Donor-Advised Fund Tax Benefits (DAF)
Giving through a donor-advised fund can be tax-efficient. Using a donor-advised fund can reduce your tax liability while increasing your charitable impact.
Grow Your Donations Tax-Free.
Your DAF's charitable dollars can be invested before being distributed. Your DAF balance can grow with the market. This increases grantmaking funds. The assets of the DAF belong to the charitable sponsor, so you will not be taxed on any growth.
Avoid a Windfall Tax Year.
DAFs can help reduce tax burdens after a windfall like an inheritance, business sale, or strong market returns. Contributions to your DAF are immediately tax deductible, lowering your taxable income. With DAFs, you can effectively pre-fund years of giving with assets from a single high-income event.
Make a contribution to reduce or eliminate capital gains.
One of the most common ways to fund a DAF is by gifting publicly traded securities. Securities held for more than a year can be donated at fair market value and are not subject to capital gains tax. If a donor liquidates assets and then donates the proceeds to their DAF, capital gains tax reduces the amount available for philanthropy. Gifts of appreciated securities, mutual funds, real estate, and other assets are immediately tax deductible up to 30% of Adjusted gross income (AGI), with a five-year carry-forward for gifts that exceed AGI limits.
Using Appreciated Stock as a Gift
Donating appreciated stock directly to a DAF rather than liquidating it and donating the proceeds reduces philanthropists' tax liability by eliminating capital gains tax and lowering marginal income tax.
In the example below, a donor has $100,000 in long-term appreciated stock with a cost basis of $10,000:
Using a DAF would allow this donor to give more to charity while paying less taxes. This strategy often allows donors to give more than 20% more to their favorite causes.
For illustration purposes, this hypothetical example assumes a 35% income tax rate. All realized gains are subject to the federal long-term capital gains tax of 20% and the 3.8% Medicare surtax. No other state taxes are considered.
The information provided here is general and educational in nature. It is not intended to be, nor should it be construed as, legal or tax advice. NPT does not provide legal or tax advice. Furthermore, the content provided here is related to taxation at the federal level only. NPT strongly encourages you to consult with your tax advisor or attorney before making charitable contributions.
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.
Sylvain Saurel
3 years ago
A student trader from the United States made $110 million in one month and rose to prominence on Wall Street.
Genius or lucky?
From the title, you might think I'm selling advertising for a financial influencer, a dubious trading site, or a training organization to attract clients. I'm suspicious. Better safe than sorry.
But not here.
Jake Freeman, 20, made $110 million in a month, according to the Financial Times. At 18, he ran for president. He made his name in markets, not politics. Two years later, he's Wall Street's prince. Interview requests flood the prodigy.
Jake Freeman bought 5 million Bed Bath & Beyond Group shares for $5.5 in July 2022 and sold them for $27 a month later. He thought the stock might double. Since speculation died down, he sold well. The stock fell 40.5% to 11 dollars on Friday, 19 August 2022. On August 22, 2022, it fell 16% to $9.
Smallholders have been buying the stock for weeks and will lose heavily if it falls further. Bed Bath & Beyond is the second most popular stock after Foot Locker, ahead of GameStop and Apple.
Jake Freeman earned $110 million thanks to a significant stock market flurry.
Online broker customers aren't the only ones with jitters. By June 2022, Ken Griffin's Citadel and Stephen Mandel's Lone Pine Capital held nearly a third of the company's capital. Did big managers sell before the stock plummeted?
Recent stock movements (derivatives) and rumors could prompt a SEC investigation.
Jake Freeman wrote to the board of directors after his investment to call for a turnaround, given the company's persistent problems and short sellers. The bathroom and kitchen products distribution group's stock soared in July 2022 due to renewed buying by private speculators, who made it one of their meme stocks with AMC and GameStop.
Second-quarter 2022 results and financial health worsened. He didn't celebrate his miraculous operation in a nightclub. He told a British newspaper, "I'm shocked." His parents dined in New York. He returned to Los Angeles to study math and economics.
Jake Freeman founded Freeman Capital Management with his savings and $25 million from family, friends, and acquaintances. They are the ones who are entitled to the $110 million he raised in one month. Will his investors pocket and withdraw all or part of their profits or will they trust the young prodigy for new stunts on Wall Street?
His operation should attract new clients. Well-known hedge funds may hire him.
Jake Freeman didn't listen to gurus or former traders. At 17, he interned at a quantitative finance and derivatives hedge fund, Volaris. At 13, he began investing with his pharmaceutical executive uncle. All countries have increased their Google searches for the young trader in the last week.
Naturally, his success has inspired resentment.
His success stirs jealousy, and he's attacked on social media. On Reddit, people who lost money on Bed Bath & Beyond, Jake Freeman's fortune, are mourning.
Several conspiracy theories circulate about him, including that he doesn't exist or is working for a Taiwanese amusement park.
If all 20 million American students had the same trading skills, they would have generated $1.46 trillion. Jake Freeman is unique. Apprentice traders' careers are often short, disillusioning, and tragic.
Two years ago, 20-year-old Robinhood client Alexander Kearns committed suicide after losing $750,000 trading options. Great traders start young. Michael Platt of BlueCrest invested in British stocks at age 12 under his grandmother's supervision and made a £30,000 fortune. Paul Tudor Jones started trading before he turned 18 with his uncle. Warren Buffett, at age 10, was discussing investments with Goldman Sachs' head. Oracle of Omaha tells all.
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 Data
def 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 Data
Signals 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.
You might also like
Nick Nolan
3 years ago
How to Make $1,037,100 in 4 Months with This Weird Website
One great idea might make you rich.
