500 bonds are now being offered for 1.1 BTC each.
That was fast. But two major problems:
1. "a prediction market for GLBSE events." What's that about? It should be a prediction market for any event, or perhaps any Bitcoin-related event.
2. Apparently this uses Inkling which 'uses an "automated market maker" to control trading.' I don't know if I like this, we should have the possibility to place bids and asks. (Well, at least the algorithms were designed by Robin Hanson who is pretty cool.)
3. Why is the currency $?
Looks like Nefario's goals with this are completely different from what I had in mind.
Thanks for pointing that out, I totally missed what algorithm it is using. From the Inkling site:
Unlike the real stock market or most other prediction markets, Inkling Markets uses an "automated market maker" to control trading.
In typical stock markets, a human buyer must be matched with a human seller of stock. For example, if Joe is selling 50 shares at $50/share, there must be a buyer willing to pay that price and vice versa. The price of the stock itself is based on the supply and demand of the finite number of shares in play. In a real stock market and even most prediction markets in existence today, these transactions are all handled by computers, enabling millions of transactions like this to take place automatically each day.
In Inkling Markets, we do not force a buyer to be matched with a seller, and vice versa. From a trader's perspective, Inkling Markets is always the buyer and seller of shares and there is no limit to the number of shares in play. Inkling Markets also sets the stock price according to demand or lack there of. If a trader buys shares, there is demand for the stock and its price goes up. If a trader sells, there is a lack of demand and the price goes down.
The principles behind our algorithms originate from research by Professor Robin Hanson at George Mason University. Here is some background about Hanson's automated market maker:
I replied to one of nefario's posts linking to this
paper: A Practical LIquidity-Sensitive Automated Market Maker.In the paper it states:
Current automated market makers over binary events suffer from two problems that make them impractical. First, they are unable to adapt to liquidity, so trades cause prices to move the same amount in both thick and thin markets. Sec- ond, under normal circumstances, the market maker runs at a deficit.
It goes on to state:
The amount of liquidity in LMSR [Hanson's logarithmic market scoring rule] is a parameter set a priori before the market maker knows what bets traders will place. Setting the liquidity is more art than science—a constant dilemma for almost everyone who has implemented LMSR. Too little liquidity makes prices fluctuate wildly af- ter every trade; too much makes prices barely budge even following large bets. Exacerbating the problem, the amount prices move for a fixed bet in LMSR is a constant. The 1,000,001st dollar moves the price as much as the first, counter to intuitive notions of liquidity.
Higher liquidity is good for traders but comes at the cost of increasing the market maker’s worst-case loss. In general, an LMSR operator can expect to lose money in proportion to the liquidity it provides (Pennock and Sami, 2007). The cost is rationalized as payment for traders’ information. Yet subsidized markets are the exception rather than the rule. The vast majority of markets run at a profit. It’s no coin- cidence that most examples of LMSR in practice are games based on virtual currency rather than real money.
I was waiting for the bond buyback feature and the contract changes to come into effect on GLBSE before making my type of insurance bonds. I was also hesitant because GLBSE was going to make its own speculative market. The GLBSE speculative market does not look viable and I think I could go with my plans.