Browsing by Subject "Johannesburg Stock Exchange"

Now showing 1 - 4 of 4
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    Open Access
    An online learning algorithm for technical trading
    (2019) Murphy, Nicholas John; Gebbie, Tim
    We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. [31] on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.
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    Open Access
    Artificial neural networks and the cross-section of equity returns: identifying nonlinear opportunities on the Johannesburg Stock Exchange
    (2025) Reed, Joshua; van Rensburg, Paul
    This study investigates the potential of Artificial Neural Networks (ANNs) to forecast stock returns on the Johannesburg Stock Exchange (JSE) using fundamental and technical factors. The optimal neural network architecture is explored, considering varying model depths and node counts. The activation function, training algorithm, learning rate, number of epochs, batch size, and loss function are kept constant across architectures. The findings suggest that portfolios constructed from ANN forecasts have the potential to outperform an equal-weighted benchmark. Model performance depends on network architecture, with a three hidden layer model with 64 nodes in the first hidden layer yielding the best results. Addition of further hidden layers or nodes is found to reduce model generalization, mainly due to overfitting, while less complex models are found to underfit. Models with a reduced variables set outperformed, confirming the importance of feature selection. While ANNs are found to underperform a linear model, the top performing ANN outperforms on risk-adjusted metrics over the test period, suggesting benefits to non-linear return forecasting on the JSE. However, with no clear relationship between in-sample and test period performance across architectures, this superior performance could be data specific, highlighting challenges in selecting an optimal model ex-ante. On the other hand, limitations in feature selection and training likely constrained model performance, with potential to improve generalization. This study provides a foundation for further research into return forecasting with ANNs on the JSE, contributing to the growing field of artificial intelligence and machine learning in finance. Future research could further optimize model hyperparameters, improve feature selection, and account for the time-varying nature of features.
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    Open Access
    Correlation emergence in two coupled limit order books in the fluid limit
    (2024) Bauer, Dominic; Gebbie, Timothy
    Weuse random walks to simulate the fluid limit of two coupled diffusive limit order books to model correlation emergence. The model implements the arrival, cancellation and diffusion of orders coupled by a pairs trader profiting from the mean-reversion between the two order-books in the fluid limit for a Lit order book with vanishing boundary conditions and order volume conservation we are able to demonstrate the recovery of an Epps effect. We show how various stylised facts depend on the model parameters and the numerical scheme and discuss various strengths and weaknesses of the approach. We demonstrate how the Epps effect depends on different choices of time and price discretisation and show how an Epps effect can emerge without recourse to market microstructure effects.
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    Open Access
    Covid-19 impact on Johannesburg Stock Exchange for the duration of the pandemic period
    (2025) Dube, Siyabonga; Alhassan, Abdul Latif
    For a considerable time and for different reasons, the financial system shocks endured during rare events continue to pique investors' and policymakers' keen interest. Consequently, this study explores COVID-19's impact on the JSE. The pandemic caused significant shocks to financial systems and economies. Uncertainties emanating from investor fear in response to the pandemic outbreak affected portfolio investment decisions. Additionally, policymakers implemented ‘social distancing' and stringent measures to restrict the contagion of the health crisis, which had a disruptive impact on global value chains. To limit these adverse effects, policymakers — subject to budgetary constraints — implemented fiscal, monetary, and other stimulus packages to lessen the adverse impact on the real economy. Extensive studies have examined the reaction and recovery of financial and economic markets following pandemic-induced shocks. These studies draw on theories from behavioural finance, financial risk contagion, and the efficient market hypothesis to analyse market responses and stability. This dissertation builds on the existing literature by examining the health crisis' transmission to the financial markets in an emerging economy. The study employed new deaths and stringency measures implemented during the pandemic period as proxies for COVID-19 and assessed their impact on ALSI returns, the variable of interest, using a quantile regression estimation technique. The results indicate a level of collinearity and multicollinearity between ALSI returns and global financial market performance indicators. The correlation between ALSI returns, the S&P 500 and Implied Volatility is statistically significant at 0.716 and –0.600 respectively. This outcome indicates the deepened integration of South African financial markets with the globe. The flight to safe havens was not observed. Contrasting ALSI returns with macroeconomic factors represented by crude oil and the rand–dollar exchange rate, the relationships are statistically insignificant. The real economy disturbances do not appear to have been transmitted to the financial markets in the long term. Progress in vaccine development and coordinated global policy interventions may have limited the sustained adverse impact on the real economy. This study offers key recommendations for portfolio design, policymaker intervention timing, and the balance between economic stimulation and containment efforts during pandemics.