The MM models here take into an account any number of possible outcomes, each outcome represented by `q_i`. Correct me if I’m wrong, but it seems like it assumes that one and only one outcome can be correct and cash in, while any shares in the other outcomes eventually become worthless.
I’m thinking of an application using play money where any number of outcomes can end up cashing in at different amounts. I would love for it to be purely trader-powered but if there are not many people playing at first, especially in a market with a large number of stocks, there wouldn’t be much activity.
Here’s a basic example of what I’m thinking: think of each “stock” (not sure what the proper term is so I’ll use stock) corresponding with a contestant at a ring toss game with 500+ contestants. Each player plays the same game individually, and each player’s performance is (for the most part) not affected by any other player’s performance. For each ring a player successfully tosses onto the peg, their stock is worth $1. So if David ends up getting 8 rings on the peg, that corresponding stock will cash in at $8. So there is no real set limit to the total price of all stocks. Would the MM have to act in a very different way than described?
Let’s also say the MM can be fed predictions to base prices on. According to my research and stats, I put an expected value of David’s ring count to be 7.429, for a price of $7.43. Is it possible to use that prediction price as a base, then use the result from a current MM model as a ratio to somehow act on it?
If David sprains a finger on his throwing hand before the game, my prediction would jump down to $3.75. Conversely, if he ate his Wheaties this morning my prediction would change to $9.62. It seems like I would want my base price to have a varying degree of effect on a stock, depending on market size (much as you control the liquidity variable `b` in your model.)
I hope someone can validate or invalidate these thoughts, and hopefully point me in the right direction of where to look for answers. I’m a software engineer looking for a model to best implement into my toy project.]]>
“Our performance in the first half has been phenomenal on all fronts,” Frear added. “As a result, we are increasing guidance across the board for subscribers, revenue, adjusted EBITDA, and free cash flow. Our adjusted EBITDA guidance includes approximately $19 million of incremental expense from the reported royalty litigation settlement that will be reflected in the second half of 2015. Reported free cash flow in the second half of 2015 will exclude the cash payment we expect to make this month under the settlement.”
Our full year 2015 guidance is as follows:
Net self-pay subscriber additions of approximately 1.6 million,
Total net subscriber additions of approximately 1.8 million,
Revenue of approximately $4.5 billion,
Adjusted EBITDA of approximately $1.62 billion, and
Free cash flow of approximately $1.3 billion.