Faheem Aslam

Dr. Faheem Aslam is Tenured Associate Professor in the Department of Management Sciences at CIIT Islamabad. He earned his Master's and Ph.D. Degrees from Hanyang University Business School, Seoul, South Korea, in 2015.
My educational background, combined with my practical experience in the financial industry, has provided me with a comprehensive understanding of finance. I hold a PHD degree in Hanyang University business school, South Korea (AACSB accredited international academic institution) with CGPA of 3.93/4. I have taught a range of finance courses, from introductory to advanced level, and have received consistently positive feedback (above 90%) from my students. In addition to my teaching experience, I have published more than 50 research papers in top-tier finance journals (including ABDC, ABS) with an impact factor of 155. My research is getting increasing attention among international research community with more than 1300 citations. Likewise, the world health organization.

EDUCATION

PHD (Finance)-Hanyang University Seoul, South Korea.
MBA (Finance)- International Islamic University Islamabad (Gold Medal)

RESEARCH, TEACHING, or OTHER INTERESTS

General Economics, Econometrics and Finance, Finance, Business, Management and Accounting, Developmental Neuroscience

Scopus Publications

Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach
Saima Latif, Faheem Aslam, Paulo Ferreira, and Sohail Iqbal
MDPI AG
Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. These models include the deep belief network with gated recurrent units, the LeNet architecture with gated recurrent units, and the LeNet architecture combined with highway networks. The results demonstrate that the proposed hybrid models achieve higher forecasting accuracy than the single deep learning models. This outcome is attributed to the complementary strengths of convolutional networks in feature extraction and recurrent networks in pattern recognition. Additionally, an analysis using the Shapley method identifies the volatility index, financial stress index, and economic policy uncertainty as the most significant predictors, underscoring the effectiveness of our data-driven approach. These findings highlight the substantial impact of contemporary uncertainty factors on stock markets, emphasizing their importance in studies analyzing market behaviour.


Exploring the connection between geopolitical risks and energy markets
Dora Almeida, Paulo Ferreira, Andreia Dionísio, and Faheem Aslam
Elsevier BV



Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
Haider Ali, Muhammad Aftab, Faheem Aslam, and Paulo Ferreira
MDPI AG
Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.


Influence of the Russia–Ukraine War and COVID-19 Pandemic on the Efficiency and Herding Behavior of Stock Markets: Evidence from G20 Nations
Bilal Ahmed Memon, Faheem Aslam, Hafiz Muhammad Naveed, Paulo Ferreira, and Omonjon Ganiev
MDPI AG
Efficiency in stock markets is essential for economic stability and growth. This study investigates the efficiency and herding behavior of the stock markets from the top economies of the world (known as G20 countries). We classify stock market indices using MSCI classification for the developed and emerging markets to provide a comparative examination using the latest data and by employing the robust multifractal detrended fluctuation (MFDFA) method. In addition to the full sample, the analysis uses sub-sample periods to reveal the hidden features and efficiencies of the G20 markets during the Russia–Ukraine War and COVID-19 for the first time. The findings show the availability of varied multifractality among all G20 stock markets during the overall and crisis periods, exhibit long-range correlations, and may support the fractal market hypothesis. In addition, Italy remains the least efficient, while Germany remains the most efficient stock market. The sub-sample results further reveal unevenness in the local fluctuations and resultant higher inefficiency considering the sheer magnitude and impact of crises on the G20 stock markets. However, the efficiency of developed stock markets performed better as compared to emerging markets. The study of G20 stock markets is useful and provides several implications for a wider audience.


Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities
Saima Latif, Nadeem Javaid, Faheem Aslam, Abdulaziz Aldegheishem, Nabil Alrajeh, and Safdar Hussain Bouk
Elsevier BV



Interplay of multifractal dynamics between shadow policy rates and energy markets
Faheem Aslam, Ahmed Imran Hunjra, Bilal Ahmed Memon, and Mingda Zhang
Elsevier BV



Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices
Haji Ahmed, Faheem Aslam, and Paulo Ferreira
MDPI AG
Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.


