The InvestmentAddict Series Post #3 Can Tesla reach $700-$1,700 by 2026-2030?
In this article, I will take an entirely different approach and try to show how I use a specific combination of the Elliot Wave Theory + Fibonacci numbers to identify POTENTIAL price targets for each of my individual stocks. In this case, I will take the example of $TSLA stock.
It is worth noting that academic research concludes that all charting techniques are absolutely irrelevant in predicting price. This finding might be true or it may be that the actual experiments which were used to test the predictable powers of these tools had a flawed methodology. You don't know me well, but let's say my expertise if I have any is to collect current research, meta research (i.e. a collection of all research into 1 about a specific topic), and as a hobby looking into the research methodology (the way in which the question was tested in practice or simply how did they actually answer the question - what did they do to test it?). Therefore, I personally believe such tools are not given their proper respect as their predictive power was never actually scientifically tested accurately.
So I will try to make an experiment of my own.
The context of this particular experiment is based on what is referred to a 1) channel, 2) Fibonacci numbers and 3) the Elliot Wave theory. Basically these 3 tools are applies to the chart of Tesla's price (shown as of 2017-2023).
The value of the channel itself would be to indicate that once price reaches the top of the channel which may be at about $715 as early as by the end of 2023 or, it may be about $1700-1800 by end of 2025, or much higher by 2030-2035. In other word, the longer it takes it to reach the top line, the bigger the gain would be.
These numbers however are not based on the channel itself. They are the so-called Fibonacci numbers. These suggest that the price Tesla could reach is between $714 and 1707.74 per stock. But like the channel, they do not give any indication about when this would happen.
Once the so-called Elliot Wave labels are applied (the purple numbers of 1,2,3,4,5, and the blue letters of A-B-C), a technical analyst would argue that according to the Elliot Wave Principle, markets tend to move in 5 waves to form what is called an impulse, this is then followed by at least a three wave drop. For instance, the move of 2019 lows to 2021 highs itself is a move of 5 waves, which is followed by the A-B-C drawn above for a correction which took place from $400 to $117. This $117 number is not random, a technical analyst would argue that 0.382% is the typical retracement level for a wave 4 (hence the purple label 4 at $117). Moreover, the view that this number is significantly important is confirmed by the channel (i.e. there is what is called a confirmation by two tools). So what does this mean?
Overall, a technical analyst would suggest that $117 is a price which we will not see again for a long time. We would first need to grow to at least $400 to see any resistance (the previous high) or need to make a new high of at least $700 before ever considering that Tesla price might drop to about $120 again. However, reaching $700 is not necessarily a pre-condition for Tesla to drop back to $120 as this rally could in reality extend for 3-5 years and reach much higher targets, before price drops by 60-80% to re-test these current levels.
This is to show that the predictions of a technical analyst, would not be very specific. This is what makes the testing of the predictive power of such technical tools greatly difficult.
However, to formulate a proper albeit simple research, I will ask the research question "Can Tesla shares reach $700-$1700 by 2026-2030?
To answer this research question I have formulated the following hypotheses
H1) Tesla share will grow from $117 to $700 by 2026
H2) Tesla share will not top until the top of the channel is reached (whenever this may be)
H3) Tesla shares will reach a price of about $1700 by 2030
H4) Tesla shares will not drop back to $117 before making it to at least $400
H5) Tesla share will grow to $700 eventually even if not by 2026
H6) Tesla share will grow to $1700 eventually even if not by 2030
H7) Tesla share will eventually be $5000 by 2038?
H8) Tesla share will reach $5000 by 2030
Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researcher predicts will be outcome of the study. It is an integral part of the scientific method that forms the basis of scientific experiments.
Unlike those in an experiment, these are called - directional hypotheses
They specify the expected direction to be followed to determine the relationship between variables and are derived from theory. Furthermore, they imply the researcher’s intellectual commitment to a particular outcome.
As mentioned, above I have outlined my 7 directional hypotheses based on this piece. I like hypotheses as a tool to outline statements which can be turned into a testable prediction. After all, we all have our hobbies and mine is to write research and test my own hypotheses.
The purpose of these hypotheses is for me to summarize my opinion in a few statements, test them and write subsequent pieces in which I discuss whether these hypotheses were accepted or rejected, as a requirement of the scientific method to be valid and a continuation of this hobby.
