More on Economics & Investing

Ray Dalio
3 years ago
The latest “bubble indicator” readings.
As you know, I like to turn my intuition into decision rules (principles) that can be back-tested and automated to create a portfolio of alpha bets. I use one for bubbles. Having seen many bubbles in my 50+ years of investing, I described what makes a bubble and how to identify them in markets—not just stocks.
A bubble market has a high degree of the following:
- High prices compared to traditional values (e.g., by taking the present value of their cash flows for the duration of the asset and comparing it with their interest rates).
- Conditons incompatible with long-term growth (e.g., extrapolating past revenue and earnings growth rates late in the cycle).
- Many new and inexperienced buyers were drawn in by the perceived hot market.
- Broad bullish sentiment.
- Debt financing a large portion of purchases.
- Lots of forward and speculative purchases to profit from price rises (e.g., inventories that are more than needed, contracted forward purchases, etc.).
I use these criteria to assess all markets for bubbles. I have periodically shown you these for stocks and the stock market.
What Was Shown in January Versus Now
I will first describe the picture in words, then show it in charts, and compare it to the last update in January.
As of January, the bubble indicator showed that a) the US equity market was in a moderate bubble, but not an extreme one (ie., 70 percent of way toward the highest bubble, which occurred in the late 1990s and late 1920s), and b) the emerging tech companies (ie. As well, the unprecedented flood of liquidity post-COVID financed other bubbly behavior (e.g. SPACs, IPO boom, big pickup in options activity), making things bubbly. I showed which stocks were in bubbles and created an index of those stocks, which I call “bubble stocks.”
Those bubble stocks have popped. They fell by a third last year, while the S&P 500 remained flat. In light of these and other market developments, it is not necessarily true that now is a good time to buy emerging tech stocks.
The fact that they aren't at a bubble extreme doesn't mean they are safe or that it's a good time to get long. Our metrics still show that US stocks are overvalued. Once popped, bubbles tend to overcorrect to the downside rather than settle at “normal” prices.
The following charts paint the picture. The first shows the US equity market bubble gauge/indicator going back to 1900, currently at the 40% percentile. The charts also zoom in on the gauge in recent years, as well as the late 1920s and late 1990s bubbles (during both of these cases the gauge reached 100 percent ).
The chart below depicts the average bubble gauge for the most bubbly companies in 2020. Those readings are down significantly.
The charts below compare the performance of a basket of emerging tech bubble stocks to the S&P 500. Prices have fallen noticeably, giving up most of their post-COVID gains.
The following charts show the price action of the bubble slice today and in the 1920s and 1990s. These charts show the same market dynamics and two key indicators. These are just two examples of how a lot of debt financing stock ownership coupled with a tightening typically leads to a bubble popping.
Everything driving the bubbles in this market segment is classic—the same drivers that drove the 1920s bubble and the 1990s bubble. For instance, in the last couple months, it was how tightening can act to prick the bubble. Review this case study of the 1920s stock bubble (starting on page 49) from my book Principles for Navigating Big Debt Crises to grasp these dynamics.
The following charts show the components of the US stock market bubble gauge. Since this is a proprietary indicator, I will only show you some of the sub-aggregate readings and some indicators.
Each of these six influences is measured using a number of stats. This is how I approach the stock market. These gauges are combined into aggregate indices by security and then for the market as a whole. The table below shows the current readings of these US equity market indicators. It compares current conditions for US equities to historical conditions. These readings suggest that we’re out of a bubble.
1. How High Are Prices Relatively?
This price gauge for US equities is currently around the 50th percentile.
2. Is price reduction unsustainable?
This measure calculates the earnings growth rate required to outperform bonds. This is calculated by adding up the readings of individual securities. This indicator is currently near the 60th percentile for the overall market, higher than some of our other readings. Profit growth discounted in stocks remains high.
Even more so in the US software sector. Analysts' earnings growth expectations for this sector have slowed, but remain high historically. P/Es have reversed COVID gains but remain high historical.
