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Folklore narratives and IPO outcomes
No full textOur primary contribution to the finance literature is the introduction of folklore narratives as a major factor in influencing corporate outcomes. Using the initial public offering (IPO) underpricing as the main focus, we demonstrate that folklore narratives depicting lower tolerance toward antisocial behavior are associated with lower IPO underpricing. The relation between folklore narratives and IPO pricing is independent of indicators of trust, religion, culture, societal preferences, or institutional democracy. This relation is weaker in countries with a more transparent information environment and following reforms that improve disclosure and corporate governance. Folklore narratives on punishment for antisocial behavior are also related to enhanced information disclosure, lower agency problems, better long-term performance for IPO firms, higher proceeds raised and free float, and overall IPO activity in the market. Collectively, we show that informal institutions, such as folklore narratives, exert a strong influence on IPO outcomes globally.</p
Hypergraph node representation learning with one-stage message passing
No full textHypergraphs as an expressive and general structure have attracted considerable attention from various research domains. Most existing hypergraph node representation learning techniques are based on graph neural networks, and thus adopt the two-stage message passing paradigm (i.e. node → hyperedge → node). This paradigm only focuses on local information propagation and does not effectively take into account global information, resulting in less optimal representations. Our theoretical analysis of representative two-stage message passing methods shows that, mathematically, they model different ways of local message passing through hyperedges, and can be unified into one-stage message passing (i.e. node → node). However, they still only model local information. Motivated by this theoretical analysis, we propose a novel one-stage message passing paradigm to model both global and local information propagation for hypergraphs. We integrate this paradigm into HGraphormer, a Transformer-based framework for hypergraph node representation learning. HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian. Extensive experiments demonstrate that HGraphormer outperforms recent hypergraph learning methods on five representative benchmark datasets on the semi-supervised hypernode classification task, setting new state-of-the-art performance, with accuracy improvements between 2.52 % and 6.70 %. Our code and datasets are available.1</p
“We were taught how to think about ourselves – as less than – and we accepted it”. Reflections on racial injustice in South Africa
No full textThis autoethnography examines how internalised racism shaped my self-perception growing up under apartheid-era South Africa. Drawing on personal memories during the turbulence of Nelson Mandela’s imprisonment and release, I explore how apartheid’s racial logics extended beyond formal segregation to produce internalised hierarchies within communities of colour. Through narrative and critical reflection, this paper interrogates how apartheid-era laws, such as the Group Areas Act and Separate Amenities Act, stratified people of colour into hierarchies of privilege based on complexion, while everyday interactions reinforced these biases long after apartheid’s dismantling. It exposes the psychological toll of internalised colourism, showing how racialised individuals were conditioned to devalue themselves and others based. By positioning autoethnography as a method of resistance, the paper contributes to scholarship on internalised racism, advocating for epistemic and psychological decolonisation and a rejection of colonial constructs of colour in the pursuit of self-acceptance, solidarity, and racial justice