Sigmoidal x Baillie Gifford
Using AI Sentiment Analysis To Track Investment Signals
Real-time sentiment analysis system developed by Sigmoidal for Baillie Gifford, one of the leaders in the investment management industry, with over $330 billion in assets under management.
Enhanced Decision Making
Baillie Gifford is an investment management firm headquartered in the UK, with over 1,200 employees, multiple offices worldwide (i.a. New York, Zurich, Hong Kong), and £262 billion (approx. $334 billion) in assets under management as of June 2020. More than 110 years on the market (founded in 1908).
Baillie Gifford reached out to Sigmoidal, as they have been searching for proven solutions to adapt to the current digital transformation trends across the industry.
We designed and developed an AI-based tool to track and analyze clients’ sentiment towards companies from their portfolio, working in real-time. It also automatically generates high-level reports, including only the crucial information.
The project’s goal was to automate the process of sentiment tracking and analysis, enabling Baillie Gifford to make better-informed decisions concerning investment and asset allocation across their portfolio.
The developed solution uses Sigmoidal’s proprietary AI engine for real-time tracking and analysis of users’ sentiment across social media. It also includes a feature to generate high-level reports automatically. Everything is packed in a user-friendly front-end, which does not require any technical knowledge.
Among the others, we used spaCy, Docker, and TensorFlow as our tools. We used Twitter API for data collection and easy maintenance in the future (in case of any updates on this social media platform).
Cut Down Workload Significantly
We augmented the process, significantly reducing the workload needed for research and analysis.
Enhanced Decision Making
Baillie Gifford can now use additional investment signals to their advantage, getting it directly from users.
The solution can automatically generate a high-level report, allowing managers to react to market behavior fast.
1. The cloud-based machine learning system core in the form of a web application, providing aggregated metrics about clients’ posts from social media with regard to their sentiment towards a company.
- Sentiment score for each post (positive or negative, varying from 0 to 1),
- Total number of posts, where a client mentions a company (in a positive or a negative manner),
- Total number of positive sentiment posts and a percentage share,
- Total number of negative sentiment posts and a percentage share,
- Statistics of posts over time – ability to analyze the trend across time and detect any anomalies for a specific date.
2. Front-end web application
- With an option to generate and download a high-level PDF report, showing changes in sentiment across time (by use of quantized, aggregated time metrics) and essential information.
- The solution also enables the user to generate and download a .csv file, containing the full list of post information.
Thanks to the AI-based sentiment analysis system, we augmented the process, significantly reducing the workload needed for research and analysis. Baillie Gifford can now use additional investment signals to their advantage, getting it directly from users, allowing managers to react to market behavior in a more informed manner. Ultimately, it leads to better portfolio management and risk minimization.
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