Predicting Bitcoin Returns Using Artificial Neural Networks - An Application of Large Datasets to Convolutional Neural Networks and Long Short-Term Memory Based Artificial Neural Networks in Finance.

Time series forecasting is one of the foremost challenges studied in finance. In this thesis various Convolutional Neural Network and Long Short Term Memory Artificial Neural Network models are used to predict Bitcoin returns. Previous literature has explored using data from Sentiment analysis of Social Media, and Blockchain information in isolation. This thesis seeks to combine the predictive power of earlier smaller models into a larger model that better utilizes a broader category of features in time series prediction. The resulting models are able to predict Bitcoin returns well, beating out simpler methods that do not utilize Artificial Neural Networks

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

A Gated Recurrent Unit Approach to Bitcoin Price Prediction

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.Comment: 8 figures, 16 page

블록체인, 가상화폐, 파생상품 시장을 위한 예측 모형

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. 이재욱.This dissertation aims to conduct the empirical analysis for the financial derivative and cryptocurrency market and to develop analytical techniques based on machine learning models suitable for prediction and estimation of each field. In the financial derivative market, a Markov chain Monte Carlo (MCMC) methods employ the candidate probability distribution nearest to the target probability distribution to acquire sample distributed from the posterior density. Choice of the candidate probability distribution affects the practical convergence speed of the MCMC methodology and the fitness of the sample. In this dissertation, we propose a MCMC framework possible to samples from the candidate distribution nearest to the target probability density without the specification of the candidate distribution. We confirm that the jump diffusion models and Bayesian neural networks have the best performance in estimating and predicting given the data of the recent day for the model estimation given S&P index options in 2012. Especially, the jump diffusion model has a very high performance in terms of domain adaptation between the American option and the European option. This difference is reflected in the fact that the jump diffusion model is based on the common asset of the American option and the European option. Based on this empirical precedent study, we proposed a machine learning model called generative Bayesian neural network (GBNN) to overcome the disadvantages of the machine learning model. GBNN maximizes posterior probability through the GBNN obtains prior information from the GBNN data learned up to the previous day, and learns likelihood probability from actual trading data of learning day. We identify that the GBNN model outperform other benchmark models in terms of model prediction. Bitcoin is a successful cryptocurrency, and it has been extensively studied in fields of economics and computer science. In this dissertation, we analyze the time series of Bitcoin price with a BNN using Blockchain information in addition to macroeconomic variables. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the Bitcoin price in Aug. 2017. In addition, we suggested the enhanced GRU model for correlation analysis between cryptocurrency markets. Assuming that the gate value obtained from the GRU model is the parameter of the VAR model, it makes possible to visualize the correlation between various alternative currencies in the cryptocurrency market. As a result, it is confirmed that there is a very significant correlation between the currencies separated from the existing currencies and the existing currencies.Chapter 1 Introduction 21 1.1 Financial derivative market analysis 21 1.2 Cryptocurrency market analysis 24 1.3 Aims of the Dissertation 26 1.4 Outline of the Dissertation 28 Chapter 2 Literature Review 29 2.1 Review of Financial Econometric Models 29 2.1.1 Time series models 29 2.1.2 Option pricing methods 34 2.2 Review of Statistical Machine Learning Models 39 2.2.1 Articial neural networks 39 2.2.2 Bayesian neural networks 39 2.2.3 Support vector regression 43 2.2.4 Gaussian process 45 Chapter 3 Predictive Models for the Derivatives Market 47 3.1 Chapter Overview 47 3.2 A Generative Model Sampler for Inference in State Space Model 51 3.2.1 Backgrounds 51 3.2.2 Proposed methods: generative model sampler 56 3.3 Machine Learning versus Econometric Models in Predictability of Financial Options Markets 59 3.3.1 Data description and experimental design 59 3.3.2 Estimation and prediction performance 62 3.3.3 Robustness and Domain Adaptation Performance of the Models 66 3.4 A Generative Bayesian Neural Networks Model for Risk-Neutral Option Pricing 70 3.4.1 Proposed method 70 3.4.2 Empirical Studies 74 3.5 Chapter Summary 86 Chapter 4 Predictive Models for Blockchain and Cryptocurrency Market 89 4.1 Chapter Overview 89 4.2 Economics of Bitcoin and Blockchain 91 4.3 An Empirical Study on Modeling and Prediction of Bitcoin Prices Based on Blockchain Information 93 4.3.1 Data Specication and Structure of the Experiment 93 4.3.2 Linear Regression Analysis 99 4.3.3 Estimation and Prediction Results of Bitcoin Price 104 4.4 Enhanced GRU Framework for Correlation Analysis of Cryptocurrency Market 111 4.4.1 Enhanced GRU Framework 111 4.4.2 Empricial Studies 113 4.5 Chapter Summary 115 Chapter 5 Conclusion 119 5.1 Contributions 119 5.2 Future Work 122 Bibliography 125 국문초록 161Docto

Modeling and Prediction of Cryptocurrency Prices Using Machine Learning Techniques

With the introduction of Bitcoin in the year 2008 as the first practical decentralized cryptocurrency, the interest in cryptocurrencies and their underlying technology, Blockchain, has skyrocketed. Their promise of security, anonymity, and lack of a central controlling authority make them ideal for users who value their privacy. Academic research on machine learning, Blockchain technology, and their intersection have increased significantly in recent years. Specifically, one of the interest areas for researchers is the possibility of predicting the future prices of these cryptocurrencies using supervised machine learning techniques. In this thesis, we investigate their ability to make one day ahead price prediction of several popular cryptocurrencies using five widely used time-series prediction models. These models are designed by optimizing model parameters, such as activation functions, before settling on the final models presented in this thesis. Finally, we report the performance of each time-series prediction model measured by its mean squared error and accuracy in price movement direction prediction