International Journal of Computational Economics and Econometrics (6 papers in press)

Regular Issues

• Machine learning approach to forecasting global energy consumption and sustainability  
  by George Sammour, Anas Irsheid 
  Abstract: This study applies machine learning specifically K-means clustering and long short-term memory (LSTM) networks to analyse and forecast global energy consumption patterns. Countries were classified into 12 clusters based on energy profiles, GDP, and environmental impacts, identifying seven unique national profiles (the USA, China, France, Germany, Russia, Japan, Canada, and Brazil). These clusters revealed varying dependencies on fossil fuels and renewable sources. LSTM models predicted primary and renewable energy consumption, offering insights for energy planning and investment. Results indicated distinct reliance patterns, renewable energy forecasts were more accurate in Germany and Canada due to stable infrastructure and policies, while Russia and Brazil showed higher variability in renewable adoption. Visual analyses captured deviations during the 20082009 financial crisis and the 2020 COVID pandemic. Overall, the findings demonstrate machine learning potential to optimise energy management, support the United Nations Sustainable Development Goals, and mitigate climate change through improved forecasting and resource utilisation.
  Keywords: energy consumption forecasting; machine learning in energy; fossil fuel dependency; sustainable energy strategies; UN-SDGs; energy demand prediction; long short-term memory; LSTM; K-means clustering.
  DOI: 10.1504/IJCEE.2025.10074820
   
  
• Small vs. large firms: exploring determinants of IPO launch decisions  
  by Muhammadriyaj Faniband, Pravin Jadhav 
  Abstract: This paper investigates the impact of macroeconomic and market specific factors on initial public offering (IPO) launch decisions of small and large firms in India using the count data models: Poisson and negative binomial models. We consider a monthly dataset from January 2012 to December 2024. We find that for small firms, industrial output growth is consistently positive and significant, emphasising their reliance on domestic industrial performance, while for large firms, its impact is weaker and delayed. Inflation and interest rates exert robust negative effects on both markets. Other variables, including exchange rate, economic policy uncertainty and foreign and domestic institutional investment flows, are largely insignificant. Market indicators such as Nifty 50 and market volatility influence large firms IPOs more than small firms, which reflects broader investor sensitivity. We provide several policy implications for the central bank and government to foster a conducive IPO environment for small and large Indian firms.
  Keywords: macroeconomic; market indicators; initial public offerings; IPO; SME; small firms; large firms; count models; India.
  DOI: 10.1504/IJCEE.2026.10075355
   
  
• Panel models in MATLABR®: fixed effects, clustered standard errors and large datasets  
  by Pavel S. Kapinos 
  Abstract: This note describes a MATLAB® program that offers two advantages over the existing software. First, it allows for flexible estimation of multi-way clustered standard errors using the methodology of Cameron et al. (2011), which, unlike other approaches, does not limit the number of clustering dimensions. Second, it efficiently computes estimates of multi-way fixed effects models, with or without clustered standard errors, using native MATLAB® commands and materially reducing computational time in large datasets. Additional optionality, such as the ability to drop singleton observations or provide standard errors for linear combinations of estimated coefficients, is also included.
  Keywords: panel models; fixed effects; clustered standard errors; large datasets; MATLAB®.
  DOI: 10.1504/IJCEE.2026.10075536
   
  
• Multi-resolution co-movement dynamics of conventional and Islamic stock markets: mapping global financial interdependencies  
  by Amel Belanes, Foued Saâdaoui, Hana Rabbouch 
  Abstract: This study examines contagion and co-movement between Islamic and conventional equity indices in the USA, Canada, the UK, and Japan during periods of financial distress. A wavelet-based multi-scale framework is applied to daily data over 2001-2017, capturing market dynamics before, during, and after the 2008 global financial crisis. To assess the stability of these relationships under a more recent systemic shock, the analysis is extended to the COVID-19 period using an updated sample covering 2017-2026. The results reveal marked heterogeneity in contagion patterns across crisis episodes, with pronounced differences in both intensity and time-frequency dependence of co-movements. Interdependence strengthens substantially during turbulent periods, reflecting heightened vulnerability under financial stress. Both global and local shocks are found to be key drivers of market dynamics across multiple investment horizons. Dependence structures are time-varying and state-dependent, exhibiting stronger synchronisation during crises and partial decoupling in calmer periods. These findings highlight important implications for portfolio allocation and risk management under evolving financial interdependencies.
  Keywords: multi-scale analytics; financial data; co-movement; contagion; wavelets; US stock market.
  DOI: 10.1504/IJCEE.2026.10079080
   
  
• Modelling and forecasting financial volatility: hybrid econometric-deep learning architectures  
  by Burç Arslan Kaleli, Ahmet Özçam 
  Abstract: Volatility forecasting plays a vital role in financial markets, particularly in asset pricing, portfolio allocation, and derivative valuation. This study proposes hybrid LSTM-based models, namely LSTM-GARCH, LSTM-EGARCH and LSTM-FIGARCH, for forecasting the volatility of the S&P 500 index. Using unidirectional and bidirectional long short-term memory (LSTM) architectures together with gated recurrent units (GRU), model performance is evaluated across different window lengths (7, 30 and 60 days) and forecast horizons (1, 14 and 21 days). The LSTM architecture is designed to balance simplicity and performance while capturing sequential patterns in financial time series. Based on out-of-sample prediction loss, the hybrid models achieve lower forecast errors than traditional approaches across multiple settings, although this improvement may partly reflect the use of a richer input structure. The findings suggest that hybrid deep learning and econometric frameworks offer meaningful improvements in volatility forecasting and can serve as effective tools for researchers and practitioners.
  Keywords: volatility forecasting; GARCH; LSTM; deep learning; hybrid econometric models; financial time series.
  DOI: 10.1504/IJCEE.2026.10080056
   
  
• Adaptive technical analysis as a regime-contingent decision-support model: evidence from an emerging equity market  
  by Mudit Gera 
  Abstract: This study evaluates technical analysis as a regime-contingent decision-support framework rather than as a universal forecasting or abnormal-return generating tool. Using daily NIFTY 50 data from January 2020 to March 2025, it compares five algorithmic decision models: trend-following, oscillator-based, volatility-momentum hybrid, volume-based, and behavioural-anchor rules. Performance is assessed through risk-adjusted metrics, CAPM inference, rolling-window analysis, bootstrap resampling, parameter-sensitivity tests, and regime-specific evaluation. Results show that volume-based and volatility-sensitive models provide episodic economic usefulness during market stress and regime transitions, whereas trend-following rules perform primarily during sustained directional persistence. Behavioural-anchor heuristics, such as Fibonacci retracements, lack statistically robust decision-support value. The findings distinguish conditional economic usefulness from persistent alpha generation and support the adaptive markets hypothesis by showing that technical indicators are best understood as state-contingent decision aids aligned with volatility, liquidity, and market regimes. The study offers practical implications for traders, portfolio managers, and decision-system designers seeking context-aware analytical tools.
  Keywords: Ttechnical analysis; regime-contingent modelling; adaptive markets hypothesis; AMH; computational econometrics; decision-support systems; CAPM alpha; emerging equity markets; NIFTY 50; algorithmic trading.
  DOI: 10.1504/IJCEE.2026.10080192