Latest / How Canadian Markets Work / Reading a Data Release Explained: Why Does Good News Crash the Market? | Canadian Investing

Transcript

Reading a Data Release Explained: Why Does Good News Crash the Market? | Canadian Investing · How Canadian Markets Work

• 0:00
  I want you to just take a slow, deep breath for
• 0:03
  a moment. Yeah, just let your shoulders drop.
• 0:06
  Exactly. We are going to step away from the noise
• 0:10
  today. Away from the flashing red and green numbers
• 0:13
  on the screens. Away from all the frantic shouting
• 0:16
  of those financial news anchors. Right. Instead,
• 0:20
  we're going to look at a really baffling paradox.
• 0:22
  Yeah. And I want you to imagine this. A highly
• 0:25
  anticipated jobs report is about to be released.
• 0:28
  The tension in the financial world is just, well,
• 0:30
  it's palpable at that moment. It really is. And
• 0:33
  then the numbers finally drop. Yeah. And they
• 0:36
  are fantastic. I mean, far, far better than anyone
• 0:38
  expected. So it looks like an absolute economic
• 0:41
  triumph for everyday people. Yeah. But the very
• 0:43
  moment that news drops, financial markets take
• 0:46
  a violent nosedive, the screen turns into just
• 0:49
  a sea of panic selling. It's wild to watch. It
• 0:51
  is. Why on earth would undeniably great news
• 0:54
  cause a massive market sell -off? It feels completely
• 0:56
  counterintuitive, right? It goes against literally
• 0:59
  every instinct we have about how good news should
• 1:01
  be received. Right. We expect applause for a
• 1:04
  strong economy. Exactly. And instead, the market
• 1:07
  treats prosperity as a threat. Welcome to our
• 1:10
  deep dive. Today we are taking a serene, unhurried
• 1:14
  walk through a fascinating stack of notes on
• 1:16
  how financial markets actually function. It's
• 1:19
  going to be a nice, quiet journey. Very quiet.
• 1:22
  Specifically, our mission is to look at how to
• 1:25
  read an economic data release. Our goal is to
• 1:28
  give you a shortcut to being genuinely well -informed.
• 1:31
  We want to teach you how to read economic data
• 1:34
  just like a professional. Yeah. So you never
• 1:36
  again find yourself misled by those sensational,
• 1:40
  breathless headlines. Because we really need
• 1:42
  to set the stage here, you know, by understanding
• 1:44
  a fundamental truth about the world of finance.
• 1:46
  And what's that? A number is never just a number.
• 1:49
  It's a story. And like any good story, it's wrapped
• 1:53
  in complex layers. Oh, definitely. Layers of
• 1:55
  expectations, quiet revisions and hidden compositions
• 1:58
  just waiting beneath the surface. Right. If you
• 2:01
  only read the headline, you are only reading
• 2:03
  the cover of the book. Okay, let's unpack this.
• 2:06
  Because to understand why markets can suddenly
• 2:09
  plummet on seemingly fantastic news, we first
• 2:12
  have to understand where the market actually
• 2:14
  lives. And it doesn't live in the present. it
• 2:18
  lives entirely in the future. That is so true.
• 2:20
  The future focus of the market changes the very
• 2:23
  definition of what constitutes news. Right, because
• 2:26
  before any significant economic data is released,
• 2:29
  whether that's inflation, employment, or GDP
• 2:32
  economists publish their forecasts. Yeah, a large
• 2:35
  group of them. And these individual forecasts
• 2:38
  are gathered together and aggregated into what
• 2:40
  we call a consensus expectation. The consensus
• 2:43
  expectation. Exactly. This consensus is what
• 2:46
  the collective of market believes is most likely
• 2:48
  to happen. And the market trades on that consensus
• 2:51
  well in advance. So let's say inflation is expected
• 2:54
  to come in at 3%. OK. And then the official release
• 2:57
  says it is exactly 3%. The market shouldn't move
• 3:00
  at all. Not a single inch. That information was
• 3:03
  already priced into every stock and bond. What's
• 3:06
  fascinating here is that in the financial markets,
• 3:08
  movement only comes from surprise. Surprise.