3. How many new buyers (i.e., non-existing buyers) entered the market?
Expansion of new entrants is often indicative of a bubble. According to historical accounts, this was true in the 1990s equity bubble and the 1929 bubble (though our data for this and other gauges doesn't go back that far). A flood of new retail investors into popular stocks, which by other measures appeared to be in a bubble, pushed this gauge above the 90% mark in 2020. The pace of retail activity in the markets has recently slowed to pre-COVID levels.
4. How Broadly Bullish Is Sentiment?
The more people who have invested, the less resources they have to keep investing, and the more likely they are to sell. Market sentiment is now significantly negative.
5. Are Purchases Being Financed by High Leverage?
Leveraged purchases weaken the buying foundation and expose it to forced selling in a downturn. The leverage gauge, which considers option positions as a form of leverage, is now around the 50% mark.
6. To What Extent Have Buyers Made Exceptionally Extended Forward Purchases?
Looking at future purchases can help assess whether expectations have become overly optimistic. This indicator is particularly useful in commodity and real estate markets, where forward purchases are most obvious. In the equity markets, I look at indicators like capital expenditure, or how much businesses (and governments) invest in infrastructure, factories, etc. It reflects whether businesses are projecting future demand growth. Like other gauges, this one is at the 40th percentile.
What one does with it is a tactical choice. While the reversal has been significant, future earnings discounting remains high historically. In either case, bubbles tend to overcorrect (sell off more than the fundamentals suggest) rather than simply deflate. But I wanted to share these updated readings with you in light of recent market activity.

Sofien Kaabar, CFA
2 years ago
Innovative Trading Methods: The Catapult Indicator
Python Volatility-Based Catapult Indicator
As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.
Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.
The Foundation: Volatility
The Catapult predicts significant changes with the 21-period Relative Volatility Index.
The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.
Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.
Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:
As stated, standard deviation is:
# The function to add a number of columns inside an array
def adder(Data, times):
for i in range(1, times + 1):
new_col = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, new_col, axis = 1)
return Data
# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)
return Data
# The function to delete a number of rows from the beginning
def jump(Data, jump):
Data = Data[jump:, ]
return Data
# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)
# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)
# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)
# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file
def volatility(Data, lookback, what, where):
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
except IndexError:
pass
return Data
The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.
The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.
RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.
def ma(Data, lookback, close, where):
Data = adder(Data, 1)
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
# Cleaning
Data = jump(Data, lookback)
return Data
def ema(Data, alpha, lookback, what, where):
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
# First value is a simple SMA
Data = ma(Data, lookback, what, where)
# Calculating first EMA
Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)
# Calculating the rest of EMA
for i in range(lookback + 2, len(Data)):
try:
Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
except IndexError:
pass
return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
# Adding a few columns
Data = adder(Data, 7)
# Calculating Differences
for i in range(len(Data)):
Data[i, where] = Data[i, close] - Data[i - width, close]
# Calculating the Up and Down absolute values
for i in range(len(Data)):
if Data[i, where] > 0:
Data[i, where + 1] = Data[i, where]
elif Data[i, where] < 0:
Data[i, where + 2] = abs(Data[i, where])
# Calculating the Smoothed Moving Average on Up and Down
absolute values
lookback = (lookback * 2) - 1 # From exponential to smoothed
Data = ema(Data, 2, lookback, where + 1, where + 3)
Data = ema(Data, 2, lookback, where + 2, where + 4)
# Calculating the Relative Strength
Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
# Calculate the Relative Strength Index
Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))
# Cleaning
Data = deleter(Data, where, 6)
Data = jump(Data, lookback)
return Datadef relative_volatility_index(Data, lookback, close, where):
# Calculating Volatility
Data = volatility(Data, lookback, close, where)
# Calculating the RSI on Volatility
Data = rsi(Data, lookback, where, where + 1)
# Cleaning
Data = deleter(Data, where, 1)
return DataThe Arm Section: Speed
The Catapult predicts momentum direction using the 14-period Relative Strength Index.
