Paper: arXiv 2308.07626

Abstract

The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced stochastic process modeling (Markov chains up to order 8) and statistical entropy concepts for data coding, while using real intra-day cryptocurrency data from three major assets with a defined backtesting framework comparing predictions against random choices.

Research Flowchart

  flowchart TD
  A["Research Goal"] --> B{"Key Methodology"}
  B --> C["Data: Intra-day Returns<br>3 Major Cryptocurrencies"]
  C --> D["Computational Process<br>Markov Chains Orders 1-8"]
  D --> E["Findings"]
  subgraph E ["Outcomes"]
      E1["Forecasts better than<br>random choices"]
      E2["Long-memory components<br>investigated"]
  end