• 3:11
  Yeah. The expected outcome has already been bought
• 3:14
  and sold millions of times over before the data
• 3:17
  is ever published. The market was waiting to
• 3:19
  find out if its collective assumption was wrong,
• 3:21
  not if it was right. I love that. Think of it
• 3:25
  like watching a movie where someone has already
• 3:27
  told you all the spoilers. Oh, that's a great
• 3:29
  analogy. Right. You are sitting in the theater,
• 3:32
  eating your popcorn. If the movie ends exactly
• 3:35
  how the spoiler predicted, You don't gasp. No,
• 3:38
  you don't jump out of your seat. You just nod.
• 3:40
  Exactly. You only react if there's a sudden surprise
• 3:44
  twist that wasn't in the spoiler. Therefore,
• 3:47
  correctly predicting an economic number doesn't
• 3:49
  actually make you money. No, you have to correctly
• 3:51
  predict how the number will differ from what
• 3:54
  everyone else already assumes. Which is incredibly
• 3:56
  hard to do. It really is. And this helps explain
• 3:59
  why the common logic of the economy is doing
• 4:02
  well so stock should rise completely fails in
• 4:05
  practice. Right. Because if everyone already
• 4:07
  knows the economy is doing well, that optimism
• 4:10
  is already baked into the price of the stocks.
• 4:12
  You are paying a premium for yesterday's revelation.
• 4:14
  Which brings us back to our opening paradox.
• 4:17
  The good news is bad news trap. The classic trap.
• 4:21
  Yeah. How can a surprisingly strong jobs report
• 4:24
  a real twist in the movie caused the market to
• 4:28
  sell off? It comes down to the ripple effects.
• 4:31
  specifically regarding interest rates. The market
• 4:34
  isn't reacting to the jobs themselves. It's reacting
• 4:36
  to how the central bank will interpret those
• 4:39
  jobs. A surprisingly strong economic data release
• 4:43
  might signal to the central bank that the economy
• 4:45
  is running too hot. Right. And to prevent inflation
• 4:48
  from taking root, they might decide to hold interest
• 4:51
  rates higher for a longer period to cool things
• 4:53
  down. Exactly. I want to pause here. Because
• 4:56
  this is where the financial jargon usually gets
• 4:58
  incredibly dense. We often hear phrases like,
• 5:01
  uh, discounting future cash flows. Oh, yeah,
• 5:04
  that one always scares people off. It really
• 5:05
  does. Yeah. But let's look at it conceptually.
• 5:08
  Think of a stock as a promise of future payouts.
• 5:10
  Okay. If interest rates are high, you can get
• 5:13
  a great, perfectly safe return just by leaving
• 5:16
  your money in a bank or buying a government bond.
• 5:18
  Right, with basically zero risk. Exactly. So
• 5:21
  for a risky stock to be worth your money today,
• 5:25
  its price has to drop to make the potential reward
• 5:27
  attractive enough. High interest rates act like
• 5:30
  gravity on stock prices. Good news for the real
• 5:35
  economy like more jobs. higher wages triggers
• 5:38
  a fear of that gravity. That is the perfect way
• 5:41
  to visualize it. The gravity analogy works so
• 5:44
  well, the market is playing a game of anticipation.
• 5:47
  Right. It sees the good news, anticipates the
• 5:50
  central bank's heavy gravitational pull on rates,
• 5:53
  and sells the stock before that gravity can drag
• 5:56
  the price down. It's all about beating the gravity.
• 5:58
  So if the market is purely reacting to how a
• 6:01
  surprise compares to that spoiler, What happens
• 6:04
  when we discover that the spoiler they reacted
• 6:06
  to was completely wrong to begin with? Oh, well,
• 6:08
  that introduces us to the concept of phantom
• 6:11
  data. Phantom data? That sounds ominous. It kind
• 6:14
  of is. Most economic data you read in the headline
• 6:16
  is just an initial estimate. It's a rough first
• 6:18
  draft. Right. It's compiled very quickly based
• 6:20
  on incomplete survey responses and early data.