As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:
A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.
When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.
Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.
The direction-finding filter in the frame
The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.
Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.
This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:
We defined the moving average function above. Create the Catapult indication now.
Indicator of the Catapult
The indicator is a healthy mix of the three indicators:
The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.
If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.
The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.
lookback_rvi = 21
lookback_rsi = 14
lookback_ma = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.
The chart below shows recent EURUSD hourly values.
def signal(Data, rvi_col, signal):
Data = adder(Data, 10)
for i in range(len(Data)):
if Data[i, rvi_col] < 30 and \
Data[i - 1, rvi_col] > 30 and \
Data[i - 2, rvi_col] > 30 and \
Data[i - 3, rvi_col] > 30 and \
Data[i - 4, rvi_col] > 30 and \
Data[i - 5, rvi_col] > 30:
Data[i, signal] = 1
return DataSignals are straightforward. The indicator can be utilized with other methods.
my_data = signal(my_data, 6, 7)Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation. Technical analysis will lose its reputation as subjective and unscientific.
After you find a trading method or approach, follow these steps:
Put emotions aside and adopt an analytical perspective.
Test it in the past in conditions and simulations taken from real life.
Try improving it and performing a forward test if you notice any possibility.
Transaction charges and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be included in your tests.
After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.
Sam Hickmann
3 years ago
What is headline inflation?
Headline inflation is the raw Consumer price index (CPI) reported monthly by the Bureau of labour statistics (BLS). CPI measures inflation by calculating the cost of a fixed basket of goods. The CPI uses a base year to index the current year's prices.
Explaining Inflation
As it includes all aspects of an economy that experience inflation, headline inflation is not adjusted to remove volatile figures. Headline inflation is often linked to cost-of-living changes, which is useful for consumers.
The headline figure doesn't account for seasonality or volatile food and energy prices, which are removed from the core CPI. Headline inflation is usually annualized, so a monthly headline figure of 4% inflation would equal 4% inflation for the year if repeated for 12 months. Top-line inflation is compared year-over-year.
Inflation's downsides
Inflation erodes future dollar values, can stifle economic growth, and can raise interest rates. Core inflation is often considered a better metric than headline inflation. Investors and economists use headline and core results to set growth forecasts and monetary policy.
Core Inflation
Core inflation removes volatile CPI components that can distort the headline number. Food and energy costs are commonly removed. Environmental shifts that affect crop growth can affect food prices outside of the economy. Political dissent can affect energy costs, such as oil production.
From 1957 to 2018, the U.S. averaged 3.64 percent core inflation. In June 1980, the rate reached 13.60%. May 1957 had 0% inflation. The Fed's core inflation target for 2022 is 3%.
Central bank:
A central bank has privileged control over a nation's or group's money and credit. Modern central banks are responsible for monetary policy and bank regulation. Central banks are anti-competitive and non-market-based. Many central banks are not government agencies and are therefore considered politically independent. Even if a central bank isn't government-owned, its privileges are protected by law. A central bank's legal monopoly status gives it the right to issue banknotes and cash. Private commercial banks can only issue demand deposits.
What are living costs?
The cost of living is the amount needed to cover housing, food, taxes, and healthcare in a certain place and time. Cost of living is used to compare the cost of living between cities and is tied to wages. If expenses are higher in a city like New York, salaries must be higher so people can live there.
What's U.S. bureau of labor statistics?
BLS collects and distributes economic and labor market data about the U.S. Its reports include the CPI and PPI, both important inflation measures.
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James Brockbank
3 years ago
Canonical URLs for Beginners
Canonicalization and canonical URLs are essential for SEO, and improper implementation can negatively impact your site's performance.
Canonical tags were introduced in 2009 to help webmasters with duplicate or similar content on multiple URLs.
To use canonical tags properly, you must understand their purpose, operation, and implementation.
