Many people who have managed portfolios for as long as I have feel that there has been too much change. I don’t think there has been enough. Stock picking has always been about trying to predict financial results, based on the assumption that financial results drive share prices.

When I first started in the industry there was a substantial return to hard work and diligence. If one was willing to gather all the accounting data of a group of companies and analyse it one could identify trends in financial performance. Assuming persistence of these trends, one could predict future results and hence drive outperformance.

However technology has commoditised financial data. In addition, what used to take days to process and analyse can now be achieved in a matter of seconds. The process of financial analysis has been industrialised.

This has spawned a new breed of portfolio manager. The quant managers will process a huge amount of financial information on a vast number of companies in a fraction of a second. Their sophisticated algorithms categorise businesses by country, sector, industry, momentum, size, value etc. enabling them to better predict the companies’ financial performance. These insights are used to build portfolios that are well diversified and that can capture much of the traditional manager’s outperformance.

How can the humble stock picker compete? You would have thought that these rational analysts would have packed up their pencils and gone home. However, the industry for traditional stock analysts continues to thrive.

This has led many asset owners, consultants and advisors to believe that markets are now highly efficient. However, data suggests that there remains a huge variation in share prices. To my mind, the problem is that the stock picking investment community has failed to turn this variation into consistent outperformance.

It is clear to me that future financial performance is driven by more than past financial performance. We need to find new and better ways of predicting future financial performance. Assuming that the financial data has been squeezed dry, we need to find new sources of outperformance – new insights to deliver alternative sources of alpha. As someone who has been picking stocks for years, I have found many instances where two businesses of similar size, location, using similar people and processes to produce identical goods or services that are sold to the same customers, have produced dramatically different financial returns over the long run. Same industry, same inputs, same outputs, different financial outcomes…why?

In my opinion, it is down to intangible or non-financial data. This intangible data includes corporate culture, employee and customer engagement, effectiveness of Research & Development (R&D) and a willingness to place a high degree of importance on Environmental, Social & Governance (ESG) factors. These are typically long-term intangible assets that can have a short-term financial cost, but will pay off handsomely over the longer term. These intangible assets are hardly ever found in financial reports and accounts. Information on them is hard to access, assimilate and conclude upon. But they are nonetheless a very powerful predictor of long-term financial performance.

These are alpha sources that asset owners are not accessing but can play a very valuable part in their portfolios. Not only do they lead to investments in better businesses with a better ESG footprint, lower portfolio turnover and reduced transaction costs but, crucially, the potential outperformance derived from non-financial sources is idiosyncratic in nature and has a low correlation to more traditional systematic, quantitative sources. They are a different flavour of outperformance, or true alpha, and so including these alpha sources can diversify the return stream of a portfolio.

Importantly we believe that the pursuit of these alpha sources must be an integral part of the investment process and enshrined in a manager’s philosophy. It is not enough merely to add on a screen or a rating once the traditional form of analysis has been concluded. Factors such as ESG should comprise a non-negotiable, essential component of fundamental stock research. As a team we make a judgment on management and ESG as part of our assessment of every company we invest in.

While this might sound attractive, there are of course significant practical challenges. These fall into two categories:

• First, alpha generation. One needs a philosophy, process, tools, skills and expertise to gather and collate the relevant information, analyse it and then draw conclusions from it. Given that this information is non-standard, not necessarily comparable, consistent or complete, the task of drawing relevant conclusions from it is far from trivial.
• Second, alpha capture. Identifying a group of companies that is likely to produce superior long-term financial results is one thing. Combining them into a portfolio that outperforms due to these alpha sources is another challenge altogether. Correlations between stocks can cause unintended concentrations which may lead to large positive or negative returns that may overwhelm the alpha associated from good stock picking. Hence it is critical to have an alpha capture framework that is able to isolate the impact of these alpha sources and to control the impact of unintended systematic exposures.

In conclusion, just as the outperformance potential from financial data has been industrialised by computers and quantitative algorithms, I believe there are a number of alternative sources of alpha that are available to the stock picker to drive outperformance. These alpha sources are non-financial in nature and are powerful catalysts of long-term financial results. Accessing and analysing these alpha sources requires a philosophy, process and team that are sensitive to this non-traditional information. Crucially, a team with different skills and the expertise to capture this alpha is key.