• 6:23
  So it's not absolute truth. It's just the best
• 6:26
  guess they have on that particular morning. Exactly.
• 6:29
  And as more complete information trickles in
• 6:31
  over the following weeks and months, the statistical
• 6:33
  agencies update their numbers. The revisions.
• 6:36
  The revisions. And they can be substantial. A
• 6:39
  month that is loudly and joyfully reported in
• 6:42
  the headlines as remarkably strong can be quietly
• 6:44
  revised to remarkably weak months later. When
• 6:48
  absolutely nobody is paying attention anymore.
• 6:50
  Precisely. The cameras are gone. Aye. Struggle
• 6:53
  to understand how a multi -trillion dollar market
• 6:55
  allows itself to be fooled like this I mean if
• 6:58
  these statistical agencies are constantly revising
• 7:01
  their numbers weeks later Why do Wall Street
• 7:04
  professionals still trade so violently on the
• 7:06
  first inaccurate draft? It's a great question.
• 7:09
  Shouldn't they know better than to bet billions
• 7:11
  on a rough draft? Oh, they absolutely know better.
• 7:13
  But they are bound by institutional mandates,
• 7:16
  algorithmic triggers, and just the sheer momentum
• 7:18
  of the herd. The herd mentality. Yeah. If an
• 7:21
  institutional manager knows the initial data
• 7:23
  is flawed and also knows that thousands of trading
• 7:26
  algorithms are about to buy based on that flawed
• 7:28
  data, they have to buy too just to ride the wave.
• 7:31
  Wow. So they are forced to trade the phantom
• 7:34
  data, because the illusion itself creates real
• 7:37
  market movement. Exactly. And here is the truly
• 7:40
  fascinating part for you, the individual reader.
• 7:44
  The revision to the old data is usually buried
• 7:46
  deep down in paragraph 6 of a news article. Right,
• 7:49
  while the flashy but highly inaccurate initial
• 7:52
  estimate for the new month gets all the bold
• 7:55
  font at the top of the page. You've got it. Wait,
• 7:58
  so to read the news correctly, you actually have
• 8:01
  to read it backward. You have to look at the
• 8:02
  quiet revision to last month before you even
• 8:05
  allow yourself to look at the headline for this
• 8:07
  month. Experienced professional readers operate
• 8:10
  exactly that way. Seriously. Seriously. The correction
• 8:13
  gets a fraction of the attention from the public,
• 8:16
  but it is often the most vital piece of the puzzle.
• 8:18
  Because it sets the actual baseline? Right. If
• 8:21
  you don't know where the baseline just moved
• 8:23
  to, the new number is completely devoid of context.
• 8:26
  OK, so once you accept that the baseline is constantly
• 8:28
  shifting and you finally allow yourself to look
• 8:30
  at the current number, our notes tell us you
• 8:33
  still cannot trust it. You really can't? Because
• 8:35
  the headline is an aggregate. It is a giant bucket
• 8:39
  holding a lot of different things, and aggregates
• 8:41
  hide all the interesting parts. This is the illusion
• 8:44
  of composition. The headline number is just the
• 8:47
  net sum of many moving parts, and those moving
• 8:50
  parts are frequently traveling in completely
• 8:53
  opposite directions. They can mask severe weaknesses
• 8:56
  with superficial strengths. Okay, let's slowly
• 8:58
  walk through a highly specific fictional scenario
• 9:02
  to really paint a picture of this. Imagine another
• 9:05
  jobs report. The consensus expectation, our spoiler,
• 9:09
  is that 15 ,000 jobs will be added. OK, 15 ,000.
