Mainstream economics haven't noticed.

What come to mind when I say the word "economics"?

Probably GDP, unemployment, and inflation.

If you've ever watched the news or listened to an economist, they'll use data like these to defend a political goal.

The issue is that these statistics are total bunk.

I'm being provocative, but I mean it:

• The economy is not measured by GDP.

• How many people are unemployed is not counted in the unemployment rate.

• Inflation is not measured by the CPI.

All orthodox economists' major economic statistics are either wrong or falsified.

Government institutions create all these stats. The administration wants to reassure citizens the economy is doing well.

GDP does not reflect economic expansion.

GDP measures a country's economic size and growth. It’s calculated by the BEA, a government agency.

The US has the world's largest (self-reported) GDP, growing 2-3% annually.

If GDP rises, the economy is healthy, say economists.

Why is the GDP flawed?

GDP measures a country's yearly spending.

The government may adjust this to make the economy look good.

GDP = C + G + I + NX

C = Consumer Spending

G = Government Spending

I = Investments (Equipment, inventories, housing, etc.)

NX = Exports minus Imports

GDP is a country's annual spending.

The government can print money to boost GDP. The government has a motive to increase and manage GDP.

Because government expenditure is part of GDP, printing money and spending it on anything will raise GDP.

They've done this. Since 1950, US government spending has grown 8% annually, faster than GDP.

In 2022, government spending accounted for 44% of GDP. It's the highest since WWII. In 1790-1910, it was 3% of GDP.

Who cares?

The economy isn't only spending. Focus on citizens' purchasing power or quality of life.

Since GDP just measures spending, the government can print money to boost GDP.

Even if Americans are poorer than last year, economists can say GDP is up and everything is fine.

How many people are unemployed is not counted in the unemployment rate.

The unemployment rate measures a country's labor market. If unemployment is high, people aren't doing well economically.

The BLS estimates the (self-reported) unemployment rate as 3-4%.

Why is the unemployment rate so high?

The US government surveys 100k persons to measure unemployment. They extrapolate this data for the country.

They come into 3 categories:

• Employed

People with jobs are employed … duh.

• Unemployed

People who are “jobless, looking for a job, and available for work” are unemployed

• Not in the labor force

The “labor force” is the employed + the unemployed.

The unemployment rate is the percentage of unemployed workers.

Problem is unemployed definition. You must actively seek work to be considered unemployed.

You're no longer unemployed if you haven't interviewed in 4 weeks.

This shit makes no goddamn sense.

Why does this matter?

You can't interview if there are no positions available. You're no longer unemployed after 4 weeks.

In 1994, the BLS redefined "unemployed" to exclude discouraged workers.

If you haven't interviewed in 4 weeks, you're no longer counted in the unemployment rate.

If unemployment were measured by total unemployed, it would be 25%.

Because the government wants to keep the unemployment rate low, they modify the definition.

If every US resident was unemployed and had no job interviews, economists would declare 0% unemployment. Excellent!

Inflation is not measured by the CPI.

The BLS measures CPI. This month was the highest since 1981.

CPI measures the cost of a basket of products across time. Food, energy, shelter, and clothes are included.

A 9.1% CPI means the basket of items is 9.1% more expensive.

What is the CPI problem?

Here's a more detailed explanation of CPI's flaws.

In summary, CPI is manipulated to be understated.

Housing costs are understated to manipulate CPI. Housing accounts for 33% of the CPI because it's the biggest expense for most people.

This signifies it's the biggest CPI weight.

Rather than using actual house prices, the Bureau of Labor Statistics essentially makes shit up. You can read more about the process here.

Surprise! It’s bullshit

The BLS stated Shelter's price rose 5.5% this month.

House prices are up 11-21%. (Source 1, Source 2, Source 3)

Rents are up 14-26%. (Source 1, Source 2)

Why is this important?

If CPI included housing prices, it would be 12-15 percent this month, not 9.1 percent.

9% inflation is nuts. Your money's value halves every 7 years at 9% inflation.

Worse is 15% inflation. Your money halves every 4 years at 15% inflation.

If everyone realized they needed to double their wage every 4-5 years to stay wealthy, there would be riots.

Inflation drains our money's value so the government can keep printing it.

The Solution

Most individuals know the existing system doesn't work, but can't explain why.

People work hard yet lag behind. The government lies about the economy's data.

In reality:

• GDP has been down since 2008

• 25% of Americans are unemployed

• Inflation is actually 15%

People might join together to vote out kleptocratic politicians if they knew the reality.

