CUD Digital Repository :: Browsing by Author "Gurrib, Ikhlaas"

Browsing by Author "Gurrib, Ikhlaas"

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A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process
(Springer Science and Business Media Deutschland GmbH, 2022) Kamalov, Firuz; Gurrib, Ikhlaas; Moussa, Sherif; Nazir, Amril
The Covid-19 pandemic has highlighted the importance of forecasting in managing public health. The two of the most commonly used approaches for time series forecasting methods are autoregressive (AR) and deep learning models (DL). While there exist a number of studies comparing the performance of AR and DL models in specific domains, there is no work that analyzes the two approaches in the general context of theoretically simulated time series. To fill the gap in the literature, we conduct an empirical study using different configurations of generalized autoregressive conditionally heteroskedastic (GARCH) time series. The results show that DL models can achieve a significant degree of accuracy in fitting and forecasting AR-GARCH time series. In particular, DL models outperform the AR-based models over a range of parameter values. However, the results are not consistent and depend on a number of factors including the DL architecture, AR-GARCH configuration, and parameter values. The study demonstrates that DL models can be an effective alternative to AR-based models in time series forecasting. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Are cryptocurrencies affected by their asset class movements or news announcements?
(Malaysian Economic Association, 2019) Gurrib, Ikhlaas; Kweh, Qian Long; Nourani, Mohammad; Ting, Irene Wei Kiong
This study analyses whether returns of top market capitalised cryptocurrencies are affected by their movements or major global macroeconomic news. Daily data are collected for the leading 10 cryptocurrencies from July 2017-December 2018. This study, (i) tests whether lagged variables can help predict other variables' returns through a vector autoregression (VAR) model, (ii) analyses the response of cryptocurrencies to one standard deviation shock on Bitcoin's returns, and (iii) decomposes factors that contribute to variance and tests for structural breaks. Findings show that most cryptocurrencies do not significantly affect other variances, except for Monero, which represented between 19% and 45% of the variances of five cryptocurrencies. Autoregressive (AR) models are superior in forecasting one day ahead return forecasts, compared to the VAR model, whereas the random walk (RW) model ranked last. Although remarkable structural breaks are observed via impulse response functions during December 2017-January 2018, no major news announcements were released on the same day the breaks occurred. Overall, this study suggests the need for high-frequency cryptocurrency prices to tackle the issue of the relationship between intraday news release and cryptocurrencies. © 2019 Malaysian Economic Association. All rights reserved.
Are energy block chain currencies affected by the major US energy markets?
(Econjournals, 2019) Gurrib, Ikhlaas
While various economies have started to embark on a gradual shift towards renewable sources of energy, energy block chain based crypto currencies have emerged. The purpose of this study is to shed fresh light into whether an energy commodity price index (ENFX) and energy block chain based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Using principal component analysis over daily data of crude oil, heating oil, natural gas, and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only the lagged ENCX was significant in estimating current ENFX values. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX, and one standard deviation shock in ENCX left ENFX unaffected. Both indices had 1 structural break on different dates. Overall findings suggest that while the ENFX and ENCX are good representative of commodity energy prices and energy block chain based cryptos respectively, the two markets are not robust determinants of each other. © 2018, Econjournals. All rights reserved.
Are key market players in currency derivatives markets affected by financial conditions?
(LLC CPC Business Perspectives, 2018) Gurrib, Ikhlaas
Tis study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous fnancial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efciency, this study compares the impact of current, 1 and 2 week lags of fnancial conditions onto foreign currency futures players' net positions. Te fnancial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. Te US dollar index futures is included as a benchmark, since the fnancial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers' net positions, lagged NFCI captured the highest correlation with both players' net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the fnancial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. Te latter could be explained by FCIs not being affected during this period's event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. Tis suggests the need to look into other components like non-reportable long or short positions in future analysis. © 2018 Ikhlaas Gurrib.