• 9:12
  But the actual release comes out and says 40
• 9:15
  ,000 jobs were added. A massive beat. Wow. Yeah,
• 9:19
  the headline writes itself, economy smashes expectations
• 9:21
  by 25 ,000 jobs. Exactly. It sounds like a massive
• 9:24
  victory on the surface. But let's look closer.
• 9:26
  Let's really look at it. We take a slow breath
• 9:28
  and read the composition of that number. We look
• 9:30
  inside the bucket. Right. And what do we see?
• 9:33
  Well, it turns out full -time employment actually
• 9:35
  fell by 10 ,000 jobs, but part -time employment
• 9:37
  rose by 50 ,000 jobs. Oh, so the net is a positive
• 9:42
  40 ,000, but the quality of the employment is
• 9:45
  visibly deteriorating. Yes. Reminds me of a company
• 9:48
  boasting that they made a million dollars in
• 9:51
  a single day. On the surface, it sounds like
• 9:53
  incredible business growth. It does. But if you
• 9:56
  look at the composition and realize they made
• 9:58
  that million dollars by selling their only factory
• 10:00
  to pay the rent, it's not actually growth at
• 10:03
  all. That's a perfect comparison. That's what
• 10:05
  happens when full -time stable jobs are quietly
• 10:08
  replaced by part -time precarious ones. The aggregate
• 10:11
  number goes up, but the foundation is cracking.
• 10:14
  And we also have to read backward. as we discussed.
• 10:17
  We look at last month's figure in this fictional
• 10:20
  release. Let's say originally it was reported
• 10:23
  as a solid positive 20 ,000 jobs, but buried
• 10:26
  in the notes it has just been revised down to
• 10:28
  a loss of 5 ,000 jobs. That is a 25 ,000 job
• 10:32
  downward swing to the past, which means the 25
• 10:35
  ,000 job downward revision almost entirely cancels
• 10:38
  out the 25 ,000 job beat. of the current month.
• 10:41
  Exactly. The baseline sank right out from under
• 10:43
  us while we were busy celebrating the headline.
• 10:46
  Unbelievable. Now let's look at another detail
• 10:48
  in this release. The headline proudly states
• 10:51
  that the unemployment rate fell. That sounds
• 10:53
  fantastic. But we look at the composition again.
• 10:57
  Let's take a moment to understand the math behind
• 10:59
  the unemployment rate. It's important math to
• 11:02
  know. It isn't just the number of jobless people
• 11:04
  divided by the total population, is it? No. It
• 11:06
  only counts people who are actively looking for
• 11:08
  work. Right. So if a million people get so discouraged
• 11:11
  by a bad economy that they simply give up and
• 11:14
  stop looking for a job, they are erased from
• 11:17
  the equation entirely. They just vanish from
• 11:19
  the math. The math formula shrinks. The unemployment
• 11:22
  rate drops, even though not a single person actually
• 11:25
  got hired. In our fictional scenario, the rate
• 11:28
  fell only because the participation rate fell.
• 11:31
  It is a mechanical mathematical improvement masking
• 11:33
  a deep human discouragement. It's quite sad when
• 11:36
  you think about it that way. It is. And finally,
• 11:39
  if we look at the last detail in this release,
• 11:42
  we see that wage growth came in below expectations.
• 11:45
  Meaning people are taking home less extra pay
• 11:48
  than anticipated. Exactly. Their purchasing power
• 11:51
  is shrinking. So what does this all mean? Well,
• 11:54
  the headline boldly screamed an economic boom.
• 11:58
  But the composition, the quiet details underneath,
• 12:01
  reveals a weakening labor market. Yeah. Full
• 12:04
  -time jobs are bleeding away. The past was much
• 12:07
  worse than we thought. People are giving up the
• 12:09
  search for work entirely, and wages are softening.
