Hello,
welcome back to Papers with Backtest podcast.
Today we dive into another algo trading research paper.
And today we're looking at something called the accruals anomaly.
Yeah,
it's a really interesting one.
Basically,
it's this observation that companies reporting higher non-cash earnings,
those are the accounting accruals.
They tend to have lower stock returns later on.
Right.
And the opposite to lower accruals,
potentially better returns.
It seems a bit.
it backward,
doesn't it?
It really does.
You'd normally think,
you know,
higher reported earnings equals good news.
Exactly.
But this suggests maybe we need to look closer at how those earnings are generated,
especially those accounting adjustments.
And the paper we're unpacking today really gets into this.
It's The Persistence of the Accruals Anomaly by Baruch Lev and Dora Nesim from back in April 2004.
And the fascinating thing,
like you said,
is persistence.
This wasn't a new discovery even then.
People had known about it for.
what,
almost a decade?
Yeah,
nearly 10 years.
And the basic trading strategy seems pretty straightforward on paper.
You mean buy the stocks with low accruals,
bet against the ones with high accruals?
Precisely.
So the big question,
the one we're really tackling for you today,
is why didn't this just disappear?
Right.
Shouldn't sophisticated investors,
you know,
the big funds,
have arbitraged this away by now?
That's the puzzle.
And what can we actually take away from this for trading rules or backtesting?
Well...
If something like this hangs around for so long,
there must be some friction,
some reason it's hard to capture.
Let's see what Lev and Nassim found.
Okay,
so first things first.
Did the anomaly actually exist consistently?
They looked at a really long period,
right?
Yeah,
1965 all the way to 2002.
A pretty solid chunk of time.
And they calculated these accruals using a couple of different methods,
I think.
That's right.
One was based on the balance sheet data.
They called it BSACCC.
It looks at changes in things like current assets minus cash.
current liabilities minus debt.
Yeah.
You know,
trying to isolate that non-cash part of earnings.
OK,
got it.
And the other?
The other used the cash flow statement,
CFSACC.
So two angles on the same idea.
So what did they find when they actually tested the strategy,
the long short one?
Well,
they confirmed it.
The anomaly was definitely there over that whole period.
How did they test it?
Like a portfolio?
Yeah,
they set up what's called a zero investment portfolio.
Basically,
you go long the bottom 10 percent of stocks,
the ones with the lowest accruals.
And short,
the top 10%,
the highest accrual.
Exactly.
And the returns.
They were statistically significant and positive.
How positive are we talking?
Depending on the exact measure and sample,
the average abnormal returns were in the range of about 7.5%
to 8.9%
per year.
That's from their table one.
Okay.
That's not trivial.
But here's the kicker you mentioned.
Right.
The magnitude,
the strength of this effect.
It hadn't really diminished much over time.
So even though people knew about it,
the potential profit was still there?
Pretty much.
Their analysis,
looking for a trend,
didn't show a significant decrease.
That's the core puzzle we keep coming back to.
It really makes you wonder about the big players then,
the institutional investors.
What were they doing?
Well,
the paper looked into that.
They examined institutional ownership changes in these companies.
Did they react?
Were they jumping on this?
They did react,
yes.
The data showed institutions were trading based on Krul's info.
Mostly in the first couple of quarters after the fiscal year ends,
which is when you'd expect that information to be digested.
OK,
so they weren't completely ignoring it.
No,
not at all.
And interestingly,
it seemed like the more active institutions,
the ones they call transients who trade more often,
they accounted for a big chunk of this reaction.
Even if they didn't hold the biggest overall position.
Right.
But here's the thing.
Even with this reaction,
the anomaly persisted.
So their trading just wasn't enough to like.
close the gap?
Apparently not.
It seems the institutional reaction was just too weak overall to make the anomaly disappear.
Hmm.
So why?
Why wasn't the response stronger?
Did the paper offer reasons?
It did.
It looked at the characteristics of the companies at the extremes,
the really high and really low accrual firms.
And what did they find?
What are these companies like?
Well,
they tend to have traits that institutions often try to avoid.
Like what?
Like being small companies.
There was a negative correlation there.
Okay.
Smaller market cap.
Makes sense.
Harder for big funds to trade.
What else?
Lower stock prices,
often.
Also,
a lower book-to-market ratio.
Now,
institutions often prefer higher book-to-market sort of value stocks,
but these extreme accrual firms tended to be lower on that scale.
Right.
Liquidity and maybe style preferences playing a role.
Exactly.
And also,
higher residual volatility,
basically,
more stock-specific risk,
and lower profitability.
So,
smaller,
lower price,
maybe more
growth oriented by the Bia member riskier and less profitable.
Yeah.
A whole cluster of characteristics that aren't typically on the institutional favorites list.
Do they confirm this statistically?
Yeah.
Table four in the paper shows regressions confirming these relationships.
Institutions generally prefer the bigger firms,
higher share prices,
etc.
So it seems the very stocks where this anomaly is strongest are the ones the big players tend to shy away from anyway.
That seems to be a big part of the story.
Their mandates,
their risk controls,
their preferences just don't align well with heavily trading these specific types of stocks.
OK,
so if the institutions aren't fully stepping in because they don't like the stocks,
what about individual investors?
Couldn't they capture this?
Well,
that brings us to the next hurdle,
transaction costs.
Right.
The practical side of actually putting the trades on?
The paper argues that trading these extreme accruals firms,
especially for individuals,
involves pretty substantial costs.
Why would they be particularly high here?
Well,
a couple of reasons.
First,
their simulations suggested that to get reliable,
statistically significant positive returns from this strategy,
you couldn't just pick one or two stocks.
You needed diversification.
Yes,
quite a bit.
They estimated you'd need around 40 securities in the long portfolio and another 40 in the short portfolio.
Wow.
80 stocks total.
That's a lot of trades.
It is.
And each trade has commissions,
potential slippage,
especially with smaller,
maybe less liquid stocks.
The fixed costs per trade really start to add up when you're trading that many names.
I can see how that would eat into profits quickly,
especially for a smaller account.
Definitely.
And maybe even more importantly,
remember where a lot of the profit comes from.
The short side,
betting against the high accrual companies.
Exactly.
And short selling has its own specific,
often significant costs.
Like borrowing fees.
Precisely.
You have to pay to borrow the stock you want to short.
And those fees can vary a lot.
The paper cited estimates anywhere from
0.20% up to nearly 4.72%
per year.
Ouch.
That top end could wipe out a big chunk of the potential anomaly return right there.
It really could.
Plus,
there's the risk of the stock being recalled by the lender,
forcing you to close your position perhaps at an inconvenient time.
So the shorting aspect,
which seems crucial for the strategy's overall return,
is particularly expensive and may be difficult for individuals.
That's a key takeaway.
High transaction costs,
amplified by the need for broad diversification,
and especially the high costs and complexities of shorting those high-curral stocks.
So let's try and summarize this for everyone listening.
The accruals anomaly,
this negative link between non-cash earnings and future returns,
it's been persistent.
Right.
And it seems to stick around likely because of a combination of factors.
First,
the big institutional players,
while aware of it,
tend to avoid the specific types of stocks small,
low priced,
volatile,
where the anomaly is strongest.
Their hands are kind of tied by their investment styles or mandates.
Yeah,
there's an institutional preference issue.
And second,
for individual investors who...
might be more willing to trade these stocks.
The transaction costs get in the way,
especially the need to hold lots of stocks and the significant costs associated with short-selling the high accruals firms.
Exactly.
The costs of actually implementing the strategy seem to be a major barrier.
It really highlights that gap between finding something interesting in the data and being able to consistently profit from it in the real world,
doesn't it?
Absolutely.
Theory versus practice.
The practical hurdles,
costs,
liquidity,
diversification needs.
Shorting difficulties,
they could prevent arbitrage from working perfectly,
allowing anomalies like this to persist longer than you might initially expect.
So the takeaway isn't just about accruals,
but maybe a broader lesson about considering implementation costs and feasibility for any strategy.
Definitely.
It's a great reminder that back-tested returns are one thing,
but net returns after all the real-world frictions are what actually matter.
This deep dive was really insightful.
It shows how market mechanics and investor constraints can explain puzzles like the persistence of the accruals anomaly.
Indeed.
And it makes you think,
doesn't it?
What other known factors or strategies might look good on paper but face similar practical roadblocks that aren't immediately obvious?
Something to keep in mind when you're looking at research.
Thank you for tuning in to Papers with Backtest podcast.
We hope today's episode gave you useful insights.
Join us next time as we break down more research.
And for more papers and backtests,
find us at https.paperswithbacktest.com.
Happy trading!