Berlin-based YUKKA Lab uses AI, machine learning and neural networks to analyse hundreds of thousands of pieces of data and news sources – giving financial professionals the tools for more strategic decision making.
There are many ways financial data and news is delivered to clients – what sets YUKKA Lab apart?
We make complicated and overwhelming masses of information easy to understand with a focus on the essential and, moreover, good looking. We know that financial professionals have access to so much information, what they need is the support to understanding the trends and movements in the markets that matter to them. We offer finance professionals insights on which companies and industries get positive or negative media presence, outline what topics are driving theses sentiment shifts, and which companies and topics are mentioned in the same articles. This provides a seismograph showing sentiment eruptions enabling finance professionals to identify new opportunities, proactively reach out to clients over actual news leads and to make better-informed investment decisions.
What was the impetus for developing the product – what problem does YUKKA Lab’s offering solve?
Humans use intuition and their gut feeling to make decisions. However, there is too much information available to be able to processes that manually. In addition, personal filter bubbles can be biased, very selective and subjective. This leads to decisions which are unprecise and not very granular. Our Augmented Language Intelligence Technology provides finance professionals with a perfect overview of market developments, and they get precisely quantified sentiment indicators to alert them of essential shifts. This means they can react fast and adjust exposure early on to improve risk management and optimize performance.
Who is the typical YUKKA Lab client?
We cover a range of different use cases. Certainly, Asset Management and Wealth Management are our core target group due to our unique offering that combines news analytics and financial modelling. Besides, we work with risk units of global banks, sales & marketing departments, support robo-advisers with our early-warning-system and have many other exciting options to make use of our technology.
How does the sentiment analysis work? How was the algorithm developed?
Content contains many words, phrases and characters. We use a whole range of different technologies, such as machine learning and state-of-the-art neural networks (deep learning) to classify statements as positive, negative or neutral and detecting the targets of those statements. Natural Language Processing (NLP) offers the possibility to track the mood of the public about a specific company, topic, index, country or industry influenced by published media information.
The technology and algorithms have been developed in-house over the last years. As a company, we have a strong commitment to R&D with a tech team made up of 13 global experts and doctors in the field of NLP, ML and AI from 17 employees.
Do you have any numbers that point to results from the decision making and efficiencies offered by using the offerings from YUKKA Lab?
How much news can you read every day? We analyze 200,000 news stories from more than 20,000 different sources every day in real-time. Sounds efficient? We cooperated with University St. Gallen HSG and they found out that using the YUKKA News & Trend Lab would make the work of a portfolio manager 15% more efficient. This efficiency was realised by Belvoir Capital, who have managed the Sentiment SICAV fund since February 2017 when they ran a proof concept for based on our early-warning-system.
Another partner used our technology for their robo-advisor, improving the performance on an average of 17%, meaning better Sharpe-ratio, return p.a., and reduced maximum drawdown. According to this robo-advisor: “Applied as a hedge overlay to a pure equity portfolio or to a multi-asset portfolio the signal dramatically increases the risk/return efficiency.”
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