Having reliable economic data is the first step.

People can't understand the situation without sufficient information. Instead of immigrants or billionaires, people would blame liar politicians.

Here’s the vision:

A decentralized, transparent, and global dashboard that tracks economic data like GDP, unemployment, and inflation for every country on Earth.

Government incentives influence economic statistics.

ShadowStats has already started this effort, but the calculations must be transparent, decentralized, and global to be effective.

If interested, email me at trevorstark02@gmail.com.

Here are some links to further your research:

1. MIT Billion Prices Project

2. 1729 Decentralized Inflation Dashboard Project

3. Balaji Srinivasan on “Fiat Information VS. Crypto Information”

DATA MINING WITH SUPERALGORES

Old pots produce the best soup.

Science has always inspired indicator design. From physics to signal processing, many indicators use concepts from mechanical engineering, electronics, and probability. In Superalgos' Data Mining section, we've explored using thermodynamics and information theory to construct indicators and using statistical and probabilistic techniques like reduced normal law to take advantage of low probability events.

An asset's price is like a mechanical object revolving around its moving average. Using this approach, we could design an indicator using the oscillator's Total Energy. An oscillator's energy is finite and constant. Since we don't expect the price to follow the harmonic oscillator, this energy should deviate from the perfect situation, and the maximum of divergence may provide us valuable information on the price's moving average.

Definition of the Harmonic Oscillator in Few Words

Sinusoidal function describes a harmonic oscillator. The time-constant energy equation for a harmonic oscillator is:

With

Time saves energy.

In a mechanical harmonic oscillator, total energy equals kinetic energy plus potential energy. The formula for energy is the same for every kind of harmonic oscillator; only the terms of total energy must be adapted to fit the relevant units. Each oscillator has a velocity component (kinetic energy) and a position to equilibrium component (potential energy).

The Price Oscillator and the Energy Formula

Considering the harmonic oscillator definition, we must specify kinetic and potential components for our price oscillator. We define oscillator velocity as the rate of change and equilibrium position as the price's distance from its moving average.

Price kinetic energy:

It's like:

With

and

L is the number of periods for the rate of change calculation and P for the close price EMA calculation.

Total price oscillator energy =

Given that an asset's price can theoretically vary at a limitless speed and be endlessly far from its moving average, we don't expect this formula's outcome to be constrained. We'll normalize it using Z-Score for convenience of usage and readability, which also allows probabilistic interpretation.

Over 20 periods, we'll calculate E's moving average and standard deviation.

We calculated Z on BTC/USDT with L = 10 and P = 21 using Knime Analytics.

The graph is detrended. We added two horizontal lines at +/- 1.6 to construct a 94.5% probability zone based on reduced normal law tables. Price cycles to its moving average oscillate clearly. Red and green arrows illustrate where the oscillator crosses the top and lower limits, corresponding to the maximum/minimum price oscillation. Since the results seem noisy, we may apply a non-lagging low-pass or multipole filter like Butterworth or Laguerre filters and employ dynamic bands at a multiple of Z's standard deviation instead of fixed levels.

Kinetic Detrender Implementation in Superalgos

The Superalgos Kinetic detrender features fixed upper and lower levels and dynamic volatility bands.

The code is pretty basic and does not require a huge amount of code lines.

It starts with the standard definitions of the candle pointer and the constant declaration :

let candle = record.current
let len = 10
let P = 21
let T = 20
let up = 1.6
let low = 1.6

Upper and lower dynamic volatility band constants are up and low.

We proceed to the initialization of the previous value for EMA :

if (variable.prevEMA === undefined) {
    variable.prevEMA = candle.close
}

And the calculation of EMA with a function (it is worth noticing the function is declared at the end of the code snippet in Superalgos) :

variable.ema = calculateEMA(P, candle.close, variable.prevEMA)
//EMA calculation
function calculateEMA(periods, price, previousEMA) {
    let k = 2 / (periods + 1)
    return price * k + previousEMA * (1 - k)
}

The rate of change is calculated by first storing the right amount of close price values and proceeding to the calculation by dividing the current close price by the first member of the close price array:

variable.allClose.push(candle.close)
if (variable.allClose.length > len) {
    variable.allClose.splice(0, 1)
}
if (variable.allClose.length === len) {
    variable.roc = candle.close / variable.allClose[0]
} else {
    variable.roc = 1
}

Finally, we get energy with a single line:

variable.E = 1 / 2 * len * variable.roc + 1 / 2 * P * candle.close / variable.ema

