Dryships (DRYS) is a popular trading stock, especially for institutions. Professional traders use all sorts of methods to determine how the stock should behave. One of those methods is calculating how tightly correlated an asset moves around with other assets. For example, when oil rises, does Exxon stock rise? If so, how much? Algorithmic trading firms often set trading models up before the opening bell to try and trade away alpha each day. The most sophisticated firms use models that adjust with each stock's own personal idiosyncrasies.
Dryships is an interesting stock because it has some measurable relationship with several factors (securities), although the factors are less reliable than many traders would think. For example, (USO) is an exchange traded fund which accurately tracks the daily price moves of crude oil. Because (DRYS) is a diversified drybulk shipping, oil tanker, and oil drilling company, one might think oil plays a large role in the price of DRYS stock on a day to day basis.
The results year to date are that oil has only the slightest of positive relationships with Dryships. For every 1% rise in oil, Dryships rises only 0.19% per day. The p-value here is only around .61, so the results are not statistically significant either.
SPY, the ticker for the S&P 500 ETF, is a much more reliable factor to trade with. For every 1% rise in (SPY), year to date, Dryships has risen 2.79% per day, and with a p-value of .000 (a super reliable measure which is statistically significant).
Ocean Rig (ORIG) is the subsidiary that Dryships owns a majority interest in, so one would expect a very high correlation between the two stocks. But in reality, year to date, for every 1% Ocean Rig rises, Dryships only rises 0.151%, actually less than its ownership percentage in the company. The p-value is about .43, so again, this is a fairly unreliable statistic to use in daily trading.
Even though two of these factors have proven to be only lowly correlated and fairly unreliable predictors of Dryships, I think combined with the rest of the stock market they create a half decent model to work from.
The model could look as follows:
Daily % change in DRYS = 2.79*(Daily % change in SPY) + .19*(Daily % change in USO) + .15*(Daily % change in ORIG).
To increase accuracy of the model, one would want to play the safe side of the two less reliable factors. Meaning, until USO or ORIG have a major change, they should probably just be ignored. But if either changes by greater than 2-3%, they become important factors to consider in valuing Dryships for the day.
In sum, we see that the DRYS daily beta with the market has been 2.79. This means if the market begins to backtrack, DRYS will without a doubt collapse nearly three times as much.
On very volatile oil trading days, the more mathematical trader likely has an advantage trading Dryships by also considering the small effect oil has.
This model would likely be most successful for intra-day trading, and to be systematically successful, it would likely need to be in a large portfolio of similarly designed trades using other assets to hedge risks.
Many, many firms completely ignore the effect of oil in their models due to laziness, even on days where it should have some measurable effect. If USO and ORIG change very little, a single factor model with SPY should be used. When using the single factor model, the beta with SPY on a daily basis is 3.19, slightly higher than the beta of the multi factor model.
And there is your chance at capturing some alpha.