4 research outputs found
Leveraging Explainable AI to Support Cryptocurrency Investors
In the last decade, cryptocurrency trading has attracted the attention of private and professional traders and investors. To forecast the financial markets, algorithmic trading systems based on Artificial Intelligence (AI) models are becoming more and more established. However, they suffer from the lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. This is particularly critical for cryptocurrency investors, because the study of the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts’ decisions. This paper proposes a new visual analytics tool to support domain experts in the explanation of AI-based cryptocurrency trading systems. To describe the rationale behind AI models, it exploits an established method, namely SHapley Additive exPlanations, which allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface. The simulations carried out on 21 cryptocurrencies over a 8-year period demonstrate the usability of the proposed tool
Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis
The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon
PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin
Bitcoin, with its ever-growing popularity, has demonstrated extreme price
volatility since its origin. This volatility, together with its decentralised
nature, make Bitcoin highly subjective to speculative trading as compared to
more traditional assets. In this paper, we propose a multimodal model for
predicting extreme price fluctuations. This model takes as input a variety of
correlated assets, technical indicators, as well as Twitter content. In an
in-depth study, we explore whether social media discussions from the general
public on Bitcoin have predictive power for extreme price movements. A dataset
of 5,000 tweets per day containing the keyword `Bitcoin' was collected from
2015 to 2021. This dataset, called PreBit, is made available online. In our
hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial
lexicons, so as to capture the full contents of the tweets and feed it to the
model in an understandable way. By combining these embeddings with a
Convolutional Neural Network, we built a predictive model for significant
market movements. The final multimodal ensemble model includes this NLP model
together with a model based on candlestick data, technical indicators and
correlated asset prices. In an ablation study, we explore the contribution of
the individual modalities. Finally, we propose and backtest a trading strategy
based on the predictions of our models with varying prediction threshold and
show that it can used to build a profitable trading strategy with a reduced
risk over a `hold' or moving average strategy.Comment: 21 pages, submitted preprint to Elsevier Expert Systems with
Application
Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management
This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services