1) We visited NYC, Silicon Valley, and spoke with different personas in regard to the economic outlook. These individuals had a high net worth and have been around multiple economic cycles.

2) Interestingly enough, one can see that with the rising interest rates, the S&P 500, as well as real estate may have a 20-25% pull-back in the next 2 years.

3) While we all wait for the market to correct, there is an interesting piece of software called https://www.mashvisor.com/ that can find real estate deals when we hit  the bottom of the market.

4) As companies begin to conduct hiring freezes, it's still important to notate that the M1 money supply went up five fold - so cash is sitting out there

​5) Use your time wisely and time this market!

​Legal Disclaimer - We preface these posts in which any investment commentary is not an  endorsement to buy or sell a security, and does not represent our customers opinion. This is just an educational post.

​While many individuals leverage google trends for markets such as ecommerce to see which products to sell, there are various other variations in which google trends could be quite useful in regards to researching investments. For individuals seeking to learn how to leverage google trends, it is vital for he or she to understand that one first must pick a programming language. For the purpose of this article, we chose to use the python programming language. Some of the benefits in regards to python are it's simple to use, quick to code, and fewer lines of code. Some of the drawbacks are it's interpreted language in nature, which means the code takes longer to run than say Java, but this could be resolved with Apache Flink or Apache Spark, which convers the code into the JVM, and is out of scope for this tutorial.. When writing code, it's vital for one not to fall in love with a programming language. Now, we get into utilizing python for google trends pertaining to investments.

Steps:

1) Download Pycharm -> https://www.jetbrains.com/pycharm/download/#section=windows
2) Install Anaconda -> https://www.anaconda.com/products/distribution
3) Run the source code below, and replace the ticker symbol with a new ticker symbol:

# import the TrendReq method from the pytrends request module
from pytrends.request import TrendReq
from pylab import *
# execute the TrendReq method by passing the host language (hl) and timezone (tz) parameters
pytrends = TrendReq(hl='en-US', tz=360)
# build list of keywords
kw_list = ["AAPL"]
# build the payload
pytrends.build_payload(kw_list, timeframe='2021-02-22 2022-02-22', geo='US')

# plot all three trends in same chart


# store interest over time information in df
df = pytrends.interest_over_time()
# display the top 20 rows in dataframe
print(df.head(20))
df.plot()
show()

Legal Disclaimer:

This post does not constitute an endorsement for an investment, does not reflect the opinions of any Investment Sciences' clients or affiliates, and is solely for educational purposes only.

While Investment Science has no strategic ties to vendors, we can objectively tell you some interesting trends. If one wanted to view the trends in regards to which industries are rising, he or she could view the color grid on CB Insights to analyze whether or not the industry being sought is the most optimal industry to select. For instance, it looks as if anything to do with Software, Mobile & Telecom, Healthcare, and Internet is growing. Similarly, due to the correlation of the markets with the industries being funded, these grids can be used across hiring, investments, and prospecting. 
​
Now we can talk about some ticker symbols. The ticker symbol VOX is up about 54%, which corresponds to the telecom. IGV is up 75%, which corresponds to software. XLV maps to healthcare and is up about 28%. We hope this helps all of our readers.

Were About To Find Out.
Thursday, September 9th, the Kansas City Chiefs host the Houston Texans on NBC in prime time.  This will almost certainly break records for betting activity on a Week 1 NFL game.  But will it last?
 
We know that growth in active trading has been explosive, fueled by startups like Robin Hood and the elimination of trading commissions by the bigger brokers in the Fall of 2019.  Trading has never been simpler or easier.  ​

In the first half of 2020, Robin Hood accounts grew by 30%. With new-to-the-game investor favorites like Tesla, Zoom, and DraftKings doubling, tripling and more in the past 6 months, and charts of the SPY and QQQ resembling the old Price is Right “Cliff-Hangers” game (https://www.youtube.com/watch?v=bWEGNe104To ), the average new retail investor/trader has likely had at least some positive trades and is feeling pretty good about it.
  
And without all of those commissions, day-trading, or at least short-term trading is no doubt looking pretty attractive relative to sports betting as a place to get some action.  For most NFL bets, whether with a casino, online sportsbook, or your Uncle Larry’s business associate, you generally bet $110 to win $100.  If the sportsbook takes in $110 on the Chiefs, and $110 on the Texans, the guy who picked the winner (including the point-spread of course) gets $210, and the sportsbook keeps the extra $10.  Over time, when you pay that 10% commission on losing bets, it means you need to win more than about 52.4% of the time to generate positive cash flow.  Most bettors don’t do that well long run, so they either accept that they enjoy the game, like the action, or kid themselves that they make money through selective memory.
 
Meanwhile, anyone with an E*TRADE account gets an easy peek at their balance and which trades made money.  You can forget about all the times the Jaguars or the Bears have disappointed you, but every time I login, I can see that GE stock is worth nothing close to what I paid for it. Though opening up a chart on Tractor Supply Company (TSCO) cheers me right back up.  
 
