It could go either way for Tesla in Q2 2019 to generate a profit or a loss. Let’s have a look at the data!
By now, almost everyone knows that Tesla had a record quarter for deliveries (95,220), including 17,650 Model S+X, and 77,550 Model 3’s. More importantly, Tesla decreased its inventory during Q2 2019, an important finding because in the last 5 quarters, when Tesla reduces its inventory, net income was positive. Here are the production, deliveries, and inventory data since Q1 2018:
For example, in Q3 and Q4 2018, Tesla reduced its inventory by 3,358 and 4,145 cars, respectively, and reported positive net income for both quarters. In contrast, during Q1 and Q2 2018, and Q1 2019, inventory increased by 4,497, 12,571, and 14,100 cars, and Tesla reported a net loss of more than $700 million in each of those quarters. When inventory increases, the costs for producing these cars accumulate in the absence of any revenue. In contrast, when inventory is reduced, no cost to produce the cars occurs, and additional revenue is generated. The importance of the reduction in inventory will become more apparent a bit later, when I’ll estimate the Q2 2019 gross margin.
In terms of total revenue, the first step is to calculate the automotive sales revenue. First, it’s important to know what the average selling price (ASP) is for Tesla’s cars. For S+X sales, I’ve estimated a $100,000 ASP. In contrast, the ASP for the Model 3 is a bit more complicated. As shown below, the ASP for the Model 3 has fluctuated greatly over the past 5 quarters, from $46,500, to $48,000, to $55,700, to $52,500, and $45,200 in Q1 2019:
At worst, I’ll assume an ASP for the Model 3 of $45,200 for the current quarter. However, it may be much higher. Conservatively, in a best-case scenario, I’ll assume $48,000. These ASPs yield a total automotive sales revenue of either $5.27 and $5.49 billion:
Now onto total revenues, which consists of automotive sales and leasing, energy generation and storage, services and other. These categories have been relatively stable over the past 5 quarters, so I used their respective 5-quarter average values to estimate their Q2 2019 amounts. Based on these data, in worst- and best-case scenarios, Tesla generated $6.24 and $6.47 billion in total revenue:
What about the cost of generating these revenues? One of the most important factors is the automotive gross margin. In quarters (Q3, Q4 2018) where Tesla reduced its inventory, automotive gross margins were 23.3% and 25%, whereas in the 3 quarters (Q2 and Q2 2018, Q1 2019) where inventory increased, gross margins were 18.4%, 18.9%, and 18.6%. Because of the inventory-gross margin association, I’ll assume that in Q2 2019, gross margins will be at worst, 23.3%, and at best, 25%. Based on these data, the cost of revenues for automotive sales is $4.042 billion at worst, and $3.953 at best.
Moreover, over the past 5 quarters, the cost of revenues for automotive leasing and energy generation and storage hasn’t fluctuated much, so I used the 5-quarter average for Q2 2019. However, services and other costs have spiked during the past 2 quarters. For a worst-case scenario, I used the difference obtained from subtracting the revenue vs cost of revenue for services and other, $190 million (obtained during Q1 2019), thereby yielding a Q2 2019 value of $569 million. For a best-case scenario, I used the 5-quarter average value, thereby yielding $513 million. I then summed all the costs of these revenues, and obtained the gross profit by subtracting the cost of revenues from total revenues. This yields $1.174 billion at worst, and $1.546 billion at best, for gross profit:
Investigating further, operating expenses (OpEx) are next. OpEx costs have been relatively stable over the past 5 quarters:
Accordingly, I used the average, 5-quarter OpEx value, $1.104 billion for Q2 2019. Subtracting that from gross profit yields a Q2 2019 loss from operations of $70 million at worst, whereas in the best case, it is $442 million.
Additional costs include interest income or expense, other net income or expense, benefit or provision for income taxes, and net losses attributable to non-controlling interests and redeemable non-controlling interests. I then subtracted the 5-quarter average for the sum of these values (-$153,664 million) from the loss from operations values to obtain the net loss (or gain) attributable to common shareholders, which is the gross profit (or loss). At worst, Tesla may report a -$84 million loss, whereas at best, they may report a $288 million profit:
So which will come true? Key factors are the Model 3 ASP, gross margins, and decreasing the cost of services losses. I’m leaning towards Tesla achieving the best case scenario…We’ll find out tomorrow!
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Deep sleep, the stage of sleep also known as “slow wave sleep” declines during aging. Based on a meta-analysis of 65 studies representing 3,577 subjects (aged 5 years to 102 years; Ohayon et al. 2004), slow wave sleep, expressed as a percentage of total sleep time decreases during aging from 25% in childhood to less than 10% in adults older than 65 years: Continue reading “Tracking Deep Sleep-Can It Be Improved?”
