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Found 12 papers, 0 papers with code
Bitcoin's Edge: Embedded Sentiment in Blockchain Transactional Data
Using a variety of Machine Learning techniques, we showcase for the first time the predictive power of blockchain-embedded sentiment in forecasting cryptocurrency price movements on the Bitcoin and Ethereum blockchains.
Scalable Signature-Based Distribution Regression via Reference Sets
Distribution Regression (DR) on stochastic processes describes the learning task of regression on collections of time series.
A Financial Time Series Denoiser Based on Diffusion Model
Finally, we show that by using classifiers trained on denoised time series, we can recognize the noising state of the market and obtain excess return.
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.
Detecting Financial Market Manipulation with Statistical Physics Tools
We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets.
A new encoding of implied volatility surfaces for their synthetic generation
In financial terms, an implied volatility surface can be described by its term structure, its skewness and its overall volatility level.
Error in the Euclidean Preference Model
Spatial models of preference, in the form of vector embeddings, are learned by many deep learning and multiagent systems, including recommender systems.
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning
Its convenience led to an exponentially increasing amount of financial data, which is however hard to use for the prediction of future prices, due to the low signal-to-noise ratio and the non-stationarity of financial time series.
The Efficient Hedging Frontier with Deep Neural Networks
The trade off between risks and returns gives rise to multi-criteria optimisation problems that are well understood in finance, efficient frontiers being the tool to navigate their set of optimal solutions.
Better Model Selection with a new Definition of Feature Importance
Feature importance aims at measuring how crucial each input feature is for model prediction.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Cryptocurrency Trading: A Comprehensive Survey
This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e. g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others).
Ascertaining price formation in cryptocurrency markets with DeepLearning
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world.