Assessing views towards energy sources with social media data: The case of nuclear energy in the UAE
(MDPI, 2021-11) Contu, Davide; Elshareif, Elgilani Eltahir; Gurrib, Ikhlaas
Insights from the analysis of views towards energy sources are of paramount importance for the setting of successful energy policies, especially in instances where the public might be reluctant towards certain projects’ implementations. This work presents an analysis of social media comments data given in response to posts around the connection to the grid of a nuclear plant reactor in the United Arab Emirates (UAE). We assessed comments on Facebook posts of local and international media, as well those written in response to a post of a social media influencer. We extracted the main themes and performed sentiment analysis. The results indicate the presence of mixed views towards nuclear energy when focusing on comments on international media’s posts as well as on the social media influencer’s post considered, whilst they were very positive when assessing comments to local media. All in all, nuclear waste and previous nuclear accidents appear to be as the top of the mind; at the same time, solar energy is often suggested in the comments as a viable energy source for the UAE. Implications for the communication of nuclear energy developments in social media are discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
An assessment of the potential VAT revenue collection for the United Arab Emirates
(Routledge, 2017) Gurrib, Ikhlaas
This study analyses the effect of a 5% VAT in the UAE for the period 2018–2022. The methodology includes collection efficiency, standard tax rate and the final consumption expenditure (FCE). Various scenarios are analysed, including a constant 5% VAT for 2018–2022; increasing it by 2.39% yearly; increasing it to reach the maximum 2014 country tax rate of 27%; or increasing it to reach an average tax rate of 19.1%. The collection efficiency values of 0.4–0.7 result in a 2018–22 tax revenue to GDP range of between 1.75 and 7.84%. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Autocorrelation for time series with linear trend
(Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz; Thabtah, Fadi; Gurrib, Ikhlaas
The autocorrelation function (ACF) is a fundamental concept in time series analysis including financial forecasting. In this note, we investigate the properties of the sample ACF for a time series with linear trend. In particular, we show that the sample ACF of the time series approaches 1 for all lags as the number of time steps increases. The theoretical results are supported by numerical experiments. Our result helps researchers better understand the ACF patterns and make correct ARMA selection. © 2021 IEEE.
Autoregressive and neural network models: A comparative study with linearly lagged series
(Institute of Electrical and Electronics Engineers Inc., 2021-09-29) Kamalov, Firuz; Gurrib, Ikhlaas; Thabtah, Fadi
Time series analysis such as stock price forecasting is an important part of financial research. In this regard, autoregressive (AR) and neural network (NN) models offer contrasting approaches to time series modeling. Although AR models remain widely used, NN models and their variant long short-term memory (LSTM) networks have grown in popularity. In this paper, we compare the performance of AR, NN, and LSTM models in forecasting linearly lagged time series. To test the models we carry out extensive numerical experiments based on simulated data. The results of the experiments reveal that despite the inherent advantage of AR models in modeling linearly lagged data, NN models perform just as well, if not better, than AR models. Furthermore, the NN models outperform LSTMs on the same data. We find that a simple multi-layer perceptron can achieve highly accurate out of sample forecasts. The study shows that NN models perform well even in the case of linearly lagged time series. © 2021 IEEE.
Bitcoin price forecasting: Linear discriminant analysis with sentiment evaluation
(Association for Computing Machinery, 2021-08-25) Gurrib, Ikhlaas; Kamalov, Firuz; Smail, Linda
Cryptocurrencies such as bitcoin have garnered a lot of attention in recent months due to their meteoric rise. In this paper, we propose a new method for predicting the direction of bitcoin price using linear discriminant analysis (LDA) together with sentiment analysis. Concretely, we train an LDA-based classifier that uses the current bitcoin price information and Twitter headline news in order to forecast the next-day direction of bitcoin price. The proposed model achieves highly accurate results beating several benchmark targets. In particular, the proposed approach produces forecast accuracy of 0.828 and AUC of 0.840 on the test data. © 2021 Association for Computing Machinery. All rights reserved.
Can an energy futures index predict us stock market index movements?
(Econjournals, 2018) Gurrib, Ikhlaas
This paper investigates if an energy futures conditions index (EFCI) can predict movements of US major stock market indices. While various financial conditions indices provide information about the financial stress of a country, the existence of an energy conditions index, using futures markets, is scarce. Using weekly data over 1992-2017, this paper proposes an energy futures index using principal component analysis and test its predictability. The EFCI captures 95% of the variability inherent in the crude oil, heating oil and natural gas futures total reportable positions. Stability in forecast errors over different lags suggests 1 week lag is sufficient in forecasting weekly Nasdaq Composite Index, Nasdaq 100 and Russell 3000 values. 95% prediction levels support that the estimated model captures all actual market indices values, except for the 2000 technology bubble. The inability of the energy futures index in predicting stock market indices during the 2000 bubble can be explained by the poor sensitivity of energy futures to this specific event. Distributions were non-normal, not serially correlated and homoscedastic under the whole sample period, with diagnostics on pre and post technology bubble crisis showing mixed results. © 2018, Econjournals. All rights reserved.
