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
  • [21] A Markov chain Monte Carlo method for simulation of wind and solar power time series
    Luo, Gang
    Shi, Dongyuan
    Chen, Jinfu
    Wu, Xiaoshan
    Luo, G. (luoganghust@163.com), 1600, Power System Technology Press (38): : 321 - 327
  • [22] Monte Carlo Simulation Forecasting of Maritime Ferry Safety and Resilience
    Dabrowska, E.
    Soszynska-Budny, J.
    2018 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM), 2018, : 376 - 380
  • [23] Probabilistic Time-Dependent Travel Time Computation Using Monte Carlo Simulation
    Tomis, Radek
    Rapant, Lukas
    Martinovic, Jan
    Slaninova, Katerina
    Vondrak, Ivo
    HIGH PERFORMANCE COMPUTING IN SCIENCE AND ENGINEERING, HPCSE 2015, 2016, 9611 : 161 - 170
  • [24] Accelerated Monte Carlo for Optimal Estimation of Time Series
    Francis J. Alexander
    Gregory L. Eyink
    Juan M. Restrepo
    Journal of Statistical Physics, 2005, 119 : 1331 - 1345
  • [25] Accelerated Monte Carlo for optimal estimation of time series
    Alexander, FJ
    Eyink, GL
    Restrepo, JM
    JOURNAL OF STATISTICAL PHYSICS, 2005, 119 (5-6) : 1331 - 1345
  • [26] Monte Carlo simulation-based traffic speed forecasting using historical big data
    Jeon, Seungwoo
    Hong, Bonghee
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2016, 65 : 182 - 195
  • [27] Development of multi-forecasting model using Monte Carlo simulation coupled with wavelet denoising-ARIMA model
    Singh, Sarbjit
    Parmar, Kulwinder Singh
    Kumar, Jatinder
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2025, 230 : 517 - 540
  • [28] The Application of Monte Carlo Method Simulation in Wind Power Investment Project
    Wu Yunna
    Hu Xinliang
    Shen Yue
    ADVANCES IN COMPUTATIONAL ENVIRONMENT SCIENCE, 2012, 142 : 207 - 211
  • [29] Water quality probability forecasting model based on Monte Carlo simulation
    Liu, Yueyi
    Zhao, Jianshi
    Huang, Yuefei
    Shi, Yong
    Chen, Liangang
    Shuili Xuebao/Journal of Hydraulic Engineering, 2015, 46 (01): : 51 - 57
  • [30] Optimal Placement of Distributed Generator using Monte Carlo Simulation
    Rao, B. Neelakanteshwar
    Abhyankar, A. R.
    Senroy, Nilanjan
    2014 EIGHTEENTH NATIONAL POWER SYSTEMS CONFERENCE (NPSC), 2014,
12345