Amazon algo priced book at $23,698,655.93

Apparently High Frequency Algorithm battles take place outside Wall St. as well.

Source: CNN 

Capital Structure Arbitrage explained

Capital Structure Arbitrage usually exploits inefficiencies between credit and equity instruments of a business entity or autonomous economy. Like all arbitrage, it isn't truly risk free; when you eliminate risks from market movement, "little" issues like credit, systemic risks become much more apparent.

Examples

1) Convertible Arbitrage: Long convertiable bond/Short the stock
This works when you have a Positive Carry = Bond Yield - (Borrow Rate + Dividend)

2) Capital Structure Arb. with credit instrument ETFs: exploiting the aggregate relationship between credit and equity markets

"
... we have created using three ETFs – IEF (a Treasury bond ETF), LQD (a corporate bond ETF), and SPY (the S&P500 equity ETF). Critically the need to use a Treasury and corporate bond ETF is due to the need to separate credit risk premia from yield – both ETFs reflect yields and we use IEF to hedge away the risk-free movements in LQD. Based on years of analysis and our proprietary credit cycle view, we find that following a Z-Score of the differential between a carefully weighted combination of these three ETFs enables profitable (though infrequent) signals to be generated.
"

So there're quite a number of things to explore!

Kelly bet sizing, equity growth probability, and stuff

(THE KELLY CRITERION IN BLACKJACK SPORTS BETTING, AND THE STOCK MARKET1

 

 

where:

• f* is the fraction of the current bankroll to wager;
• b is the net odds received on the wager ("b to 1"); that is, you could win $b (plus the $1 wagered) for a $1 bet
• p is the probability of winning;
• q is the probability of losing, which is 1 − p.

 These are the guys who created the original theory of card counting in blackjack, way before the MIT team. Some important points off the above linked research paper, keep in mind everything's proved numerically

1) Kelly's Criterion for optimal bet sizing

2) Probability estimate for reaching specific future equity levels with respect to n trials(bets)

3) Estimate of trials needed to reach specific future equity levels

4) Actual examples applied to black jack card counting, sports betting, and of course financial trading

This was the result of a 3 month sports betting strategy, applying the Kelly Ratio for each bet size. They placed 5-15 bets per day to allow for the Law of Large Numbers to kick in sooner than later. With an initial bankroll of $50k, we can see that the actual profits beat the expected a bit and ended close to 100% for the period.

Backtesting strategies in R

Noticed a pretty good tutorial of this over at FOSS Trading. R is great for several reasons, mainly that it's free and efficient with system resources.

I've already uploaded a basic guide to time series analysis with R here. Enjoy!

Marriage risk management

So Paul Elam wrote a piece around the risks of marriage, divorce, and has made the whole ordeal quite horrifying. While I don't agree with everything in the article, it does provoke some critical, practical thoughts.


"
Marriage is quite literally an investment of not only your heart, but all of your work, income—and future income, especially when children are involved. Now, if an investment broker told you he had a deal in which you could invest, and there was more than a 50 percent chance that you would be wiped out and spend most of the rest of your life paying the margin call or going to jail, how much would you invest?
"


So from the standpoint of cold risk management, the choice appears simple; yet it defies every bit of traditional belief. Of course what about the successful marriages? There is probably more to it than simple statistics, and that may be where Elam, and 50%+ of the failures had missed; stuff to maintain/improve human relationships, I'm sure somethings' there.

LTCM strategies

Long Term Capital Managment  was run by a few Wall Streeters along with a couple of economics nobel prize winners, and as expected they had failed spectacularly in the late 1990s when volatility jumped, expectedly. Of course this again raises the earlier question of whether complex mathematical models are really necessary to make money in the markets.

Probably not.

Of all the mathematical ingenuity, the LTCM guys traded simple statistical arbitrage strategies e.g. interest rate convergence and basic pair trading that don't really require anymore than a basic understanding of applied statistics and no more math than calculus.

So again, making a profit is easier than most people think. It's the stuff that makes a KILLING (e.g. alpha generating high speed algo) that requires extraordinary intellect, talent; and these folks have a bit more ambition than academic recognition.