On the Dynamic Changes in the Global Stock Markets’ Network during the Russia–Ukraine War
Kashif Zaheer, Faheem Aslam, Yasir Tariq Mohmand, and Paulo Ferreira
MDPI AG
Analysis of the relationships among global stock markets is crucial for international investors, regulators, and policymakers, particularly during a crisis. Complex network theory was applied to analyze the relationship between global stock markets during the Russia–Ukraine war. Daily data from 55 stock markets from 6 August 2021 to 23 September 2023 were retrieved and used to investigate the changes in global stock market networks. The sample period was divided into 22 subsamples, using a 100-day rolling window rolled forward a trading month, and then long-range correlations based on distance matrices were calculated. These distance matrices were utilized to construct stock market networks. Moreover, minimum spanning trees (MSTs) were extracted from these financial networks for analytical purposes. Based on topological and structural analysis, we identified important/central nodes, distinct communities, vulnerable/stable nodes, and changes thereof with the escalation of war. The empirical findings reveal that the Russia–Ukraine war impacted the global stock markets’ network. However, its intensity varied with changes in the region and the passage of time due to the level of stock market integration and stage of war escalation, respectively. Stock markets of France, Germany, Canada, and Austria remained the most centrally connected within communities; surprisingly, the USA’s stock market is not on this list.


Bitcoin’s multifractal influence: deciphering the relationship with conventional and renewable energy markets
Ayesha Rasool Malik, Faheem Aslam, and Paulo Ferreira
Informa UK Limited



Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic
Bilal Ahmed Memon, Faheem Aslam, Shakhnoza Asadova, and Paulo Ferreira
Elsevier BV



The dynamics of market efficiency of major cryptocurrencies
Faheem Aslam, Bilal Ahmed Memon, Ahmed Imran Hunjra, and Elie Bouri
Elsevier BV



Interplay of multifractal dynamics between shadow policy rates and stock markets
Faheem Aslam, Wahbeeah Mohti, Haider Ali, and Paulo Ferreira
Elsevier BV



Investigating efficiency of frontier stock markets using multifractal detrended fluctuation analysis
Faheem Aslam, Paulo Ferreira, and Wahbeeah Mohti
Emerald
PurposeThe investigation of the fractal nature of financial data has been growing in the literature. The purpose is to investigate the multifractal behavior of frontier markets using multifractal detrended fluctuation analysis (MFDFA).Design/methodology/approachThis study used daily closing prices of nine frontier stock markets up to 31-Aug-2020. A preliminary analysis reveals that these markets exhibit fat tails and clustering patterns. For a more robust analysis, a combination of Seasonal and Trend Decomposition using Loess (STL) and MFDFA has been employed. The former method is used to decompose daily stock returns, where later detected the long rang dependence in the series.FindingsThe results confirm varying degree of multifractality in frontier stock markets, implying that they exhibit long-range dependence. Based on these multifractality levels, Serbian and Romanian stock markets are the ones exhibiting least long-range dependence, while Slovenian and Mauritius stock markets indicating highest dependence in their series. Furthermore, the markets of Kenya, Morocco, Romania and Serbia exhibit mean reversion (anti-persistent) behavior while the remaining frontier markets show persistent behaviors.Practical implicationsThe information given by the detection of the fractal measure of data can support for investment and policymaking decisions.Originality/valueFrontier markets are of great potential from the perspective of international diversification. However, most of the research focused on other emerging and developed markets, especially in the context of multifractal analysis. This study combines the STL method and a physics-based robust technique, MFDFA to detect the multifractal behavior of frontier stock markets.


ASYMMETRIC RESPONSE OF DISAGGREGATED IMPORT DEMAND TO EXCHANGE RATE MOVEMENTS: A SMALL OPEN ECONOMY PERSPECTIVE
KHURRUM S. MUGHAL, SADDAM ILYAS, YASIR TARIQ MOHMAND, FAHEEM ASLAM, and MUKHTAR UL HASAN
World Scientific Pub Co Pte Ltd
Net importing countries are very susceptible to changes in the value of their currency. Pakistan, being a small open economy, faces a constant pressure in its current account and BOP, which leads to unavoidable stress on its exchange rate. Exchange rate movements affect the cost of imports directly which have been studied extensively in empirical literature. However, these studies ignore the possibility of asymmetric effects of exchange rate and its impact on import demand in Pakistan. An appreciation in exchange rate may have a different impact on demand for imports than depreciations depending upon the level of rigidity in consumer preferences as well as the availability of substitutes for consumer goods, capital goods and raw materials. We use quarterly data of Pakistan’s consumer goods, capital goods and raw material imports from 2005:Q1 to 2018:Q4 and employ a relatively recent econometric methodology, namely, Nonlinear Autoregressive Distributed Lag (NARDL) technique. The results confirm the existence of asymmetric impact of exchange rate in the long-run. The appreciation of currency has more pronounced impact in increasing imports relative to the depreciation of it in decreasing imports. There are further differences of this effect within imports across consumer goods, capital goods and raw materials. We present policy implications of this asymmetric effect of exchange rate on disaggregated consumer imports.


Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks
Kashif Zaheer, Faheem Aslam, Yasir Tariq Mohmand, and Paulo Ferreira
Emerald
PurposeCOVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.Design/methodology/approachDaily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.FindingsThe findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.Originality/valueIt is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.


The footprints of Russia–Ukraine war on the intraday (in)efficiency of energy markets: a multifractal analysis
Faheem Aslam, Skander Slim, Mohamed Osman, and Ibrahim Tabche
Emerald
PurposeThis paper examines the impact of Russian invasion of Ukraine on the intraday efficiency of four major energy markets, namely, diesel oil, Brent oil, light oil and natural gas.Design/methodology/approachThis study applies the multifractal detrended fluctuation analysis (MFDFA) to high-frequency returns (30-min intervals) for the period from October 21, 2021, to May 20, 2022. The data sample of 5,141 observations is divided into two sub-samples, before and after the invasion of 24th February 2022. Additionally, the magnitude of long memory index is employed to investigate the presence of herding behavior around the invasion period.FindingsResults confirm the presence of multifractality in energy markets and reveal significant changes of multifractal strength due to the invasion, indicating a decline of intraday efficiency for oil markets. Surprisingly, the natural gas market, being the least efficient before the invasion, turns out to be more efficient after the invasion. The findings also suggest that investors in these energy markets are likely to show herding, more prominently after the invasion.Practical implicationsThe multifractal patterns, in particular the long memory property of energy markets, can help investors develop profitable investment strategies. Furthermore, the improved efficiency observed in the natural gas market, after the invasion, highlights its unique traits and underlying complexity.Originality/valueThis study is the first attempt to assess the impact of the Russia–Ukraine war on the efficiency of global commodity markets. This is quite important because the adverse effects of the war on financial markets may potentially cause destabilizing outcomes and negative effects on social welfare on a global scale.


On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis
Faheem Aslam, Ijaz Ali, Fahd Amjad, Haider Ali, and Inza Irfan
Springer Science and Business Media LLC



The nexus between twitter-based uncertainty and cryptocurrencies: A multifractal analysis
FAHEEM ASLAM, ZIL-E-HUMA, RASHIDA BIBI, and PAULO FERREIRA
World Scientific Pub Co Pte Ltd
We take the novel Twitter-based economic uncertainty (TEU) to examine if it has cross-correlation characteristics with four major cryptocurrencies i.e. Bitcoin, Ethereum, Litecoin, and Ripple. To conduct a more thorough analysis, we apply multifractal detrended cross-correlation analysis (MFDCCA) on seasonal-trend decomposition using Loess (STL) decomposed series as well as without decomposed series on the daily data, ranging from 1 June 2011 to 30 June 2021. The findings of this study indicate that: (i) all pairs of TEU with cryptocurrencies are multifractal and have power-law behavior; (ii) the pairs of Ethereum and Bitcoin with TEU are found to be the most multifractal while Litecoin with TEU has the lowest multifractal characteristics; (iii) all STL decomposed series of cryptocurrency have persistent cross-correlation with TEU with the exception of Ethereum which has anti-persistent cross-correlation with TEU; (iv) all without decomposed series of cryptocurrencies show significant persistent cross-correlation characteristics with TEU; (v) the highest linkage is found for the pair of Bitcoin with TEU. Moreover, to reveal the dynamic characteristics in the cross-correlation of TEU with cryptocurrencies, the rolling window is employed for MFDCCA. These findings have important managerial and academic implications for policymakers, investors, and market participants.


Islamic vs. Conventional Equity Markets: A Multifractal Cross-Correlation Analysis with Economic Policy Uncertainty
Faheem Aslam, Paulo Ferreira, Haider Ali, Arifa, and Márcia Oliveira
MDPI AG
There is ample evidence that Islamic stock markets perform differently from conventional stock markets, particularly when economic policy uncertainty (EPU) or any other uncertainty such as geopolitical uncertainty is present. Considering this context, this paper examines the US EPU’s cross-correlation with both conventional and Islamic stock markets from the perspective of multifractality. Daily stock market prices of five main countries are considered: US, Thailand, Indonesia, Pakistan, and India. Using the multifractal detrended cross-correlation analysis (MF-DCCA), we validate the existence of long-range cross-correlation between US EPU and all the stock markets considered, demonstrating that all pairs of US EPU have strong power law and multifractal characteristics. Furthermore, all pairs display varying levels of multifractal strength, with the US EPU and US conventional stock market exhibiting the strongest multifractal patterns. Additionally, a cross-correlation between US EPU and the different stock markets is found to be persistent. The results of this study are pertinent to the various market participants in both conventional and Islamic markets, particularly investors, who may be able to draw useful conclusions from them for purposes such as portfolio diversification.


