With the White Home downplaying the worth of quarterly reporting for firms, buyers face a well-recognized query: does the price of producing data outweigh the advantages?
Utilizing Robert Shiller’s long-run information, this submit exhibits that quarterly earnings include data that’s doubtless useful to each long-term allocators and short-term merchants. Its advantages, which I don’t try to quantify, ought to be weighed in opposition to any financial savings from less-frequent reporting.
Quarterly vs. Semi-Annual: What’s at Stake
The White Home this week known as for a change from quarterly to semi-annual earnings reporting. President Donald Trump argued that such a shift would save firms time and cash.
That could be true. However would buyers lose useful data?
To reply this query, I take advantage of earnings information from Robert Shiller’s on-line information from January 1970 (1970:1), the 12 months through which the Securities and Alternate Fee made quarterly earnings obligatory, to 2025:6 to check relationships among the many change in three-month earnings, six-month earnings, and the development in earnings. I outline the development as a 61-month centered transferring common change in earnings. Particularly, I take a look at whether or not understanding three-month earnings’ adjustments helps an investor higher estimate adjustments within the longer-term development in earnings.
Chart 1 exhibits three-month earnings in inexperienced, six-month earnings in pink, and development earnings in blue. Collection begin in January 2000 (2000:1), moderately than 1970:1, for ease of visualization.
Chart 1. 3-month, 6-month, and development earnings, 2000:1 to 2025:6.
Supply: Robert Shiller on-line information, writer calculations.
In fact, three-month earnings are choppier than six-month earnings. However it’s not apparent from visible inspection that understanding three-month earnings along with six-month earnings would assist a long-term investor predict adjustments in development earnings. (I take a look at this beneath and discover that they might).
It’s, nevertheless, apparent {that a} short-term investor, one maybe inquisitive about earnings adjustments in durations of lower than a 12 months, would profit from understanding three-month earnings. This remark is confirmed empirically beneath.
I begin with the long-term investor, who I assume is within the long-term development in earnings. A pure method to gauge the worth of getting three-month earnings along with (or as an alternative of) six-month earnings is to mannequin the change in development earnings as a operate of 1 or each, estimate that mannequin utilizing peculiar least squares, and examine mannequin accuracy. On this submit, I take advantage of R-squared as my measure of match (or adjusted R-squared) — the bigger, the higher.
At any level, the investor is aware of one-half the present development in earnings. That’s, they know the primary 30 months’ earnings of the present 61-month window, my proxy for the development in earnings. And so they know both the final three months of earnings, or the final six months of earnings, or each.
To find out whether or not receiving earnings data each three months versus each six months would assist the long-term investor to higher predict the development, I estimated specs the place the change in 30-month-ahead development inflation is defined by the change in six-month earnings alone plus the prior earnings-trend change (Mannequin 1). In Mannequin 2, the development change is defined by the identical variables plus the three-month change in earnings. Outcomes are proven in Desk 1.
Desk 1. Regressions of development inflation change on 3- and 6-month earnings adjustments, 1970:1 – 2025:6.
| Dependent variable = Pattern inflation (30-month lead) | ||
| Mannequin 1 | Mannequin 2 | |
| Six-mo. change (three-mo. lag) | 0.073 (0.013) | 0.061 (0.013) |
| Three-mo. change | – | 0.124 (0.029) |
| Pattern change | -0.223 (0.041) | -0.234 (.040) |
| Adjusted R-squared | 0.098 | 0.126 |
| Obs | 547 | 547 |
Supply: Robert Shiller on-line information, writer calculations.
Since I’m not inquisitive about inference, I omit dialogue of estimated coefficient values, aside from to notice that they enter with the anticipated signal. However this, I embody the prior development in earnings to cut back bias in my estimates and customary errors seem in parenthesis subsequent to every estimate.
The important thing result’s that including quarterly earnings (three-month change) improves match — the adjusted R-squared will increase from 0.098 for Mannequin 1 to 0.126 for Mannequin 2. Whereas neither match is spectacular, these outcomes counsel that quarterly earnings might assist the long-term investor predict development earnings. Different measures of match, particularly the Akaike and Bayesian data standards (AIC and BIC), verify that the specification which incorporates 3-month earnings is extra correct.
As for what could also be of curiosity to merchants (short-term buyers), one would possibly guess that the three-month earnings change is said to the subsequent three-month change. Quarterly earnings adjustments are certainly persistent. The scatter in Chart 2 exhibits the autocorrelation of quarterly earnings, the place excessive values (earnings adjustments larger than 100%) have been eliminated for simpler viewing. The estimated slope is 0.601 (se = 0.031) — the blue finest match line is flatter than the black 45-degree diagonal line — and the R-squared is 0.361.
Chart 2. Three-month lagged earnings change vs. three-month earnings change, 1970:1 – 2025:6.
Supply: Robert Shiller on-line information, writer calculations.
And on the threat of estimating the plain, the R-squared of a mannequin explaining 12-month earnings with six-month earnings (from six-months earlier than) is 0.699, whereas together with three-month earnings (from three-months earlier than) improves the match to 0.953.
Price vs. Profit
It’s practically axiomatic that, in most functions, extra information is preferable to much less. And the outcomes mentioned right here counsel that quarterly earnings include useful data for buyers. However producing earnings is expensive.
As regulators contemplate lowering reporting frequency, they need to weigh not simply the financial savings but additionally the potential losses — losses to buyers ensuing from much less transparency and to the financial system ensuing from impaired market effectivity.
Extra to Suppose About
Previous CFA Institute member surveys present clear help for quarterly earnings.