Crossing Over
We know that some of the football betting regulars are crossing over.  Outspoken Tweeter and sports betting guru, David Portnoy (founder of Barstool Sports) has made multiple appearances on CNBC’s Mad Money with Jim Cramer, and DraftKings is now running commercials during that show.
 
Is stock trading a substitute for betting in the NFL?
The NFL gets the largest share of sports betting action.  Academic research finds that baseball betting is a substitute for betting on NFL games (https://journals.sagepub.com/doi/abs/10.1177/1527002511417630 ).  When the NFL season starts, even the preseason, betting activity clearly declines for baseball as it grows for football.  In author Gary Mayer’s true story of life as a bookie, (https://www.amazon.com/Bookie-My-life-disorganized-crime/dp/0874770238/ref=sr_1_1?dchild=1&keywords=bookie%2C+my+life+in+disorganized+crime&qid=1599692442&sr=8-1 ), he explains that he only handled baseball bets to keep his football betting clients active until the next football season. 
 
The football bettors want action.  This Thursday night, they’ll get it.  With the double-stuff mustachioed Andy Reid and the Kansas City Chiefs, and a Las Vegas betting total of 54.5, this should be a high scoring game with plenty of fireworks.

Will the bettors forget about trading stocks? 
I doubt it, but there will be some trade-offs.  If the NFL keeps everyone healthy and the games are good, I’m predicting a dip in the Monday morning trades.  Some of their attention and the need for action will once again be met by the NFL.  If the market keeps going higher, NFL betting/viewing may lose some steam as people focus their attention on making money in the stock market.  However, if the markets hit a rough patch, with a sustained downward trend this Fall, NFL betting and viewership will be stronger than ever.  I’m rooting for good games and a strong market, but no matter what happens, there will be some tradeoffs between sports betting and stock trading.

- Dr. Weinbach

​
Enhance Your Financial Portfolio with the Wilcox Model
At our firm, we continually seek out unique and compelling data points to inform our financial instrument analysis. In developing the Wilcox Model, we have incorporated a diverse range of data, including astronomical phenomena such as star positions, sunspots, and moon cycles. While we maintain the proprietary nature of our algorithm, we can share that our back-testing of this model from 2010 to 2016 yielded an impressive 25% return.
Although a 4% annual return might not seem extraordinary at first glance, what sets this strategy apart is its potential independence from broader stock market trends. Integrating a portion of your investment portfolio with the Wilcox Model could provide valuable diversification and risk mitigation.

​Understanding Back-Testing and Investment Positions
Back-testing involves applying a trading strategy or analytical method to historical data to evaluate its effectiveness in predicting actual results. This rigorous process helps us ensure that our models are not only theoretically sound but also practically viable.
When it comes to investment positions, maintaining a “long” position in a security means that you own the security, anticipating that its value will increase over time. Conversely, “short” positions are taken with the expectation that the security's price will decline.
By leveraging advanced back-testing techniques and a keen eye for unique data points, the Wilcox Model offers a robust addition to any investment strategy. Enhance your financial portfolio's performance and resilience with our innovative approach

​(We back tested the results in python):

Investment Science has a product called Investment Lab where one is able to push a button and make money through numerous parameters. While 'Investment Lab' itself is not for sale, we can help firms solve issues such as finding optimal prices of financial instruments or build a similar product for your needs. Our firm created a graph in python, which is a general purpose programming language. The python library used was pylab, a graphing library in python. The purpose of this post is not to teach you how to code, but rather how to build interesting products from complex financial instruments in a simple programming language such as python.  What we did here was graph out a put option (a put option is a contract giving the owner the right, but not the obligation to sell or sell short a specified amount of an underlying security at a pre-determined price within a specified time frame). For the graph above, we used the black scholes formula (a mathematical model for pricing an options contract. In particular, the model estimates the variation over time of financial instruments. It assumes these instruments (such as stocks or futures) will have a lognormal distribution of prices. Using this assumption and factoring in other important variables, the equation derives the price of a call option).

What is interesting about the graph above is on August 7th, 2020 the near-term price of the put option, that expired on August 7th, is worth more than the other option prices into the future. Simply speaking, when there are no economic events (such as a company merger or a corporate earnings event) one would expect the derivatives price on 08/07/2020 (i.e. today) for the company, Activision Blizzard (Ticker symbol ATVI), to be less than the future option prices. - but it's not!

Now that you understand this post, please go to our next post on  Options Trading Arbitrage

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​Unfortunately, many models used on wall-street are assuming a normal distribution, and many schools are teaching with assumptions of a normal distribution. What we have done is actually go all the way back to the 1960's, and from the attached spreadsheet one can actually see across sector which industries belong to which statistical distributions, and it does change every few years. On top of data science and algorithmic trading, these models are often inaccurate, and need augmentation to consider all of the data points. We are actively working on a product that should be available in two years time that addresses these inefficiencies in the markets. In the meantime, feel free to play with the spreadsheet.

statistical_distributions.xlsx

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