When the world is changing so rapidly and profoundly, how can we trust the underlying data used in our investment models?

The global coronavirus pandemic has created positioning and liquidity challenges for all types of investors. At the same time, it has created a unique complication for quantitative strategists—when the world is changing so rapidly and profoundly, how can one trust the typically backward-looking underlying data used in investment models?

With interest rates at historic lows and the unusual level of uncertainty surrounding markets, it has been challenging for investors to generate income while managing risk. That’s why we have been particularly focused on the equity Income factor: prudently constructed, it can be a considerable source of yield.

The Income factor is primarily constructed using the relative dividend and buyback yields of individual companies. These metrics can, however, be especially vulnerable to stale data in the current environment, as companies rapidly adapt their cash distribution policies to reflect a new and unfamiliar corporate paradigm. Cash flow within numerous industries has been put on hold, but even those companies still able to return excess capital to shareholders are facing political and social pressure that calls such action into question. This could mean that the data received from providers may not represent the most accurate and up-to-date information.


Let’s take a look at a few examples from mid-April, 2020 (Source: Bloomberg).

  • Oilfield Services Company – 13.7% Dividend Yield
  • Motorcycle Manufacturer – 7.8% Dividend Yield
  • Airline – 20.1% Buyback Yield
  • Bank – 15.3% Buyback Yield

How confident can we be that the companies listed above are going to be able and willing to sustain these levels of increased yields? The oilfield services company and motorcycle manufacturing company listed above have already announced that they will be cutting their dividends by 75% and 95%, respectively. However, it can take time before the economic reality of these decisions is reflected in the data we receive from providers.

As we know, past performance is not a guarantee of future results. But in an environment like the current one, it is barely even a guide. So how can we ensure that our models are capturing the most up-to-date information possible?

During normal environments, combining these signals with other factors, such as Quality, can help identify companies that are in a healthier or more stable financial position, thereby mitigating some of this data risk. Quantitative investors often use other methods of handling data, such as forward filling and eliminating (or “winsorizing”) extreme outliers in their data sets. These techniques can all help, but they may not be sufficient for the challenges of the current situation.

This is where we think the quantitative investing team at Neuberger Berman has an advantage over certain of our peers, in that we are able to leverage the expertise of a fully scaled fundamental equity research team.


Collaborating with fundamental analysts helps us to identify where dividends and buybacks are likely to be most vulnerable at the sector, industry, and security level and apply these insights to our quantitative models through ongoing data refinements.

Broadly speaking, we have found that the vast majority of companies—with a few notable exceptions, mostly within IT and Non-bank Financials—would be unlikely to buy back stock in the current environment. On the other hand, we think most companies are likely to defend their dividends aggressively—though many companies, particularly in Energy, Consumer Discretionary and Industrials, may simply be unable to do so even if they wanted to. Furthermore, different regions can present unique difficulties. For example, the European Central Bank has asked euro zone banks to halt dividends until at least the fall of 2020, and we will need to take these types of requirements into consideration.

Once we have worked with our colleagues on the fundamental research team to identify these company and industry insights, we can start to think about how we incorporate this additional level of fundamental depth into our models. Importantly, we can also corroborate and continually update information as it becomes available using various event databases and secondary sources, such as Bloomberg and broker reports. This allows us to identify key developments and adjust our data accordingly.

In light of all this information, we made a large scale adjustment to the underlying data that feeds into our models for thousands of companies across the globe. This is an evolving process, but at the time of writing we had made dividend adjustments to 5,830 of the stocks in our universe, or 89%; and buyback adjustments to 2,842, or 43%. In order for efforts like this to be scalable across sectors and regions, our team also needed to build out the infrastructure to maintain, adjust and expire these kinds of data refinements when they are no longer needed.

These insight-driven actions around sustainability of dividends and buybacks give us a higher degree of conviction in our data and ultimately in our portfolio positions—which we believe can help obtain better outcomes for clients. Though the implications of the pandemic on corporate balance sheets are likely still to working themselves out well into the future, we already see public announcements and data slowly beginning to confirm some of our findings. We believe we have positioned ourselves ahead of this curve.