Q: Can you briefly describe yourself and your investment style?

When I began in the investment business 34 years ago, I was completely focused on fundamental investing as a style. Before joining BlackRock in 2012, I was with Putnam Investments, and they had a pretty significant quantitative effort. Despite this, like a lot of fundamentalists, I didn’t trust backward-looking tools as much as I trusted fundamental research.

However, in 2003–2005, I noticed that quantitative tools were doing a fabulous job of trend riding. At that point, a light went on in my head: I realized if I could take the best of my fundamental stock-picking execution and add to it improved trend riding—that would be very complementary and improve my ability as an investment manager. At the end of the day, it remains a fundamental process, and the quantitative model does not put a stock into the portfolio. 

Nearly a decade later, the successful implementation of this enhanced investment approach led to my current position managing the Basic Value Fund for BlackRock.

Q: What is the current size of assets under management in the fund?

Our flagship Basic Value mutual fund has $4.4 billion, while total assets managed in the strategy are about $13 billion.

Q: What core beliefs guide your investment philosophy?

First of all, we are long-term value investors. We believe strongly in cash-on-cash returns and buying a dollar at a significant discount. The amount of the discount varies, depending on interest rates and the time horizon for returns.

We also seek to generate alpha, but in a risk-adjusted fashion. To me, the Holy Grail of a good investment style is consistency. We don’t want to be like a broken clock that performs well only at particular times in the cycle. We are confident that we have enough diversity and idiosyncratic drivers in our stock selection and portfolio construction to perform at each point in the cycle, as long as we execute well. 

Regarding risk, factor bets and even sector bets are only a minority of the risk allocation that is built into the portfolio. The majority of risk allocation—at least 70% and is currently over 80% —is channeled into stock-specific and industry risk. That is consistent with the focus of our research. We don’t extend our research into factor risks and sector analysis. Our research is on a more granular level.

As already mentioned, we do actively seek diversification and typically run 90+ names. Right now we are running around 97. For us, diversification is by name, as well as by risk, risk factor, industry, and frankly by the fundamental drivers that are targeted for the portfolio. We don’t place the same bet over and over again in our portfolio, because that would not be diversification.

We believe in active risk. Our active risk is over 80%, and in that way, we are extremely different from our benchmark, the Russell 1000 Value Index. But we’re managing risk at the very granular industry and company level.

Q: What is your investment process, and where do you get ideas?

We know the management teams, we know the industries, and we know the companies where we invest. So our investment strategy is more of an ongoing flow process.

That being said, we basically have three different ways to identify misvaluation, either in full valuations for sells or undervaluations for buys. My team and I look for (1) stocks that are appropriate to come out of the portfolio, (2) stocks already in the portfolio that could be at a greater weight, or (3) stocks not in the portfolio that have become misvaluation opportunities. 

We look for as much misvaluation as we can find to build as robust an opportunity set as possible. Remember, our objective is not only alpha but also risk adjustment and consistency. Hence, we are unwilling to concentrate because we feel that’s not something that would meet our objectives. Each of my analysts covers a portion of the market, and I give them discretion to find equities that they think are misvalued. I then review their ideas with them in-depth. 

Next, we try to identify reasons for misvaluations, as well as to assess the fair value of that particular stock when something happens. Finally, we try to identify the catalyst that is most likely to get an equity moving on the revaluation journey. So that’s one way that we generate ideas.

I also initiate ideas, overseeing the whole universe both fundamentally and using a quantitative tool. It is a static tool but it’s moderately sophisticated. It helps focus attention on areas of the market where we might have missed an opportunity. The quantitative tool looks for factors that have identified misvaluation in equities relative to their upcoming performance. 

Because quantitative is essentially a backward-looking view of alpha, it tends to work when the future looks like the past. And quant in general does work, because on average, the future does look like the past. However, we all know there are times—like market inflection points or company turnarounds—when the future does not look like the past. At those times, we don’t pay attention to the quant signal.

I also don’t share quant with my analysts, because I want the sources of alpha to be uncorrelated. That further enhances our ability to identify misvaluation. I don’t want analysts to be biased and think that a stock is or isn’t interesting because of its quant score. I want their purely fundamental, forward-looking opinions about where misvaluation in the market may lie.

Q: Could you illustrate your research process with an example?

We got involved with Microsoft Corporation when former CEO Steve Ballmer was still running the company. At the time investors were fed up with the company’s performance. There was a lot of concern about Windows eroding in the PC business, as well as the cloud dis-intermediating the Microsoft franchise.

However, when we looked more closely, we saw that the company had a very large cash balance. The price-earnings ratio of the stock was around 11 times earnings. Additionally, we found that Microsoft was the largest company in cloud, and that its cloud business was growing approximately 100% a year. The Windows business was a cash cow that we felt had a much longer life left than doubters on the Street believed. And, although Microsoft was losing money in Xbox and Bing, we also saw opportunities to improve results in those peripheral businesses.

