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Dave Allen
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Dave is the VP of Sales and Marketing of EidoSearch. He has over 14 years of proven success working in the Financial Services vertical. Mr. Allen was most recently GM for Americas at TIM Group, the world's leading Alpha Capture network, where he ran sales and service functions for the region and... More
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EidoSearch
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  • If Netflix (NFLX) Beats Expectations The Stock Will...

    "You must always be able to predict what's next and then have the flexibility to evolve" - Marc Benioff, Founder of Salesforce.com

    Netflix (NASDAQ:NFLX) is up 42% over the past three months. Although the stock gave back a bunch of gains in March, it has popped another 9% over the past week after announcing that their members watched over 10 billion hours in the past quarter. I have accounted for a few of those myself.

    We're now two days out from their Q1 earnings announcement, and investors have done their fundamental work. Models and price forecasts have been adjusted, consensus is being built for expectations and asset managers and hedge funds are, in many cases, adjusting their positions going into the print. Some managers are looking to take advantage of better than expected results, and others are getting smaller or mitigating risk based on their fundamental view of the company's performance vs. expectations.

    There are questions we need to ask to impact profitability. When has a peer stock (we'll use Sector here) traded over the quarter and into the earnings event (specifically two days out) in a similar fashion in recent history, and what are the range of outcomes? How can you use this data point?

    We've made it easy for investors to quantify the range of likely outcomes. We found 72 similar instances in the peer group over 10 years. Some of these companies blew out the number, and some had disastrous results or forward guidance compared to expectations, while most fell somewhere in between.

    We are able to provide the statistics for this range. If you feel the company will beat expectations, how much is the stock likely to run up in the next month? If the stock misses, how badly will it likely get beaten up. We can show the range of outcomes so investors can get a much better gauge for position sizing and the inherent risk premium. Consider us scenario analysis for earnings to complement your fundamental opinion and drivers.

    For Netflix, the statistics show that the typical outcome is up, but that there's more than 2x the upside to the downside when looking at the past results of the 72 peer companies that traded into the print in a similar fashion. We're not predicting results here! Again, we're giving an objective gauge on how the market and stock will react under similar conditions no matter what scenario takes place. Here are the visuals:

    The average return of peers historically is 4.3%

    (click to enlarge)

    (SOURCE: EidoSearch data dated 4/13/15)

    More importantly, here are the range of returns under similar conditions in the peer group historically. The stock is up 63.9% of the time, and for the tails, you can see there is only one instance down more than -20% in the next month and 6 that are up more than 20%.

    (click to enlarge)

    (SOURCE: EidoSearch data dated 4/13/15)

    Netflix is one of those matches that is up more than 20% historically. Per the charts below, you can see the similar trajectory for the stock price this past quarter vs. their earnings release in Q1 of 2013. Two days out from the release in both instances. The stock was up 40% in the next month two years ago.

    (click to enlarge)

    (SOURCE: EidoSearch data dated 4/13/15)

    If you use Options to hedge around events like earnings, you can see that we can also overlay implied volatility on our forecasted return distributions to show investors where options might be cheap or expensive based on upside, downside or overall magnitude differences. In this example, we are projecting less downside risk than the symmetrical cone of implied vol (based on normal distributions) suggests.

    (click to enlarge)

    (SOURCE: EidoSearch data dated 4/13/15)

    In summary, Netflix is up 42% over the past 3 months and 9% last week on viewer consumption. We have found 72 similar instances in peer group of stocks trajectory two days before earnings results. Scenarios Analysis shows the range of probable outcomes with 2.3x the upside to downside and an Avg 1 Month Return of 4.3%.

    Tags: NFLX, long-ideas
    Apr 13 1:15 PM | Link | Comment!
  • The S&P At An All-Time High...and Going Higher?

    Phil Connors (Bill Murray): "Do you ever have déjà vu, Mrs. Lancaster?" Mrs. Lancaster: "I don't think so, but I could check with the kitchen" Groundhog Day, 1993

    Here we go again. The S&P 500 ended 2013 at an all-time high, sitting at 1,848 on December 31st of that year. It was still around that level last January 22nd, at 1,844, when the markets got spooked and the S&P dropped 6% over the next couple weeks. But, by mid-February, the market was back to hitting new highs and ended the first quarter of 2014 up a respectable 2%.

    This past December 29th, the S&P 500 was at an all-time high again of 2,090. The markets have once again been volatile, and the S&P pulled back to the tune of a 5% drop to 1,992 by January 15th. But, here we are a few weeks later, and the S&P is once again is sitting at all-time highs. Do we repeat last year's recovery from a poor start and finish the first quarter on a positive note?

    There are ongoing concerns with Greek debt and the potential trickle down effect, and although it's not on the front page, U.S. market valuations are becoming a growing concern for many especially with the specter of rising interest rates on the horizon. With that being said, the positives clearly continue to outweigh the negatives.

