ChatGPT Informed Graph Neural Network for Stock Movement Prediction

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.Comment: Under Review. 10 pages, 2 figure

Designing IS service strategy: an information acceleration approach

Information technology-based innovation involves considerable risk that requires insight and foresight. Yet, our understanding of how managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies a new experimental method based on “discrete choice analysis” and “information acceleration” to directly examine how decisions are made in a way that is behaviourally sound. The method is highly applicable to information systems researchers because it provides relative importance measures on a common scale, greater control over alternate explanations and stronger evidence of causality. The practical implications are that information acceleration reduces the levels of uncertainty and generates a more accurate rationale for IS service strategy decisions

Redacted disclosure and analysts' weighting of private and public information

This paper investigates whether and how redacting proprietary information in regulatory filings affects financial analysts' weighting of private and public information. I examine this issue in the context of initial public offerings (IPO) where firms are allowed to redact value-relevant, proprietary information in relation to material agreements. To the extent that redaction affects firm information environment, I expect redaction to incentivize analysts to overweight their private information relative to public information. As predicted, I find that analysts' overweighting of private information is greater for redacted IPO firms. Moreover, this result prevails particularly when analysts involved rely more on private information. Next, I find analysts' overweighting of private information is more pronounced for analysts who have limited resources, ability, and attention, and when IPO firms do not receive venture capital financing. Finally, I find that the redaction-overweighting relation is attenuated after the passage of Regulation Fair Disclosure. I also find that analysts' overweighting of private information increases redacted IPO firms' idiosyncratic return volatility. Overall, my results extend prior research by examining the role of firm information environment on analysts' decision-making process.Includes bibliographical reference

Embracing Nonlinearities: Further Exploring Machine Learning Applications to Inflation Forecasting

In this thesis, I employ a number of machine learning (ML) methods on the inflation forecasting problem space. I utilize macroeconomic indicators alongside textual data and apply ML methods to an updated time horizon. Ultimately, I find that ML methods are a viable alternative to traditional benchmarks under certain time horizon conditions, particularly with the inclusion of textual data. However, in contrast with the previous literature, I demonstrate that some ML models are particularly sensitive to the treatment of outliers. When a full time horizon is employed and outliers are included, certain ML models that performed well in previous analyses are not able to outperform other forecasting methods.Bachelor of Scienc

The history of the French tableau de bord (1885-1975): evidence from the archives

The history of the tableau de bord in France has never really been written. This paper sets out to draw up a history using the archives of three large industrial companies - Lafarge, Pechiney, and Saint-Gobain – as source material. This paper seeks to revisit the myth of the French tableau de bord as presented in a great many comparative management studies (typically, Tableau de bord vs. Balanced ScoreCard). This myth rests on more or less implicit assumptions regarding, for instance, the central role played by engineers in the emergence of tableaux de bord, the single and unified way in which this instrument is used in companies from top to bottom and, or course, its French specificity.French tableau de bord, scorecard, managerial innovation