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Friday, 1 July 2022

Predicting bitcoin returns using high-dimensional technical indicators☆,

Predicting bitcoin returns using high-dimensional technical indicators☆,


There has been much diction about  environment comeback on financial benefit , such as stock  answer or item  answer , are excepted ;  thought , few studies have deviled victual currency return  cutlery . In this article we examine  environment  bitcoin returns are  excepted by a large set of bitcoin price-based technical index. especially , we build a  ranking  tree-based model for return  forecast using 124 technical measure. We provide verification  that the suggest model has strong out-of-sample anticipation  power for taped ranges of daily returns on bitcoin. This finding specify  that using big data and technical inquiry can help predict bitcoin returns that are hardly driven by bailiwick.

]1. Introduction

Cryptocurrency is a digital currency that  apply cryptography to secure the producer involved in transactions and generation of units. As the world's first  disapear cryptocurrency, bitcoin was created in 2009 based on a white paper written by a person with the pseudonym of Satoshi Nakamoto.1 In central currencies, the government or other corporate entities have control over the supply of currency by printing new fiat money. In contrast, bitcoin is a despearzed currency, meaning that no single entity is  incharge for the  maker  of new units or bitcoins (see, e.g., Harvey2).

2. Related literature

This study is related to the new actually on cryptocurrency. Several studies have examined the valuation of bitcoin and other cryptocurrencies. For instance, Athey, Parashkevov, Sarukkai, and Xia3 develop a model of bitcoin pricing and provide mixed  proof  about the ability of the model to explain bitcoin prices. Pagnotta and Buraschi4 consider the apriciyal of bitcoin and deprecate network assets using an belance model. Several other studies inspect  the suggestion of blockchains and related technologies for other areas in finance. For example, Raskin and Yermack5 consider the implications for central banking. Yermack6 focuses on corporate governance. Easley, O'Hara, and Basu7 and Huberman, Leshno, and Moallemi8 investigate bitcoin mining costs. Lastly, Harvey9 provides an in-depth discussion of the mechanics of cryptocurrencies.

3. Data  details 

The data used in this paper is a BTC-USD data set from  specluation .com, which  aaded the daily open, high, low and close prices of bitcoin from January 1st, 2012 to December 29th, 2017. After cleaning the data set, we have 2168 watching in total. We feature the whole selected into three  selected. The first one covers the period January 2, 2012–April 29, 2012, consisting of 120 observations. This selected is used to calculate the initial values of technical gauge that serve as inputs (predictors) in our decision-tree analysis. The second  selected is from April 30, 2012 to July 19, 2016 and used as the so-called training set in the decision-tree analysis. The third  selected is the period July 20, 2016–December 29, 2017 and used as the test set. The split between the training and test  selected here is done such that the size of the training  selected is about 3 times of the test  selected

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