Can energy commodities affect energy blockchain-based cryptos?
(Emerald Group Publishing Ltd., 2019-10) Gurrib, Ikhlaas
Purpose: The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach: Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings: Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications: Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value: As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets. © 2019, Emerald Publishing Limited.
Can the leading US energy stock prices be predicted using the ichimoku cloud?
(Econjournals, 2021) Gurrib, Ikhlaas; Kamalov, Firuz; Elshareif, Elgilani
The aim of this study is to investigate if Ichimoku Cloud can serve as a technical analysis indicator to improve stock price prediction for leading US energy companies. The methodology centers on the application of the Ichimoku Cloud as a trading system. The daily stock prices of the top ten constituents of the S&P Composite 1500 Energy Index-spanning the period from 12th April, 2012 to 31st July, 2019-were sourced for experimentation. The performance of the Ichimoku Cloud is measured using both the Sharpe and Sortino ratios to adjust for total and downside risks. The analysis is split into pre and post oil crisis to account for the drop in energy stock prices during the July 2014-December 2015. The model is also benchmarked against the naïve buy-and-hold strategy. The capacity of the Ichimoku indicator to provide signals during strengthening trends is analyzed. Despite the drop in energy stock prices, number of trades continued to increase along with profit opportunities. The PSX stock ranked first, with the highest Sharpe ratio, Sortino ratio, and Sharpe per number of trade. As expected, a number of buying signals occurred during strengthening bullish periods. Surprisingly, various sell signals also occurred during similar strengthening bullish trends. Most of the buy and sell signals under the Ichimoku indicator occurred outside of strengthening of bullish or bearish trends. The overall findings suggest that speculators can benefit from the use of the Ichimoku Cloud in analyzing energy stock price movements. In addition, it has the potential to reduce susceptibility to changes in energy prices. Last, the strength of the trend in place needs to be captured as it served as an additional layer of information which can improve the decision making process of the trader. © 2021, Econjournals. All rights reserved.
COVID-19, Short-selling Ban and Energy Stock Prices
(Asia-Pacific Applied Economics Association (APAEA), 2021) Gurrib, Ikhlaas; Kweh, Qian Long; Contu, Davide; Kamalov, Firuz
We examine the short-selling ban imposed by the National Commission for Companies and the Stock Exchange of Italy, the authority that regulates the Italian securities market, on three Italian energy stocks. We find that the effect of the short-selling ban was temporary.
COVID-19, Short-selling Ban and Energy Stock Prices
(Asia-Pacific Applied Economics Association, 2020) Gurrib, Ikhlaas; Kweh, Qian Long; Contu, Davide; Kamalov, Firuz
We examine the short-selling ban imposed by the National Commission for Companies and the Stock Exchange of Italy, the authority that regulates the Italian securities market, on three Italian energy stocks. We find that the effect of the short-selling ban was temporary. © 2020, Asia-Pacific Applied Economics Association. All rights reserved.
Cross-Market Price Mechanism Between the US Copper Futures Market and a Newly Proposed Chinese Dollar Index
(Springer Science and Business Media B.V., 2017) Gurrib, Ikhlaas
Recent changes in China’s copper demand have lately received much attention due to its close relationship to the country’s economic activity. Although an emerging market, China accounts for around 40 % of the world’s copper demand and the USA is the third biggest market for exports, making it imperative to assess the relationship between copper futures prices and a newly proposed Chinese dollar index. The purpose of this study is to analyse if changes in the copper futures prices can be used as a market timing tool to predict movements in the Chinese dollar index, and vice versa. To enhance the predictive market timing ability, an adaptive relative strength index model is used to track changes in market conditions better. The analysis is conducted using both daily and weekly data over the June 2007–December 2015 period. Findings will suggest if the technical analysis tool can be used to forecast copper prices based on changes in the Chinese dollar index, or if accurate forecasts can be made on the Chinese dollar index based on movements in copper’s prices, over different frequency intervals. More importantly, this would have policy implications in that it would reveal whether global copper prices can be affected by Chinese Yuan’s movements against other major global currencies, suggesting a need for regulatory bodies to relook at the effect of non-fundamental factors on commodity and currency markets. © 2017, Springer International Publishing Switzerland.
