Macro Roundup

The Verdad team shows that from 2001-22, firms with the highest analyst growth forecasts actually delivered the lowest total returns, as EBITDA multiple contraction more than offset high EBITDA growth that fell short of initial analyst forecasts.

“…The above chart shows the total returns of each quintile of growth prediction broken out into categories. The data above suggests that while firms with high forecast growth indeed grew at impressive rates, they also experienced the greatest contractions in their multiples. In fact, the same three factors that predicted growth—analyst forecast EBITDA growth, improvement in the TEV/EBITDA multiple, and trailing EBITDA growth—were the exact same factors that predicted multiple contraction. As a result, higher growth net of capital consumption for these firms was more than offset by higher multiple contraction. This is the cruel result of a lack of persistence in growth. Valuation multiples contract because growth does not persist….”

Greg Obenshain and Brian Chingono, "Predicting Growth,"Verdad, October 2022, https://mailchi.mp/verdadcap/predicting-growth
Predicting Growth

Two weeks ago, we extended the 2001 findings in the paper The Level and Persistence of Growth Rates through June 2022 and confirmed that, indeed, there is no persistence in long-term earnings growth beyond chance. The chart below shows this graphically. The straight black line at the top is what we’d expect if growth perfectly persisted, meaning that companies that were above the median growth rate in year 1 were again above that rate in years 2, 3, 4 and 5. The gray line is what we would expect if growth does not persist at all, meaning that a company that was above median growth in year 1 had a coin flip of a chance at being above median growth in year 2, so that that probability of being above median growth for two years in a row is 25% and just 3.215% for five years running. The blue line is Chan et al.’s original finding from that paper and the dotted black line is ours.

This result led us to quip that analysts might as well plug in the same long-term growth assumptions for a SaaS company as a coal mine. And we stand by this. The data shows that we need to be very humble when plugging a terminal growth assumption into a discounted cash flow model. A good assumption is the GDP growth rate.

But the observation that growth rates do not persist is not the same as saying they are not predictable in the short run. And last week we showed that, while analysts are wildly over-optimistic in their growth estimates, they do, in fact, sort companies correctly into the high and low growers. While it may indeed be true that growth does not persist, surely we can predict with some confidence that the IT services firm with a growing customer backlog is likely to grow faster next year than the auto parts supplier who just lost their largest contract.

So can we make some reasonable predictions about growth? One way to do this is just to select those variables that have predicted growth in the past. When we run regressions on EBITDA growth, the three variables that are most significant and have the highest magnitude of impact are, in order, analyst expected EBITDA growth, followed closely by both the trailing improvement in the TEV/EBITDA multiple and trailing EBITDA growth.

We can build a simple growth factor that equal weights these three variables. And this factor works very well to predict the relative magnitude of short-term growth. Below we show the same persistence chart, except this time we show the persistence of growth for the top quintile of our growth factor and the bottom quintile of our growth factor. The black line is again random chance.

But, alas, there is a problem: predicting growth isn’t the same as predicting returns. And it turns out that our growth prediction is actually negatively correlated with total returns. Below we show the realized EBITDA growth by predicted growth quintile and the realized total return.

The key to understanding this apparent paradox is to decompose total returns into the contributions of growth, changes in multiple, leverage, and distributions. The below chart shows the total returns of each quintile of our growth prediction broken out into these categories.

This is the cruel result of a lack of persistence in growth. Valuation multiples contract because growth does not persist. In fact, the lack of dispersion of returns between the high growers and the low growers points to the efficiency of markets. Growth is more or less correctly priced. And that’s what we’d expect. As every good analyst learns, it’s not the forecast that matters, but the forecast relative to what is already priced in.

Stocks that price high growth are particularly susceptible to multiple contraction. So an analyst building a DCF model should remind themselves that the higher the growth rate, the lower the exit multiple relative to the entry multiple should be. Analysts should assume that growth mean reverts to the market within five years. This is the argument we have made from the beginning and an eminently reasonable approach, even for the fastest growers, as we showed in Figure 2. To give a sense of what this should look like, we provide a cheat sheet below. For each projected growth quintile, we show the starting annualized two-year growth projection and the subsequent realized median annual growth rates.

We’ve spent three weeks now looking at growth, only to come to the conclusion that predicting growth well doesn’t lead to being able to forecast total returns any better. Next week we plan to look at what fair multiples should be for a terminal value assumption and why some companies deserve higher valuations, independent of their growth rates.

Analysis from New York Fed shows that Americans spend 60 million fewer commuting hours each day vs. pre-pandemic. They allocated this time primarily toward leisure activities and sleeping, while reducing overall work hours.