• 12:12
  The aggregate lied, but the composition told
• 12:14
  the truth. And every single one of those details
• 12:17
  was in the exact same release. freely available
• 12:20
  to the public at the exact same moment This completely
• 12:23
  explains the wild chaotic nature of the market
• 12:26
  in the immediate aftermath of a data release
• 12:28
  The first few minutes are fiercely noisy Algorithmic
• 12:32
  trading bots might cause the market to plummet
• 12:34
  on the strong headline, assuming interest rates
• 12:36
  will rise. And then the market might completely
• 12:39
  reverse direction 10 or 15 minutes later as the
• 12:42
  human professionals actually sit down, read the
• 12:45
  composition, realize the economy is actually
• 12:47
  softening, and change their view on what the
• 12:50
  central bank will actually do. It's a roller
• 12:52
  coaster. Reacting immediately is clearly a terrible
• 12:56
  idea for anyone but a highly specialized high
• 12:58
  -frequency trader. Completely terrible. So now
• 13:02
  that we know the treacherous traps of expectations,
• 13:05
  the ghosts of revisions, and the illusions of
• 13:08
  aggregates. How do we practically apply this
• 13:10
  to our lives? Yeah. How do you, the listener,
• 13:13
  use this without feeling entirely overwhelmed
• 13:16
  by the sheer volume of data? Because it is overwhelming.
• 13:19
  It is. But we can outline a very practical, very
• 13:22
  deliberate defense plan. It consists of four
• 13:25
  steps, and it takes roughly three minutes per
• 13:28
  data release. Only three minutes. Just three.
• 13:30
  You don't need a degree in economics to do this.
• 13:32
  you just need patience. Okay, I love that. Step
• 13:35
  one, find the consensus expectation before you
• 13:38
  read the number. Right, because if you don't
• 13:40
  know what the spoiler was, you have no way of
• 13:42
  knowing if the movie actually had a twist. Exactly.
• 13:44
  You have to know what the collective market expected
• 13:47
  to understand if the news is truly news. Step
• 13:50
  two, read the revision to the prior period first.
• 13:53
  The backwards reading. Yes. Before you look at
• 13:55
  the shiny new number, look at how history was
• 13:58
  just rewritten in the footnotes. Understand where
• 14:01
  your baseline currently sits so you aren't comparing
• 14:04
  today's reality to a yesterday that never existed.
• 14:07
  That is so crucial. Step three, read the composition.
• 14:11
  Don't stop at the aggregate bucket. Look inside
• 14:13
  the bucket. Yeah. Is the job growth full -time
• 14:17
  or part -time? Or, you know, if we were looking
• 14:19
  at inflation, is it core inflation, which tracks
• 14:23
  the underlying persistent trend of prices? Or
• 14:26
  is it just a volatile headline number driven
• 14:28
  entirely by a temporary spike in gasoline prices
• 14:31
  that will reverse next week? Beautifully put.
• 14:34
  And step four, go directly to the raw source.
• 14:37
  Skip the news articles. Yes. If you are in Canada,
• 14:40
  go to Statistics Canada's website. If you are
• 14:42
  in the United States, go to the Bureau of Labor
• 14:44
  Statistics, read the actual release. Because
• 14:46
  the summary you read on a financial news site
• 14:48
  or a blog was likely written in 20 minutes by
• 14:50
  a rushed journalist. A journalist who was under
• 14:52
  immense pressure to file a story the very second
• 14:55
  the data dropped. Right. The original source
• 14:58
  is free. It is calm and it contains all the necessary
• 15:01
  nuance without the panic. Taking those three
• 15:04
  minutes changes you from a reactive consumer
• 15:07
  of news into an informed reader of data. It really
• 15:10
  does. But even with this defense plan, our notes
• 15:14
  highlight four major complications, four subtle
• 15:17
  traps hidden in the data itself that we need
• 15:20
  to gently untangle. These are the statistical
• 15:22
  quirks that can distort reality even when you're
• 15:25
  reading carefully. So what's the first trap?
• 15:28
  The first trap is seasonal adjustment. Oh, right.
• 15:31
  Because raw economic data is enormously seasonal.
• 15:34
  For example, retail hiring naturally spikes every
• 15:37
  December for the holidays. Right. Everyone needs
• 15:39
  extra help for the shopping season. Exactly.
