What TLT Won't Do – Probably

TLT trade taken on 02/24/2023 based on what it will not do based on it’s own historical data.

Wrote PUTs for 04/21/23 expiry

Strike price: 84

Quantity: 100 PUTs

Premium: $700

Portfolio Margin: $4,140

“No lose since inception” strike is 83.94

Most, if not all, stock market analysts attempt to predict what a stock or the stock market will do in the future. They use fundamental analysis, technical analysis, and experience to predict the direction and potential levels to be reached before a potential turning point. We, on the other hand, analyze what a stock, bond, ETF, or index is unlikely to do in the future based upon its own historical data. Unlike RSI, Bollinger Bands, or other technical indicators that provide similar information, our measure uses the entire stock/ETF/index trading history to determine what the stock/ETF/index is unlikely to do.

So what is it ??

We have developed a machine learning system that measures a symbols' percentage price change versus time for multiple time periods and compares the symbols recent price behavior to its own past to determine if the current behavior is anomalous. Think of this as a measure of momentum run amok for the symbol. Our experience has shown that there are two levels of anomalous stock/ETF/index behavior that are of interest: (1) When the stock/ETF/index current performance is outside of 99% of all prior instances (this is called a Level 1 overbought or oversold condition) and, (2) When the stock/ETF/index current performance is outside the bounds of all of its prior history (this is called a Level 2 overbought or oversold condition).

We currently screen 600 stock/ETF/index symbols every day - with more being added each week. These symbols are all traded US stock market exchanges during normal US trading hours. Figure 1, below, shows a subset of symbols screened for February 24, 2023

Figure 1. February 24, 2023 Stock Screener Subset

In the first column of Figure 1, we have sets of 3 rows that have Level 1, Level 2, and Data Since labels. The Data Since rows describe the start date for each symbol associated with the date. For instance, SPX has 1/2/1962 which means that the machine learning system contains SPX data since January 2, 1962 for the computations of SPX (S&P 500). Likewise, the symbol INDY has data since November 23, 2009 for its computations, etc. Next are the rows for Level 1 and Level 2. Let's look at Level 1 first. In the rows for Level 1, a number greater than or equal to 1 indicates that on a price change % versus time basis, the symbol is overbought because it moved farther to the upside and faster than 99% of all other equivalent time periods in its history. These cells are highlighted light blue in the screener data. In the rows for Level 1, a number less or equal to zero indicates that on a price change % versus time basis, the symbol is oversold because it moved farther to the downside and faster than 99% of all other equivalent time periods in its history. These cells are highlighted light red in the screener data file. Level 2 is similar to Level 1 except that instead of 99% of the history, it is 100% of the history implying the stock/ETF/index has never moved that far that fast for any equivalent period of time in the data set. Yellow highlighted cells simply highlight symbols that are near overbought or oversold on a Level 1 or Level 2 basis.

In Figure 1, we can see that, AGG and UNG screened as Level 1 oversold AND SHOP and BAX as almost Level 1 oversold. However, in this article, we are focusing on TLT (iShares 20+ Year Treasury Bond ETF ) as it is neither overbought or oversold.

Figure 2, below, shows a chart of the TLT price action over the last six months.

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https://preview.redd.it/u9yz18fyxika1.png?width=936&format=png&auto=webp&v=enabled&s=3f192c0d9fdb67c21159c6d416aa8a8c7f448e88

So how can we use this overbought/oversold information ??

The machine learning system that determines the overbought/oversold condition also provides threshold levels that correspond to what the symbol is unlikely to do in the FUTURE based upon where it is trading now. Table 1, below, shows various levels and limits with different probabilities of occurrence for TLT for a subset of future dates.

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Table 1. TLT Upper and Lower Closing Limits through March 23rd, 2023

In Table 1, above, we show five (5) columns corresponding to "No loss since data start", "Once Every 10 Years", "Once Every 5 Years", “Once Every 1 Years”, and "1% Raw Data". Under each column header are sub-headers titled "Lower Limit" and "Upper Limit". Under each of these sub-headers are prices that reflect what TLT is unlikely to close below or above for varying probabilities defined the column header. For instance, in the row corresponding to the 2/28/2023 date, we see a lower limit of 89.16 and upper limit of 116.56 under the No loss since inception column header. This implies that TLT closing below $89.16 or above $116.56 on Tuesday, February 28, 2023 would be historical (something that's never happened before) on a percentage price change versus time basis. Likewise, for the 03/23/2023 date, we see a lower limit of $90.22 and upper limit of $122.44 under the Once Every 5 Years header. This implies that closing below $90.22 or above $122.44 on Friday, March 23rd, 2023 would be expected to happen once every 5 years. Given that there are approximately 252 trading days every year, once every 5 years corresponds to a likelihood of 1 in 1260 or 0.079%. Finally, in the 1% Raw Data column, the prices reflect a 1% probability of closing below the indicated future lower limit prices or above the indicated future upper limit prices on the corresponding date based upon the entire history of TLT - including what it's done recently.

So who could benefit from this data ??

• Options Traders. The primary beneficiaries of this type of information are options traders. By providing closing price levels for a symbol for a given future date, an option trader can write option positions with a known probability of success for both Put and Call options.

• Long/short strategies. Knowing when a stock/ETF/index has moved too far, too fast is an important input in a buying/selling/shorting decision. This screening decision is done automatically by the machine learning computers on a daily basis. The only thing the computer doesn't do is push the buy or sell button for you.

• Elliott Wave Practitioners. Elliott Wave analysis can be a powerful tool for analyzing the likely path a stock/ETF/index may take higher or lower. However, there are times when multiple paths may present themselves with near equal probability using Elliott Wave analysis alone. However, many times, one or more possible paths would require the stock/ETF/index to move in a manner that would be unlikely to happen based upon the machine learning analysis.

• Fundamentalists. All traders have two decisions to make: When to buy and when to sell or vice versa. A fundamental trader chooses to screen companies based upon fundamental analysis and makes buying decisions based upon this decision. However, when does the fundamental trader choose to sell, buy more, etc. The stock screening method explained in this paper can be a useful tool in helping make those decisions with exact price levels.