Project description
Forecasting cryptocurrency value by measuring sentiment
It is difficult to forecast the cryptocurrency market. Unlike stocks and foreign exchange rates, which are influenced by economic indicators, cryptocurrency prices are driven by sentiments. The EU-funded CryptoVolatility project will explore new ways to forecast cryptocurrency volatility. Specifically, it will build a machine that produces discrete sentiment phases each day using news articles and internet search data. Using artificial neural networks to measure sentiments, this tool is expected to assist regulators and investors learn more about cryptocurrency volatility. In the long-term, this new tool will also make it possible to design policies to help overcome financial crises.
Objective
Forecasting cryptocurrency volatility is a topic of interest in quantitative finance. A growing number of studies argue that compared to equty price cryptocurrency prices are to a large and perhaps abnormal degree driven by sentiments. However, econometric studies focus on forcing conditional volatility models developed for equity return volatility to fit on cryptocurrency data despite being aware that estimation techniques developed for analyzing equity price or commodity price volatility lack robustness and do not work as intended. Is it possible to propose solutions to deal with the mentioned shortcomings? Is it possible to suggest a new family of models? If so, how? The purpose of New, realistic and robust models for cryptocurrency volatility is to answer these questions by suggesting new and more realistic conditional volatility models accompanied with reliable cross-disciplinary estimation techniques to forecast cryptocurrency price volatility. What is novel and innovative about the suggested framework is that contrary to the current literature our point of departure is the empirical features observed in cryptocurrency prices combined with a useful tool, namely, artificial neural networks used to measure sentiments. Our aim is to build a machine that produces discrete sentiment phases each day using news articles and internet search data. Once we have identified the number of phases and determined, which phase an observation at a given time-period belongs to following neural network estimation, we can estimate the model parameters, jumps and filter out the continuous conditional volatility process contemporaneously using particle filtering techniques. Besides academics, this proposal is also relevant for regulators and investors as they can learn a great deal by understanding how cryptocurrency volatility actually behaves. Regulators can use sentiment labels from the neural network to design policies to contrast and overcome financial crises in the future.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business economics
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
33100 TAMPERE
Finland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.