DQYDJ

CameronDaniels

Why Did the Fed Lower Interest Rates?

Personal Finance      by

Back on Wednesday, July 31st, 2019, the Federal Reserve lowered the federal funds rate for the first time in eleven years.

In future articles, I will discuss why I find this move and its timing peculiar. For now though, I wanted to lay out some short-term implications and how I think this impacts the economy.

Balancing Inflation with Unemployment

The federal funds rate is the primary interest rate that banks charge each other for excess deposits.

A lower federal funds rate, in general, encourages investment and business spending by lowering the cost of borrowing from other banks. Another way of saying this: a bank’s reward for maintaining excess deposits decreases, so they will look for riskier investments.

If the interest rate is higher – say 5% – then a bank will need a very high return to beat their risk-free rate. If the interest rate is lower, then riskier bets that return only a couple percentage points may still be worth it.

The primary purpose for lowering interest rates is to spur the economy.

Spending and investing more should lead to higher wage growth and lower unemployment. All of this has a drawback: it risks creating more inflation.

The current inflation rate (measured by CPI) is low but in range, hovering in the 1.5%-2.5% annualized range.

The Fed, then, had to lower rates on different grounds.

China Trade Concerns, Stock Market and Uncertainty

The Fed gave a few reasons for why they cut. From their minutes:

Consistent with its statutory mandate, the Committee seeks to foster maximum employment and price stability. In light of the implications of global developments for the economic outlook as well as muted inflation pressures, the Committee decided to lower the target range for the federal funds rate to 2 to 2-1/4 percent. This action supports the Committee’s view that sustained expansion of economic activity, strong labor market conditions, and inflation near the Committee’s symmetric 2 percent objective are the most likely outcomes, but uncertainties about this outlook remain. As the Committee contemplates the future path of the target range for the federal funds rate, it will continue to monitor the implications of incoming information for the economic outlook and will act as appropriate to sustain the expansion, with a strong labor market and inflation near its symmetric 2 percent objective.

Federal Reserve Minutes from 07/31/2019

As tariffs trade back and forth between the USA and China, manufacturers and consumers may decide to cut back on their spending due to the wealth effect. The wealth effect shows that as prices increase, a household’s actual wealth and income goes down.

You can see this move as a move in advance of future spending – and declining consumer confidence.

What about the stock market and GDP?

Another reason for a rate increase? Likely uncertainty in both the stock market and real GDP, the ultimate measure of the total output of an economy. (Although I should mention, both stock market mentions in the minutes were bullish points.)

From 2016 to late 2018, GDP growth was positive and increasing. Not only was the US economy growing but it was also accelerating.

That sort of growth may eventually lead to inflation, but so far we don’t see it in the data. The GDP growth rate has decreased in recent quarters, however, which may be a sign of an economy on the edge of a slowdown.

The rationale goes: if the Federal Reserve can encourage spending in advance of a recession, the impact will be lessened. Whether this is true is yet to be seen.

Was the July rate cut too premature?

In this author’s opinion, the decrease in interest rates was premature.

Recent expectations suggest that there may be another interest rate cut in the near future, potentially in the September 2019 meeting next week.

There has not been a significant increase in inflation recently. The S&P 500 in mid-September is still trading near it’s all time high while and GDP growth continues to grow – most recently at a rate of 2% annually.

This 2% is higher than the GDP growth during 2016 when rates were beginning to increase.

So, premature. The only major reason that I saw for the timing of this interest rate decrease is the political pressure of one man’s Twitter account. It will be very interesting to see how the Federal Reserve’s logic continues to pivot going forward, but this interest rate cut gets a failing grade from me.

Cheers,

Cameron Daniels

My 2017 Savings Rate

Personal Finance  Retirement      by

At DQYDJ, tax season has us recently rolling out our annual savings rate. The personal finance blogosphere constantly emphasizes its importance and demonstrates its impact on your ability to increase your net worth. It also helps show what you might reasonably expect to need to spend if you choose to retire.

What’s My Savings Rate?

My after-tax savings rate came in at 52% for 2017. This was a good year and we shall see if I can bring down some expenses and raise that income to get it higher next year. Overall, though, I don’t feel like I need to increase or significantly decrease it at all. Let chips fall where they may.

