Ep 5: How To Profitably Spend $100M on Data for Trading
May 16, 2023
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In this episode, industry expert Rich Brown discusses the importance of data in trading, including the use of alternative data to forecast fundamental performance. The podcast also explores the complexities of privacy and data protection regulations in Asia, the use of Snowflake as a common platform for data vendors, and the potential of jet GPT in financial services. Additionally, it discusses Microsoft Azure's role in analyzing financial models and how data providers are leveraging fundamental metrics for forecasting. The episode concludes by highlighting the development of tools for data management and operational alpha.
Data plays a crucial role in enabling informed investment decisions for hedge funds and trading firms.
Advancements in machine learning models and artificial intelligence have made data analysis more efficient and sophisticated.
Data sourcing and procurement teams play a crucial role in vetting data vendors and assessing the quality and relevance of data sources.
Deep dives
Importance of Data in Investment Process
Data plays a crucial role in enabling hedge funds, asset managers, and proprietary trading firms to make informed investment decisions. In order to utilize data effectively, a lot of work goes on behind the scenes to manage data at scale. This includes sourcing data from various external sources, such as alternative data providers, and ensuring that the data is reliable and relevant to the investment process. The process involves data engineers, data scientists, and portfolio teams who work together to analyze the data and determine its value for specific portfolio strategies. Data management tools and platforms, such as Snowflake, are used to streamline the data ingestion, normalization, and analysis process, allowing for faster decision-making and better portfolio construction.
The Evolution of Data in Financial Services
Over the years, the financial services industry has witnessed significant advancements in data utilization. Initially, machine-readable news and sentiment analysis were considered groundbreaking developments. However, with the rise of machine learning models and artificial intelligence, data analysis has become even more sophisticated and efficient. Machine learning models have made it easier to analyze and extract insights from various types of data, including documents, social media posts, and alternative data sources. This evolution has enabled investment firms to make better use of data in their investment processes and gain valuable insights for generating alpha.
Challenges in Evaluating Data Sources
Evaluating the value and relevance of data sources can be a complex process. It requires a thorough understanding of the investment goals and requirements, as well as the ability to assess the quality and reliability of the data. Data sourcing and procurement teams play a crucial role in vetting various data vendors and conducting due diligence on their data sets. They consider factors such as data consistency, frequency, timeliness, and relevance to specific investment strategies. Additionally, compliance and legal aspects need to be considered, ensuring that the data is used in accordance with licensing agreements and regulatory requirements. The use of AI and machine learning can aid in the evaluation process by automating some aspects of data analysis and streamlining the decision-making process.
The Role of Aggregators in Data Procurement
Data aggregators and marketplaces play a significant role in simplifying the data procurement process for investment firms. They provide a centralized platform where firms can access a wide range of data sources and quickly evaluate their offerings. Aggregators offer catalogs of data vendors, enabling firms to efficiently search for specific data sets or providers. By leveraging aggregators, firms can benefit from standardized data formats, streamlined onboarding processes, and potential cost savings. However, for more proprietary data needs, firms may still need to engage directly with vendors and negotiate separate licensing agreements.
The Integration of Blockchain and Decentralized Finance
The integration of blockchain technology and decentralized finance (DeFi) brings new opportunities and challenges to the financial industry. Blockchain enables secure and transparent transactions, making it ideal for applications such as securitization, real estate, and insurance. Data plays a crucial role in these applications, ensuring the integrity and accuracy of transactions and facilitating the automation of processes. The use of blockchain in data management can enhance data security, privacy, and interoperability. Additionally, data providers are leveraging blockchain to bring off-chain data onto the chain, making it accessible for smart contract execution. As the space continues to evolve, it will be essential for organizations to navigate regulatory frameworks and privacy concerns to fully leverage the potential of blockchain and DeFi in their operations.
When it comes to using technology to be at the cutting edge, the quality of the data you are using is the name of the game. In trading that some times means spending tens or even hundreds of millions of dollars for data that gives you insight into the world that very few have. In this episode we bring on Rich Brown who has lead data and sourcing teams at some of the most successful and well hedge funds in the world to shed to light on this industry.
We talk about the different ways that data is used for discretionary and systematic managers. This can include everything from real time exchange feeds, data from bbg terminals, to the most exotic data you could image. We touch on how some alternative data is used to forecast not just price action but more fundamental performance of KPIs that a separate process may then use to forecast future returns.
We also discuss how to think about licensing data to train LLMs where the licensed data my be embedded in the model weights but not easily traced back to the original source. Rich points out that some of this is new but mostly already solved problems, at least contractually, where the products are conscidered derived work products and are likely covered depending on the licensing model used. There is a wealth of insight and we hope that you enjoy this episode as much as we did in creating it.