So hypotheses 1-3 are tested in a manner similar to past research because I give a time deadline. This is not a perfect way to test these tools because none of them gave us a time deadline). Therefore, I defined those in time because this would already be a step up from existing research, giving them about 3-7 years as opposed to existing research (which gave them months or a year) as a basis to conclude the results. In other words, my time limits for this research are about 3y for hypothesis 1 and about 7 years for hypothesis 3. With hypotheses 5 and 6, I actually test the tools as they are meant to (to simply predict future price, but not TIME + PRICE). Also I added hypothesis 7, which is a kind of a longer term test and H8 which is a shorter term time test (of the stock splits as tool to predict future value). These two hypotheses specifically are formulated by another piece - Can Tesla reach $5,000 in 10-15y which is by 2033/2038 as of 2023, which you can read here. Hypotheses 2 and 4 are not limited by time like 5,6 and 7.
Practical implications for my investing
This is most practical to me in the sense that it tells me I should not consider selling any of my Tesla shares below $400, and a reasonable first sell might be about $700. It also tells me that by any chance if by mid 2025, we are already about 1600, we might have grown a bit too aggressively and may have to have at least 3-6 months correction. In other words, these are the practical implications for me based on some of the 2016 Tesla I have purchased and some of the new Tesla I have recently purchased.
Because the near term future of Tesla as defined by less than 5-10 years, a key question I ask myself is what other stocks can I buy that can allow me to buy more Tesla. I answer this research question here.
Based on these expectations and deriving testable hypotheses with the help of technical analysis tools, I will also have provided a unique piece of evidence on the topic - "do technical analysis tools actually have predictive power?". That is important because I suspect a methodology design flaw in the current research, which may have dismissed these tools too early by analysing them in the wrong way. I am conducting the experiment with my own investment in Tesla. In that way, I have an open interest to discuss my real findings, be it that they worked out or not. So see you in Q1 2026 for the first update on this piece (or sooner if price is already at $700).
Lastly, I answer the research question of what I think is the sector and its stocks I'd buy to buy both more Tesla and Nvidia here. Finally, I have a research question about a specific company, which I wonder whether can be thought of as "a new type of Google/Baidu". You can read more about this company here.
Difference with existing experiments (this for people who are more interested about learning about how research is and should be conducted).
All the experiments I read regarding the tests of the predictive power of technical analysis tools, are conducted over a period of less than a year. It's somehow confusing how one section of a book about investing explains that 30 years are needed to determine whether a manager has actual skill or not, but at the same time, the methods used to test the predictive powers of the so-called technical analysis tools never examine a period longer than a year.
From the experiment methodology it becomes clear that those researchers did not understand that these tools predictive power does not lay in predicting price for a specific period, but to predict price and possible price targets.
If I had to do this properly, I would need to take about 100 pages to define a methodology, show mistakes in existing methodology and name my PhD thesis, debunking misconceptions about the value of technical analysis tools in investing. However, I'd much rather do this as a hobby in such an informal and practically-oriented way. At any university, the purpose is to create research with as wider applications as possible. The biggest thing one could do is formulate what is known as a "ground theory" (something like gravity or Einstein's theories in the world of physics). This is a type of theory that serves as the basis of other researchers to write further research based on the assumptions of a grand theory. So writing a grand theory makes a person like the superstar of the academic world. In the business world, an example would be Michael Porter with his 5 Forces of industry analysis (this is not necessarily a grand theory, but let's pretend that it is because his fame is nearly as high as the one of Einstein, albeit in a different field).
The key point I am trying to make is that I possess the skills to create and properly research this topic to the level of at least a Master's thesis (and potentially a PhD), I can make an experiment which requires about 1% on a PhD thesis (this piece) to conduct my own experiment with a single stock and its assumptions (I know, I will need at least another 299 examples, because for yes/no data, we need 300 data points to decide whether there is actually any predictive power). But let's begin with 1 of 300.
In this I am also trying to show that to have been properly researched this topic would require so much more time and effort than researchers have currently put. However, me being a nerd and all, I'd like to perform my own kind of test and this article and whether the hypotheses are accepted or rejected would provide me with further evidence regarding the predictive power of the tools used to predict these price targets.
Reminder
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Disclaimer
No one should buy or sell anything because of anything I post inside my blog. This is all that it is, a way for me to track my own thoughts, organize my research and share my unique opinions. Any opinion posted on this blog is just that - an opinion. The purpose of these pieces are to act as a motivational information tool. This is a blog about my life goals and the decisions I use to try to achieve them.
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In short, nothing in this blog is financial advice! Do your own research & Do your own due diligence. If you have investment questions, I highly recommend you seek help from an authorized (regulated) financial planner, which I am not.
