I've used Matlab intensively, it has a number of limitations for really big projects, it's not evident at first, and it takes too long to explain. The biggest limitation is that there is no data-management included. But it's very useful for "small" research projects. Don't bother about using it real-time, it's not worth the trouble, even if it may look good in some small demos.
On the other hand 20,000 hours is a bit excessive. IMHO people waste a lot of time trying to come up with nice GUI's or interfaces with spreadsheets. Take t****station, looks good too, but it's completely useless to me.
Speaking of holy grail machines, it seems that pair trading, relative value strategies, etc. for equities are getting arbitraged away... see below... any opinions?
when don bright started talking about daytraders doing pairs, i saw the writing on the wall...
On the other hand 20,000 hours is a bit excessive. IMHO people waste a lot of time trying to come up with nice GUI's or interfaces with spreadsheets. Take t****station, looks good too, but it's completely useless to me.
Speaking of holy grail machines, it seems that pair trading, relative value strategies, etc. for equities are getting arbitraged away... see below... any opinions?
when don bright started talking about daytraders doing pairs, i saw the writing on the wall...
FINANCIAL TIMES
April 12, 2002, Friday USA Edition 2
SECTION: GLOBAL INVESTING; Pg. 27
HEADLINE: Statistical arbitrage is stung by a more efficient market
BYLINE: By ROBERT CLOW
DATELINE: NEW YORK
Fund of hedge fund investors are becoming increasingly sceptical of statistical arbitrage as a hedge fund strategy.
They point to declining equity volatility and the increased efficiency of the equity market, arising from decimalisation, as two reasons why it is increasingly difficult for statistical arbitrage hedge funds to make money.
"Lower volatility and higher active trading makes it difficult to make money," said Barry Colvin, chief investment officer of Tremont Advisers, noting that some statistical arbitrageurs, who trade less frequently, continue to produce results. Mr Colvin noted that statistical arbitrage can take several forms, but generally the strategy involves buying and selling equities as they deviate from a historical range.
For example, if IBM rose sharply in price, a statistical arbitrageur might sell the stock short while buying a number of peer companies, such as Hewlett Packard. Such a trade should allow the arbitrageur to benefit from IBM's reversion to a more reasonable price while offering protection from a more general rally in the industry.
Statistical arbitrageurs usually rely on complex mathematical models to provide them with buy or sell signals. As a result some of the best-known quantitative names in hedge funds are active in this area.
DE Shaw is one of the best-known statistical arbitrage funds. Its results are understood to have held up well over the last year, but many of its peers are already suffering from the tough environment.
"Those that have tilted towards value have actually done OK," said Mr Colvin, noting that some of these funds have a growth or value bias like traditional equity funds. "Those that have no bias whatsoever have been pretty flat."
Most relative value hedge fund strategies either rely on volatility being high, as with this strategy, convertible arbitrage and global macro economic trading, or on volatility decreasing.
Mortgage arbitrage is one example of a so-called short volatility strategy, because the mortgage traders are short on the underlying mortgage refinancing options.
Equity volatility is declining now, partially as a result of the declining volume of money being pumped into the stock market by mutual fund investors.
Arthur Samberg, head of Pequot Capital Management, has said he expects hedge funds to struggle over the next few years as a result of this declining volatility.
The recent decimalisation of the stock market, which led to stocks going from being priced in eighths, quarters and halves to ten cent increments, has meant stock prices often move in smaller increments and therefore tend to overshoot less in response to news or data.
But that kind of increased efficiency is bad news for statistical arbitrageurs, whose whole strategy is based on profiting from stock prices overshooting for brief periods either on the upside or the downside.
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