Understanding the Dynamic Between Human and Machine in Data Analysis
Text-based data has consistently been a rich source of alpha for both discretionary and systematic traders. Historically, a tension has existed between the two camps, with humans excelling in the interpretation of nuanced language and machines capable of processing information at unparalleled speeds. Recent research from LSEG Data & Analytics reveals that advancements in natural language processing (NLP) and high-performance computing are not only enhancing the speed advantage enjoyed by systematic traders but are also significantly narrowing the gap in comprehension between humans and machines when analyzing text data.
The Evolution of Deep Learning in Trading
The rapid advancements in deep learning technology have transformed the financial landscape. Artificial, convolutional, and recurrent neural networks have become essential tools in many top-performing funds, consistently outperforming traditional statistical and machine learning methods. More broadly, large language models (LLMs) have emerged as pivotal tools across various sectors. Generative models, particularly GPT, are being deployed at an unprecedented pace, while discriminative LLMs are making equally noteworthy progress, though less publicized.
BERT’s Dominance in Sentiment Analysis
Google’s BERT model has become a leading transformer architecture in the realm of sentiment analysis. According to LSEG Data & Analytics, BERT has achieved remarkable performance improvements over previous state-of-the-art models, demonstrating double-digit enhancements on the General Language Understanding Evaluation (GLUE) benchmark. This leap forward holds significant potential for systematic traders aiming to extract increased value from textual data.
Adaptability Through Fine-Tuning
One of BERT’s most valuable features is its ability to be fine-tuned with relatively small amounts of labeled data, making it adaptable to specialized domains that may involve technical jargon or unconventional language. This flexibility enables traders to harness the model’s capabilities in niche areas, broadening its application and effectiveness in financial contexts.
Accessibility and Implementation of BERT
From a practical perspective, accessibility stands out as one of BERT’s compelling advantages. Developing a language model from the ground up is a daunting task; for instance, the base BERT model consists of 110 million trainable parameters and demands substantial data for effective training. Fortunately, many versions of these models are open-sourced, allowing traders and institutions to focus on fine-tuning existing models for their specific applications, greatly reducing the barriers to entry.
Deploying BERT in Real-Time Data Streams
LSEG Data & Analytics emphasizes the operational aspects of integrating these models into live data pipelines. Utilizing Hugging Face’s FinBERT, a single CPU thread running at 2.3GHz can process approximately 20 text pieces per second in a basic setup. By switching to a faster tokenizer—achievable with just a line of code in Python—users can improve throughput by around 74%. Transitioning to a GPU infrastructure further escalates these capabilities, where a 9.1 TFLOP GPU can handle approximately 261 predictions per second, representing a tenfold increase over the CPU baseline. Today’s cloud providers offer even greater computational power, enabling quicker and more efficient data analysis.