Early COVID-19 policy response on healthcare equity prices
(Emerald Group Holdings Ltd., 2021-10-18) Gurrib, Ikhlaas
Purpose: This paper aims to investigate the implementation of the short selling ban policy imposed by the Italian stock exchange on health-care stock prices, as a tool to mitigate COVID-19 price effects. Important contributions are in terms of assessing the effect of the temporary short selling ban on restricted health-care stocks; the effect of COVID-19 cases and crude oil price volatility onto health-care stocks; and whether COVID-19 resulted in a change in the risk and average stock price of health-care stocks. Design/methodology/approach: The methodology involves impulse responses to capture the shock of the short selling ban onto health-care stocks, and Markov switching regimes to capture the effect of COVID-19 onto the risk and prices in the health-care industry. Daily data from 9 November 2018 till 23 December 2020 is used. Findings: Findings suggest there were significant changes in average prices in health-care technology and health-care services stocks before, during and after the short selling ban. Shocks to the number of COVID-19 cases and crude oil price volatility impacted health-care stocks but lasted only for a few days. While daily changes in the number of COVID-19 cases impacted some health-care stocks in the presence of a two-state Markov regime, insignificant coefficients and relatively low duration suggest that the short selling policy did not significantly change the average price and risk in health-care stocks to explain a two-state regime in the health-care industry. Research limitations/implications: Insignificant coefficients in a two-state Markov regime reinforce that short-selling policies have a short-lasting effect onto health-care equity prices. The findings are limited by the duration of the short selling policy, the pandemic event and the health-care industry. Originality/value: This is the first study to look at the impact of early COVID-19 and short selling ban policy on health-care stocks. © 2021, Emerald Publishing Limited.
The effect of energy cryptos on efficient portfolios of key energy listed companies in the S&P composite 1500 energy index
(Econjournals, 2020) Gurrib, Ikhlaas; Elshareif, Elgilani; Kamalov, Firuz
This paper investigates if energy block chain based crypto currencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 energy index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as autoregressive moving average – Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and machine learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and ML models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. Different models are assessed using the Sharpe performance measure. Daily data is used, spanning from November 21, 2017 to January 31, 2019. Findings suggest that energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks. © 2020, Econjournals. All rights reserved.
Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?
(Springer Science and Business Media Deutschland GmbH, 2022-12) Gurrib, Ikhlaas; Nourani, Mohammad; Bhaskaran, Rajesh Kumar
This 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. © 2022, The Author(s).
Environmental initiatives: impact on firm wealth creation
(Inderscience Publishers, 2022) Kumar, Rajesh B.; Sujit K.S.; Gurrib, Ikhlaas
This study investigates the impact of environmental initiatives adopted by firms on wealth creation. A sample consisting primarily of 4886 developed and emerging market firms from Thomson Reuters ESG database is used for the study. Findings support that firms from emerging market had higher use of resources and emission reduction efficiency than the developed counterparts. Environmental initiatives were higher for polluting firms compared to non-polluting firms. Non-health sectors had higher environmental initiatives like efficient use of resources, emission reduction and innovation strategies targeted towards the reduction of environmental costs. Food sector had higher environmental initiatives compared to the non-food sector. Firms from sin industries had positive market valuation effects. Using regression analysis, our study examines the impact of environmental initiatives on three models of performance. The study suggests that environmental initiatives to reduce environmental emissions and activities targeted at environmental innovation are not value enhancing activities for entities. Empirical findings supporting that environmental initiatives do not lead to improved financial performance lead to have implications for managers and policy makers. With markets not perceiving environmental initiatives as value creating activities by firms, actions targeted towards emission reduction, adoption of environmental innovation strategies and resource efficiency are not positively viewed by markets. Copyright © 2022 Inderscience Enterprises Ltd.
Financial Forecasting with Machine Learning: Price Vs Return
(Science Publications, 2021) Kamalov, Firuz; Gurrib, Ikhlaas; Rajab, Khairan
Forecasting directional movement of stock price using machine learning tools has attracted a considerable amount of research. Two of the most common input features in a directional forecasting model are stock price and return. The choice between the former and the latter variables is often subjective. In this study, we compare the effectiveness of stock price and return as input features in directional forecasting models. We perform an extensive comparison of the two input features using 10-year historical data of ten large cap US companies. We employ four popular classification algorithms as the basis of the forecasting models used in our study. The results show that stock price is a more effective standalone input feature than return. The effectiveness of stock price and return equalize when we add technical indicators to the input feature set. We conclude that price is generally a more potent input feature than return value in predicting the direction of price movement. Our results should aid researchers and practitioners interested in applying machine learning models to stock price forecasting. © 2021 Firuz Kamalov, Ikhlaas Gurrib and Khairan Rajab. This open access article is distributed under a Creative

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