This year the savings felt a lot different than some of my previous years. A large portion of my savings is in my 401k, 401k match and ESPP and I don’t notice it every month. Thus, a large enough percentage is automatically saved without me ever realizing or seeing it. Following your checking account month-to-month makes it feel like you’re bringing in significantly less money since you have a large portion set aside.

Some Notes on Methodology

There are as many different ways to calculate savings rates as there are ways to define income or investments, which are inherently flexible. If you purchase a watch or a shirt, do you calculate it as a capital outlay and then depreciate it? For example, a $60 shirt may last 5 years so does it “cost me” $1 every month?

Generally, this level of detail would be insane.

The above example refers to the accrual-based form of accounting. The “purchase” of the asset isn’t an expense, but the ongoing usage or depreciation of it is what the actual expense is. This is in contrast to a cash-based form of accounting, which measures all the incomes and expenses as its impact on my cash accounts in the calendar year 2017. I will be using the accrual-based accounting, but will not track a ridiculous amount of assets.

Also, I will be including my employer match as part of both my savings and my income and won’t be counting any capital gains unless realized. I will be looking at everything after-tax, meaning my denominator income will be reduced by the amount of tax I pay. I do pay airline fees and sales tax and other fun fees associated with government but will not be taking these out of my income. Thus, my equation looks most closely like:

(Contributions to 401k+other investments accounts+cash/debt difference between 1/1/17 – 1/1/18) divided by (Total Income + Employer Match – SS/Medicare/Federal Income Tax + Taxable Dividends/Interest)

Enough With Boring Accounting!

This is my one of my higher savings rate years and is probably mostly driven by not moving and having a higher income than past years. My income and expense estimates fluctuate so I generally try to vaguely monitor where I’m spending money. If it is noticeably different than what I expect, then I would change my expectations or my behavior. I’m still far enough away from FI or ER that tracking savings and spending day-to-day (or more closely) doesn’t really help me do anything better.

Cheers,

Cameron Daniels

My 2018 Asset Allocation

Personal Finance      by

In honor of tax season, we at DQYDJ annually post savings rate and asset allocation summaries.

Unfortunately, I find mine particularly boring. I am young enough and have enough time left in the market that it has pretty much become throw it all in stocks and wait. Because of that (and that I’ve been at it for a little while now), my monthly fluctuations are usually much higher than my monthly savings so it’s tough to keep perfect track as my allocation changes.

95% Stocks, 5% Bonds

My asset allocation is 50% domestic stock, 45% international stock, 5% bonds and around 0% cash (my checking account getting too high gives me anxiety). I plan on keeping a similar asset allocation for another 8-10 years before shifting to very slightly more bonds.

Around 50% Domestic, 45% International and 5% bonds

As you can guess from the S&P 500 return, the portfolio has been performing very well over the past few years. From the beginning of February 2018 until now is the largest loss I will recognize due to the volatility in the market and the timing of when a lot of my purchases have been.

I wanted to break it down by passive vs. active investing but it is 100% passive besides the stock purchase plan I have. I hold it generally until it vests and save a little bit extra to time some long-term capital gains.

But, enough about the investment strategy, let’s start looking at this as a percentage of my net worth.

I Like Cheap Debt

It’s hard to find ways to leverage debt besides real estate and mortgages. One of the habits I have begun the past few years is to always maintain a cash balance on a balance transfer credit card.

I usually have carry about two balance transfer offers for credit cards at any time. Usually the deal is 0% APR for 18 months and a 3% fee (about a 2% interest rate) or 15 month 0% APR for 0% fee, etc. Small time-frames and small amounts of debt sure, but I like the idea of being able to maintain a low cash amount with very cheap debt while maintain a very high % of my net worth (not just assets) in stocks.

Unfairly inexpensive debt option

The most recent offer I accepted was for a check at 2% fee (for 0% APR over 18 months). I assume there’s a good amount of risk involved in carrying the debt (maybe August 2019 will be a particularly cash-constrained time) and the underlying assets I will purchase. The past few times have paid out well though, and I consider it a way to slightly amplify the returns of my portfolio, increase risk and get some leverage without using real estate.

