Hello.
Welcome back to Papers with Backtest podcast.
Today,
we dive into another algo trading research paper.
Hi there.
Yeah,
today we're looking at a really interesting one called percent accruals.
It's by Hasala,
Lundholm,
and Van Winkle from 2010.
Right.
And what's neat is how it challenges the sort of standard way we think about accruals,
especially for trading.
Exactly.
It puts forward this idea that maybe the traditional calculation isn't the most effective for spotting opportunities.
For traders listening,
the big question this paper tackles is,
does this new definition they call percent accruals actually work better?
Is it a sharper tool for finding potentially mispriced stocks compared to,
you know,
the old way?
That's the core of it.
And our mission today is really to dig into the trading rules they tested and importantly,
the backtest results they got.
What can we actually use?
Okay,
let's do that.
Before we jump into their new idea,
maybe we should quickly cover the basics of the traditional accrual strategy.
What were people doing before?
Sure.
So the standard definition,
the one most people know,
is you take net income,
subtract cash from operations,
and then you divide that whole thing by the company's average total assets.
It's basically trying to isolate the non-cash part of earnings relative to the company's size.
Exactly.
It's a measure of earnings quality in a sense.
And this wasn't just theoretical,
right?
Sloan's work famously built a strategy on this.
Oh,
yeah.
Sloan's 1996 paper was huge.
it showed.
Pretty significant returns from going long,
low accrual stocks and short,
high accrual stocks.
That really put the accrual anomaly on the map for quant traders.
But like a lot of anomalies,
the initial shine maybe wore off a bit over time,
or at least people started questioning it.
That's fair to say.
Yeah,
later research definitely raised questions about how big that effect really was and if it persisted consistently,
which conveniently leads us right back to this paper and their different approach.
Perfect setup.
So what is this percent accruals measure?
How did Hafzal and his team redefine it?
Okay,
the key difference is the denominator.
They still use net income minus cash from operations in the numerator.
But instead of dividing by average total assets,
they divide by the absolute value of net income.
So net income.
Ah,
okay.
So scaling by earnings itself,
not the asset base.
What's the thinking there?
Why make that change?
Well,
their argument is that this gets closer to the composition of earnings.
It asks,
how much of this reported net income number is actual cash versus accounting adjustments?
Right.
Focusing on the cash versus accrual mix within the earnings figure itself.
Exactly.
And they suggest investors might fixate on that bottom line net income figure without fully distinguishing between the cash and accrual components,
potentially leading to mispricings.
OK.
That makes intuitive sense.
How does this translate into a trading rule then?
How do they sort stocks?
It's a pretty standard portfolio sorting approach.
Firms that have large negative accruals compared to their absolute earnings,
meaning cash flow is much stronger than reported income.
Yeah.
They go into the low accrual portfolios.
Those are the potential longs.
And the opposite for shorts.
Firms with large positive accruals relative to their absolute earnings,
where reported income is much higher than cash flow,
they land in the high accrual portfolios,
the potential shorts.
Can you give us a feel for the numbers?
Like what kind of
percent accrual value puts a firm in that bottom low accrual group?
Sure.
The paper mentions that for the lowest decile,
the 10th of firms with the lowest percent operating accruals,
the values were typically in the range of,
let's see,
minus 5.7 down to about minus 3.1.
And what's interesting about the firms in that bucket?
What's really key here is that these firms generally had positive cash from operations,
but they also had large negative accruals.
which pull their net income figure down,
sometimes close to zero.
That seems like a crucial distinction from the traditional measure.
Absolutely.
Because with traditional accruals scaled by assets,
that lowest decile could often contain firms with really big losses and significant negative cash flows.
Percent accruals seems to filter those out of the long portfolio.
The paper used Georgia Pacific and supervision as examples,
didn't they,
to highlight this?
They did.
It's a great illustration.
Both firms,
despite being very different in size,
ended up in the lowest percent operating accruals decile in 2001.
Both had strong positive cash flow,
but big negative accruals.
So the percent measure picked up on that similar earnings composition,
regardless of the fact Georgia Pacific was way bigger overall.
Exactly.
Traditional accruals being scaled by assets would treat them very differently.
Percent accruals focuses purely on that cash versus non-cash makeup of the reported profit.
Oh,
okay.
So we have this new measure,
this different way of slicing the data.
Now for the payoff,
did it actually lead to better back-tested results?
Let's talk table four.
Right.
Table four is the direct head-to-head.
Panel A has the percent operating accruals results.
Panel B has the traditional operating accruals.
Same period,
same methodology otherwise.
What's the first thing that hits you looking at panel A versus panel B?
The hedge return is just dramatically different.
For percent accruals,
The hedge portfolio long of bottom decile,
short the top decile,
returned 11.68%
per year.
And crucially,
this was highly statistically significant,
P-value less than 0.001.
Wow,
nearly 12%
and very significant.
What about the long and short legs individually for the percent accrual strategy?
Also strong.
The long portfolio,
the low percent accrual stocks,
returned a positive 5.53%
per year,
also highly significant,
P0.001.
The short portfolio,
the high percent accrual stocks,
had a significant negative return of made at 6.15%,
P.COL 0.009.
So both sides seem to contribute.
Okay.
Now compare that to panel B,
the traditional accruals.
How did that look?
Well,
the hedge return for traditional accruals was 6.51%.
But,
and this is a big,
but it was not statistically significant.
The P value was 0.186.
So based on their tests,
you couldn't be very confident that the 6.5%
wasn't just random chance.
Pretty much.
At conventional significance levels,
you'd say it wasn't reliably different from zero.
And the long short legs for traditional.
This is where it gets really interesting.
The long portfolio for traditional accruals returned only 1.27%
per year,
and that was highly insignificant.
P0.5 years,
zero.
Basically zero.
Wow.
Yeah.
The short side,
however,
did work.
Selling the highest traditional accrual stocks yielded a significant negative 5.24%
per year.
P equals 0.029.
Similar negative return to the percent accruals short side.
OK,
so pulling that together,
it looks like the percent accruals strategy generated a much stronger,
much more reliable hedge return overall.
And the big driver of that difference seems to be the long side,
right?
Buying low percent accruals stocks worked much better than buying low traditional accruals stocks.
It's exactly the story table foretells.
That long portfolio difference,
5.53%
versus 1.27%
is really striking.
It suggests percent accruals is much better at identifying
potentially underpriced stocks.
Did they run the same comparison for total accruals,
not just operating accruals?
They did,
yes.
That's in table five.
And the story is pretty much the same,
quite consistent.
Okay.
What did they find there?
For percent total accruals,
they again found a significant hedge return,
8.53%
per year,
peak of 0.000.
And again,
the long side was strong,
a significant positive,
5.49%,
peak of 0.005.
And traditional total accruals,
let me guess,
weaker.
You got it.
The hedge return for traditional total accruals was insignificant.
The long portfolio return was also insignificant,
just 1.60%.
The short side did show significance again,
negative 4.23%,
P yield 0.048.
So it really seems robust across both definitions,
operating and total.
This percent scaling seems to unlock more performance,
particularly from the long book.
That's certainly what these back tests strongly indicate.
Did the paper look at what kinds of companies end up in these extreme deciles?
Are they different based on the measure used?
Yes.
Table 2 provides some characteristics,
and there's some interesting differences,
especially in that lowest decile,
the potential longs.
That's what?
Well,
for one,
the average market cap of firms in the lowest percent accrual decile was much larger,
about $1.5 billion,
compared to only $474 million for the lowest traditional accrual decile.
Ah,
that's potentially quite practical,
isn't it?
Larger firms usually mean better liquidity,
maybe a lower trading cost to implement the strategy.
Exactly.
That could be a real advantage.
Also,
those low percent accrual firms tended to have higher average return on assets and,
maybe more importantly,
higher cash return on assets.
Suggesting they are actually,
well,
fundamentally healthier businesses on average in that long portfolio.
It seems that way.
It suggests percent accruals might be better at picking firms that are performing okay on a cash basis,
but where the accounting is perhaps temporarily obscuring that.
That ties back nicely to the original rationale.
And
Did they check how much overlap there actually is between the two measures?
Are they picking the same stocks?
Good question.
Table 3 looks at that overlap.
And it's surprisingly low,
especially in those crucial extreme deciles.
For instance,
only about 11.78%
of the firms in the lowest decile using percent accruals were also in the lowest decile using traditional accruals.
Wow.
Only about 12%
overlap.
That really drives home the point that this isn't just a minor tweak.
It's identifying a substantially different group of stocks.
Precisely.
It's finding a different set of potential opportunities.
And like any good academic paper,
they surely ran some robustness checks to see if the results held up under different conditions.
Oh,
yes,
they did several.
For example,
Table 10 examined whether including or excluding special items from the earnings calculation changed things.
They found the percent accrual strategy worked well either way.
Which is good.
It means it's not just driven by weird one-off accounting things.
Right.
And Table 11 looked at performance separately.
for firms making profits versus firms making losses.
Percent accrual showed effectiveness for both groups.
That's interesting because the traditional anomaly has sometimes been shown to be weaker among loss-making firms.
OK,
another point in its favor.
Anything else?
They also looked at performance across different levels of arbitrage risk,
basically,
how easy or hard it is to trade these stocks.
The percent accrual strategy seemed to remain significant across most of these risk levels,
suggesting it's not just working in tiny,
illiquid,
hard to trade stocks.
That's important,
too.
It suggests broader applicability.
OK,
so wrapping this all up after diving into this paper,
what's the main message for an algo trader listening today?
I think the key takeaway is that the evidence here,
based on their back tests,
it's pretty compelling.
Using percent accruals as a signal
particularly for identifying potential long candidates,
appears to offer a potentially more profitable approach compared to the traditional accruals measure.
The performance,
especially on that long side,
was significantly better in their tests.
Yes,
significantly better and more statistically robust.
It suggests this alternative definition might capture something about earnings quality or investor misperception more effectively.
So for traders looking to refine existing quality or value strategies or develop new ones,
This percent accruals idea definitely warrants a closer look.
Absolutely.
The paper hints that its success might come from better identifying situations where investors underestimate the persistence of cash flows versus accruals.
And maybe the fact that it relies less on very small,
potentially high-risk stocks is also a factor.
Definitely food for thought and further testing.
Thank you for tuning in to Papers with Backtest podcast.
We hope today's episode gave you useful insights.
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find us at https.paperswithbacktests.com.
Happy trading.