Bitcoin

BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching ArXiv ID: 2308.08558 “View on arXiv” Authors: Unknown Abstract Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements. ...

Exploring the Bitcoin Mesoscale ArXiv ID: 2307.14409 “View on arXiv” Authors: Unknown Abstract The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses. ...

Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis ArXiv ID: 2305.13123 “View on arXiv” Authors: Unknown Abstract We are interested in the nonparametric estimation of the probability density of price returns, using the kernel approach. The output of the method heavily relies on the selection of a bandwidth parameter. Many selection methods have been proposed in the statistical literature. We put forward an alternative selection method based on a criterion coming from information theory and from the physics of complex systems: the bandwidth to be selected maximizes a new measure of complexity, with the aim of avoiding both overfitting and underfitting. We review existing methods of bandwidth selection and show that they lead to contradictory conclusions regarding the complexity of the probability distribution of price returns. This has also some striking consequences in the evaluation of the relevance of the efficient market hypothesis. We apply these methods to real financial data, focusing on the Bitcoin. ...

Metcalfe’s Law as a Model for Bitcoin’s Value ArXiv ID: ssrn-3078248 “View on arXiv” Authors: Unknown Abstract This paper demonstrates that bitcoin’s medium- to long-term price follows Metcalfe’s law. Bitcoin is modeled as a token digital currency, a medium of exchange w Keywords: Metcalfe’s Law, Bitcoin, Network Effects, Cryptocurrency Valuation, Cryptocurrency Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper uses high-level concepts like Metcalfe’s law (n^2) and Gompertz curves but presents them without heavy derivations or complex mathematics. Empirical work is discussed (price fits, manipulation investigation) but the excerpt lacks detailed backtesting methodology, code, or robust statistical metrics. flowchart TD A["Research Goal: Model Bitcoin's value via Metcalfe's Law"] --> B["Methodology: Time-series Regression Analysis"] B --> C["Data Input: Historical Bitcoin Price & Active Addresses"] C --> D["Computational Process: Log-linear Regression of Price vs Network Value"] D --> E{"Key Findings/Outcomes"} E --> F["Strong correlation confirms Metcalfe's Law applies"] E --> G["Price follows power law: P ~ n²"] E --> H["Valuation tool for long-term trends"]