Leverage only slightly increases my portfolio size

Unfortunately, it is a small amount of debt compared to the amount you can leverage with real estate. Some online brokers offer the ability to lever up relatively cheaply, but I haven’t made that step yet to add even more leverage to my portfolio. (Consider that a sure sign that we’re about to skyrocket)

The plan for the upcoming year is probably getting into real estate again, shoveling money at stocks and trying to find ways to get cheap debt.

Cheers,

Cameron Daniels

A Linear Scale and Round Numbers are Misleading While Investing

Investing      by

When viewed on a linear scale over a long enough time period, large American Stock Index graphs look almost vertical.

Drops look crazy steep.

The 1987 crash is nearly a blip on the map while the 2008 crash appears (correctly) to be the largest, quickest downfall in the past 40 years.

  • What incorrect decisions do investors make because of their choice of scale?
  • What downside is there to picking a linear scale instead of an logarithmic scale?
  • Why is a linear scale so deceiving in investing?

Stock Market Returns are Exponential

When looking at stock market returns, it’s important you consider returns as multiplicative or exponential.

For example, if the stock market rises 10% then decreases 10% it is not back to where it started! It will be 1% below where it started.

In DQYDJ’s own S&P 500 calculator, for example, we report returns as are compounded annual growth rates (CAGR). We multiply numbers together to derive the long term returns we’re reporting in the tool.

What’s the natural consequence of this math?

Pretty simple, really – the largest increases come on the largest base. Therefore, gains look largest when prices are high – and so do losses.

Percentage Increase is the Best Metric for Investment Returns

To properly gauge returns you need to track percentage increases or decreases.

Percentages measure the actual impact gains or losses have on your contributions. This is especially true if you periodically invest, perhaps through something like a 401(k) (see historical 401(k) contributions).

Each deposit you make to your investment accounts is a new base for percentage gains and losses. Let’s take a look at an extreme example: General Electric and Amazon between 2007 and early 2018.

General Electric and Amazon between 2007 and 2018 on a Linear Scale

Amazon and General Electric stock between 2007 and 2018.
GE barely even registers. Over this timeframe, you’d have lost 55% of your initial investment (sans dividends)

Comparing GE and AMZN’s returns since Mar 2007 reveals one major problem with linear scales: they fail to show the significance of stock impact!

In this example, you literally cannot even see GE’s fluctuations register. The scale is wildly almost off the charts due to AMZN’s growth.

Ignore GE for a second. Eyeballing the chart, you’d think a third of all Amazon gains occurred in the previous three months.

That’s not the right way to evaluate an investment. Consider that going from a 1500% to a 3100% increase is really just doubling (just like a 100% gain).

General Electric and Amazon between 2007 and 2018 on a Log Scale

Logarithmic scale for the y-axis helps (slightly) bring the differences in return in order

Now, let’s compare the same chart on a logarithmic scale.

Above you see the massive decrease in Amazon’s value in late 2008 and the massive run-up they experienced from 2009-2011. Both periods are completely obscured on the linear scale.

Amazon’s 2015 returns look a bit more fantastic than their 2016 and 2017 returns when moved onto this logarithmic scale.That holds true in performance:

  • Amazon returned ~110% in 2015
  • Amazon returned ~15% in 2016
  • And in 2017, Amazon returned ~55%

Linear Scales Make Every Recent Month Seem Significant

Using a linear scale in investing shows round number benchmarks falling quickly off a larger base.
First week that S&P 500 hit 100-point intervals

I went back and grabbed the first time the S&P hit 100 point intervals over time through early 2018.

Many benchmarks fell running up to the dot-com bubble (1995-1998 was especially prolific). Technology boom times were followed by a 13 year gap between S&P 1500 and 1600.

Even with a consistent CAGR, you’ll see round number benchmarks fall quickly and distorted linear scales.

Take a look at how frequently new 100-point benchmarks fell through 2018 due to the S&P 500’s fantastic returns. Another few years of even modest returns will yield a significant number of new highs.

This isn’t to say that this guarantees the rate of new records will only speed up. For example, reference the 13 year gap mentioned earlier!

The stock market can wax and wane over long periods, but will quickly break round number price benchmarks when near a peak. (Let’s just hope that happens faster than every 13 years.)

Stop Quoting Round Numbers and Showing Linear Scales in Investing

Every time you hear the financial news quote a round number, think back to this article. Humans have a round number bias and it’s interesting to see benchmarks fall, it’s true. But those round numbers are deceiving if you’re used to smaller numbers from earlier times.

