ
    FINCH, S
    CROP PROTECTION, 1993, 12 (05) : 335 - 342