The Z calculation reuses code from Z-Normalization-based indicators:

variable.allE.push(variable.E)
if (variable.allE.length > T) {
    variable.allE.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allE.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allE[i]
    }
    variable.MA = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allE[i] - variable.MA, 2)
    }
    variable.sigma = Math.sqrt(variable.SQ / T)
variable.Z = (variable.E - variable.MA) / variable.sigma
} else {
    variable.Z = 0
}
variable.allZ.push(variable.Z)
if (variable.allZ.length > T) {
    variable.allZ.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allZ.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allZ[i]
    }
    variable.MAZ = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allZ[i] - variable.MAZ, 2)
    }
    variable.sigZ = Math.sqrt(variable.SQ / T)
} else {
    variable.MAZ = variable.Z
    variable.sigZ = variable.MAZ * 0.02
}
variable.upper = variable.MAZ + up * variable.sigZ
variable.lower = variable.MAZ - low * variable.sigZ

We also update the EMA value.

variable.prevEMA = variable.EMA

Conclusion

We showed how to build a detrended oscillator using simple harmonic oscillator theory. Kinetic detrender's main line oscillates between 2 fixed levels framing 95% of the values and 2 dynamic levels, leading to auto-adaptive mean reversion zones.

Superalgos' Normalized Momentum data mine has the Kinetic detrender indication.

All the material here can be reused and integrated freely by linking to this article and Superalgos.

This post is informative and not financial advice. Seek expert counsel before trading. Risk using this material.

Bear markets don't last forever, but that's hard to remember. Jamie Cullen's illustration

A bear market is a 20% decline from peak to trough in stock prices.

The S&P 500 was down 24% from its January highs at its low point this year. Bear market.

The U.S. stock market has had 13 bear markets since WWII (including the current one). Previous 12 bear markets averaged –32.7% losses. From peak to trough, the stock market averaged 12 months. The average time from bottom to peak was 21 months.

In the past seven decades, a bear market roundtrip to breakeven has averaged less than three years.

Long-term averages can vary widely, as with all historical market data. Investors can learn from past market crashes.

Historical bear markets offer lessons.

Bear market duration

A bear market can cost investors money and time. Most of the pain comes from stock market declines, but bear markets can be long.

Here are the longest U.S. stock bear markets since World war 2:

Stock market crashes can make it difficult to break even. After the 2008 financial crisis, the stock market took 4.5 years to recover. After the dotcom bubble burst, it took seven years to break even.

The longer you're underwater in the market, the more suffering you'll experience, according to research. Suffering can lead to selling at the wrong time.

Bear markets require patience because stocks can take a long time to recover.

Stock crash recovery

Bear markets can end quickly. The Corona Crash in early 2020 is an example.

The S&P 500 fell 34% in 23 trading sessions, the fastest bear market from a high in 90 years. The entire crash lasted one month. Stocks broke even six months after bottoming. Stocks rose 100% from those lows in 15 months.

Seven bear markets have lasted two years or less since 1945.

The 2020 recovery was an outlier, but four other bear markets have made investors whole within 18 months.

During a bear market, you don't know if it will end quickly or feel like death by a thousand cuts.

Recessions vs. bear markets

Many people believe the U.S. economy is in or heading for a recession.

I agree. Four-decade high inflation. Since 1945, inflation has exceeded 5% nine times. Each inflationary spike caused a recession. Only slowing economic demand seems to stop price spikes.

This could happen again. Stocks seem to be pricing in a recession.

Recessions almost always cause a bear market, but a bear market doesn't always equal a recession. In 1946, the stock market fell 27% without a recession in sight. Without an economic slowdown, the stock market fell 22% in 1966. Black Monday in 1987 was the most famous stock market crash without a recession. Stocks fell 30% in less than a week. Many believed the stock market signaled a depression. The crash caused no slowdown.

Economic cycles are hard to predict. Even Wall Street makes mistakes.

Bears vs. bulls

Bear markets for U.S. stocks always end. Every stock market crash in U.S. history has been followed by new all-time highs.

How should investors view the recession? Investing risk is subjective.

You don't have as long to wait out a bear market if you're retired or nearing retirement. Diversification and liquidity help investors with limited time or income. Cash and short-term bonds drag down long-term returns but can ensure short-term spending.

Young people with years or decades ahead of them should view this bear market as an opportunity. Stock market crashes are good for net savers in the future. They let you buy cheap stocks with high dividend yields.

