August 17, 2007 here's more information about what Wall Street Gecko said about Hedge Funds there exists Wall Street folklore about some hedge funds having had amazing returns. my understanding is that George Soros’s Quantum Fund made more than $1 billion betting against the Brittish pound in the European exchange-rate crisis of September 1992. however there has been a lot of recent headline news reporting that some of the top (and/or largest) hedge funds, (such as Goldman Sachs 9 billion dollar Global Alpha Fund) had been experiencing significant difficulties recently. hedge funds are private investment funds which often obtain large profits by leverage (borrowing money to invest more than the money actually invested in the fund) small profits from statistical arbitrage investments can thereby be magnified; which works very nicely, as long as the market remains favorable; however, leverage also magnifies losses if the market turns unfavorable; such downside risk is not always obvious during favorable periods the recent problems with subprime mortgage loans exposed the underlying risk of the financially engineered derivitive products which re-packaged (and redistributed) the risk involved in such loans. CDO's (Collateralized Debt Obligations) one of the most dramatic hedge fund incidents in recent history was Long-Term Capital Management (LTCM), which was a hedge fund put together in 1994 by some of the top people on Wall Street and academia, including: John Meriwether (formerly the vice-chairman and head of bond trading at Salomon Brothers), and Myron Scholes and Robert C. Merton, who were awarded the 1997 Nobel Memorial Prize in Economics. initially, LTCM began with impressive annualized returns of over 40% for its first years, however in July 1998 the Russian government devalued the ruble and declared a moratorium on future debt repayments. in 1998 LTCM lost $4.6 billion in less than four months. along with such losses equity capital declined by 50% causing LTCM leverage ratio to climb to over 45 to 1 with that high leverage ratio being during a period of high volatility. by September 19, 1998 LTCM capital was down to only $600 million in the fund, however the fund had an asset base of $80 billion at that point. October 1, 1998 SEC testimony to the House Committee on Banking and Financial Services well, in September 1998 the Federal Reserve rescued Long-Term Capital Management the title of "hedge" fund implies there they are hedged against risk. some hedge funds utilize computers and databases to invest in arbitrage opportunities which might arise from perceived deviances from normal patterns, which are expected to return to normal patterns; simultaneously taking offsetting positions are expected to hedge against any risks involved. such arbitrage opportunity pricing differentials are often rather small, however financial leverage makes the effort worth while; Myron Scholes explained the functioning of LTCM with a simile of a giant vacuum cleaner picking up nickles that everyone else had overlooked. however Nassim Taleb compared the LTCM strategies as: "picking up pennies in front of a steamroller" economist John Maynard Keynes, is said to have warned investors that although markets do tend toward rational positions in the long run, "the market can stay irrational longer than you can stay solvent." mathematical covariance matrices quantify the relative price movements of stocks, more specifically, which stocks are likely to have price movement in opposite directions; problem is that such covariance matrices (upon which the risk hedges are based) are not static, and can dramatically change quite rapidly. there also exists some skepticism as to whether all "hedge" funds are actually hedged against risk. in the United States, financial securities are regulated by the Securities Act of 1933 the Securities Exchange Act of 1934, and the Investment Company Act of 1940. however hedge funds with fewer than 100 shareholders are exempt from regulation some "hedge" funds might be nothing much more then over-leveraged long funds. anyone remember the accounting issues which arose in the Enron/WorldComm era of the previous decade?

November 24, 2007 practitioners of quantitative analysis construct mathematical models in attempts to mathematically model the actions within a stock market environment Nobel prize winner (1963) William S. Sharp was one of the first mathematicians to utilize a factor model (each market variable being quantified by mathematical factors in equations) quantitative analysis tries to mathematically describe the interactive effects of stock prices my understanding is that when IBM tried to program a supercomputer to play chess IBM - Deep Blue supercomputer it was realized that because of processing and memory limitations it would be physically impossible to build a computer which would contain every possible move, for every square, for every combination of chess pieces. instead, a practical system was implemented which was programmed with the most likely (useful) chess move strategies well, in the stock market a similar problem arises if one tries to quantify every correlation, between every stock price. the most popular quant solution for stock market applications is to quantifiy only how every stock moves relative to a "proxy" for the market, the "proxy" usually being some standard market index (such as: S&P 500) if one is thus able to quantify the characteristcs of how each stock moves relative to the market as a whole, well then patterns can be found which quantify which stocks seem to move together such a quant model is called a "Single-Index Model" such models quantify "systematic" risk, id est: risks effecting the entire market, which are expected to show up as effects to the entire market index; and company specific risks, which in theory would account for specific characteristics of individual company stocks; of course, although mathematics itself is quite predictible, those with experience in the stock market would most likely tell you that the stock market is not as reliably predictable example: in math: 2 + 2 = 4 in the stock market: price is whatever the most powerful trading force says that it is; such theory being based upon the herd type characteristics of a significant percentage of market participants "quants", being quite mathematical, communicate with a mathematical alphabet symbolically representing the factors of their mathematical equations Alpha and Beta are the most basic quant symbols, (the word "alphabet" itself is a contraction of the first 2 ancient symbols: alpha and beta) and are shown in the following equation:

in practical application: a stock's beta mathematically quantifies the direct correlation of how much the price of a stock is expected to move (up or down) based upon how much the market index moves (up or down) (notice in the equation above, it multiplies the Market return) also notice however that alpha factor stock price movements are not correlated to entire market index price movements (no multiplicative (direct) connection to index price movements) an example of a stock with a beta of exactly 1 is mathematically expected to move by the same percentage (100%) as the index does the beta factors for each individual stock being mathematically derived by statistical regression; id est: for anyone who remembers some geometry: beta is the slope of the stock price line graph, statistical regression obtains the mathematical factors for that line from statistical analysis of the observed cloud of stock prices negative beta indicates a stock which tends to have price movements opposite to the direction of the index; short funds are expected to have such negative beta a beta of less then 1 exhibit less stock price volatility relative the whatever volatility the entire market is exhibiting betas of more then 1 indicate stocks which exhibit price changes which tend to be more then what the entire market experiences example: an ultra-bear short fund with beta of -2.5 would be expected to go up 5% if the market declines by 2% the high negative beta exhibiting magnification (leverage) well, the point is that during bull market environments high beta stocks are nice in that they tend to provide investors with higher yield then the market index; however if the market trend turns into a bear market environment high beta stocks are expected to have greater losses then the market

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this webpage was last updated on: December 5, 2007