TL;DR:
- The Bloomberg Terminal utilizes AI and ML to analyze a vast amount of financial data for valuable insights.
- ML algorithms uncover patterns and anomalies in data that surpass human capabilities.
- Machines excel at identifying hidden trends and patterns in millions of documents.
- The Terminal employs AI and ML techniques in various applications, including information extraction and anomaly detection.
- Insight generation through AI/ML analysis uncovers investment signals from large datasets and news stories.
- ML is a collaborative effort between humans and machines, with humans defining methodologies and ensuring quality assurance.
- Human expertise is transferred to machines gradually, with humans collecting, labeling, and providing feedback on ML models’ performance.
- Human involvement remains critical for optimal model performance and continuous improvement.
- Annotation plays a vital role in training and evaluating models, using “golden” copies of data for comparison.
- Bloomberg’s data analysts possess domain expertise and collaborate with engineers and data scientists to develop automation solutions.
Main AI News:
The Bloomberg Terminal, an indispensable tool for financial professionals, grants access to an extensive array of over 35 million financial instruments spanning various asset classes. With such a vast volume of data, the Terminal leverages the power of artificial intelligence (AI) and machine learning (ML) to drive its ongoing transformation.
Machine learning enables the rapid and comprehensive analysis of data, surpassing the capabilities of human analysts. By scrutinizing data at an unprecedented scale, ML algorithms uncover intricate patterns and anomalies that serve as the basis for generating valuable insights. Furthermore, these insights can guide the automation of laborious and repetitive tasks that were previously carried out manually by humans.
While AI may not match human intelligence in every domain, it outperforms human agents in specific areas. Machines excel at identifying hidden trends and patterns buried within millions of documents, continuously improving their abilities over time. Moreover, machines exhibit consistent and unbiased behavior, free from the errors and biases inherent to human decision-making.
Gideon Mann, the Head of ML Product & Research at Bloomberg’s CTO Office, affirms the strengths of machines, stating, “Humans are skilled at deliberate actions, but when we make decisions, we start from scratch. Machines execute tasks consistently, so even if they err, their mistakes possess consistent characteristics.”
Presently, the Bloomberg Terminal extensively utilizes AI and ML techniques across numerous exciting applications. The integration of these technologies is poised to expand rapidly in the years to come. The journey began approximately two decades ago.
Before the turn of the millennium, Bloomberg relied on manual labor for all data-related tasks, including collection, analysis, and distribution. Automation was in its nascent stages, and primitive models operated through sets of if-then rules manually coded by humans. However, as the 2000s drew to a close, true machine learning took flight within the company.
This novel approach involved human annotation of data in training machines to make associations based on labeled information. Over time, the machine “learns” from this training data, resulting in increasingly accurate decision-making capabilities. This data-driven approach far surpasses the limitations of traditional rule-based programming.
Over the last decade, the use of ML applications at Bloomberg has experienced explosive growth. According to James Hook, the Head of Bloomberg’s Data department, AI/ML and data science find broad applications within the company.
One such application is information extraction, where computer vision and natural language processing (NLP) algorithms analyze unstructured documents, typically challenging for machines to comprehend. By extracting semantic meaning from these documents, the Terminal delivers valuable insights to users. These documents include videos, audio recordings, blog posts, tweets, and more.
Anju Kambadur, the Head of Bloomberg’s AI Engineering group, elaborates on the process, stating, “It typically starts by asking questions about each document. For example, in a press release, we identify the entities mentioned, the executives involved, and the companies they collaborate with. We also uncover supply chain relationships mentioned in the document. Subsequently, the salience of the relationships between entities is measured, and the content is associated with specific topics. Machine learning plays a crucial role in assigning ‘topic codes’ to documents, whether they relate to electric vehicles, oil, the United States, or the APAC region.”
Utilizing natural language processing models, Bloomberg effectively extracts a wealth of information from unstructured documents, enabling comprehensive analysis and actionable insights. The integration of AI and ML continues to revolutionize the Bloomberg Terminal, empowering financial professionals with advanced tools for making informed decisions.
Quality control is another vital area where AI and ML techniques come into play. Anomaly detection methods are employed to identify issues related to dataset accuracy, among other aspects. The Bloomberg Terminal leverages this capability to uncover potential hidden investment opportunities or flag suspicious market activity.
For instance, if a financial analyst alters their rating for a specific stock following a quarterly earnings announcement, anomaly detection provides context to determine whether this behavior is typical or if it should be presented as a noteworthy data point for investment decisions.
Insight generation is another domain where AI/ML proves invaluable. By analyzing vast datasets, the Terminal uncovers investment signals that may remain unnoticed through traditional means. One example involves analyzing highly correlated data, such as credit card transactions, to gain insights into recent company performance and consumer trends. Additionally, the analysis and summarization of millions of news stories ingested into the Terminal daily shed light on key questions, themes, market drivers, economic sectors, and trading volume related to specific securities.
In reality, the practice of ML is a collaborative effort between humans and machines. Humans currently play a crucial role in defining ontologies and methodologies, performing annotations, and ensuring quality assurance. Bloomberg has swiftly increased its workforce capacity to tackle these tasks at scale. The integration of machines in workflows doesn’t replace human workers but redirects their focus from tedious, repetitive tasks toward higher-level strategic oversight.
According to Gideon Mann, Head of ML Product & Research at Bloomberg’s CTO Office, this shift represents a transfer of human skill from manual data extraction to workflow definition and creation. Ketevan Tsereteli, a Senior Researcher in Bloomberg Engineering’s AI group, explains this transfer in practice.
Previously, a team of data analysts with domain expertise manually identified mergers and acquisitions news in press releases and extracted relevant information. Today, these experts contribute by collecting, labeling, and providing feedback on the performance of ML models, highlighting correct and incorrect assumptions. Gradually, domain expertise is transferred from humans to machines.
Human involvement remains critical at every step to ensure optimal model performance and continuous improvement. This collaborative effort involves ML engineers building learning systems and underlying infrastructure, AI researchers and data scientists designing and implementing workflows, and annotators such as journalists and subject matter experts collecting and labeling training data while conducting quality assurance.
Tina Tseng, ML/AI Data Strategist, emphasizes the importance of domain expertise possessed by thousands of analysts in Bloomberg’s Data department, who understand the data and its significance to customers in areas like finance, law, and government. They closely collaborate with engineers and data scientists to develop automation solutions.
Annotation plays a vital role not only in training models but also in evaluating their performance. “Golden” copies of data are created as truth sets, against which the model’s outputs are automatically compared to calculate statistics that quantify its performance. Evaluation sets are utilized in both supervised and unsupervised learning approaches, ensuring robust evaluation methodologies.
Conlcusion:
The integration of artificial intelligence (AI) and machine learning (ML) techniques in the Bloomberg Terminal represents a significant advancement for the market. By leveraging these technologies, financial professionals gain access to unparalleled insights and analysis capabilities, enabling them to make informed investment decisions. AI and ML empower users by uncovering hidden patterns, identifying anomalies, and generating valuable signals from vast datasets and unstructured documents.
This revolution in data analysis and automation provides a competitive edge in the market, allowing professionals to navigate complexities and seize potential investment opportunities with greater precision. As AI and ML continue to evolve and expand within the Bloomberg Terminal, the market can expect further enhancements that drive efficiency, accuracy, and strategic decision-making for financial professionals worldwide.
