On the drivers of technical analysis profits in cryptocurrency markets: A distributed lag approach

Walter Bazán-Palomino; Daniel Svogun

doi:10.1016/j.irfa.2023.102516

On the drivers of technical analysis profits in cryptocurrency markets: A distributed lag approach

Walter Bazán-Palomino
, Daniel Svogun

Universidad del Pacífico

Research output: Contribution to journal › Article in a journal › peer-review

12 Scopus citations

Abstract

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.

Original language	English
Article number	102516
Journal	International Review of Financial Analysis
Volume	86
Early online date	11 Jan 2023
DOIs	https://doi.org/10.1016/j.irfa.2023.102516
State	Published - Mar 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure
SDG 17 Partnerships for the Goals

Keywords

Asset bubbles
Cryptocurrency
Technical analysis
Transaction costs

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10.1016/j.irfa.2023.102516

Cite this

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title = "On the drivers of technical analysis profits in cryptocurrency markets: A distributed lag approach",

abstract = "The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.",

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doi = "10.1016/j.irfa.2023.102516",

language = "English",

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T1 - On the drivers of technical analysis profits in cryptocurrency markets

T2 - A distributed lag approach

AU - Bazán-Palomino, Walter

AU - Svogun, Daniel

PY - 2023/3

Y1 - 2023/3

N2 - The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.

AB - The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.

KW - Asset bubbles

KW - Cryptocurrency

KW - Technical analysis

KW - Transaction costs

UR - https://www.scopus.com/pages/publications/85146060187

UR - https://www.mendeley.com/catalogue/9235fd82-aa27-3a06-814c-3d8889f1683e/

U2 - 10.1016/j.irfa.2023.102516

DO - 10.1016/j.irfa.2023.102516

M3 - Article in a journal

AN - SCOPUS:85146060187

SN - 1057-5219

VL - 86

JO - International Review of Financial Analysis

JF - International Review of Financial Analysis

M1 - 102516

ER -

On the drivers of technical analysis profits in cryptocurrency markets: A distributed lag approach

Abstract

Bibliographical note

UN SDGs

Keywords

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