Dependence structure across equity sectors: Evidence from vine copulas
Faheem Aslam, Ahmed Imran Hunjra, Elie Bouri, Khurrum Shahzad Mughal, and Mrestyal Khan
Elsevier BV



Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets
Faheem Aslam, Paulo Ferreira, and Haider Ali
MDPI AG
The investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions.


The use of transfer entropy to analyse the comovements of European Union stock markets: a dynamical analysis in times of crises




Insurance fraud detection: Evidence from artificial intelligence and machine learning
Faheem Aslam, Ahmed Imran Hunjra, Zied Ftiti, Wael Louhichi, and Tahira Shams
Elsevier BV



Interplay multifractal dynamics among metal commodities and US-EPU
Leonardo H.S. Fernandes, José W.L. Silva, Fernando H.A. de Araujo, Paulo Ferreira, Faheem Aslam, and Benjamin Miranda Tabak
Elsevier BV



Classification of m-payment users’ behavior using machine learning models
Faheem Aslam, Tahir Mumtaz Awan, and Tayyba Fatima
Springer Science and Business Media LLC

RECENT SCHOLAR PUBLICATIONS

The Complexity of Jump Dynamics in Cryptocurrencies and Forex Markets: A Multifractal Cross-Correlation Framework
H Ali, M Aftab, F Aslam
Journal of Managerial Sciences 19 (1), 74-120 2025



Exploring the connection between geopolitical risks and energy markets
D Almeida, P Ferreira, A Dionsio, F Aslam
Energy Economics 141, 108113 2025



Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach
S Latif, F Aslam, P Ferreira, S Iqbal
Economies 13 (1), 6 2024



Bitcoin’s multifractal influence: deciphering the relationship with conventional and renewable energy markets
A Rasool Malik, F Aslam, P Ferreira
Cogent Economics & Finance 12 (1), 2395413 2024



Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets
H Ali, M Aftab, F Aslam, P Ferreira
Fractal and Fractional 8 (10), 571 2024



Influence of the Russia–Ukraine War and COVID-19 Pandemic on the Efficiency and Herding Behavior of Stock Markets: Evidence from G20 Nations
B Ahmed Memon, F Aslam, HM Naveed, P Ferreira, O Ganiev
Economies 12 (5), 106 2024



Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities
S Latif, N Javaid, F Aslam, A Aldegheishem, N Alrajeh, SH Bouk
Heliyon 10 (6) 2024



Interplay of multifractal dynamics between shadow policy rates and energy markets
F Aslam, AI Hunjra, BA Memon, M Zhang
The North American Journal of Economics and Finance 71, 102085 2024



On the Dynamic Changes in the Global Stock Markets’ Network during the Russia–Ukraine War
K Zaheer, F Aslam, YT Mohmand, P Ferreira
Economies 12 (2), 41 2024



Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices
H Ahmed, F Aslam, P Ferreira
Fractal and Fractional 8 (2), 96 2024



Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic
BA Memon, F Aslam, S Asadova, P Ferreira
Heliyon 9 (12) 2023



The dynamics of market efficiency of major cryptocurrencies
F Aslam, BA Memon, AI Hunjra, E Bouri
Global Finance Journal 58, 100899 2023



Interplay of multifractal dynamics between shadow policy rates and stock markets
F Aslam, W Mohti, H Ali, P Ferreira
Heliyon 9 (7) 2023



The Nexus Between Twitter-Based Uncertainty And Cryptocurrencies: A Multifractal Analysis
F Aslam, Zil-E-Huma, R Bibi, P Ferreira
Fractals 31 (03), 2350027 2023



Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks
K Zaheer, F Aslam, Y Tariq Mohmand, P Ferreira
China Finance Review International 13 (1), 23-45 2023



On the inner dynamics between Fossil fuels and the carbon market: A combination of seasonal-trend decomposition and multifractal cross-correlation analysis
F Aslam, I Ali, F Amjad, H Ali, I Irfan
Environmental Science and Pollution Research 30 (10), 25873-25891 2023



Islamic vs. Conventional equity markets: a multifractal cross-correlation analysis with economic policy uncertainty
F Aslam, P Ferreira, H Ali, Arifa, M Oliveira
Economies 11 (1), 16 2023



Dependence structure across equity sectors: Evidence from vine copulas
F Aslam, AI Hunjra, E Bouri, KS Mughal, M Khan
Borsa Istanbul Review 23 (1), 184-202 2023