Canonical URLs and Tags
Canonical tags tell search engines that a certain URL is a page's master copy. They specify a page's canonical URL. Webmasters can avoid duplicate content by linking to the "canonical" or "preferred" version of a page.
How are canonical tags and URLs different? Can these be specified differently?
Tags
Canonical tags are found in an HTML page's head></head> section.
<link rel="canonical" href="https://www.website.com/page/" />These can be self-referencing or reference another page's URL to consolidate signals.
Canonical tags and URLs are often used interchangeably, which is incorrect.
The rel="canonical" tag is the most common way to set canonical URLs, but it's not the only way.
Canonical URLs
What's a canonical link? Canonical link is the'master' URL for duplicate pages.
In Google's own words:
A canonical URL is the page Google thinks is most representative of duplicate pages on your site.
— Google Search Console Help
You can indicate your preferred canonical URL. For various reasons, Google may choose a different page than you.
When set correctly, the canonical URL is usually your specified URL.
Canonical URLs determine which page will be shown in search results (unless a duplicate is explicitly better for a user, like a mobile version).
Canonical URLs can be on different domains.
Other ways to specify canonical URLs
Canonical tags are the most common way to specify a canonical URL.
You can also set canonicals by:
Setting the HTTP header rel=canonical.
All pages listed in a sitemap are suggested as canonicals, but Google decides which pages are duplicates.
Redirects 301.
Google recommends these methods, but they aren't all appropriate for every situation, as we'll see below. Each has its own recommended uses.
Setting canonical URLs isn't required; if you don't, Google will use other signals to determine the best page version.
To control how your site appears in search engines and to avoid duplicate content issues, you should use canonicalization effectively.
Why Duplicate Content Exists
Before we discuss why you should use canonical URLs and how to specify them in popular CMSs, we must first explain why duplicate content exists. Nobody intentionally duplicates website content.
Content management systems create multiple URLs when you launch a page, have indexable versions of your site, or use dynamic URLs.
Assume the following URLs display the same content to a user:
A search engine sees eight duplicate pages, not one.
URLs #1 and #2: the CMS saves product URLs with and without the category name.
#3, #4, and #5 result from the site being accessible via HTTP, HTTPS, www, and non-www.
#6 is a subdomain mobile-friendly URL.
URL #7 lacks URL #2's trailing slash.
URL #8 uses a capital "A" instead of a lowercase one.
Duplicate content may also exist in URLs like:
https://www.website.com
https://www.website.com/index.php
Duplicate content is easy to create.
Canonical URLs help search engines identify different page variations as a single URL on many sites.
SEO Canonical URLs
Canonical URLs help you manage duplicate content that could affect site performance.
Canonical URLs are a technical SEO focus area for many reasons.
Specify URL for search results
When you set a canonical URL, you tell Google which page version to display.
Which would you click?
https://www.domain.com/page-1/
https://www.domain.com/index.php?id=2
First, probably.
Canonicals tell search engines which URL to rank.
Consolidate link signals on similar pages
When you have duplicate or nearly identical pages on your site, the URLs may get external links.
Canonical URLs consolidate multiple pages' link signals into a single URL.
This helps your site rank because signals from multiple URLs are consolidated into one.
Syndication management
Content is often syndicated to reach new audiences.
Canonical URLs consolidate ranking signals to prevent duplicate pages from ranking and ensure the original content ranks.
Avoid Googlebot duplicate page crawling
Canonical URLs ensure that Googlebot crawls your new pages rather than duplicated versions of the same one across mobile and desktop versions, for example.
Crawl budgets aren't an issue for most sites unless they have 100,000+ pages.
How to Correctly Implement the rel=canonical Tag
Using the header tag rel="canonical" is the most common way to specify canonical URLs.
Adding tags and HTML code may seem daunting if you're not a developer, but most CMS platforms allow canonicals out-of-the-box.
These URLs each have one product.
How to Correctly Implement a rel="canonical" HTTP Header
A rel="canonical" HTTP header can replace canonical tags.
This is how to implement a canonical URL for PDFs or non-HTML documents.