You need discipline, patience, and planning to buy stocks when it doesn't feel right.

Bear markets aren't fun because no one likes seeing their portfolio fall. But stock market downturns are a feature, not a bug. If stocks never crashed, they wouldn't offer such great long-term returns.

It's ok and even desired to be unprofitable if you're gaining revenue at a reasonable cost and have 100%+ net dollar retention, meaning you never lose customers and expand them. To estimate the acceptable cost of new SaaS revenue, we compare new revenue to operating loss and payback period. If you pay back the customer acquisition cost in 1.5 years and never lose them (100%+ NDR), you're doing well.

To evaluate payback period, we compared new revenue to net operating loss for the last 73 SaaS companies to IPO since October 2017. (55 out of 73). Here's the data. 1/(new revenue/operating loss) equals payback period. New revenue/operating loss equals cost of new revenue.

Payback averages a year. 55 SaaS companies that weren't profitable at IPO got a 1-year payback. Outstanding. If you pay for a customer in a year and never lose them (100%+ NDR), you're establishing a valuable business. The average was 1.3 years, which is within the 1.5-year range.

New revenue costs $0.96 on average. These SaaS companies lost $0.96 every $1 of new revenue last year. Again, impressive. Average new revenue per operating loss was $1.59.

Loss-in-operations definition. Operating loss revenue COGS S&M R&D G&A (technical point: be sure to use the absolute value of operating loss). It's wrong to only consider S&M costs and ignore other business costs. Operating loss and new revenue are measured over one year to eliminate seasonality.

Operating losses are desirable if you never lose a customer and have a quick payback period, especially when SaaS enterprises are valued on ARR. The payback period should be under 1.5 years, the cost of new income < $1, and net dollar retention 100%.

Apple's latest revelation may shock iPod fans and former owners.

Apple discontinued the iPod touch on May 11, 2022. After 21 years, Apple killed the last surviving iPod, a device Steve Jobs believed would revolutionize the music industry.

Jobs was used to making bold predictions, but few expected Apple's digital music player to change the music industry. It did.

This chaos created new business opportunities. Spotify, YouTube, and Amazon are products of that chaotic era.

As the digital landscape changes, so do consumers, and the iPod has lost favor. I'm sure Apple realizes the importance of removing an icon. The iPod was Apple like the Mac and iPhone. I think it's bold to retire such a key Apple cornerstone. What would Jobs do?

iPod evolution across the ages

Here's an iPod family tree for all you enthusiasts.

iPod vintage (Oct 2001 to Sep 2014, 6 generations)

The original iPod had six significant upgrades since 2001. Apple announced an 80 GB ($249) and 160 GB ($349) iPod classic in 2007.

Apple updated the 80 GB model with a 120 GB device in September 2008. Apple upgraded the 120 GB model with a 160 GB variant a year later (2009). This was the last iteration, and Apple discontinued the classic in September 2014.

iPod nano (Jan 2004 to Sep 2005, 2 generations)

Apple debuted a smaller, brightly-colored iPod in 2004. The first model featured 4 GB, enough for 1,000 songs.

Apple produced a new 4 GB or 6 GB iPod mini in February 2005 and discontinued it in September when they released a better-looking iPod nano.

iTouch nano (Sep 2005 to July 2017, 7 generations)

I loved the iPod nano. It was tiny and elegant with enough tech to please most music aficionados, unless you carry around your complete music collection.

Apple owed much of the iPod nano's small form and success to solid-state flash memory. Flash memory doesn't need power because it has no moving parts. This makes the iPod nano more durable than the iPod classic and mini, which employ hard drives.

Apple manufactured seven generations of the iPod nano, improving its design, display screen, memory, battery, and software, but abandoned it in July 2017 due to dwindling demand.

Shuffle iPod (Jan 2005 to Jul 2017, 4 generations)

The iPod shuffle was entry-level. It was a simple, lightweight, tiny music player. The iPod shuffle was perfect for lengthy bike trips, runs, and hikes.

Apple sold 10 million iPod shuffles in the first year and kept making them for 12 years, through four significant modifications.

iOS device (Sep 2007 to May 2022, 7 generations)

The iPod touch's bigger touchscreen interface made it a curious addition to the iPod family. The iPod touch resembled an iPhone more than the other iPods, making them hard to tell apart.

Many were dissatisfied that Apple removed functionality from the iPod touch to avoid making it too similar to the iPhone. Seven design improvements over 15 years brought the iPod touch closer to the iPhone, but not completely.

