123 research outputs found
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
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
In this paper, we analyze the time-series of minute price returns on the
Bitcoin market through the statistical models of generalized autoregressive
conditional heteroskedasticity (GARCH) family. Several mathematical models have
been proposed in finance, to model the dynamics of price returns, each of them
introducing a different perspective on the problem, but none without
shortcomings. We combine an approach that uses historical values of returns and
their volatilities - GARCH family of models, with a so-called "Mixture of
Distribution Hypothesis", which states that the dynamics of price returns are
governed by the information flow about the market. Using time-series of
Bitcoin-related tweets and volume of transactions as external information, we
test for improvement in volatility prediction of several GARCH model variants
on a minute level Bitcoin price time series. Statistical tests show that the
simplest GARCH(1,1) reacts the best to the addition of external signal to model
volatility process on out-of-sample data.Comment: 17 pages,11 figure
Artificial Intelligence & Machine Learning in Finance: A literature review
In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market.
JEL Classification: C80
Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market.
JEL Classification: C80
Paper type: Theoretical Researc
Sensing Social Media Signals for Cryptocurrency News
The ability to track and monitor relevant and important news in real-time is
of crucial interest in multiple industrial sectors. In this work, we focus on
the set of cryptocurrency news, which recently became of emerging interest to
the general and financial audience. In order to track relevant news in
real-time, we (i) match news from the web with tweets from social media, (ii)
track their intraday tweet activity and (iii) explore different machine
learning models for predicting the number of the article mentions on Twitter
within the first 24 hours after its publication. We compare several machine
learning models, such as linear extrapolation, linear and random forest
autoregressive models, and a sequence-to-sequence neural network. We find that
the random forest autoregressive model behaves comparably to more complex
models in the majority of tasks.Comment: full version of the paper, that is accepted at ACM WWW '19
Conference, MSM'19 Worksho
Understanding the Relationship between Online Discussions and Bitcoin Return and Volume: Topic Modeling and Sentiment Analysis
This thesis examines Bitcoin related discussions on Bitcointalk.com over the 2013-2022 period. Using Latent Dirichlet Allocation (LDA) topic modeling algorithm, we discover eight distinct topics: Mining, Regulation, Investment/trading, Public perception, Bitcoin’s nature, Wallet, Payment, and Other. Importantly, we find differences in relations between different topics’ sentiment, disagreement (proxy for uncertainty) and hype (proxy for attention) on one hand and Bitcoin return and trading volume on the other hand. Specifically, among all topics, only the sentiment and disagreement of Investment/trading topic have significant contemporaneous relation with Bitcoin return. In addition, sentiment and disagreement of several topics, such as Mining and Wallet, show significant relationships with Bitcoin return only on the tails of the return distribution (bullish and bearish markets). In contrast, sentiment, disagreement, and hype of each topic show significant relation with Bitcoin volume across the entire distribution. In addition, whereas hype has a positive relation with trading volume in a low-volume market, this relation becomes negative in a high-volume market
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