• 15:42
  If statisticians didn't adjust for that, every
• 15:44
  December would look like an unprecedented economic
• 15:47
  boom. And every January when those temporary
• 15:49
  workers are let go would look like a severe depression.
• 15:52
  So statisticians use mathematical models to smooth
• 15:55
  this out, allowing us to see the true underlying
• 15:58
  trend beneath the seasonal noise. They do. The
• 16:01
  trap, however, is that these adjustments are
• 16:03
  merely historical models. They rely on the assumption
• 16:06
  that human behavior repeats itself reliably.
• 16:09
  Which it usually does until it doesn't. Precisely.
• 16:12
  When human behavioral patterns dramatically shift,
• 16:15
  like they did globally in the year 2020 during
• 16:17
  the pandemic, those historical models can violently
• 16:21
  distort reality. Because they're trying to smooth
• 16:23
  out seasonal patterns that simply no longer exist
• 16:25
  in the modern world. Exactly. Wow. OK, trap number
• 16:28
  two is single month noise, something we touched
• 16:31
  on earlier regarding the phantom data. A lot
• 16:34
  of this data comes from surveys. And surveys
• 16:36
  have inherent sampling errors. I mean, you are
• 16:39
  trying to measure the employment status of over
• 16:42
  150 million people by calling a few thousand
• 16:45
  households. It's an incredibly difficult task.
• 16:48
  And a single month release might show a massive
• 16:50
  unexpected jump or drop, but it might just be
• 16:53
  statistical noise. Just a quirk of who happened
• 16:56
  to answer the phone that week. Exactly. This
• 16:58
  is why looking at three month or six month rolling
• 17:00
  averages is vastly more informative than obsessing
• 17:03
  over a single month's fluctuation. The long term
• 17:06
  trend reveals the truth. The single data point
• 17:09
  is often a statistical liar. Very well said.
• 17:13
  OK. Trap number three is something called base
• 17:16
  effects. Yes, base effects. This one is incredibly
• 17:20
  important when we talk about inflation. Most
• 17:22
  gated is reported as year over year. We compare
• 17:25
  the price of a basket of goods today to the price
• 17:28
  of that exact same basket exactly 12 months ago.
• 17:32
  That rolling 12 month window is usually a very
• 17:35
  reliable way to measure price pressures. But
• 17:38
  it becomes deceptive if something highly unusual
• 17:40
  happened exactly one year ago. Let's walk through
• 17:42
  the math of a base effect to make it concrete.
• 17:45
  OK, let's do it. Imagine a geopolitical shock
• 17:47
  causes oil prices to spike to an unprecedented
• 17:50
  $150 a barrel last March. Right. And because
• 17:54
  oil is in everything, inflation for that month
• 17:56
  looks absolutely massive. Exactly. But let's
• 17:59
  say by this March, oil has settled back down
• 18:01
  to normal $80 a barrel. When the year over year
• 18:04
  inflation number is calculated today, it compares
• 18:06
  today's normal prices to last year's extreme
• 18:09
  spike. Oh, I see. The math will show a massive
• 18:12
  plunge in inflation. Yes. A politician might
• 18:16
  point to that plunge and declare that the cost
• 18:18
  of living crisis is solved. But the reality is,
• 18:20
  prices today didn't suddenly become cheap. No.
• 18:23
  The bizarre anomaly from 12 months ago simply
• 18:26
  fell out of the back of the rolling window. Wow.
• 18:28
  So the strange number you're seeing today...
• 18:30
  is actually being caused by the ghost of the
• 18:33
  past, not by anything structural happening in
• 18:36
  the economy right now. Exactly. It's a mathematical
• 18:38
  illusion. Here's where it gets really interesting.
• 18:41
  The final trap is a linguistic trap, as much
• 18:44
  as a mathematical one. It is the trick of changes
• 18:48
  in rates of change. This is a remarkably subtle
• 18:51
  manipulation. But it is incredibly powerful because
• 18:54
  it preys on our desire for good news. I think
• 18:57
  we all just want to hear that things are getting
• 18:59
  better. We do. Imagine a situation that is actively
• 19:01
  getting worse. Let's say a government deficit
• 19:05
  is growing larger every single year. The hole
• 19:07
  is getting deeper and the debt is piling up.