    Crude Oil prices seem to have found some footing and lower prices at the pump are fueling consumers. The economy continues to strengthen here at home, the run of the U.S. dollar is starting to slow and 4th quarter earnings data looks very good. In fact, 391 of the S&P 500 companies have reported earnings so far, and 71% have beaten expectations according to Thomson Reuters.

    Last year is certainly a good gauge for how the market is likely to react in the next few months, but is there additional precedence?

    Using EidoSearch, we decided to look back a little further to include the past 6 months trend in the S&P 500 (so we could include the higher volatility and drop and recovery from Q4) to try and identify similar instances historically. We found 9 similar instances dating back to 1984, and the S&P is up in 7 of the 9 instances and the average return in the next 3 months is 1.7%.

    Although the average return is not overwhelming, the two down instances from 2005 and 2006 only give back -1.4% and -1.5% respectively. Here's a table of all 9 instances:

    (Source: EidoSearch data dated 2/17/15)

    6 most similar historical comps to the current price trend

    (click to enlarge)

    (Source: EidoSearch data dated 2/17/15)

    3 month forward return distributions of the 9 most similar historical instances of the current 6 month price trend in the S&P 500.

    (click to enlarge)

    (Source: EidoSearch data dated 2/17/15)

    Overall, the S&P has recovered losses from January and is now up for the year, similar to 2014. Including the last 6 months, the market has traded in a similar fashion 9 times dating back to 1984. The S&P is up in 7 of 9 instances and the average return in the next 3 months is 1.7%.

    Feb 18 10:10 AM | Link | Comment!
  • A Forecast For Netflix...and The Weather

    C-3PO: "But sir, the possibility of successfully navigating an asteroid field is approximately 3,720 to one.
    HAN SOLO: "Never tell me the odds."

    There is probably no profession on planet Earth more criticized than meteorology. If they say it's going to be 80 degrees and sunny on Sunday, and we plan a beach day and it ends up being cloudy and rainy, we are not happy campers. We don't consider the last 5 forecasts that the weather man might have nailed, it's just the ones they don't get right which stick in our craw.

    Forecasting the weather has in fact become a much more precise science, but the modeling of it is challenging based on all of variables especially for longer range forecasts. Per Wikipedia:

    "The chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes mean that forecasts become less accurate as the difference in current time and the time for which the forecast is being made (the range of the forecast) increases."

    Ok, so modelling all of the factors that go into a weather forecast is challenging to no surprise. They are also opaque. The average person has no idea what goes into a weather forecast and therefore we can't apply our own opinion to validate or discredit the forecast. We basically have no insight into the process which we generally do not like.

    Although the process is not clear, Meteorologists are generally pretty good with their forecasts given their current limitations. In my opinion the biggest issue is that they are not effective at managing our expectations! They are taking a forecasting model built to generate a RANGE of likely temperatures and weather conditions and they are trying to provide a precise temperature and forecast to the public.

    Wouldn't it be great if the forecast told you, "This Sunday there's 85% probability that the weather will be between 77 and 82 degrees and Partly Sunny."? That would allow us to apply our own logic about whether or not it made sense to pack up the car with the beach gear, or what to pack on a trip, etc. Basically, we could add value based on how we wanted to use the forecast.

    I used EidoSearch to simplified the modelling of forecasts for all "Big Data" problems like weather forecasts. The technology doesn't need models and data scientists, it simply uses search by example to provide a range of forecasts based on similar environments or patterns historically, e.g. weather patterns. For now it's only focused on applying it to price and economic time series data to provide investors with probabilities to support their fundamental investment process.

    I've validated the accuracy of predicting return probabilities going forward using actual return distributions historically. Literally based on tens of millions of predictions. A few examples of questions I have been able to quantifying through probabilities:

    1. We like the fundamental story, but is this a good time to build a position?

    2. The stock is up 30% in two months and at our original forecast price. Do we lock in gains?

    3. What positions in the book have the biggest downside risk?

    4. Where are opportunities in our sector with a ton of upside potential we should do some work on?

    I decided to take a look at Netflix (NASDAQ:NFLX). The stock was at $484 on September 10th, closed at $448 on October 15th, and dropped 20% a couple weeks ago on a bad quarter. The price now sits at around $380.

    I found 97 historical instances of the current trading pattern in Netflix across Consumer Cyclical stocks historically. Per the chart below, the average return in the next 3 months is 10.8% and based on the RANGE of historical return distributions, there's more than 3x the upside to downside and 85% probability the price will be above $351.30 in three months.

    (click to enlarge)

    (SOURCE: EidoSearch data dated 10/27/14)

    The below chart shows the skew of the 3 month forward returns of all 97 historical instances. There are only 3 instances down more than 25% in the next 3 months, and 25 instances that are up more than 25%.

    (click to enlarge)

    (Source: EidoSearch Data dated 10/27/14)

    A range of historical outcomes and probabilities in the hands of Fundamental Investors that can incorporate them. With a 85% probability the stock will be above $351.30 in the three months, I see a good buying opportunity.

    Tags: NFLX, long-ideas
    Oct 28 2:49 PM | Link | Comment!
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