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Uniting Data-scientists For Good: How We Annotated A Dataset To Predict Financial Trends

In August, we had a community challenge on financial news annotation. Read on to see how it went and what our community members had to share about this experience.

Uniting Data-scientists For Good: How We Annotated A Dataset To Predict Financial Trends

Ukraine war, high inflation rate, high volatility of oil prices, aggressive rate action by central banks, upcoming recession cycle… Investing in the financial market or not, we have been greatly impacted by the ever-changing economy from different aspects of our life. The question is, therefore, how do we protect ourselves from the uncertainty of market volatility and seize opportunities? That’s exactly why we brought about the August Community Challenge

For the longest time, people try to get more insights into what’s happening in the market, so as to make sense of the next action. Information is always the key. During this challenge, we went through a selected dataset of financial articles and annotated the most valuable information. After a whole week of annotation, we are proud to have created a dataset that can help people classify and predict financial trends. 

This challenge was made possible by our community of data scientists & ML engineers. From different parts of the world, our community contributed to our dataset and eventually made it happen.

There are a few members that we’d like to highlight here, and the first one is ​​Isabelle Francis:

“Sometimes small achievements make my day. I was one of the two winners of Kili Technology's Financial News Analysis challenge, where thousands of participants annotated thousands of data. It was the first time that I got to use my knowledge in the financial industry, and put it to use to train and annotate data for a Machine Learning (labeling data and sentiments) model. In the newsroom, we call these corporate event articles ‘market movers’ i.e. we anticipate stocks will move up or down… Then there's also macro/microeconomic data. 

Nonetheless, I had fun learning Kili Technology's tool and was humbled to work alongside such a brilliant team. Never knew that I would contribute to training an AI model; and try to figure out how typical investors and financial journalists/analysts would perceive, when it comes to ‘market moving’ reports”


The other winner Khadidiatou Laye shared:

“Thank you Kili Technology for this educational opportunity for me. Thanks to Benjamin Fourio for assisting us throughout this adventure. Congratulations to Isabelle Francis who undoubtedly contributed to my motivation.

Merci à tous!”

We thank everyone who participated and contributed to creating this dataset. We look forward to meeting every one of you in our community, and of course, seeing you during our next challenge! Till next time!

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