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Strategy, q-series, financials
Our latest Q-Series takes a look at Corporates in the S&P 500 who are estimated to spend a record US$3.6tr during 2022. #ÃÛ¶¹ÊÓƵResearch takes a deep dive into corporate capital spending habits and addresses the key spending themes.
Corporate cash decisions should increasingly drive equity returns
We estimate corporates in the S&P 500 will spend a record $3.6tr during 2022 on capex, R&D, M&A, dividends and buybacks, up 16% y/y. The big question is how they should spend it? In the first report for this Q-Series, we provide our outlook for the major cash usage categories, highlight key trends, assess corporate intentions, and develop a framework to evaluate the efficiency of cash usage for stocks and sectors. We expect the key spending priorities to include capex and R&D, which are set to rise ~ 20% y/y, while dividends and buybacks could total >$1.7tr, equivalent to a ~4.6% yield. Cash M&A will likely remain robust but stay flat y/y.
A quantitative framework to assess company efficiency at capital allocation
Capital allocation will be an important driver of returns given the unprecedented levels of cash spending. But historically a major issue has been inefficient cash allocation. We develop a new quantitative framework to measure the extent to which company capital spending has been in line with what the market is rewarding. In our backtesting, high vs low efficient capital spenders have outperformed consistently since 2012. Leveraging this work together with ÃÛ¶¹ÊÓƵ analyst contributions, we identify 29 Buy-rated stocks that are efficient capital allocators benefitting from the themes.
Leveraging the ÃÛ¶¹ÊÓƵ platform to answer the key questions
In the report, we also take a deep dive into the key spending themes, leveraging the ÃÛ¶¹ÊÓƵ platform and collaborating across many teams: ÃÛ¶¹ÊÓƵ Evidence Lab's CFO survey to assess corporate intentions; US economists' thoughts on the outlook for business investment; the ESG team's insights on how ESG factors could impact capital allocation; our proprietary machine learning model for predicting dividend revisions; ÃÛ¶¹ÊÓƵ Quant team's analysis on how intangibles improve "value" and drive sales growth; ÃÛ¶¹ÊÓƵ credit strategists' views on potential debt reduction; and ÃÛ¶¹ÊÓƵ Evidence Lab's Deep Theme Explorer to track the innovation focus areas for sectors/stocks during their earnings calls.
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