Applied Data Science Approaches in FinTech: Innovative Models for Bitcoin Price Dynamics

Living in a data-intensive environment is a natural consequence to the continuous innovations and technological advancements, that created countless opportunities for addressing domain-specific challenges following the Data Science approach. The main objective of this thesis is to present applied Data Science approaches in FinTech, focusing on proposing innovative descriptive and predictive models for studying and exploring Bitcoin Price Dynamics and Bitcoin Price Prediction. With reference to the research area of Bitcoin Price Dynamics, two models are proposed. The first model is a Network Vector Autoregressive model that explains the dynamics of Bitcoin prices, based on a correlation network Vector Autoregressive process that models interconnections between Bitcoin prices from different exchange markets and classical assets prices. The empirical findings show that Bitcoin prices from different markets are highly interrelated, as in an efficiently integrated market, with prices from larger and/or more connected exchange markets driving other prices. The results confirm that Bitcoin prices are unrelated with classical market prices, thus, supporting the diversification benefit property of Bitcoin. The proposed model can predict Bitcoin prices with an error rate of about 11% of the average price. The second proposed model is a Hidden Markov Model that explains the observed time dynamics of Bitcoin prices from different exchange markets, by means of the latent time dynamics of a predefined number of hidden states, to model regime switches between different price vectors, going from "bear'' to "stable'' and "bear'' times. Structured with three hidden states and a diagonal variance-covariance matrix, the model proves that the first hidden state is concentrated in the initial time period where Bitcoin was relatively new and its prices were barely increasing, the second hidden state is mostly concentrated in a period where Bitcoin prices were steadily increasing, while the third hidden state is mostly concentrated in the last period where Bitcoin prices witnessed a high rate of volatility. Moreover, the model shows a good predictive performance when implemented on an out of sample dataset, compared to the same model structured with a full variance-covariance matrix. The third and final proposed model, falls within the area of Bitcoin Price Prediction. A Hybrid Hidden Markov Model and Genetic Algorithm Optimized Long Short Term Memory Network is proposed, aiming at predicting Bitcoin prices accurately, by introducing new features that are not usually considered in the literature. Moreover, to compare the performance of the proposed model to other models, a more traditional ARIMA model has been implemented, as well as a conventional Genetic Algorithm-optimized Long Short Term Memory Network. With a mean squared error of 33.888, a root mean squared error of 5.821 and a mean absolute error of 2.510, the proposed model achieves the lowest errors among all the implemented models, which proves its effectiveness in predicting Bitcoin prices

Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators

This study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -convolutional neural network, hybrid CNN-LSTM network, multilayer perceptron and radial basis function neural network- to predict whether six popular cryptocurrencies -Bitcoin, Dash, Ether, Litecoin, Monero and Ripple- will increase their value vs. USD in the next minute. The results, based on 18 technical indicators derived from the exchange rates at a one-minute resolution over one year, suggest that all series were predictable to a certain extent using the technical indicators. Convolutional LSTM neural networks outperformed all the rest significantly, while CNN neural networks were also able to provide good results specially in the Bitcoin, Ether and Litecoin cryptocurrencies.We would also like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin

Empirical Forecasting Analysis of Bitcoin Prices: A Comparison of Machine learning, Deep learning, and Ensemble learning Models

Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies

Deep State-Space Model for Predicting Cryptocurrency Price

Our work presents two fundamental contributions. On the application side, we tackle the challenging problem of predicting day-ahead crypto-currency prices. On the methodological side, a new dynamical modeling approach is proposed. Our approach keeps the probabilistic formulation of the state-space model, which provides uncertainty quantification on the estimates, and the function approximation ability of deep neural networks. We call the proposed approach the deep state-space model. The experiments are carried out on established cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been to predict the price for the next day. Benchmarking has been done with both state-of-the-art and classical dynamical modeling techniques. Results show that the proposed approach yields the best overall results in terms of accuracy