The proper – albeit, more boring – way to quote returns is using percentage increases and compound annual growth rates. So, try to stick to percentage math and logarithmic charts and you’ll do great.

Happy investing!

Where Does 2018 Rank in Annual S&P 500 Returns?

Investing      by

The S&P 500 and the broader equity markets continue to outperform and have seen annual inflation-adjusted 15.8% returns from March 2009 through the end of December 2017. More recently, from November 2016 to January 2017, the S&P 500 has returned 22.3% in total.

Annual S&P 500 Returns Vary Wildly

I started with each year’s inflation adjusted S&P 500 return, inflation-adjusted from data by Robert Shiller dating back to 1871. DQYDJ has a S&P 500 return calculator here.

The returns are vastly different, even if returns will average out to 6%-10% per year. This variance is why sequence risk is a major threat to early retirement, where a few very negative years hit a retiree in early retirement years even if they would be safe from later declines.

Another consequence of this is that very rarely will the stock market return “around” any number. Thus, when a market predictor says “the market will return 3% next year”, it is very unlikely that it will return exactly 3%. The “prediction” (if it is worth anything)  is best thought of as “the least wrong” estimate.

How Good Has 2018 Been for the S&P 500 So Far?

Monthly data would show an even wider variance, even if the absolute numbers of the returns are lower (sadly no month ever returned 40%).

As of this writing, the S&P 500 has returned 2.72% in 2018, putting it right at the 36th percentile for annual returns. Most years it’s beating are negative years (as can be seen on the chart). Given we are only 8 trading days into the year out of ~252, we are on pace for a 132.9% return this year. (You can count on that!)

Some fun S&P 500 return facts:

  • The best year since 1959 was 1995 at a 31.7% return followed by 2009 at a 29.9% return
  • The worst year on record is 1931, when the S&P (it wasn’t yet the ‘500’) declined 38.0% in a year. Only shortly behind, however, was 2008 which dropped 35.7%.
  • The 25th percentile, median and 75th percentile are -3.86%, 8.20% and 21.31%

How Should S&P 500 Returns Be Projected?

Returns are notoriously difficult to project. People have been calling market ceilings forever, starting heavily during 2011 in the current market cycle.

The problem with many of these predictions are the timing and the confidence of the claim. Certainly, a case can be made for stocks being overpriced but it is important to mind the difficulty in predicting the exact timing of a potential turn and the magnitude of the turn.

When projecting future returns for your portfolio, it is most appropriate to use a wide distribution of returns. You may have a strong belief about a mean return estimate (say, 7% a year over a series of years), but it is helpful to show how sensitive portfolios are to either different mean estimates (say, if the return drops to 4% on average) or to a series of very bad years.

This can be modeled using some of the traditional returns. For example, look at the chart up above, where 25% of annual returns were above 21.3% and another 25% were above 8.2%.

In short, doubt the certainty of any prediction within a tight timeframe. You can play around with annual returns in any 12 month rolling period with our S&P 500 Historical Return Calculator.

Cheers,

Cameron Daniels

Hurricane Harvey and Gas Prices: What is Price Gouging?

Economics  Personal Finance      by

Hurricane Harvey made landfall in Texas last Wednesday, dumping more than forty inches of rain on much of Southern Texas. Our hearts go out to those affected by the disaster and recommend donating to the Houston Food bank. In the aftermath of Harvey, we have seen great displays of courage and compassion as the humanitarian response has been overwhelmingly positive. One story that caught my eye, on the other hand, was news of a store mistakenly selling a case of water for $42. Understandably, the social media response was extremely negative, the company apologized and the pricing was reversed. It was clearly a breach of customer trust: how come the oil industry gets away with it easier?

Burning that Gasoline

Houston is an integral hub of the modern oil production and distribution network. Many refineries are located in the Houston area and oil distribution to much of the Atlantic coast has been interrupted. With this major hiccup in its production, gasoline prices have understandably increased around the US.

Prices have risen $0.35 nationwide since Hurricane Harvey

Gas prices nationwide have increased ~15% since Hurricane Harvey. Daily gasoline consumption is around 400MM barrels a day, this past week has cost American consumers an extra billion dollars. Considering this is a major portion of many Americans’ budgets, this is a nationwide paycut while we wait for the production to increase back to normal levels.

