Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?

Author:

Gurrib Ikhlaas,Nourani MohammadORCID,Bhaskaran Rajesh Kumar

Abstract

AbstractThis paper investigates the role of Fibonacci retracements levels, a popular technical analysis indicator, in predicting stock prices of leading U.S. energy companies and energy cryptocurrencies. The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy. Daily crypto and stock prices were obtained from the Standard & Poor's composite 1500 energy index and CoinMarketCap between November 2017 and January 2020. This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk. Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos. Furthermore, most price violations were frequent during price falls compared to price increases, supporting that the Fibonacci instrument does not capture price movements during up and downtrends, respectively. Also, fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break. Furthermore, the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model. Finally, complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents. This study’s overall findings elucidate that, despite significant drops in oil prices, speculators (traders) can implement profitable strategies when using technical analysis indicators, like the Fibonacci retracement tool, with or without price crossover rules.

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Finance

Reference78 articles.

1. Aggarwal R (1988) Stock index futures and cash market volatility. Rev Futures Mark 7(2):290–299

2. Aragon GO, Ferson WE (2007) Portfolio performance evaluation. Found Trends® Finance 2(2):83–190

3. Ball R (1978) Filter rules: interpretation of market efficiency, experimental problems and Australian evidence. Acc Educ 18(2):1–17

4. Beyaz E, Tekiner F, Zeng X, Keane J (2018) Comparing technical and fundamental indicators in stock price forecasting. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on smart city; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS), 28–30 June 2018, pp 1607–1613. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00262

5. Bhandari R (2014) ibonacci and stock analysis. Futures. http://www.futuresmag.com/2014/04/30/fibonacci-and-stock-analysis

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3