Slashdot Mirror
Stock-Picking Computers
eldavojohn writes "A while ago, Slashdot ran an article on Algorithms used to augment or replace analysts. Today, the NY Times is running an article on stock-picking computers with quotes from the lovable Ray Kurzweil." From the article: "'Investment firms fall over themselves advertising their latest, most esoteric systems,' said Mr. Lo of M.I.T., who was asked by a $20 billion pension fund to design a neural network. He declined after discovering the investors had no real idea how such networks work. 'There are some pretty substantial misconceptions about what these things can and cannot do,' he said. 'As with any black box, if you don't know why it works, you won't realize when it's stopped working. Even a broken watch is right twice a day.'"
While the idea of stock picking algorithms is neat; market history suggest it won't work a a way to predict performance. What would be interesting is to better search for arbitrage opportunities to exploit faster than others. Of course, eventually others do the same and it becomes an arms race.
I'm a consultant - I convert gibberish into cash-flow.
First you have to get a license for the exotic pet, then you have to import it, then you have get it vaccinated and take out liability insurance in case it throws feces at a passing dignitary.
No, no, no, there's a better way. Get me a case of beer and a copy of the WSJ and a dartboard. I'm at least as random as a monkey after 24 beers.
The only surefire protection against Microsoft infections is abstinence. - The Onion
That's not the same as automated stock picking.
Automated trading systems 'generally' are used to take a position in a stock that has already been picked.
So, trader A in Goldman Suchs wants to take a long position (buy) 100K shares of IBM, so he assigns that trade to the algorithmic trading engine, which might offer him various algorithms to help fulfill his position at the best possible price, ranging from %vol, VWAP, 'iceberg' or other type of algorithm.
notice, though, that the trader already had the stock to trade chosen, he didn't let the algorithmic engine choose it for him.
[ Yes, I am joking. I'm quite sure Mr. Lo is brilliant -- just maybe a touch too honest. :-) ]
John
In some cases, the result seems to work more consistently.
I had a friend who worked in the AI department of Lockheed (about 20 years ago) and they developed the software that was used for the Robert Prector's Elliot Wave newsletter. Every two years they would give the program to a couple of people to try for 6 months. These people would invest $10,000, use the program and follow the guidelines, then evaluate the results. I was privy to the outcomes of three of these tests in the mid-nineties, and the lowest was earned $15,000 and the highest was earned $36,000. These are pretty good results. (However, the stock market was steadily climbing during those years and I wasn't able to compare results with EWT competition. Still, if I was able to consistently get 30%/year on my investments...)
Back in the 70's, Dean Witter had a program called PACE. I know two people who had a system for using it that earned them over $100,000/year, and they never deviated from the program while I knew them.
Then I have a friend who is a very conservative money manager (manages a couple hundred million of other people's money), and over the period that stocks crashed (remember Enron and Worldcomm?) he only had two clients lose any money, and the biggest loss was less than %15. He claims that these programs are mostly bunk. (This guy is a perfectionist, and I bet a computer is no more disciplined than he is.)
These programs are not investment management programs. The principles of investment management are pretty simple. The best book I know on the subject is still Benjamin Graham and David Dodd's book, "Security Analysis". However, the problem is finding opportunities that comply with the principles. Systematic data analysis by computer could have a profound effect, and that's what most of these programs do.
BTW, the article mentions that profits are slowing down: In Robert Prector's book, "The Elliott Wave Theory" and in his newsletter, he sort of predicts that as information becomes more available for analysis trading will be done more rapidly on spreads that may show profits as low as 1.5%
"The mind works quicker than you think!"
First of all, the article summary seems a bit misleading because people might think we're talking about "stock picking" as in analyst opinions/monkeys picking stocks. That's not so, this is about software driven trading. Often this is of a very short term nature, so in a way the growing use of program trading is the "new day trading" fad.
In the industry it's called "program trading" and refers to automated, algorithmic trading of instruments such as stocks, futures, forex. This is regularly done by many banks and large funds, and also small investors. In fact there is a discount brokerage which I'll just call IB here, that has an API which lets anyone program their own computerized trading. It's a bit "too easy" to do.
That doesn't mean it's always profitable in the long term, but without a doubt people are profiting at least in the short term. The software has multiple strategies, well documented approaches and algorithms. Generally the trading robot is trying to ride trends.
As someone who follows these things, here are a few criticisms I'm aware of:
1. These short term trading activities require high leverage, because trades have to be for large amounts of money to make them worthwhile. You need large amounts of money to make this work, because things like trading costs eat into profits tremendously. Again, like day trading.
2. High leverage is risky because one big mistake or unpleasant event could wipe out tons of past small gains. Risk management becomes a key issue. Some would argue that perceived risk in markets these days is unreasonably low. Does this unbalance the risk/reward equation?
3. Market-wide, we know program trading has increased dramatically on US exchanges. Add to this the undocumented program trading (smaller traders who don't have to report it to anyone) and basically there are a ton of computer algorithms out there today trading stocks. Everybody can't make money at the same time, so to profit the participants have to use even greater leverage = more risk.
4. Programming flaws, bugs, or improper risk management could have tremendous market-wide implications. Take for example the huge market moves in 1987; the drop was a "20-sigma" event and not anywhere within the realm of possibility back then. Obviously the models failed to handle it. Similarly, the next time we have a "big event" in markets, today's algorithms might fail. If a large number of computers choke while trading, could bad things happen?
5. So under unstable market conditions, the program trading could lead to increased volatility (like daytrading caused volatile markets during the crash). But under stable market conditions, like we have today, program trading seems to smooth out daily movements. Notice that the US markets hardly move as much as 1% in a day; trends are smooth and volatility is extremely low. The VIX, a volatility measure, has hit historic lows.