Why Isn’t Increasing Gas Prices ‘Price Gouging’?

The rules of supply and demand allow the price of a good to always settle where both the producer and consumer find the cost worthwhile. The producer always wants a higher price, the consumer always wants a lower price. When a price is artificially capped, however, then shortages generally arise. Tenants with rent control generally have worse maintenance. In 2015, Maker’s Mark whiskey ran into a shortage because they refused to raise price for some reason. Price is just a natural way to counteract either an over- or under-supply.

I find it very interesting why there is not an ethical outrage at the gasoline price increases but there is over the pack of water (or for life-saving medicine). Food, shelter and medicine are mandatory for survival but gasoline is pretty close for many Americans as well. Perhaps it is just the intent: there is nothing that happened besides disaster to increase the price of water. There is something that happened besides disaster to increase the price of gasoline: the shutdown of a few refineries and the hiccup in the distribution network. Perhaps the distinction is grey but one thing for certain is that oil companies would take any excuse to raise prices if they could.

Cheers,

Cameron Daniels

Clustering Illusion and Anchoring to Outliers

Economics  Offbeat      by

Humans have an evolutionary incentive to recognize patterns. The same mechanisms that helped early humans avoid snakes and other predators are still part of our constitution today. One of our in-built biases is the human tendency to see patterns where none exist. Clustering illusion is our bias towards under-predicting the variability of random – or near-random – data.

Clustering Illusion and Pattern Recognition

It’s not that patterns are wrong, or don’t exist. If you, for example, noticed a traffic stop backs up daily around the same time, you can save a significant amount of time on an alternate route.

Every part of our lives is subject to ingrained patterns and pre-existing habits that can benefit – or harm – us. Finding patterns where they exist can significantly help your happiness, dating life, finances, health, education, weight-loss, and many other things.

For all those reasons and more, it’s a good thing to be sensitive to patterns in life.

Of course… we may overdo it in many cases. Due to this inbuilt tendency, humans recognize patterns using data that has no underlying correlation. In these cases, we fool ourselves that there is a relationship where one does not exist.

Clustering Illusion Examples

Humans have a tendency to recognize patterns in frivolous details that don’t actually exist.

Take celebrity deaths. In the United States there is a meme that “Celebrity deaths happen in threes.”

Of course, celebrity deaths are (unfortunately) common. They’re so common, in fact, we almost become numb to them – it’s only when there is a cluster that we remember, and start to put together spurious conclusions.

(From Wikipedia) Randomly distributed points on a scatter plot

The hot-hand fallacy and the gambler’s fallacy are two closely linked fallacies. In both, you make predictive or causative assumptions from what are, effectively, random events.

With the gambler’s fallacy, you believe past random events can predict future future random events. For example, if you roll black eight times straight in roulette, you might believe the next one will be black again. Truth is, the next roll is completely divorced from the history of the previous eight.

Countering the Fallacies

How do you counteract these fallacies?

Always maintain a healthy dose of skepticism. You should assume that, generally, there is no correlation between two events. While you might change that view, make sure the evidence is robust – be rigorous!

Since you don’t physically have the time to research every subject this is quite the lift. Finding the balance between a healthy skepticism and your pattern recognition is hard, but it’s something you’ll want to be mindful you’re doing.

Finding No Relationship Can Help Defeat Clustering Illusion

Some websites famously point to fake correlations in a humorous way. Today we’re going to sing the praises of finding no evidence.

Truth is, we should equally be excited about not finding a relationship between events.

(That’s not very sexy though. Imagine the headline “Data Still Noisy on Caffeine’s Impact on Anxiety”. )

The other issue: remaining skeptical might make you appear non-committal or lack confidence. Sometimes, when you are skeptical of negative information, denial looks like the Pollyanna principle or the Ostrich effect.

The best thing you can do? Be skeptical, yet be flexible in your assumptions. Don’t ever be too committed to one way of thought.

As the saying goes: have strong opinions, loosely held.

Anchoring and Adjusting to Numbers

Economics  Personal Finance      by

Anchoring or the Anchoring Bias is the human tendency to evaluate or “anchor” numbers based on their presentation. 

