[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116938-en":3,"doc-seo-116938-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116938,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Bitcoin - Sentiment Analysis and the Efficient Market Hypothesis - A Machine Learning Approach","This dissertation examines the Efficient Market Hypothesis in the cryptocurrency market through a machine learning framework centered on support vector machines. Weekly datasets spanning 23/10/2017 to 28/10/2020 (167 weeks) are tested out-of-sample from 04/01/2021 to 02/08/2021 (31 weeks). The study builds technical, asset-based, and sentiment-based variables and forms four datasets. The non-sentiment datasets support efficiency, while incorporating sentiment via Google Trends substantially improves accuracy and predictability, yielding satisfactory forecasting results for Bitcoin price movements.","Bitcoin, Sentiment analysis and the Efficient Market Hypothesis, a Machine Learning Approach.  \nToulias Georgios  \nUNIVERSITY CENTER OF INTERNATIONAL PROGRAMMES OF STUDIES SCHOOL OF HUMANITIES, SOCIAL SCIENCES AND ECONOMICS  \nA thesis submitted for the degree of  \nMaster of Science (MSc) in banking & finance  \nNovember 2021  \nThessaloniki – Greece  \nStudent Name: Toulias Georgios  \nSID: 1103200009  \nSupervisor: Prof. Periklis Gogas  \nI hereby declare that the work submitted is mine and that where I have made use of another’s work, I have attributed the source(s) according to the Regulations set in the Student’s Handbook.  \nNovember 2021  \nThessaloniki -Greece  \nAbstract  \nThis dissertation was written as part of my MSc in banking & finance at the International Hellenic University.  \nCryptocurrency has become extremely popular among investors during the last decade. Scientists around the globe predict that this turn toward crypto is still at its foundations. Investors are intrigued by the extreme volatility that results in extreme returns. When it comes to making money, benefits are followed by disadvantages, and extreme volatility is associated with riskier investments. Since investors are willing to take the extra risk by investing in Cryptosand especially Bitcoin, the least they expect is to invest in a Market that is Efficient. When a Market is efficient, all the available information to any investor, are incorporated into the price of the assets. This mean that the chances of beating the market are eliminated. This paper is testing the Efficient Market Hypothesis in the Crypto Market.  \nThe methods we are using on our research are various Machine Learning Models, with a focus on support vector machines. The frequency of the data used is weekly, with a timeline for the test sample from 23/10/2017 until 28/10/2020 which is a total of 167 weeks. The out of sample timeline is from the 04/01/2021 until 02/08/2021 which is a total of 31 weeks. The data we collected are technical, asset based, and sentiment based. We gather our data and create four different data sets. The results of the two first data sets, that do not include sentiment data, seem to back up the efficient market hypothesis. We also perform Sentiment Analysis, which we manage to do with the addition of Google trends as variables to our data set. By adding the sentiment variables, we observe a huge improvement to our model’s accuracy and predictability making them result in satisfactory tests.  \nThese results seem to be supporting the claim that machine learning models can be used as a reliable tool for predicting cryptocurrencies in the future. Even though other scientists have tried to predict the Price movement of various cryptos in the past, our diversification is the implementation of various Google Trends as variables and the influence they have on Bitcoins  \nPrice Movements.  \nKeywords: (Support Vector Machines, Machine Learning, Cryptocurrency, Bitcoin, Google Trends)  \nToulias Georgios Date 15/11/2021  \nAcknowledgements  \nAt this point, I would like to express my gratitude to my supervisor, Professor Periklis Gogas. He was more than welcome to offer me his valuable knowledge and expertise in the area, while atthe same time he guided me through this intense period of academic study and research. I would also like to extend my deepest gratitude to my parents and older brother, without whom it would not be possible to complete my postgraduate studies.  \nTable of Contents  \n1 Introduction ............................................................................................................................................... 1  \n2 Literature Review ....................................................................................................................................... 4  \n3 Data ............................................................................................................................................................ 6  \n4 M","cbCaicccGc2DPgEc","https://ap.wps.com/l/cbCaicccGc2DPgEc","pdf",1869732,1,91,"English","en",105,"# Introduction\n# Literature Review\n# Data\n# Methodology\n## Gather & Manage Data\n## Train Models - Prediction Models\n## Evaluate Models - Evaluation Criteria\n# Results\n## First Data Set\n## Second Data Set","[{\"question\":\"What research question does the dissertation address?\",\"answer\":\"It tests the Efficient Market Hypothesis in the crypto market, focusing on whether available information is reflected in asset prices through machine learning-based prediction.\"},{\"question\":\"Which machine learning approach is emphasized in the study?\",\"answer\":\"The methodology uses various machine learning models, with a primary focus on support vector machines (SVM).\"},{\"question\":\"How does sentiment information influence the results?\",\"answer\":\"By adding sentiment variables using Google Trends, the models show a significant improvement in accuracy and predictability compared with datasets that exclude sentiment.\"}]","Bitcoin - 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