Share registers are dynamic, with constant, unpredictable, and sometimes uncontrollable changes. Listed companies need to carefully and continually consider where the next marginal investor may come from and how managing the ‘flow’ of new potential investors can reduce the impact of inevitable fluctuations in the register. Changes in the share register are driven by fundamental and non-fundamental factors and can often lead to significant share price volatility, directly impacting a company’s cost of capital. So, a comprehensive and proactive approach to the composition of the share register is a critical component of any company’s IR strategy.
Historically, the role of helping a company to identify and source potential investors has rested almost entirely with banks and brokers acting as intermediaries. Their principal goal is to successfully identify a fund management client for whom they believe an individual investment opportunity may be of interest. Where the intermediary knows both the investor and the investment opportunity well, there is a good chance that this approach will be successful. However, companies need to be aware of several significant potential drawbacks:
We believe a more fruitful strategy is to apply a rigorous and quantitative-driven investor targeting approach.
Investors have become far more open to direct contact from a company, especially when they can articulate a clear rationale for the meeting with evidence of why they might be a good fit for a particular fund’s investment strategy.
Investor targeting is, and always has been, a critical tool in a company’s IR tool kit. Our successful partnership with Valuation Metrics, a US-based algorithm-driven system owned by Citadel Securities, brings a new analytical approach to this essential process.
Using a set of multi-year backtested algorithms, we can determine a fund manager’s investment strategy and qualify the degree to which a company’s financial dynamics correlate with those strategies. By analysing the stocks an investor buys, sells and holds, we can identify the composition of a particular fund in relation to the entire universe of stocks for 29 descriptive, fundamental, and technical investment models.
An ‘importance’ rank or ‘quantitative fit’ is calculated for each of the 29 models for each fund. The ranking calculation uses ‘t-stat.’ A low t-stat score for a particular model is evidence that the fund’s use of the model is not significantly different than the average for all funds. On the flip side, models with high t-stat scores are strong evidence that a fund prioritises the model as part of its investment strategy. By looking at the scores for all the models, we can get a good sense of the strategies that a particular fund is employing in its portfolio with respect to size, growth/value, quality, safety, liquidity, etc. Using this information, we can then look at a company’s financial characteristics to determine their quantitative fit with a particular fund’s investment strategy and vice versa.
But a quantitative fit on its own is not enough. It is essential to recognise the other key investment factors impacting a fund manager’s decision-making process. Industry and sector exposure, geographical remit, market capitalisation, and peer holdings are just some of the other critical variables determining a company’s relevance in a given fund manager’s portfolio. By bringing all these elements together, we can generate a Match Score for each company for each fund. Comprehensive backtesting has proven that companies with high or very high Match Scores accounted for 84% of all new purchases made by funds.
Our innovative partnership with VM is proven to help bring funds together with companies that fit their particular strategy, much like dating services match individuals with similar goals and interests.
It is essential to recognise that a quant-driven approach to investor targeting represents a significant step forward but is not a silver bullet. Experience and common sense are essential ingredients needed to deliver meaningful outcomes. A Sat Nav system is highly effective at planning the fastest route from A to B, but delivering the best results relies heavily on human interaction and a degree of common sense.