The iPod touch uses the same iOS operating system as the iPhone, giving it access to many apps, including handheld games.

The iPod touch's long production run is due to the next generation of music-loving gamers.

What made the iPod cool

iPod revolutionized music listening. It was the first device to store and play MP3 music, allowing you to carry over 1,000 songs anywhere.

The iPod changed consumer electronics with its scroll wheel and touchscreen. Jobs valued form and function equally. He showed people that a product must look good to inspire an emotional response and ignite passion.

The elegant, tiny iPod was a tremendous sensation when it arrived for $399 in October 2001. Even at this price, it became a must-have for teens to CEOs.

It's hard to identify any technology that changed how music was downloaded and played like the iPod. Apple iPod and iTunes had 63% of the paid music download market in the fourth quarter of 2012.

The demise of the iPod was inevitable

Apple discontinuing the iPod touch after 21 years is sad. This ends a 00s music icon.

Jobs was a genius at anticipating market needs and opportunities, and Apple launched the iPod at the correct time.

Few consumer electronics items have had such a lasting impact on music lovers and the music industry as the iPod.

Smartphones and social media have contributed to the iPod's decline. Instead of moving to the music, the new generation of consumers is focused on social media. They're no longer passive content consumers; they're active content creators seeking likes and followers. Here, the smartphone has replaced the iPod.

It's hard not to feel a feeling of loss, another part of my adolescence now forgotten by the following generation.

So, if you’re lucky enough to have a working iPod, hang on to that relic and enjoy the music and the nostalgia.

Yes, client meetings are difficult. Especially when I'm alone.

Clients must tell us their problems so we can help.

In-meeting challenges contribute nothing to our work. Consider this:

• Clients are unprepared.

• Clients are distracted.

• Clients are confused.

Introducing Jack Dorsey's Google Doc approach

I endorse his approach to meetings.

Not Google Doc-related. Jack uses it for meetings.

This is what his meetings look like.

• Prior to the meeting, the Chair creates the agenda, structure, and information using Google Doc.

• Participants in the meeting would have 5-10 minutes to read the Google Doc.

• They have 5-10 minutes to type their comments on the document.

• In-depth discussion begins

There is elegance in simplicity. Here's how Jack's approach is fantastic.

Unprepared clients are given time to read.

During the meeting, they think and work on it.

They can see real-time remarks from others.

Discussion ensues.

Three months ago, I fell for this strategy. After trying it with a client, I got good results.

I conducted social control experiments in a few client workshops.

Context matters.

I am sure Jack Dorsey’s method works well in meetings. What about client workshops?

So, I tested Enterprise of the Future with a consulting client.

I sent multiple emails to client stakeholders describing the new approach.

No PowerPoints that day. I spent the night setting up the Google Doc with conversation topics, critical thinking questions, and a Before and After section.

The client was shocked. First, a Google Doc was projected. Second surprise was a verbal feedback.

“No pre-meeting materials?”

“Don’t worry. I know you are not reading it before our meeting, anyway.”

We laughed. The experiment started.

Observations throughout a 90-minute engagement workshop from beginning to end

For 10 minutes, the workshop was silent.

People read the Google Doc. For some, the silence was unnerving.

“Are you not going to present anything to us?”

I said everything's in Google Doc. I asked them to read, remark, and add relevant paragraphs.

As they unlocked their laptops, they were annoyed.

Ten client stakeholders are typing on the Google Doc. My laptop displays comment bubbles, red lines, new paragraphs, and strikethroughs.

The first 10 minutes were productive. Everyone has seen and contributed to the document.

I was silent.

The move to a classical workshop was smooth. I didn't stimulate dialogue. They did.

Stephanie asked Joe why a blended workforce hinders company productivity. She questioned his comments and additional paragraphs.

That is when a light bulb hit my head. Yes, you want to speak to the right person to resolve issues!

Not only that was discussed. Others discussed their remark bubbles with neighbors. Debate circles sprung up one after the other.

The best part? I asked everyone to add their post-discussion thoughts on a Google Doc.

After the workshop, I have:

• An agreement-based working document

• A post-discussion minutes that are prepared for publication

• A record of the discussion points that were brought up, argued, and evaluated critically

It showed me how stakeholders viewed their Enterprise of the Future. It allowed me to align with them.

Finale Keynotes

Client meetings are a hit-or-miss. I know that.

Jack Dorsey's meeting strategy works for consulting. It promotes session alignment.

It relieves clients of preparation.

I get the necessary information to advance this consulting engagement.

It is brilliant.