• 19:09
  Okay. But this year, the deficit grew by a slightly
• 19:13
  smaller amount than it grew last year. It is
• 19:16
  still growing. You are still digging the hole
• 19:18
  deeper. But a skilled communicator will deliberately
• 19:22
  weaponize the phrasing and announce the deficit
• 19:26
  grew more slowly. It's so sneaky. They take a
• 19:30
  negative reality, the fact that the hole is getting
• 19:32
  deeper, and frame the second derivative, the
• 19:35
  rate of change, to make a worsening situation
• 19:38
  sound like an absolute improvement. It exploits
• 19:41
  the cognitive load of the listener. It really
• 19:43
  does. Processing a slowing rate of deterioration
• 19:46
  requires a lot more mental effort than simply
• 19:49
  hearing the word slowing and assuming it means
• 19:51
  improving. It sounds like a victory when in reality
• 19:54
  it is just a slightly less aggressive defeat.
• 19:57
  It is a brilliant, entirely factual, and totally
• 19:59
  misleading way to communicate. Politicians, pundits,
• 20:02
  and corporate executives on every side of every
• 20:05
  argument use that exact trick. deliberately because
• 20:08
  they rely on the fact that most people do not
• 20:10
  take those three minutes to read the composition
• 20:12
  and understand the math exactly they rely on
• 20:15
  the emotional impact of the aggregate headline
• 20:17
  which brings us to the end of our quiet walk
• 20:19
  through the data it's been quite a journey let's
• 20:21
  slowly gather up the core message here economic
• 20:25
  data is not an absolute unvarnished truth handed
• 20:28
  down from on high no it is a rough messy first
• 20:32
  draft of history constantly subject to revision
• 20:34
  and reinterpretation The headline you see flashing
• 20:37
  across the screen in bright red or green is almost
• 20:41
  without fail the least informative part of the
• 20:44
  entire release. True. Actionable insight only
• 20:46
  comes from understanding the expectations that
• 20:48
  were already priced into the market. And acknowledging
• 20:50
  the quiet revisions to the past. Yes. And bravely
• 20:53
  looking inside the bucket to read the composition
• 20:55
  of the numbers. It requires a little bit of time
• 20:58
  and a little bit of stillness. Just three minutes.
• 21:00
  And if we connect this to the bigger picture.
• 21:02
  It leads to a rather profound thought to leave
• 21:05
  you with today. We live in a modern financial
• 21:08
  world dominated by algorithmic trading bots.
• 21:11
  These are vast server farms programmed to scan
• 21:14
  economic data releases and execute massive multi
• 21:18
  -million dollar trades based on aggregate headlines
• 21:20
  in mere microseconds. They're infinitely faster
• 21:23
  than we could ever hope to be. If you try to
• 21:26
  compete on speed, you will lose every single
• 21:28
  time. But perhaps the human inability to react
• 21:31
  that fast is actually a superpower. I love that
• 21:33
  idea. If the algorithms are ruthlessly programmed
• 21:36
  to trade the noisy, unrevised, and deeply flawed
• 21:40
  aggregate headline in a fraction of a second,
• 21:43
  well then, taking three minutes to sit quietly,
• 21:46
  breathe, and calmly read the composition might
• 21:48
  just be the greatest structural advantage a human
• 21:51
  investor has left in the market today. Our slowness
• 21:53
  really is our edge. Taking the time to wait for
• 21:56
  the movie's real ending rather than reacting
• 21:58
  blindly to the spoiler is how you actually understand
• 22:01
  the story. Thank you so much for joining us on
• 22:04
  this deep dive. We hope you feel a little more
• 22:06
  grounded and a little more prepared for the next
• 22:07
  time the numbers flash across your screen.