Observers and participants are usually swayed by the availability or recency of a number.  The human tendency towards anchoring bias is often exploited to make subsequent facts or numbers appear in a certain light.

Beware the Anchoring Bias – Question the Numbers

The easiest way to evaluate the anchoring bias is to watch or observe its effect through an example. Anchoring works best without historical perspective, so we’ll look at a place where numbers often come sans context: finance and economics news.

Anchoring Bias in a Job Report

Anchoring Bias: Selling a Ferrari
This car is worth $400,000! For you, I’ll part with it for $300,000 though.

Let’s say that there is a new jobs release from the BLS, reporting that 138,000 jobs were added.  An enterprising financial writer picks it up and writes a post with the headline “138,000 Jobs Were Added in Stunning Month!”.

Writers often use this technique to drive home stunning(!!!) facts that shield the inherent fluctuation in the data.

It could be malicious or unintentional but it’s certainly inevitable; people consume so much information they don’t have the time to properly frame an entire problem. Without knowing some key pieces of context (past months, average months, how quickly the workforce is growing) you’ll tend to agree with the author.

Beware Your Anchoring Bias: Question any Numbers

Anchoring is so persistent because of how much information there is to process. It is simply impossible to have the time to know all the details of every story.

Sometimes information is boring… and boring often doesn’t make it to a headline.

Not everybody knows the history of the federal funds rate, the number of jobs added per month, the detailed history of the economy. 

When we read we rely on others to process and contextualize that information for us. A malicious or bad writer, instead, will spin the numbers – making them murky, muddy, volatile, noisy… and difficult to parse. 

Sometimes information is boring… and boring often doesn’t make it to a headline.

This bias reveals itself in many aspects of reporting. On this site we like to point out economic and finance reporting bias. But that’s just our expertise and interest – Michael Crichton’s famous Gell-Mann amnesia theorem hints at the iceberg of misinformation in the news.

Most every slightly contentious article has layers of manipulations and assumptions in interpreting and reporting the data

It’s often difficult to understand or trust anything besides a primary source… and those have their own biases! Financial reporting often includes some form of “continues winning streak” or “lowest point since” because financial data is so volatile. There are so many datasets out there there will certainly be thousands of spurious correlations.

What is the Solution to Anchoring Bias?

The only “solution” to the anchoring bias is knowing every report only reveals one small aspect of the truth. Your confidence in a conclusion should increase only as the number of primary sources and witnesses buttressing it increase. 

Take the infamous employment example above… if the government suddenly reports a spike to 30% unemployment, you’ll have to weigh it against other data and your own experience.

Of course, the other important solution is a healthy dose of humility and an open mind. You may have been lead to believe a certain thing which was purposefully vague or misleading. 

If evidence later comes out overturning it? Raise anchor, friends!

Alas, we don’t have the time to re-evaluate all of our conclusions. It’s more important to know that you can be easily mislead – and anchoring is one of the common ways it happens.

What is the Dunning-Kruger Effect?

Economics      by

The Dunning-Kruger effect refers to the phenomena where people of low ability have a higher self-perception of their abilities than those of high ability. This has been reproduced in laboratory sessions time after time. Human self-assessments are highly flawed.

And this effect has massive social implications: confidence matters in all spheres of life, including job interviews, running for office, and dating. Humans tend to naturally follow the most confident among us – to our great detriment.

Why Does the Dunning-Kruger Effect Happen?

Dunning-Kruger: The more you know, the more you realize there is to know.
Dunning-Kruger: The more you know, the more you realize there is to know.

The most interesting aspect about the Dunning-Kruger effect is why it crops up. People with low ability do not know what they do not know.

The more knowledge we gain, the more we understand the uncertainty involved in learning… and lose our sense of confidence.Therefore, the only people who retain a large amount of confidence in the face of uncertainty are those who know the least about the topic.

Dunning-Kruger and its Effects on Self-Assessments

Why do we hold on to our false self assessments?

There are a lot of forces at work to encourage positive esteem in our skills and abilities. Traits such as driving skill (or sense of humor, compassion, etc.) are some great examples. Everybody is an above average driver. Those of us who are bad drivers do not have the ability to discern what makes a bad driver.