We had been in and out of the stock a number of times over the last few years so we weren’t coming to it new. However, we needed an update of what was going on with the fundamentals, so we held a number of meetings with management.

We were attracted to Microsoft because we felt its valuation was much lower than it had been. As we analyzed quarterly performance, we found that the Windows franchise had demonstrably more sustainable cash generation than the market reflected. So we purchased Microsoft in the mid $20s and rode it for two plus years. We reached our valuation target and recently exited the stock – it was then in the mid $40s. During the period we owned it, Satya Nadella took over as CEO. I think his appointment was instrumental to the stock’s rise in value.

We didn’t call that change, but we knew there had been pressure on Ballmer to either fix things or move on. Ultimately, he moved on. We were encouraged by what Satya was doing; however, longer term, that didn’t raise the price target sufficiently for us to remain in the stock.

Q: Can you cite another example?

The story of Hospira, Inc. is an interesting one relative to our style, and it demonstrates our valuation flexibility. 

Hospira provides generic injectable drugs. Its manufacturing operations had failed some FDA testing on cleanliness, so the company had been under an FDA warning. This impacted its revenues and volumes negatively. In the fall of 2012 my team and I met with the company’s relatively new management. What we saw was a broken company but in a hot area. Moreover, since the new CEO had fixed his last company and sold it, we knew that he would not be afraid to be shareholder friendly in an exit strategy. 

At the time, Hospira was trading at about 20 times earnings but at slightly less than two times revenue. Most companies in that business were trading at about four times revenue. The company was building a new plant in India, and volumes were expected to step up substantially over the next two to three years.

Hospira was an interesting story in an interesting sector but with horrible execution. The stock was not inexpensive unless you assumed management could gain traction on a turnaround. So we went into the stock with that in mind. We knew that if we held it a couple years, we would likely be rewarded. The company fixed its quality issues. I believe its India plant is now coming online. 

Pfizer, Inc. decided to buy the company to strengthen its generics business before spinning that out. As of the first quarter of this year we got a bid from Pfizer for four times revenue, which we felt was fully valued. We exited the stock, making about two-and-a-half times what we put into our first purchases two years ago. We had continued to buy Hospira until a month before the bid. We correctly identified that the thesis was on track, and that we should continue to build our position.

In this situation, we felt the company’s future was going to be different from its past. In keeping with how we use quant, we knew the quant signal would not be trustworthy—and it wasn’t.

Q: How do you construct your portfolio?

In building a portfolio there are many rules, but we aim to diversify and to express our alpha and risk confidence as much as possible. We also build with not just one most likely outcome in mind for the market and the global economy. We instead consider a probability curve and stress test each idea against the most likely event but also against the tail events.

We aim to include a fair number of names, 90 or more. At times it has been quite a bit more. Our sector weights are plus or minus 650 basis points relative to the Russell Value 1000 sector weightings. We limit our tracking error in individual stocks, as well as in factor and sector bets, with daily monitoring.

The product has a 200 to 600 basis points tracking error range. We don’t manage to a particular number, but on an empirical basis it’s been low 200s to almost 400. Right now we are in the high 200s. 

Q: What is your sell discipline?

There are a couple of different reasons that sells happen. The first is when we hit our price target. The second is when a stock has not hit our price target, but that the horizon rate of return is such that a competing investment idea would be better for our shareholders. Finally, if the thesis breaks, and we decide we have made a mistake, we sell.

Q: Is there any end of the market cap that you look at?

We use value stocks that are in the Russell 1000 Value Index. We also screen for companies that aren’t in the index that we think might be value stocks. But in defining what those are, we look for market caps of $3 billion or greater, as well as stocks being cheap to either price-to-earnings, price-to-book, or price-to-sales, relative to a core benchmark.

Q: What is your definition of risk? How do you measure and manage it?

Our philosophy on risk management has both a quantitative and a fundamental side to it. 

Let’s take the quantitative side first. We have tracking error budgets for the whole portfolio, for factor risks, for each stock risk, and for industry risk. We have active weight limitations, both overweight and underweight on sector bets. However, when we’re picking stocks, we don’t try to pick ones for one most likely scenario. That would show excess confidence in the future. 

Instead, when I review an individual holding with my analyst, I will think about various “what if” scenarios, like what if China blows up, what if interest rates spike, what if we go into a world of deflation, or what if the dollar gets really strong. We think about things that are going on in the investment environment and try to do our stress testing on a company-by-company basis. In our experience, that rolls up into a robust risk control that isn’t completely captured by the quantitative tools.