There is a science based method used to try to predict the future, it is known as predictive modeling. How does this work and what can we learn from it? Dr. Data, aka Michael Rechenthin, Ph.D joins the guys to use his special skills in Data Science to explain this concept.
An example of extrapolation (which estimates the future based on past values or trends) was shown using the growth of Dr Data’s new baby, “Jr. Data” (congratulations Mike). The height of the baby at one day and one week and at 20 years was displayed. The example showed that at the current growth rate the baby would be 27 feet by the age of twenty. Mike used Silver as an example next and showed how using prices only going back 6 months, a prediction of Silver reaching $37 per ounce by the end of October in 2018 would be projected. Changing the data to a longer time frame of 6 years would project the price of Silver going down below zero on the same date in October 2018.
Mike explained how there are two types of models used for predicting streaming data such as the direction of stock prices. There are “Adaptive Online Algorithms” which incorporates data as it arrives, while “forgetting” old data. The other type are “Batch Learning Algorithms”. These are designed to learn from “chunks” of data but they may quickly become obsolete. All prediction models though have problems with concept drift. Predictability decreases as market efficiency kicks in and any edge will cease to exist as others spot the opportunity. So what can we do if we can’t predict the future of prices?
The segment of Skinny on Options Data Science from May 5, 2016: “Spreadsheet for Calculating Expected Moves” showed us how while we can’t predict the daily future price direction using algorithms, we can use options to calculate the expected range of the market. A 1 year graph of the price of the S&P 500 showed that by using the Implied Volatility (IV) of options we can calculate a future expected range. Mike explained that because “Implied Volatility often overstates realized volatility” we can use that to make money. Since about 80% of the time the underlying will be in that projected range we can make money from Theta (time decay) by selling option premium and we don’t have to be right about the direction of the underlying.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike teaches us while we can’t predict the future with any regularity we can predict future ranges most of the time and use Theta decay to give ourselves a good chance to make money.
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