Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cam a22000004b 4500 | ||
| 001 | MAP20220011952 | ||
| 003 | MAP | ||
| 005 | 20220911185548.0 | ||
| 008 | 220421s2022 esp|||| ||| ||spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a922.135 | ||
| 245 | 1 | 0 | $aApplication of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin |
| 260 | $aMadrid$bBanco de España$c2022 | ||
| 300 | $a35 | ||
| 520 | $aSo-called cryptocurrencies are becoming more popular by the day, with a total market capitalization that exceeded $3 trillion at its peak in 2021. Bitcoin has emerged as the most popular among them, with a total valuation that reached an all-time high of $68,000 in November 2021. However, its price has historically been subject to large and abrupt fluctuations, as the sudden drop in the months that followed once again proved. Since bitcoin looks all set to continue growing while largely concentrating its activity in unregulated environments, concerns have been raised among authorities all over the world about its potential impact on financial stability, monetary policy, and the integrity of the financial system. As a result, building a sound and proper regulatory and supervisory framework to address these challenges hinges upon achieving a better understanding of both the critical underlying factors that influence the formation of bitcoin prices and the stability of such factors over time. In this article we analyse which variables determine the price at which bitcoin is traded on the most relevant exchanges. To this end, we use a flexible machine learning model, specifically a Long Short Term Memory (LSTM) neural network, to establish the price of bitcoin as a function of a number of economic, technological and investor attention variables. Our LSTM model replicates reasonably well the behaviour of the price of bitcoin over different periods of time. We then use an interpretability technique known as SHAP to understand which features most influence the LSTM outcome. We conclude that the importance of the different variables in bitcoin price formation changes substantially over the period analysed. Moreover, we find that not only does their influence vary, but also that new explanatory factors often seem to appear over time that, at least for the most part, were initially unknown | ||
| 650 | 4 | $0MAPA20200021223$aCriptomonedas | |
| 650 | 4 | $0MAPA20160009583$aBlockchain | |
| 650 | 4 | $0MAPA20160009590$aMoneda electrónica | |
| 650 | 4 | $0MAPA20170005476$aMachine learning | |
| 650 | 4 | $0MAPA20080545062$aPrecios | |
| 650 | 4 | $0MAPA20150006585$aValor de mercado | |
| 710 | 2 | $0MAPA20080439880$aBanco de España |