You can specify a canonical URL in your site's.htaccess file using the code below.
<Files "file-to-canonicalize.pdf"> Header add Link "< http://www.website.com/canonical-page/>; rel=\"canonical\"" </Files>301 redirects for canonical URLs
Google says 301 redirects can specify canonical URLs.
Only the canonical URL will exist if you use 301 redirects. This will redirect duplicates.
This is the best way to fix duplicate content across:
HTTPS and HTTP
Non-WWW and WWW
Trailing-Slash and Non-Trailing Slash URLs
On a single page, you should use canonical tags unless you can confidently delete and redirect the page.
Sitemaps' canonical URLs
Google assumes sitemap URLs are canonical, so don't include non-canonical URLs.
This does not guarantee canonical URLs, but is a best practice for sitemaps.
Best-practice Canonical Tag
Once you understand a few simple best practices for canonical tags, spotting and cleaning up duplicate content becomes much easier.
Always include:
One canonical URL per page
If you specify multiple canonical URLs per page, they will likely be ignored.
Correct Domain Protocol
If your site uses HTTPS, use this as the canonical URL. It's easy to reference the wrong protocol, so check for it to catch it early.
Trailing slash or non-trailing slash URLs
Be sure to include trailing slashes in your canonical URL if your site uses them.
Specify URLs other than WWW
Search engines see non-WWW and WWW URLs as duplicate pages, so use the correct one.
Absolute URLs
To ensure proper interpretation, canonical tags should use absolute URLs.
So use:
<link rel="canonical" href="https://www.website.com/page-a/" />And not:
<link rel="canonical" href="/page-a/" />If not canonicalizing, use self-referential canonical URLs.
When a page isn't canonicalizing to another URL, use self-referencing canonical URLs.
Canonical tags refer to themselves here.
Common Canonical Tags Mistakes
Here are some common canonical tag mistakes.
301 Canonicalization
Set the canonical URL as the redirect target, not a redirected URL.
Incorrect Domain Canonicalization
If your site uses HTTPS, don't set canonical URLs to HTTP.
Irrelevant Canonicalization
Canonicalize URLs to duplicate or near-identical content only.
SEOs sometimes try to pass link signals via canonical tags from unrelated content to increase rank. This isn't how canonicalization should be used and should be avoided.
Multiple Canonical URLs
Only use one canonical tag or URL per page; otherwise, they may all be ignored.
When overriding defaults in some CMSs, you may accidentally include two canonical tags in your page's <head>.
Pagination vs. Canonicalization
Incorrect pagination can cause duplicate content. Canonicalizing URLs to the first page isn't always the best solution.
Canonicalize to a 'view all' page.
How to Audit Canonical Tags (and Fix Issues)
Audit your site's canonical tags to find canonicalization issues.
SEMrush Site Audit can help. You'll find canonical tag checks in your website's site audit report.
Let's examine these issues and their solutions.
No Canonical Tag on AMP
Site Audit will flag AMP pages without canonical tags.
Canonicalization between AMP and non-AMP pages is important.
Add a rel="canonical" tag to each AMP page's head>.
No HTTPS redirect or canonical from HTTP homepage
Duplicate content issues will be flagged in the Site Audit if your site is accessible via HTTPS and HTTP.
You can fix this by 301 redirecting or adding a canonical tag to HTTP pages that references HTTPS.
Broken canonical links
Broken canonical links won't be considered canonical URLs.
This error could mean your canonical links point to non-existent pages, complicating crawling and indexing.
Update broken canonical links to the correct URLs.
Multiple canonical URLs
This error occurs when a page has multiple canonical URLs.
Remove duplicate tags and leave one.
Canonicalization is a key SEO concept, and using it incorrectly can hurt your site's performance.
Once you understand how it works, what it does, and how to find and fix issues, you can use it effectively to remove duplicate content from your site.