“…In the aggregate, Americans now spend 60 million fewer hours traveling to work each day. We investigate how people spend this saved time on other activities. The chart above shows how one hour of foregone commute time is allocated toward other activities. First, we find a substantial fall in time spent working. Overall paid-work hours fell because of substitution toward other activities throughout the day: we see notable increases in leisure time and sleeping. The rise in leisure was particularly pronounced among younger Americans, who reported spending more time at social events, eating at restaurants or bars, and exercising….”

David Dam, Davide Melcangi, Laura Pilossoph, and Aidan Toner-Rodgers, "What Have Workers Done with the Time Freed up by Commuting Less?,"Federal Reserve Bank Of New York, https://libertystreeteconomics.newyorkfed.org/2022/10/what-have-workers-done-with-the-time-freed-up-by-commuting-less/

What Have Workers Done with the Time Freed up by Commuting Less?

The COVID-19 pandemic has dramatically changed the way Americans spend their time. One of the most enduring shifts has occurred in the workplace, with millions of employees making the switch to work from home. Even as the pandemic has waned, more than 15 percent of full-time employees remain fully remote and an additional 30 percent work in hybrid arrangements (Barrero, Bloom, and Davis). These changes have substantially reduced time spent commuting to work; in the aggregate, Americans now spend 60 million fewer hours traveling to work each day. In this post, we investigate how people spend this saved time on other activities. Using detailed data from the American Time Use Survey (ATUS), we find that employed individuals allocate their saved commute time toward leisure activities and sleeping, while reducing overall work hours.

Measuring Time Spent

The ATUS is a nationally representative survey that measures both the amount of time people spend on various activities and where these activities take place. ATUS respondents are randomly selected from a subset of households in the Current Population Survey, allowing us to link demographic and employment information to respondents’ time use information. Because COVID-19 disrupted the survey’s data collection between March 18 and May 9 of 2020, the survey results for 2020 reflect data only from May 10 to December 31. To make comparisons with prior years, we restrict the data to this period in the other years as well.

To examine how workers allocate their saved commute time, we apply the methodology of Aguiar, Hurst, and Karabarbounis to the COVID-19 period. For those interested in the details, we aggregate individual ATUS responses to the state-level and exploit variation across states in the extent of commute time reduction. After categorizing more than 400 time use activities into nine major categories, we regress yearly changes in time use for each category on changes in commute time. Our first-differences approach enables us to account for time-invariant state characteristics, and we also include a variety of state-level control variables to account for worker composition. Across specifications, we restrict our analysis to employed individuals in order to exclude reductions in commute times due to job loss. Additionally, to account for the possibility that COVID-19 induced firms to cut hours just as employees were shifting to remote work, we also consider a sample of only full-time employees and find similar results.

Changes in Time Use

The main results are presented in the chart below in which we show how one hour of foregone commute time is allocated toward other activities. First, we find a substantial fall in time spent working; the decrease in hours worked away from home is only partially offset by an increase in working at home. This is in line with results from Bloom, Han, and Liang, who find that remote work led to a decrease in overall hours worked. Moreover, our findings help reconcile earlier work on commute times, which found that even though employees reported allocating 35 percent of their saved commute time to work, they spent fewer total hours in paid employment (Barrero, Bloom, and Davis). Our results from the ATUS suggest that although individuals may have increased time working in the precise time-slot they used to commute, overall paid-work hours fell because of substitution toward other activities throughout the day.

Second, we see notable increases in leisure time and sleeping. The rise in leisure was particularly pronounced among younger Americans, who reported spending more time at social events, eating at restaurants or bars, and exercising. Older age groups, on the other hand, tended to allocate more time to nonmarket work, such as activities related to childcare, the maintenance of the household, repairs, and meal preparation.

The ATUS also includes information on who physically accompanied the respondent during each activity. We use this variable to classify which activities were done with people outside of the immediate household. We categorize solitary activities and those with household members into one category, and activities with friends, co-workers, clients, and non-household family members into the other. As we would expect, the next chart shows that time spent on activities done alone or with household members increased, such as leisure (+2.30 hours) and nonmarket activities (+0.69). Differentiating by age, we see that younger people were the only group to significantly increase their leisure time with non-household members (+1.13), likely reflecting the differential risks of COVID-19.

