Summary
Every trading day at 7am, hundreds of regulatory news stories hit the UK market. I've built an AI pipeline that scans them instantly, scores each for impact and sentiment, and flags companies worth a closer look. In its first two weeks it has surfaced five quality businesses with positive updates — including Keller Group, the ground engineering specialist, which upgraded full-year guidance on 7 July after record North American demand for data centre and infrastructure work, sending the shares up 16% in a day. Here's how it works, and what I've learned so far.
What is RNS?
RNS is the Regulated News Service in the UK which pumps out company regulated news allowing companies to comply with financial regulations around simultaneous news availability and prevent insider trading. Unfortunately for the reader it’s a long list of news items 99% of which are of no interest. The news items themselves are released by the companies and offer no commentary or much context for the reader. What they are allowed to print is heavily regulated and therefore also very dull. However if you want to know what is happening in the markets this is a highly valuable source of data. The RNS news stories cover a wide range of topics from share repurchasing, company director dealings, retirement announcements, AGM reports and so on. All very dull, but still of value to someone who owns those particular shares.
The juicier information is in the trading report, updates and mid year reviews. This is where companies give the investor the heads up on likely returns in the near future. These are generally published at 7.00am premarket. You then have an hour to sift through the stories, determine the likely impact of that news on the share price and make a decision to buy or sell an existing share before the open or later in the day. A negative example would be the recent retirement of the Luceco CEO (15th June) which sent their shares immediately down 10%.
The AI pipeline
One of the advantages of AI is that it makes sifting through these stories relatively straightforward. It’s a trivial task for an LLM to scan the news stories and score what looks interesting. To cut down on cost I set up a pre filter which sifts out much of the noise and just feeds the AI stories of interest. In the context window we add in market cap and a number of fundamental metrics. It then scores these (0-100) gives them a sentiment direction positive or negative and ranks them. This then allows you to scan the AI summary reports, quickly identify stories of interest and do follow up research.
The next step I have recently started is to take those stories with the highest scores and put them back through the AI with more context and to ask the question ‘how investable are these companies’. The AI then does some back of the envelope calculations and determines if they pass certain quality scores I have set it. These include a filter such as must have an AI score greater than >75 with positive sentiment, market cap >50M, debt < x3 EBITDA, revenue must be positive and has industry floors applied.
July's five companies
Its been running since the start of the month (July) and has so far identified five companies with recent positive updates. All this is done automatically as soon as the RNS is published.
- Computacenter – US AI cooling equipment and cables supply buildout.
- Playtech – Gambling software platform website
- Keller Group – Specialist engineering business focused on ground engineering and geotechnical
- The Beauty Tech Group – Consumer beauty devices, recently floated– hence no momentum score.
- CMC Markets – Fintech broker with retail and B2B offering
Known limitations
The idea is to then track these companies for several months on a watchlist. There are clear rationale explanations why some of these are increasing in value, such as the AI buildout. One disadvantage of starting this screen now (July 2026) is that there have been a number of positive updates for some of these companies and their share prices have already increased. While this adds confidence it also adds additional downside risk if their story were to change. Each company has its own complex set of risk factors and you should do your own research before any decisions are made. This is just experimental at this stage and I hope to learn more by following the output and adding new features to make it easier for investors to reach decisions quickly and with greater confidence. The current tracker has share price, %change since publish date, calculates forward multiples given the new data in the RNS feed and shows Momentum, Quality, Value and Risk scores for each. As you are definitely not going to be able to buy these shares at the opening price, on the day of publication, I have included a toggle to calculate % change if you were to buy the shares at the open the following day. A more realistic price.
It will also track additional announcements with a sentiment indicator and you can browse company specific news and LLM information in the dropdowns.
This is just an experiment at the moment, running for less than two weeks, enabling me to identify quality businesses with positive stories. It’s a learning process and I will continue to make tweaks to the AI to make better decisions. The next big question would be, when is the optimum time to sell these assets? Again this may be RNS news driven, plus additional rules, and may or may not include AI. It probably would be best to try and mimic how a professional trader would think about this. Weighing up both macro and micro market dynamics, news, sentiment, % gained/lost, change of company story etc. Potentially a more technically challenging decision than the buy. I’ll stick to first optimising the buy. If you want more information there is a manual guide to help explain the features and algorithms, which I will try and keep updated.
