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Bitcoin price forecasting using hidden : Markov models
مقدم أطروحة جامعية
مشرف أطروحة جامعية
الجامعة
جامعة الأخوين
الكلية
كلية إدارة الأعمال
دولة الجامعة
المغرب
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2016
الملخص الإنجليزي
In this thesis we develop a prediction method based on continuous Hidden markov model with gaussian mixtures.
We apply this method to predict Bitcoin returns and build a systematic trading strategy based on this forecast.
Systematic trading rose exponentially on all asset classes in the last two decades, and prediction models helped hedge funds to build very pro table strategies on FX, Commodities, Equities, Interest rates.
The Bitcoin is a rising digital currency and its trading is gaining an increasing interest among institutionals, individual investors and technology enthusiasts.
Building a model on Bitcoin is an emerging eld, as Bitcoin started only in 2009 and started to be known around 2013.
The Hidden Markov model is a stochastic prediction method that has been used in voice recognition and is gaining interest in nancial forecasting.
We applied HMM model to Bitcoin forecasting in order to take advantage of its ability to nd non-linear paths in historical data.
Empirical results shows that the HMM algorithm can, for a given sets of parameters, give a very pro table strategy.
Using HMM with a sliding window and multivariate model enhanced our strategy.
Changing the other parameters have a limited impact on pro tability.
We tried di erent estimation methods such as Viterbi Algorithm and density calculation method.
We also varied the number of states and mixtures and tried both k-means and random initialization methods.
We used a simple and multivariate model in HMM and changed the triggering threshold of the strategy.
In addition, we switched to Multivariate HMMGM by adding more variables to data.
This change improved the sharp of the strategy.
As a conclusion, we succeeded to build a pro table strategy using HMM to predict Bitcoin, but its pro tability stays very sensitive to parameters.
التخصصات الرئيسية
عدد الصفحات
32
قائمة المحتويات
Table of contents.
Abstract.
Chapter One : Introduction.
Chapter Two : Hidden markov models.
Chapter Three : Methodology.
Chapter Four : Empirical results.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Faik, Muhammad. (2016). Bitcoin price forecasting using hidden : Markov models. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-775262
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Faik, Muhammad. Bitcoin price forecasting using hidden : Markov models. (Master's theses Theses and Dissertations Master). Al Akhawayn University. (2016).
https://search.emarefa.net/detail/BIM-775262
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Faik, Muhammad. (2016). Bitcoin price forecasting using hidden : Markov models. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-775262
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-775262
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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