407 research outputs found
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
In this paper, we study the ability to make the short-term prediction of the
exchange price fluctuations towards the United States dollar for the Bitcoin
market. We use the data of realized volatility collected from one of the
largest Bitcoin digital trading offices in 2016 and 2017 as well as order
information. Experiments are performed to evaluate a variety of statistical and
machine learning approaches.Comment: Full version of the paper published at IEEE International Conference
on Data Mining (ICDM), 201
An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics
In this study, we introduce a physical model inspired by statistical physics
for predicting price volatility and expected returns by leveraging Level 3
order book data. By drawing parallels between orders in the limit order book
and particles in a physical system, we establish unique measures for the
system's kinetic energy and momentum as a way to comprehend and evaluate the
state of limit order book. Our model goes beyond examining merely the top
layers of the order book by introducing the concept of 'active depth', a
computationally-efficient approach for identifying order book levels that have
impact on price dynamics. We empirically demonstrate that our model outperforms
the benchmarks of traditional approaches and machine learning algorithm. Our
model provides a nuanced comprehension of market microstructure and produces
more accurate forecasts on volatility and expected returns. By incorporating
principles of statistical physics, this research offers valuable insights on
understanding the behaviours of market participants and order book dynamics
Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance
The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors
A Gated Recurrent Unit Approach to Bitcoin Price Prediction
In today's era of big data, deep learning and artificial intelligence have
formed the backbone for cryptocurrency portfolio optimization. Researchers have
investigated various state of the art machine learning models to predict
Bitcoin price and volatility. Machine learning models like recurrent neural
network (RNN) and long short-term memory (LSTM) have been shown to perform
better than traditional time series models in cryptocurrency price prediction.
However, very few studies have applied sequence models with robust feature
engineering to predict future pricing. in this study, we investigate a
framework with a set of advanced machine learning methods with a fixed set of
exogenous and endogenous factors to predict daily Bitcoin prices. We study and
compare different approaches using the root mean squared error (RMSE).
Experimental results show that gated recurring unit (GRU) model with recurrent
dropout performs better better than popular existing models. We also show that
simple trading strategies, when implemented with our proposed GRU model and
with proper learning, can lead to financial gain.Comment: 8 figures, 16 page
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
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