Imagine having a million-dollar concept in college that made a million.
2005 precisely.
Alex Tew, 21, from Wiltshire, England, created The Million Dollar Homepage in August 2005. The idea is basic but beyond the ordinary, which is why it worked.
Alex built a 1,000,000-pixel webpage.
Each website pixel would cost $1. Since pixels are hard to discern, he sold 10x10 squares for $100.
He'd make a million if all the spots sold.
He may have thought about NFTs and the Metaverse decades ago.
MillionDollarHomepage.com launched in 2005.
Businesses and individuals could buy a website spot and add their logo, website link, and tagline. You bought an ad, but nobody visited the website.
If a few thousand people visited the website, it could drive traffic to your business's site.
Alex promised buyers the website would be up for 5 years, so it was a safe bet.
Alex's friend with a music website was the first to buy real estate on the site. Within two weeks, 4,700 pixels sold, and a tracker showed how many were sold and available.
Word-of-mouth marketing got the press's attention quickly. Everyone loves reading about new ways to make money, so it was a good news story.
By September, over 250,000 pixels had been sold, according to a BBC press release.
Alex and the website gained more media and public attention, so traffic skyrocketed. Two months after the site launched, 1,400 customers bought more than 500,000 pixels.
Businesses bought online real estate. They heard thousands visited the site, so they could get attention cheaply.
Unless you bought a few squares, I'm not sure how many people would notice your ad or click your link.
A sponge website owner emailed Alex:
“We tried Million Dollar Homepage because we were impressed at the level of ingenuity and the sheer simplicity of it. If we’re honest, we didn’t expect too much from it. Now, as a direct result, we are pitching for £18,000 GBP worth of new clients and have seen our site traffic increase over a hundred-fold. We’re even going to have to upgrade our hosting facility! It’s been exceptional.”
Web.archive.org screenshots show how the website changed.
“The idea is to create something of an internet time capsule: a homepage that is unique and permanent. Everything on the internet keeps changing so fast, it will be nice to have something that stays solid and permanent for many years. You can be a part of that!” Alex Tew, 2005
The last 1,000 pixels were sold on January 1, 2006.
By then, the homepage had hundreds of thousands of monthly visitors. Alex put the last space on eBay due to high demand.
MillionDollarWeightLoss.com won the last pixels for $38,100, bringing revenue to $1,037,100 in 4 months.
Many have tried to replicate this website's success. They've all failed.
This idea only worked because no one had seen this website before.
This winner won't be repeated, but it should inspire you to try something new and creative.
Still popular, you could buy one of the linked domains. You can't buy pixels, but you can buy an expired domain.
One link I clicked costs $59,888.
You'd own a piece of internet history if you spent that much on a domain.
Someone bought stablesgallery.co.uk after the domain expired and restored it.
Many of the linked websites have expired or been redirected, but some still link to the original. I couldn't find sponge's website. Can you?
This is a great example of how a simple creative idea can go viral.
Comment on this amazing success story.
Sam Hickmann
3 years ago
The Jordan 6 Rings Reintroduce Classic Bulls
The Jordan 6 Rings return in Bulls colors, a deviation from previous releases. The signature red color is used on the midsole and heel, as well as the chenille patch and pull tab. The rest of the latter fixture is black, matching the outsole and adjacent Jumpman logos. Finally, white completes the look, from the leather mudguard to the lace unit. Here's a closer look at the Jordan 6 Rings. Sizes should be available soon on Nike.com and select retailers. Also, official photos of the Air Jordan 1 Denim have surfaced.
Jordan 6 Rings
Release Date: 2022
Color: N/A
Mens: $130
Style Code: 322992-126
Camilla Dudley
3 years ago
How to gain Twitter followers: A 101 Guide
No wonder brands use Twitter to reach their audience. 53% of Twitter users buy new products first.
Twitter growth does more than make your brand look popular. It helps clients trust your business. It boosts your industry standing. It shows clients, prospects, and even competitors you mean business.
How can you naturally gain Twitter followers?
Share useful information
Post visual content
Tweet consistently
Socialize
Spread your @name everywhere.
Use existing customers
Promote followers
Share useful information
Twitter users join conversations and consume material. To build your followers, make sure your material appeals to them and gives value, whether it's sales, product lessons, or current events.
Use Twitter Analytics to learn what your audience likes.
Explore popular topics by utilizing relevant keywords and hashtags. Check out this post on how to use Twitter trends.
Post visual content
97% of Twitter users focus on images, so incorporating media can help your Tweets stand out. Visuals and videos make content more engaging and memorable.
Tweet often
Your audience should expect regular content updates. Plan your ideas and tweet during crucial seasons and events with a content calendar.
Socialize
Twitter connects people. Do more than tweet. Follow industry leaders. Retweet influencers, engage with thought leaders, and reply to mentions and customers to boost engagement.
Micro-influencers can promote your brand or items. They can help you gain new audiences' trust.
Spread your @name everywhere.
Maximize brand exposure. Add a follow button on your website, link to it in your email signature and newsletters, and promote it on business cards or menus.
Use existing customers
Emails can be used to find existing Twitter clients. Upload your email contacts and follow your customers on Twitter to start a dialogue.
Promote followers
Run a followers campaign to boost your organic growth. Followers campaigns promote your account to a particular demographic, and you only pay when someone follows you.
Consider short campaigns to enhance momentum or an always-on campaign to gain new followers.
Increasing your brand's Twitter followers takes effort and experimentation, but the payback is huge.
👋 Follow me on twitter