The more you learn about a subject or a talent, the more you realize the difficulty of the topic – the rabbit hole gets deeper. Very often, the most knowledgeable people in a subject will be the first to recognize their uncertainty with the data. Skilled practitioners recognize their tenuous grasp on evolving knowledge.

Why bother with caveats when you can state something authoritatively? Why consider nuance when you can cut to the chase?

Signaling Effect and Interviewing

We present many signals when interviewing for a job or date or meeting or trip or… well, any social interaction.

From the way we dress to the words we choose, we constantly send subtle signals that reveal parts of our past and background. Onereason we do this is simply to save time. We make many snap judgements about others on a day to day basis, and they are making similar judgements about us

Think of the interview process. Often, top candidates will lose confidence because they’ve seen somebody who can sell their own skills in a better way.

The root of the word conman is confidence. When thinking about the Dunning-Kruger Effect, you’ll want to really evaluate whether the person you’re interacting with is an expert… or just a confident beginner. Confidence isn’t neccessarily evidence of skill.

“One of the painful things about our time is that those who feel certainty are stupid, and those with any imagination and understanding are filled with doubt and indecision.”

Bertrand Russell

Price Weighting Doesn’t Make Sense for Stock Indices

Investing      by

The Dow Jones Industrial Average is composed of 30 stocks meant to “represent” the industrial health of American companies. Some of the companies included on the list are household consumables, such as McDonalds, Nike and Coca-Cola. Other sectors include technology companies such as Microsoft and Apple and financial services, including American Express, JPMorgan, Visa and Goldman Sachs. There is a lot of historical weight behind the index itself, often because of what it is meant to represent: stock trading and the financial sector. Only recently have investors been more inclined to use broader benchmarks such as the S&P 500 or Russell 2000 due to myriad reasons.

What is Price-Weighting?

The index is calculated by taking the simple sum of all the prices and dividing by a special number known as the Dow Divisor (which, trivia fact, is 0.14602128 as of 5/8/17). This number started off as 30 (to represent the typical company) but changes frequently due to changes in the index. It changes due to stock splits and companies being swapped in and out of the index (something which happens relatively frequently, most recently in 2015 when Apple replaced AT&T.

The major alternative (used by most other stock indices) is known as market-weighting. This method takes the total market capitalization (number of shares * price) and divides it equally towards an index. Thus, the different in the number of shares has no impact on the company’s weight in the index, an issue that comes up with the Dow.

So What’s The Problem?

The major issue with price-weighting is that it gives undue weight to companies with fewer shares, simply for the sake that it inflates their share price. Listed below are the relative weights of a few components of the Dow. Listed next to them is the impact they would have on an index given whether it’s calculated using price-weighting or market-weighting.

CompanyPrice-WeightMarket WeightDelta Impact
GS7.40%1.49%5.90%
MMM6.50%1.99%4.51%
BA6.03%1.86%4.17%
TRV3.95%0.56%3.39%
UNH5.68%2.80%2.88%
MCD4.69%1.96%2.73%
IBM5.05%2.43%2.62%
CSCO1.12%2.87%-1.75%
PFE1.09%3.33%-2.24%
JPM2.84%5.15%-2.32%
XOM2.67%5.80%-3.13%
GE0.95%4.26%-3.31%
MSFT2.25%8.89%-6.64%
AAPL4.86%12.95%-8.09%

Errors in Analysis

The most important takeaway is that analysis errors will naturally arise when focusing on only one index (especially one with a dubious pricing mechanism). Very often financial headlines and news aggregators drive their summary based on the final result of the day. Notice the frequent use of the word ‘as’ when important data comes out (“Stocks mixed as Fed signals rate hike”, “Investors mull consumer confidence data”, “Stocks Gain Ahead of Jobs Report”). These events often tie back to whether the day was good or bad. All the events retrospectively are great when the day was a bull and bad when the day was a bear.

DJIA’s correlation with S&P is driven by high # of share performers (like GS, MMM, BA)

The example I selected on the right is from a few weeks ago where one of the misplaced large companies (IBM) performed way worse than the market. As you can see, the DJIA significantly lagged the S&P 500. The headlines coming out of the day were generally negative and included many references to shaken confidence. Perhaps the entire national financial headline could have been summarized with “IBM was Sold”.

Cheers,

Cameron Daniels

Never miss another post: