During the Berkshire-Hathaway annual shareholders meeting a
week ago, a questioner asked how Berkshire’s reinsurance unit priced Super Cat
(super catastrophic) policies re-insuring other insurance carriers against
immense losses. Warren Buffett fielded
the question from the audience and passed it to Ajit Jain, Berkshire’s Vice
Chairman of reinsurance operations. The
two-part answer was instructive.
Say No:
Both Jain and Buffett said they say ‘no’ to far more
opportunities than they price or accept.
In fact, Warren Buffett and Charlie Munger have stated numerous times
over the years they believe they’re successful because they say no to most
things. Warren went farther saying that
he and Charlie didn’t have the record they do because of any great skill they
have, but because of their ability to say no to as often as they do.
This ability to say no is important to
Dividend
Farmers.
The financial world
and media covering it are a frenzy of day-trading, impulse buying, and “act
now” advice and direction.
When MSNBC,
Bloomberg, Forbes, your broker, brother-in-law, and a universe of actors are
telling you this is the next Apple or Amazon you shouldn’t miss, it’s a
challenge to hold fire saving your investment money for select
opportunities.
But that’s exactly what
Warren Buffett and Ajit Jain counseled 40,000+ attendees.
Considering the record of the men involved,
that’s good advice.
Risk Mitigation:
Beyond saying no, Jain ventured into the process his team
goes through in pricing Super Cat insurance.
They start with as much historical data as they can find. They want to know all they can about the
likelihood of an event, how, where, and why it may occur, and the magnitude of the
consequences if it does. Jain and team are
conducting risk analysis coupled with an expected cost estimate.
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| Risk Assessment Matrix |
The data helps them define the hazard e.g., a typhoon in a
specific region, as well as the estimated probability said typhoon will happen
during a given time frame.
They’re using
a process analogous to the
risk
assessment matrix I previously blogged about.
In the Berkshire analysis, the hazard generates a
combination of likelihood and severity resulting in a risk level.
Once the is risk identified, Jain and company
determine what price can be applied to account for that risk plus Berkshire’s
much discussed
margin
of safety by baking expected cost into the equation.
Expected cost is the probability of an event happening times
the cost of the event if it does. For
example, if the probability of an event happening is 1%, but the cost of the
event, should it happen, be $1,000,000, the expected cost of the event is
$10,000 (.01 x 1,000,000 = 10,000).
Estimating the expected cost of a risk (part science, part
art) allows Berkshire’s reinsurance group to apply a price that accounts for
this cost while ensuring Berkshire remains financially healthy. This is the margin of safety.
In my million dollar example, an insurer may price the
policy at $500 per year knowing there’s an estimated 1% chance of paying out
$1,000,000 and a 99% chance of collecting $500.
If the insurer has the capacity to pay $1,000,000 in the unlikely event
of such a claim, it can pocket $500 frequently with little effort and do so
repeatedly while using that $500 as investment cash as it waits on possible claims
payout. This no interest ‘loan’ is what
Warren frequently refers to as float.
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| Economic Moat |
In the case of Berkshire’s reinsurance division, the dollar
figures are considerably greater allowing the firm to take on risks and
expected costs no other entity has the capacity to accept. This financial strength allows Berkshire’s
reinsurance arm to capture premium business at premium prices with few
competitors to drive down the price.
Talk about a financial moat!
DIY Investor Application:
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| Investment Research |
Although this example stems from Berkshire-Hathaway and its
super cat business, the principle is applicable to us DIY investors.
If I begin by
reviewing
as much data as possible about a prospective investment opportunity,
I’m off to a good start.
Doing so helps me
gain a sense of how likely the company is to provide a desired return.
The higher the desired return, the lower the
likelihood it is to happen, in general, and vice versa.
I’m establishing an expected payout (probability of return x
return) against which I weigh an expected cost to invest (probability of cost x
cost). For example, if I believe the
probability of a return is 80% and the return is $5 a share in dividends, my
expected payout is $4 per share per year.
If my cost of investing is $50 a share and the probability of that cost
happening is 100% (I buy the share) my expected cost is $50 a share with an
expected return of $4 a share in a given year.
This represents an 8% return on my money which is solid.
On the risk side, if I estimate the chance of losing my $50
share is low, say 1%, then my expected cost of losing my money is .01 x $50 or 50
cents. Against this I have an expected
return of $4 which is 8 times greater than my expected cost when
investing. Probability is on my side and
my risk, although not zero, is quite low.
As long as I’m capable of losing my $50 without going broke, I’ll gladly
accept $4 a year in returns for the very low possibility I’ll lose the
$50.
This is the same kind of bet Ajit Jain and Warren Buffett
make with super cat insurance policies and do so successfully. If I’m going to be a better investor,
learning from those who have proven highly successful themselves offers a
template I’m not going to ignore.
The thoughts expressed here
are those of the author, who is not a financial professional. Opinions
should not be considered investment advice. They are presented for
discussion and entertainment purposes only. For specific investment
advice or assistance, please contact a registered investment advisor, licensed
broker, or other financial professional.
