Characterising the Nigerian Stock Exchange for Machine Learning-Based Portfolio Research: An Empirical Analysis of Return Distributions, Volatility Dynamics, Liquidity, and Correlation Structure

Nnamdi A. Isichei, Kehinde Oduwole
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:1-15, 2026.

Abstract

Machine learning portfolio systems encode the statistical assumptions of the markets they were trained on. Deploying such systems on the Nigerian Stock Exchange (NGX) without first characterising that market is methodologically indefensible, yet the NGX is almost entirely absent from the empirical ML-finance literature. This paper fills that gap. Using daily OHLCV data on 22 NGX stocks from 2015–2025, we document five structural features of the market. All 22 stocks reject normality under the Jarque–Bera test; median excess kurtosis is 4.79 and 19 of 22 stocks are positively skewed. Mean annualised volatility is 43.3%, with GARCH(1,1) persistence ($\alpha + \beta$) exceeding 0.90 in 12 stocks and a cross-sectional median of 0.912. The average pairwise return correlation is 0.109, far below typical developed-market levels, with a dense banking-sector cluster as the only dominant structure. Liquidity risk operates through volume episodicity rather than zero-volume illiquidity: only 7 zero-volume days occur across the entire dataset, yet the mean volume coefficient of variation is 1.85. At the index level, the NGX composite returns a Sharpe ratio of 1.40 over the sample period, nearly double that of the S&P 500 (0.71) at comparable volatility, though nominal Naira figures are materially affected by the 2023–2024 devaluation episode. Each finding is translated into a concrete design requirement for the two-stage Graph Neural Network and Reinforcement Learning portfolio framework that constitutes the broader research programme.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-isichei26a, title = {Characterising the Nigerian Stock Exchange for Machine Learning-Based Portfolio Research: An Empirical Analysis of Return Distributions, Volatility Dynamics, Liquidity, and Correlation Structure}, author = {Isichei, Nnamdi A. and Oduwole, Kehinde}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {1--15}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/isichei26a/isichei26a.pdf}, url = {https://proceedings.mlr.press/v319/isichei26a.html}, abstract = {Machine learning portfolio systems encode the statistical assumptions of the markets they were trained on. Deploying such systems on the Nigerian Stock Exchange (NGX) without first characterising that market is methodologically indefensible, yet the NGX is almost entirely absent from the empirical ML-finance literature. This paper fills that gap. Using daily OHLCV data on 22 NGX stocks from 2015–2025, we document five structural features of the market. All 22 stocks reject normality under the Jarque–Bera test; median excess kurtosis is 4.79 and 19 of 22 stocks are positively skewed. Mean annualised volatility is 43.3%, with GARCH(1,1) persistence ($\alpha + \beta$) exceeding 0.90 in 12 stocks and a cross-sectional median of 0.912. The average pairwise return correlation is 0.109, far below typical developed-market levels, with a dense banking-sector cluster as the only dominant structure. Liquidity risk operates through volume episodicity rather than zero-volume illiquidity: only 7 zero-volume days occur across the entire dataset, yet the mean volume coefficient of variation is 1.85. At the index level, the NGX composite returns a Sharpe ratio of 1.40 over the sample period, nearly double that of the S&P 500 (0.71) at comparable volatility, though nominal Naira figures are materially affected by the 2023–2024 devaluation episode. Each finding is translated into a concrete design requirement for the two-stage Graph Neural Network and Reinforcement Learning portfolio framework that constitutes the broader research programme.} }
Endnote
%0 Conference Paper %T Characterising the Nigerian Stock Exchange for Machine Learning-Based Portfolio Research: An Empirical Analysis of Return Distributions, Volatility Dynamics, Liquidity, and Correlation Structure %A Nnamdi A. Isichei %A Kehinde Oduwole %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-isichei26a %I PMLR %P 1--15 %U https://proceedings.mlr.press/v319/isichei26a.html %V 319 %X Machine learning portfolio systems encode the statistical assumptions of the markets they were trained on. Deploying such systems on the Nigerian Stock Exchange (NGX) without first characterising that market is methodologically indefensible, yet the NGX is almost entirely absent from the empirical ML-finance literature. This paper fills that gap. Using daily OHLCV data on 22 NGX stocks from 2015–2025, we document five structural features of the market. All 22 stocks reject normality under the Jarque–Bera test; median excess kurtosis is 4.79 and 19 of 22 stocks are positively skewed. Mean annualised volatility is 43.3%, with GARCH(1,1) persistence ($\alpha + \beta$) exceeding 0.90 in 12 stocks and a cross-sectional median of 0.912. The average pairwise return correlation is 0.109, far below typical developed-market levels, with a dense banking-sector cluster as the only dominant structure. Liquidity risk operates through volume episodicity rather than zero-volume illiquidity: only 7 zero-volume days occur across the entire dataset, yet the mean volume coefficient of variation is 1.85. At the index level, the NGX composite returns a Sharpe ratio of 1.40 over the sample period, nearly double that of the S&P 500 (0.71) at comparable volatility, though nominal Naira figures are materially affected by the 2023–2024 devaluation episode. Each finding is translated into a concrete design requirement for the two-stage Graph Neural Network and Reinforcement Learning portfolio framework that constitutes the broader research programme.
APA
Isichei, N.A. & Oduwole, K.. (2026). Characterising the Nigerian Stock Exchange for Machine Learning-Based Portfolio Research: An Empirical Analysis of Return Distributions, Volatility Dynamics, Liquidity, and Correlation Structure. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:1-15 Available from https://proceedings.mlr.press/v319/isichei26a.html.

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