Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

—The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories

Why are convertible bond announcements associated with increasingly negative issuer stock returns? An arbitrage-based explanation

While convertible offerings announced between 1984 and 1999 induce average abnormal stock returns of −1.69%, convertible announcement effects over the period 2000–2008 are more than twice as negative (−4.59%). We hypothesize that this evolution is attributable to a shift in the convertible bond investor base from long-only investors towards convertible arbitrage funds. These funds buy convertibles and short the underlying stocks, causing downward price pressure. Consistent with this hypothesis, we find that the differences in announcement returns between the Traditional Investor period (1984–1999) and the Arbitrage period (2000–September 2008) disappear when controlling for arbitrage-induced short selling associated with a range of hedging strategies. Post-issuance stock returns are also in line with the arbitrage explanation. Average announcement effects of convertibles issued during the Global Financial Crisis are even more negative (−9.12%), due to a combination of short-selling price pressure and issuer, issue, and macroeconomic characteristics associated with these offerings

FDI, terrorism and the availability heuristic for U.S. investors before and after 9/11

We record the existence of an availability heuristic that is reflected in disaster myopia of U.S. investors and exists prior to the attacks of 9/11. We argue that this is fueled by an aggregate experience hypothesis effect, resulting in a pronounced increase in the sensitivity of U.S. stock prices to terrorist attacks on foreign soil. After 9/11, stock prices react proportionally to the size of an attack and the share of FDI stock held in the region by the sector in which firms operate. This effect, non-existent prior to 2002, has become increasingly strong in recent years

Architecture for Analysis of Streaming Data

While several attempts have been made to construct a scalable and flexible architecture for analysis of streaming data, no general model to tackle this task exists. Thus, our goal is to build a scalable and maintainable architecture for performing analytics on streaming data. To reach this goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software. Our study shows that this architecture yields a good balance between scalability and maintainability due to high cohesion and low coupling of the solution, as well as asynchronous communication between the layers. This architecture can help practitioners to improve their analytic solutions. It is also of interest to academics, as it is a building block for a general architecture for processing streaming data

CFGPT: Chinese Financial Assistant with Large Language Model

Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Generative Pre-trained Transformer framework, named CFGPT, which includes a dataset~(CFData) for pre-training and supervised fine-tuning, a financial LLM~(CFLLM) to adeptly manage financial texts, and a deployment framework~(CFAPP) designed to navigate real-world financial applications. The CFData comprising both a pre-training dataset and a supervised fine-tuning dataset, where the pre-training dataset collates Chinese financial data and analytics, alongside a smaller subset of general-purpose text with 584M documents and 141B tokens in total, and the supervised fine-tuning dataset is tailored for six distinct financial tasks, embodying various facets of financial analysis and decision-making with 1.5M instruction pairs and 1.5B tokens in total. The CFLLM, which is based on InternLM-7B to balance the model capability and size, is trained on CFData in two stage, continued pre-training and supervised fine-tuning. The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application. Our codes are released at https://github.com/TongjiFinLab/CFGPT.Comment: 12 pages, 5 figure