Thread by @LeftHandPole

1\ Do you feel lucky: A Monte Carlo Estimation of $TSLAQ Fourth Quarter Profits

(Inaugural Thread on this fine Christmas morning. Not investment advice; academic discussion only. Short via put options)

2\ Sometimes the burden of prescience becomes too great for our profit estimates. It is difficult and error-prone for us to speculate on so many production parameters, oftentimes under reasonable assumptions, that are thrown into disarray on Tesla’s whim.
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3\ Monte Carlo analysis can help predict the probability of certain events even under these highly variable random scenarios. Analysts often pick the best numbers available to them and perform just one final profit/loss calculation.
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4\ But what if analysts performed that exercise 10000 times using different randomized input values and plotted the varying results? Randomized values should behave according to a reasonable distribution, but at its core we have just described a Monte Carlo analysis.
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5\ Each estimated parameter then becomes a random variable that may take on one of any given value within a range. Probability distributions describe the range of those values and the probability of selecting any given value within that range.
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6\ Carrying forward this concept, we can create a model using random variables for delivery Volume, Average Sales Price, Margins, and other income statement line items. Two convenient probability distributions are normal (gaussian) distributions and uniform distributions.
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7\ Normal distributions are bell curves that appear throughout nature with a well-defined mean and standard deviation that describe where most of the random values will tend to cluster. 95% of all values occur within 2 deviations of the mean, though range is infinite.
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8\ Uniform distributions only have possible values over a minimum and maximum range, with all values being equally likely within that range. Think of a die as an example: rolling a 1 is just as likely as a rolling a 6, and rolling a 99 is impossible.
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9\ Now let volume, ASP, and other revenues be normal distributions with means and deviations shown; let all margins be uniform distributions with min and max values shown. Finally, run this calculation 10000 times using randomized parameters with these distributions.
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10\ The output (profit) is also a random variable, taking the form of a normal distribution. Below shows a kind of histogram known as a Probability Density Function, identifying which dollar ranges are most and least likely. The area under the curve must sum to 1
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11\ A related concept is the Cumulative Density Function, which represents the probability that the value of the random variable is less than or equal to X. Below the value at X = 0 describes the probability that $TSLAQ will be unprofitable.

12\ In this simulation, the result is about a 75% chance of unprofitability. Taking the complement we can find the implied odds of profitability, denoted P(X>0), at 25%. Below is a table of other relevant statistics.
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13\ So the odds aren’t good for Tesla having positive earnings this quarter. But we also learn that the odds of exceeding last quarter’s $300M profit are nearly impossible at < 1%. A betting man might put money on the mean for a $90M loss.
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14\ Early next year, $TSLAQ will release Q4 delivery volumes, at which point volume is no longer a random variable. We can re-run the simulation and obtain a narrower PDF curve with a tighter confidence band. Updates to be posted.

15\ Meanwhile, estimates for Q1 won’t be any more favorable than they are now. Those PDF/CDF curves will be shifting more & more to the left with time. Profit may be technically possible, but only if the stars align for $TSLAQ in a way that defies belief. This is the bear thesis

16\ I welcome others to try this modeling approach with whatever values you deem reasonable. I am especially interested in the opinions of the venerable @CoverDrive12. I also have to self-report my chart crime to @TeslaCharts. Apologies, my good bro.

Merry Christmas to all!