“…Between 1998 and 2020 a rise in China’s firm births is correlated with higher firm exits and slower growth one and two years later for U.S. firms in corresponding sectors. A 1% rise in employment in China’s firms predicts a 0.1% decline in employment in the corresponding U.S. sector. The evidence suggests that U.S. industries were not outcompeted by subsidized Chinese firms prior to their being subsidized. However, in the years following a new FYP, the number of China’s firms in targeted sectors surged by 15% compared to non-targeted sectors, while firms and total job postings at U.S. firms in the corresponding sectors experienced a roughly 5% decrease. U.S. firms in these sectors also experienced a decline in earnings and output….”

Xiao Cen, Vyacheslav Fos, and Wei Jiang, "Race to Lead: How China’s Government Interventions Shape U.S.-China Industrial Competition,"Stanford University, August 1, 2022,
https://sccei.fsi.stanford.edu/china-briefs/race-lead

Race to Lead: How China’s Government Interventions Shape U.S.-China Industrial Competition

Between 1998 and 2020, a rise in China’s firm births is correlated with higher firm exits and slower growth one and two years later for U.S. firms in corresponding sectors.

Sectors targeted and subsidized through China’s Five-Year Plans experience a surge in new firms, while the corresponding sectors in the U.S. see declines in firm births, output, employment, and earnings.

China’s earlier Five-Year Plans tend to displace labor-intensive sectors in the U.S., while more recent ones displace capital-intensive sectors in the U.S.

China has become a serious competitor in economic sectors previously dominated by U.S. industry. In recent years, trade conflicts between the two countries have become more common, especially given the view that China’s government intervenes a great deal to help domestic firms compete. Research explores to what extent industries targeted and subsidized through China’s Five-Year Plans (FYPs) have displaced U.S. firms up and down the value chain.

The data. Researchers collected data on 1,643,000 U.S. firms and 1,100,000 Chinese firms from the U.S. Census Longitudinal Business Database (LBD) and the China Industrial Enterprises Database (CIED). Both databases capture firm-level characteristics and changes in economic activities over time, including firm births and deaths, employment, sales, exports, total assets, and others. China’s CIED database also includes data on the amount of state subsidies firms receive. Researchers then organized the merged data from both countries according to industry sector and identified those sectors that China’s government targeted for development in each of its FYPs from 2001, 2006, 2011, and 2016. Researchers supplemented this database with job posting data from 1.28 million U.S. firms between 2007 and 2020 from Burning Glass.

China’s firms displace same-sector U.S. firms. To examine the relationship between trends in industrial activity in China and the U.S., researchers compared the rates of firm births, firm exits, and employment by sector in the U.S. and China between 1998 and 2020. They found that a rise in China’s firm births is correlated with higher firm exits and slower growth one and two years later for U.S. firms in corresponding sectors. Further, they found that a 1% rise in employment in China’s firms predicts a 0.1% decline in employment in the corresponding U.S. sector.

Government subsidies may bolster China’s firms in targeted industries at the expense of U.S. firms. Prior to the release of a FYP, U.S. industries that were counterparts to those supported by the government in China exhibit growth trends similar to unsubsidized industries in China. In other words, the evidence suggests that U.S. industries were not outcompeted by subsidized Chinese firms prior to their being subsidized.

However, in the years following a new FYP, researchers found that the number of China’s firms in targeted sectors surged by 15% compared to non-targeted sectors, while firms and total job postings at U.S. firms in the corresponding sectors experienced a roughly 5% decrease. U.S. firms in these sectors also experienced a decline in earnings and output.

The strength of the correlation held even when removing China’s fastest-growing firms from the analysis, suggesting that the changes were driven by government targeting and subsidies rather than “natural” dynamism occurring irrespective of government support. Researchers also found that capital markets in the U.S. failed to predict any decline in fortunes for U.S. firms in sectors later targeted by a FYP, further suggesting that competition with China’s newly-subsidized firms played a role in the observed decline at U.S. firms.

China’s industrial policy differentially affects U.S. firms, industries. Certain U.S. firms and industries were able to offset the negative impact of China’s industrial policy. U.S. firms that depended heavily on China as a source of revenue and those that relied heavily on China’s imports benefited somewhat from rapid growth in the upstream sectors fostered by China’s industrial policies. In addition, while firms with limited presence in China suffered significantly, some U.S. firms offset the negative effects by offshoring production capacity to China.

China’s government support for industry important in its race to lead. The analysis shows that China’s government support plays a significant role in enhancing the competitive standing of China’s firms. Decisions by China’s government to elevate its global prowess in certain industries has led to the relative decline of such industries in the U.S., not just in low-skill “sunset” industries from which the U.S. may have been happy to retreat, but also in higher-skill industries that both countries are eager to lead.