Canonicalization SEO Myths

Yuga Labs
3 years ago
Yuga Labs (BAYC and MAYC) buys CryptoPunks and Meebits and gives them commercial rights
Yuga has acquired the CryptoPunks and Meebits NFT IP from Larva Labs. These include 423 CryptoPunks and 1711 Meebits.
We set out to create in the NFT space because we admired CryptoPunks and the founders' visionary work. A lot of their work influenced how we built BAYC and NFTs. We're proud to lead CryptoPunks and Meebits into the future as part of our broader ecosystem.
"Yuga Labs invented the modern profile picture project and are the best in the world at operating these projects. They are ideal CrytoPunk and Meebit stewards. We are confident that in their hands, these projects will thrive in the emerging decentralized web.”
–The founders of Larva Labs, CryptoPunks, and Meebits
This deal grew out of discussions between our partner Guy Oseary and the Larva Labs founders. One call led to another, and now we're here. This does not mean Matt and John will join Yuga. They'll keep running Larva Labs and creating awesome projects that help shape the future of web3.
Next steps
Here's what we plan to do with CryptoPunks and Meebits now that we own the IP. Owners of CryptoPunks and Meebits will soon receive commercial rights equal to those of BAYC and MAYC holders. Our legal teams are working on new terms and conditions for both collections, which we hope to share with the community soon. We expect a wide range of third-party developers and community creators to incorporate CryptoPunks and Meebits into their web3 projects. We'll build the brand alongside them.
We don't intend to cram these NFT collections into the BAYC club model. We see BAYC as the hub of the Yuga universe, and CryptoPunks as a historical collection. We will work to improve the CryptoPunks and Meebits collections as good stewards. We're not in a hurry. We'll consult the community before deciding what to do next.
For us, NFTs are about culture. We're deeply invested in the BAYC community, and it's inspiring to see them grow, collaborate, and innovate. We're excited to see what CryptoPunks and Meebits do with IP rights. Our goal has always been to create a community-owned brand that goes beyond NFTs, and now we can include CryptoPunks and Meebits.

Jenn Leach
3 years ago
How Much I Got Paid by YouTube for a 68 Million Views Video
My nameless, faceless channel case study
The Numbers
I anonymize this YouTube channel.
It's in a trendy, crowded niche. Sharing it publicly will likely enhance competition.
I'll still share my dashboard numbers:
A year ago, the video was released.
What I earned
I'll stop stalling. Here's a screenshot of my YouTube statistics page displaying Adsense profits.
YouTube Adsense made me ZERO dollars.
OMG!
How is this possible?
YouTube Adsense can't monetize my niche. This is typical in faceless niches like TikTok's rain videos. If they were started a while ago, I'm sure certain rain accounts are monetized, but not today.
I actually started a soothing sounds faceless YouTube channel. This was another account of mine.
I looped Pexels films for hours. No background music, just wind, rain, etc.
People could watch these videos to relax or get ready for bed. They're ideal for background noise and relaxation.
They're long-lasting, too. It's easy to make a lot from YouTube Adsense if you insert ads.
Anyway, I tried to monetize it and couldn’t. This was about a year ago. That’s why I doubt new accounts in this genre would be able to get approved for ads.
Back to my faceless channel with 68 million views.
I received nothing from YouTube Adsense, but I made money elsewhere.
Getting paid by the gods of affiliate marketing
Place links in the video and other videos on the channel to get money. Visitors that buy through your affiliate link earn you a commission.
This video earned many clicks on my affiliate links.
I linked to a couple of Amazon products, a YouTube creator tool, my kofi link, and my subscribe link.
Sponsorships
Brands pay you to include ads in your videos.
This video led to many sponsorships.
I've done dozens of sponsorship campaigns that paid $40 to $50 for an end screen to $450 for a preroll ad.
Last word
Overall, I made less than $3,000.
If I had time, I'd be more proactive with sponsorships. You can pitch brand sponsorships. This actually works.
I'd do that if I could rewind time.
I still can, but I think the reaction rate would be higher closer to the viral video's premiere date.
