How investment analysts can embrace AI to become the rising star in their funds

Anyone connected to the investment and private equity (PE) industries will know that they are extremely competitive, demanding and research-intensive. This is especially true for junior analysts and associates who are constantly working to prove their value in the early stages of their careers.

PE professionals demand a huge amount from their junior peers. The hours are long and analysts are expected to be more than just number crunchers, which means their level of responsibility is often much higher than many people would expect.

For example, analysts regularly get involved at a strategic level. They work on financial models for specific deals, build business forecasts for senior executives and develop new investment ideas for their firms. But the work isn’t always so glamorous. Investment analysts also have to spend hours sifting through news alerts and market information on a daily basis, searching for that one piece of information that could be key to their fund’s next acquisition.

It’s this area – the tactical, time-consuming research and monitoring tasks – where technologies such as artificial intelligence (AI) and machine learning can make all the difference. In a world that’s more data-heavy than ever, next-generation tools can take analysts’ productivity to a whole new level.

Too much data, too little time

With so much competition in the market, analysts are under a huge amount of pressure to find and flag relevant information as quickly as possible, in order to identify viable investment targets for their funds earlier than the rest of the market. The problem is that several key challenges hinder analysts’ ability to identify relevant investment and acquisition opportunities in a timely manner.

These challenges primarily centre on the sheer volume of information involved, starting with the origination of investment ideas that feed into the fund’s portfolio deal pipeline. Furthermore, the origination task may extend to Bolt-on, Roll-up, Add-on, or Buy-and-Build strategies, whereby the fund seeks to originate M&A opportunities for their portfolio. Either way, the origination process involves scouring a whole sector for emerging businesses that are showing the right growth signals and financial potential, all while staying one step ahead of competitors.

It’s then the job of an investment analyst or associate to continuously track these companies of interest, identifying any information that could be relevant for an investment opportunity. For example, any PE firm could have around 300-400 companies in its pipeline – companies that are in its sweet spot in terms of acquisition value, industry focus, stage of growth and revenue generation.

These analysts will receive dozens of potentially significant emails a day, providing news alerts or industry coverage through sources such as Bloomberg. That news and the summary of those articles then has to be read, digested and shared with colleagues.

After a deal has been completed, the focus shifts towards closely monitoring the acquired company’s market. Investment analysts need to know about any competitor movements, industry trends and potential add-on investment opportunities, thereby multiplying the number of companies that have to be monitored. 

This all equates to a huge amount of information for teams to sift through every single day. We’re talking a few hours each morning just to get through all the alerts (which probably still don’t capture everything) and perform the filtering process in their own minds. And, because one analyst will typically cover multiple companies, it can be hard for mid-size firms to cover entire markets, making it more likely that they will miss relevant investment opportunities.

The AI difference

This is where AI and machine learning tools can make a real difference. With so much time spent monitoring the market and filtering swathes of information for relevant signals, these technologies can empower analysts to get the right information to the right people in a timely manner – covering both ends of the acquisition cycle. 

During pre-acquisition, AI and machine learning tools can act as a significant time-saver by filtering all of the market and news information as analysts trawl through data and scan for relevant signals. This enables analysts to monitor more companies than they can currently cover, significantly improving their chances of finding great investments earlier than competitors. Automating the discovery of data across multiple sources in this way also frees their capacity to actually analyse the results – resulting in far more informed recommendations.

For example, Syfter can automate and scale the research and deal origination function by providing daily insights on companies and markets of interest based on a PE firm’s investment thesis. The data-agnostic tool intelligently scrapes company websites and public data sources, using proprietary algorithms to identify and flag companies and topics of interest.

Irrelevant information such as generic marketing information or sponsorship announcements can be filtered out, while important information will be highlighted in near real-time. This increases analysts’ productivity and drives better market intelligence, helping them get ahead of the crowd.

On the post-acquisition side, AI can increase analysts’ efficiency when monitoring portfolio companies and their markets. Analysts can filter metrics such as sector, geography and signal of interest to ensure they get the most relevant and useful information, helping them stay on top of everything that happens in their portfolio companies and their markets.

Ultimately, AI and machine learning-powered tools such as Syfter can aid analysts by streamlining both ends of the acquisition process in terms of pipeline monitoring and identifying post-deal growth opportunities.

By automating the gathering of intelligence on companies and sectors of interest, analysts can use these tools to save themselves a significant amount of time every day, freeing them up to investigate data rather than simply find it. Not only that, they can also use AI and machine learning to distinguish themselves in their firms – moving them from just being one of the pack, to the rising star.