Dr. Muhammad Ikhlaas Gurrib

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https://hdl.handle.net/20.500.12519/982
Dr. Gurrib holds a PhD in Finance and a Bachelor of Commerce (Economics and Finance) from Curtin University (Australia), a Master of Finance and a Master of Professional Accounting from Victoria State University, Australia. Prior to joining CUD he was an Assistant Professor at Prince Sultan University, Saudi Arabia, and also taught at Curtin Business School, Australia. Dr. Gurrib is also experienced in the finance industry, having worked as a Finance Manager with Westpac Bank, Australia.

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    Drivers of the next-minute Bitcoin price using sparse regressions
    (Emerald Publishing, 2023) Gurrib, Ikhlaas; Kamalov, Firuz; Starkova, Olga; Elshareif, Elgilani Eltahir; Contu, Davide
    Purpose: This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading? Design/methodology/approach: Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted. Findings: Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information. Originality/value: To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information. © 2023, Emerald Publishing Limited.
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    Momentum in Low Carbon and Fossil Fuel Free Equity Investing
    (Econjournals, 2023-09-16) Gurrib, Ikhlaas
    The Conference of the Parties to the United Nations Framework Convention on Climate Change (COP27) firmly echoed that climate change is a critical issue for humanity. Particularly, it stressed on the need to encourage a clean energy mix, including renewable and low-emission energies, as part of the continuing transition toward a cleaner and sustainable energy. Using daily indices data over the period January 1st, 2017–February 28th, 2023, this paper studies the performance of 2 family classes among sustainability indices, namely, low carbon and fossil fuel free indices. Specifically, this study sheds light by assessing the performance of trading strategies which are based on the momentum of low carbon and fossil-fuel free based indices. The performance is based on a thorough analysis of the relative strength index (RSI) and is captured through the Sharpe and Sharpe per trade measures. We decompose the analysis into pre and post COVID-19 to provide some insights how these sustainable energy investments were impacted by the coronavirus pandemic. Findings support an adjusted overbought/oversold RSI 75 (25) model resulted in fewer false signals than the traditional 70 (30) model. Relative to the post COVID-19 period, all selected equity indices performed poorly in the pre-COVID-19 period, with negative returns, except for the MSCI World Low Carbon Leaders and the SPDR MSCI Emerging Markets Fossil Fuel Free equity indices. Comparatively, in the post COVID-19 period, all indices witnessed superior return performance, though with increased risk levels. SPDR MSCI Emerging Markets Fossil Fuel Free index ranked first after adjusting for transaction costs. Investments in the post COVID-19 early impact period performed better than a naive buy-and-hold strategy for greener investments like low carbon and fossil fuel free equity indices. © 2023, Econjournals. All rights reserved.