Knowledge that Transforms

Financial sentiment analysis models, which extract meaning from vast amounts of unstructured data, play a crucial role in sentiment-driven financial decisions. However, the complex and domain-specific language used in finance poses unique challenges for adversarial attacks. To address these challenges, we propose a novel, white-box attack methodology leveraging a pre-trained general-purpose language model (GPT-4o). We employ carefully designed instructions and incorporate a new loss function based on embedding similarity to ensure semantic coherence while producing syntactically diverse samples. Our experimental results demonstrate that both FinBERT and Fin-GPT, leading models in financial sentiment analysis, exhibit significant susceptibility to our proposed adversarial attacks. Specifically, the sentiment predictions of these models were successfully altered for a substantial proportion of the samples across three public datasets, including Financial Phrase Bank (FPB), Twitter Financial News Sentiment (TFNS), and Sentimence and Entity Annotated Financial News (SEntFiN). Our findings emphasize the need for enhanced robustness in financial classification models against adversarially targeted attacks. By understanding and addressing these vulnerabilities, it is possible to improve the reliability and security of automated financial systems.

Regulatory punishment in an oligopolistic credit rating market can be costly. Utilizing the Chinese bond market’s unique features, particularly a third-party rating agency, we investigate the regulatory suspension of Dagong Rating by Chinese regulators and its market impact. The punishment initially deters Dagong but diminishes the quality of its ratings post-punishment, altering market competition. Upon returning, Dagong inflates ratings to regain market share, reflecting a “temporary suppression” strategy. Non-Dagong agencies respond by adjusting their ratings; higher power agencies lower ratings, while lower power agencies raise them to stay competitive. Investors remain skeptical of these inflated ratings. Despite Dagong’s suspension, we find no significant differences in bond or stock price reactions between Dagong-rated and non-Dagong-rated firms, suggesting investors did not penalize Dagong-rated entities. This study highlights the complex dynamics and unintended consequences of regulatory interventions in the credit rating market.

In this work, we study statistical arbitrage strategies in international crude oil futures markets. We analyse strategies that extend classical pairs trading strategies, considering the two benchmark crude oil futures (Brent and WTI) together with the newly introduced Shanghai crude oil futures. We document that the time series of these three futures prices are cointegrated and we model the resulting cointegration spread by a mean-reverting regime-switching process modulated by a hidden Markov chain. By relying on our stochastic model and applying online filter-based parameter estimators, we implement and test a number of statistical arbitrage strategies. Our analysis reveals that statistical arbitrage strategies involving the Shanghai crude oil futures are profitable even under conservative levels of transaction costs and over different time periods. On the contrary, statistical arbitrage strategies involving the three traditional crude oil futures (Brent, WTI, Dubai) do not yield profitable investment opportunities. Our findings suggest that the Shanghai futures, which has already become the benchmark for the Chinese domestic crude oil market, can be a valuable asset for international investors.