Analysis of the impact of COVID-19 pandemic on the intraday efficiency of agricultural futures markets
F Aslam, P Ferreira, H Ali
Journal of Risk and Financial Management 15 (12), 607 2022



The use of transfer entropy to analyse the comovements of European Union stock markets: a dynamical analysis in times of crises
P Ferreira, D Almeida, A Dionsio, D Quintino, F Aslam
Revista Galega de Economa 31 (3), 1-21 2022

MOST CITED SCHOLAR PUBLICATIONS

Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak
F Aslam, TM Awan, JH Syed, A Kashif, M Parveen
Humanities and Social Sciences Communications 7 (23), 1-9 2020
Citations: 350



On the efficiency of foreign exchange markets in times of the COVID-19 pandemic
F Aslam, S Aziz, DK Nguyen, KS Mughal, M Khan
Technological forecasting and social change 161, 120261 2020
Citations: 257



How different terrorist attacks affect stock markets
F Aslam, HG Kang
Defence and Peace Economics 26 (6), 634-648 2015
Citations: 201



Network Analysis of Global Stock Markets at the beginning of the Coronavirus Disease (Covid-19) Outbreak  
A Faheem, M Yasir Tariq, F Paulo, K Maaz, K Mrestyal
Borsa istanbul review 2020
Citations: 175



Insurance Fraud Detection: Evidence from Artificial Intelligence and Machine Learning
F Aslam, I Hunjra, Z Ftiti, W Louhichi, T Shams
Research in International Business and Finance 101744 (101744), 101744 2022
Citations: 125



Evidence of Intraday Multifractality in European Stock Markets during the recent Coronavirus (COVID-19) Outbreak
F Aslam, W Mohti, P Ferreira
International Journal of Financial Studies 8 (2), 1-13 2020
Citations: 105



Intraday Volatility Spillovers among European Financial Markets during COVID-19
F Aslam, P Ferreira, KS Mughal, B Bashir
International Journal of Financial Studies 9 (1), 1-5 2021
Citations: 104



Herding behavior during the COVID-19 pandemic: A comparison between Asian and European stock markets based on intraday multifractality
F Aslam, P Ferreira, H Ali, S Kauser
Eurasian Economic Review 12 (2), 333-359 2022
Citations: 73



Investigating the causal relationship between transport infrastructure, economic growth and transport emissions in Pakistan
YT Mohmand, F Mehmood, KS Mughal, F Aslam
Research in Transportation Economics 88, 100972 2021
Citations: 65



Prediction of daily COVID-19 cases in European countries using automatic ARIMA model
TM Awan, F Aslam
Journal of Public Health Research 9 (1765) 2020
Citations: 57



The impact of terrorism on financial markets: Evidence from Asia
F Aslam, A Rafique, A Salman, HG Kang, W Mohti
The Singapore Economic Review 63 (05), 1183-1204 2018
Citations: 46



Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis
F Aslam, Z Huma, R Bibi, P Ferreira
Resources Policy 102473 2022
Citations: 33



The dynamics of market efficiency of major cryptocurrencies
F Aslam, BA Memon, AI Hunjra, E Bouri
Global Finance Journal 58, 100899 2023
Citations: 29



Interplay multifractal dynamics among metal commodities and us-epu
LHS Fernandes, JWL Silva, FHA De Araujo, P Ferreira, F Aslam, ...
Physica A: Statistical Mechanics and its Applications 606, 128126 2022
Citations: 28



The footprints of COVID-19 on Central Eastern European stock markets: an intraday analysis
F Aslam, F Nogueiro, M Brasil, P Ferreira, KS Mughal, B Bashir, S Latif
Post-Communist Economies 33 (6), 751-769 2021
Citations: 28



Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis
F Aslam, S Latif, P Ferreira
symmetry 8 (2) 2020
Citations: 28



Application of Weighted Linear Combination approach in a Geographical Information System environment for nuclear power plant site selection: The case of Ghana
EB Agyekum, F Amjad, F Aslam, A Ali
Annals of Nuclear Energy 162, 108491 2021
Citations: 27



Dependence structure across equity sectors: Evidence from vine copulas
F Aslam, AI Hunjra, E Bouri, KS Mughal, M Khan
Borsa Istanbul Review 23 (1), 184-202 2023
Citations: 26



Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks
K Zaheer, F Aslam, Y Tariq Mohmand, P Ferreira
China Finance Review International 13 (1), 23-45 2023
Citations: 25



Investigating the Implications of COVID-19 on PM2. 5 in Pakistan
H Sipra, F Aslam, JH Syed, TM Awan
Aerosol and Air Quality Research 20 2020
Citations: 25