Any financial market can be approached similar to how an insurance company might approach their payouts. Each trend has a particularly amount of days you expect it to last, and aside from general statistical baselines, there are other risk factors that determine the health of the market that may indicate whether it is likely to outlast the historical norm or not.
Say for example, an average price move in the month of April over the past 20 years is 2.1%. If the markets have already gained 2.1%, you may have approximately 50% of outcomes that outperform this and you'd expect to continue higher, and 50% of outcomes that you expect would continue lower. While you might account for the risk that the downside is worse than has occurred historically, as well as the statistical expected value, and weigh many other factors, you can start with 50% chance as a baseline and adjust from there.
If you establish maximum bullishness and minimum bullishness, you might weight the odds according to the data. For example, if on a risk adjusted basis you suspected about a 45% chance of an equal move upwards as downwards, and your maximum risk was 100% and minimum was 0%, you might position in 45% stocks, and 55% cash. Of the stocks you deploy, you would focus on groups you expect to outperform.
By using historical handicapping, and a disciplined approach, you more often than not will avoid being very long near highs like most people do.
There are many metrics to historically measure a move vs other outcomes.
1)Monthly seasonal data
2)Election cycle annual data
3)10 year cycle data (the years 1966,1976,1986,1996,2006,2016 would compare to each other as the 6th year of a 10 year cycle for example)
4)Peak to trough duration and magnitude statistical analysis.
5)1-2-3 Trend reversal vs markets
Once you can assess the probability that the move continues higher and use that data in concert with your targets and stops, you can define the risk and reward of the move and estimate probability. This will give you an edge which can be measured through the use of a "Kelly criterion calculator" to assess the edge on a risk adjusted basis to other markets at the time.
Although the Kelly Criterion advocates risking way too much for normal individuals, it can still be useful on a comparative basis to other decisions. For those that understand how to fractionally adjust from that risk (depending on certainty of data and individual risk factors), you can use it to maximize your gains at a defined risk, or weight your total risk allocation according to your defined edge.
Since options market has a time duration, a clearly defined cost, and an upside defined by price movement, if you had all of the available data on market movement, knew the cost, and could calculate the intrinsic value gain, this sort of analysis would be extremely helpful for trading options on the broad market index funds. You could use those same kelly criterion calculators to calculate the long term expected growth at one full Kelly bet at each strike price. You could then choose the strike price that represents the best return with an equal amount of long term risk.
Unfortunately, some of this could be skewed by the extremes. For example, with 100 data points, the top performance represents a 1% of a happening. If that performance was high enough, betting on strike prices very far out of the money may be most profitable. However, that single data point could easily be much more likely or much less likely than 1% to occur, and the average top 1% of performances in the future could be much better or much worse than that single data point.
So if you use options, you probably should stick to the strike prices nearest to the money, or at least bet with the understanding that the farther out of the money you go, the less reliable historical data is.
While the baseline data is very useful, you have to put it in context with a variety of factors. If the average lifespan of a US male is 80 and a person is 40, you may expect them to live 40 years +/- the margin of error. However, if the person is 80, he's already proven he's not one of the individuals to die from 0-80, so the odds of him being an outlier may go up slightly. The expectation for someone who is 80 with a clean bill of health might be 85 or 90.
Unfortunately with the market, the greater the upside move without a pullback, traditionally the more steep the decline has the potential to be. Take 1920-1929 for example and 1929-1932 that followed. Unlike an insurance company, the payout to the individual that dies is fixed, while the risk if a decline begins is somewhat undefined.
You would have to instead measure the birth of a bear market, and the duration and magnitude. If the bear market starts here, how long does it last, and what is the loss if you are long until your exit conditions are met? If the bull continues, what is the gain? And what is the probability of each outcome?
This gives you the ability to handicap the move, apply an upside, and a downside and the probability of each and use the Kelly criterion to provide an allocation. You can use sites like multpl.com to look at historical prices by month or year dating back to the 1800s.
http://www.multpl.com/s-p-500-historical-prices/table/by-year
You can then use tools like Microsoft excel or open office. You can measure highs and lows over specific holding periods or from lows to highs and so on. You can also use charting software like thinkorswim's platform (TD ameritrade) and measure peak to trough or trends following a particular signal. Each measurement can be given a weighting.
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Of course up until now we have only discussed handicapping a market as a whole such as stocks. We haven't uncovered measuring ratios from one market to another, valuation metrics, valuation metrics from one market to another, and submarkets (sectors), or subsectors (industries) or even individual stocks. But you certainly can try to handicap these as well and make individual stock bets. That can be done as well.
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