Assessing machine learning performance in cryptocurrency market price prediction

Cryptocurrencies, which are digitally encrypted and decentralized, continue to attract attention of  nancial market players across the world. Because of high volatility in cryptocurrency market, predicting price of cryptocurrencies has become one of the most complicated  elds in  nancial markets. In this paper, we use Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to predict price of four well-known cryptocurrencies of Bitcoin (BTC), Ethereum(ETH), Litecoin (LTC), and Ripple (XRP). These models are subdivisions of Articial Intelligence, machine learning and data science. The main aim of this paper is to compare the accuracy of above-mentioned models in forecasting time series data, to  nd out which model can better predict price in these four cryptocurrencies. 43 variables consisting of 28 technical indicators and t+10 lags were calculated and appended to the Open, High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies. Applying random forest as feature selection, 25 variables werechosen, 24 of them selected as feature (independent variables) and one as a dependent variable. Each attribute value was converted into a relative standard score, followed by Min-max scaling; we compare models and results of Dieblod Mariano test that is used to examine whether the differences in predictive accuracy with these two models are signi cant, reveal that LSTM reaches better accuracy than GRU for BTC and ETH, but both models convey the same accuracy for LTC and XRP

Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin

Las criptomonedas son cada día más populares. Sin ir más lejos, en 2021 su capitalización agregada llegó a superar los 3 billones de dólares, una cifra nunca antes registrada. Dentro de este amplio ecosistema, destaca el caso del bitcoin, cuyo precio alcanzó los 68.000 dólares en 2021, que marca un máximo histórico. Sin embargo, la evolución de esta cotización dista de ser consistente en el tiempo, pues se observan, con frecuencia, fluctuaciones considerables y abruptas, como las ocurridas en los meses que siguieron a los valores récord antes señalados. Ante el más que previsible crecimiento del bitcoin y la concentración de su actividad mayoritariamente en ambientes no regulados, crece la preocupación entre las autoridades financieras de todo el mundo acerca de su potencial impacto en la estabilidad financiera, en la política monetaria y en la integridad del sistema financiero. En consecuencia, apremia avanzar en la construcción de un marco regulatorio y supervisor sólido y consistente ante estos desafíos. A estos efectos, resulta necesario mejorar el grado de comprensión tanto de los factores subyacentes que influyen en la formación del precio del bitcoin como de su estabilidad a lo largo del tiempo. En este documento analizamos cuáles son las variables que determinan el precio al que se negocia el bitcoin en las plataformas de intercambio más relevantes. Para ello, utilizamos un modelo flexible de aprendizaje automático; concretamente, una red neuronal Long Short Term Memory (LSTM), para establecer el precio del bitcoin en función de una serie de variables que captan factores económicos, tecnológicos y de atención por parte de los inversores. Nuestro modelo LSTM replica razonablemente bien el comportamiento del precio del bitcoin en diferentes períodos. A continuación, empleamos una técnica de interpretabilidad —SHAP— para determinar las características que influyen más en los resultados del modelo LSTM. Conforme a lo anterior, concluimos que la importancia de las diferentes variables cambia sustancialmente a lo largo del período analizado. Además, encontramos que no solo varía su influencia, sino que, paulatinamente, aparecen nuevos factores explicativos que, al menos en su mayor parte, permanecen desconocidos.So-called cryptocurrencies are becoming more popular by the day, with a total market capitalization that exceeded $3 trillion at its peak in 2021. Bitcoin has emerged as the most popular among them, with a total valuation that reached an all-time high of$68,000 in November 2021. However, its price has historically been subject to large and abrupt fluctuations, as the sudden drop in the months that followed once again proved. Since bitcoin looks all set to continue growing while largely concentrating its activity in unregulated environments, concerns have been raised among authorities all over the world about its potential impact on financial stability, monetary policy, and the integrity of the financial system. As a result, building a sound and proper regulatory and supervisory framework to address these challenges hinges upon achieving a better understanding of both the critical underlying factors that influence the formation of bitcoin prices and the stability of such factors over time. In this article we analyse which variables determine the price at which bitcoin is traded on the most relevant exchanges. To this end, we use a flexible machine learning model, specifically a Long Short Term Memory (LSTM) neural network, to establish the price of bitcoin as a function of a number of economic, technological and investor attention variables. Our LSTM model replicates reasonably well the behaviour of the price of bitcoin over different periods of time. We then use an interpretability technique known as SHAP to understand which features most influence the LSTM outcome. We conclude that the importance of the different variables in bitcoin price formation changes substantially over the period analysed. Moreover, we find that not only does their influence vary, but also that new explanatory factors often seem to appear over time that, at least for the most part, were initially unknown

Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables

The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models