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How Much Does the US Stock Market Fall/Decline on Average in a Market Crash?

How much does the stock market decline on average is a market crash? The average fall of the US stocks analyzed. 100 years of data examined. + Tesla's (TSLA) Expected fall in a market crash, and mitigation strategies.

 

Summary:

1. As per the data of over 100 years, the average stock market crash in the US stands at 25.5%.

2. As per the assessment, the next crash is expected to be in the range of 8.5% - 42%

3. Severe negative event related strategizing is discussed in the report.

4. Analysis of expectations regarding Tesla's (TSLA) decline is discussed in conjunction with risk mitigation strategies.

5. Tesla (TSLA) is expected to fall by 17% in the next market crash, in the best-case scenario, and 84% in the worst-case scenario.

 

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This report examines over 100 years of market crashes and declines to determine the average price fall in these events. This examination can aid us in formulating future expectations regarding a severe price decline event, which, of course, can always be around the corner. The example of Tesla (TSLA), and the impact of a possible severe negative event on its price is also discussed in this report.


An understanding of previous severe falls in equity mar can also aid us in formulating short-selling strategies. An accurate estimation of an average decline can be an integral part of the hedging and short-selling strategy formulation process.


To determine the average market crash, this report utilizes the Wilshire 5000 Full Cap Index (Reference) for severe price decline events from 1987 to 2020, the S&P 500 (Reference) from 1929 to 1962, and the Dow Jones Industrial Average (Reference) for 1907. The reason for using the Wilshire 5000, which represents the entire US equity market, is to accurately determine the impact of a crash or decline on all US equities, and not a specific sector or a specific sector heavy index.


Certain other indices, for example, the Nasdaq composite index, may have a higher exposure to a specific sector, i.e., Nasdaq is tech-heavy. For an accurate estimate of crashes from 1929-1962, the S&P 500, which is the most prominent and reliable market proxy for the time period in question, is used, and the Dow Jones Industrial Average, the most appropriate index for the 1907 crash/decline is used in the analysis.


Peaks to troughs around the crash periods have been assessed in this report. Furthermore, the confidence interval for price decline event, mean (average), has also been calculated. A confidence interval highlights the possible range around an estimate with a specific level of probability. For example, with a 98% confidence level, the expected mean should fall between a range calculated using the confidence interval, 98 out of 100 times, as per the past data.


Confidence interval:

Results

M: 98% CI [0.085, 0.42]

SD: 98% CI [0.18, 0.43]

Mean confidence interval: [0.085 , 0.42].

Alternatively: 0.25 ± 0.17

Margin of Error (MOE): 0.17.

Standard Error (S.E): 0.063.

Since the population's σ is not known, the formula uses the T distribution with n-1 degrees of freedom.

If you would calculate the confidence interval over an infinite number of samples with a sample size of 16, 98% of the calculated confidence intervals will contain the mean's true value.

What does this mean, simplistically? How much does the stock market crash/decline on average is a crash?

The average stock market crash/decline, as per the assessment of over 100 years of data, stands at 25.5%. 98% confidence interval indicates that the market crash, as per the data, has a range of 8.5% to 42%.


Assessment of over 100 years of data indicates that a logical expectation for the next market crash/decline would be about 25%, and we can claim with 98% confidence that the next crash, as per the data, should be in the range of 8.5% to 42%.


How can this analysis aid an investor? Example of Tesla (TSLA). Analysis of expected Tesla (TSLA) market crash

This analysis can aid investors in estimating potential portfolio or position losses in a severe price decline event, so that hedging strategies may be formulated, to mitigate the risk exposure. A further crucial element for a risk assessment, for a specific portfolio or position, would be the beta of the portfolio or position in relation to the overall market (calculated through a proxy).

For example, an investor holding (TSLA) may want to determine possible losses in case of a severe price decline event. The investor can multiply the average market decline and the range determined through the confidence interval to determine possible future losses in a crash/decline.


With a beta of 2, the average decline of Tesla (TSLA), as per the data, in a market crash/decline should be 51% (25.5 × 2). The 98% confidence interval indicates that losses should be in the range of 17% (best case scenario in a severe price decline event), and 84% in the worst case.


Thus, for every $1000 position in (TSLA), an investor is at risk of losing $170 in a price decline event in the best-case scenario, and an enormous 84%, or $840 in the worst-case (peak to trough). Understanding the potential decline can aid us in formulating risk-mitigating strategies and evaluating the cost of the mitigating options. For example, an investor concerned about a severe price decline event may be considering acquiring put options to transfer the risk of a crash to the seller of the put options. To consider the economic viability of the put options, the investor should determine the cost of the put option against the risk exposure.


In the discussed case, at the highest, if an investor is exceedingly concerned about a decline, put options priced lower than 17% of the total position in question would be a viable option. For example, in our hypothetical $1000 (TSLA) position, put options priced under $170, that cover the entire position should be economically viable. Of course, the cheaper the put options, the better for the investor.


Finally, it is worth noting that in the acquisition of the put options and their price, the investor must factor in the future gains from the position as well, before making a decision. For example, in terms of our hypothetical $1000 position in Tesla (TSLA), if the investor expects potential future profits over the life of the put to be higher than 17% of the position, and the put is worth 17%, then it would make sense to acquire the put, otherwise, if the put is more expensive than the potential gains, it would make more sense to close the position, as risks pertaining to market volatility increase. This condition indicates the importance of foresight; the earlier an investor is able to foresee a decline, the lower the price of the puts will be for the investor, as put options, of course, are priced lower when overall volatility is low.


Data



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