I’ve posted individual dietary days as an example of what and how much I eat (https://michaellustgarten.com/2015/12/31/130-grams-of-fiber-2400-calories/). However, a few days of examples may not represent the whole dietary picture. To address this, below is my average nutrient intake for the past 100 days (from October 24, 2018-Feb 5 2019):
Notice that my average values for many of these variables (i.e. potassium, selenium, Vitamin C, Vitamin K, etc.) are way above the RDA. For more info on that, I have several blog posts that explain the “why” behind that. Where am I getting those nutrients from? Shown below are 100-day averages for my food intake, ranked in order from most consumed (in grams, or ounces, if it’s a drink) to least:
During the past 100 days, my top 5 foods in terms of daily intake include carrots, strawberries, red peppers, watermelon, and cauliflower. Scroll through the list to see how much I average on a daily basis for each food!
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One of the goals of my exercise program is to reduce my resting heart rate (RHR). A stronger heart beats less times per minute, but pumps more blood per beat. In contrast, a weaker heart beats more times per minute, but less blood per beat.
Is there an optimal level for RHR? Based on a meta-analysis of 59 studies that included 1,810,695 subjects, RHR values < 50 beats per minute (bpm) are associated with maximally reduced risk of death from all causes. Conversely, RHR values > 50 bpm are associated with a higher mortality risk (Aune et al. 2017):
What’s my resting heart rate? Shown below is that data, tracked by WHOOP since August. Note that my RHR wasn’t significantly different from August until October, ranging from 51-53 bpm (average, 51.7). However, because I was tracking my RHR, I noticed that I was overtraining, leading to very high HRs, lower heart rate variability, and less deep sleep (topics for another post!) the day(s) after exercise. So early in November, I changed my exercise routine. As a result, from November until the end of January, my average RHR (49.7 bpm) has been significantly less (p-value =1E-10), and based on January’s average RHR, I’m trending closer to 47 bpm! Also note that * = significantly different when compared with August.
What did I change in my exercise program? Since I’ve been in Boston (~9 years), I’ve walked 15-20 miles per week: it’s 1.1 miles to and from work, plus at least an hour of walking on Saturdays and Sundays. That’s a constant that hasn’t changed. In contrast, I split my 3-day weight training routine, which totaled ~5-6 hours/week into 3-5 days at less than an hour each session, and at a lower intensity with more reps. My strength is still as good as it was before, and as a result, my recovery HRs aren’t as high, thereby leading to a lower average RHR over time,. I’ve been training like that consistently for the past 30 years, but it took wearing a fitness tracker to change it!
Aune D, Sen A, ó’Hartaigh B, Janszky I, Romundstad PR, Tonstad S, Vatten LJ. Resting heart rate and the risk of cardiovascular disease, total cancer, and all-cause mortality – A systematic review and dose-response meta-analysis of prospective studies. Nutr Metab Cardiovasc Dis. 2017 Jun;27(6):504-517.
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With use of a food scale, I’ve been tracking my daily macro- and micronutrient intake every day since April 2015. In addition, I have 15 blood test measurements during that period, and accordingly, I’m able to examine correlations between my dietary intake with my circulating biomarkers. In this post, I’ll address the question, is my dietary cholesterol intake significantly correlated with plasma levels of cholesterol?
1. Plasma levels of total cholesterol vs. dietary cholesterol:
In the plot we see a borderline significant (p = 0.06), moderate correlation (r = 0.5) between my plasma total cholesterol with my dietary cholesterol intake. However, note that total cholesterol is comprised of “good” and “bad” parts, with HDL as the “good”, and with non-HDL cholesterol, including LDL and VLDL, as the “bad”. What does that data look like?
2. Plasma levels of non-HDL (LDL+VLDL) cholesterol vs. dietary cholesterol:
In the plot we see a highly significant (p = 0.006), strong correlation (r = 0.67) between my non-HDL cholesterol levels with my dietary cholesterol intake. It’s not possible to show causation via correlation, but this data suggests that my dietary cholesterol intake may be driving increased levels of non-HDL cholesterol.
3. Plasma levels of HDL cholesterol vs. dietary cholesterol:
In the plot, first note that in contrast with the positive correlations between total and non-HDL cholesterol with my dietary cholesterol intake, the correlation between HDL with my dietary cholesterol intake is negative (i.e., going in the opposite direction; r = 0.51), and borderline significant (p = 0.054).
Cumulatively, it looks like my dietary cholesterol intake may be related to increased “bad” cholesterol and decreased “good” cholesterol. As a limitation of this approach, although I’ve shown blood test data for 15 measurements (which is a decent sample size), I only have 4 measurements with an average daily cholesterol intake around 200 mg or greater. In the near future, I expect to average 200 mg of daily cholesterol (or more) per day, so let’s see if these correlations hold up!
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