Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Finance
Reexamination Certificate
2000-08-16
2004-12-14
Trammell, James P. (Department: 3621)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Finance
C705S001100, C705S037000
Reexamination Certificate
active
06832210
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to a computerized system and a method for implementing a market neutral pairtrade model producing a low risk, high yearly return. Specifically, the invention relates to a computerized system for implementing a pairtrade strategy, wherein the correlation strength between two stocks of a pairtrade is determined such that fortuitous high correlation between these two stocks is substantially excluded. Particularly, the invention relates to a computerized system utilizing a market neutral relative value strategy wherein an optimal symmetric financial hedge ratio between stocks of selected pairtrades is calculated to produce the trading profits substantially overcoming transaction costs.
BACKGROUND OF THE INVENTION
Nobel Laureate Bill Sharpe says it will “revolutionize the investment management industry.” Famed financial planner Harold Evensky calls it “the best thing since sliced bread.” That thing is the market-neutral strategy.
In theory, market-neutral strategies insulate investors from stock-market volatility. Typically, a market-neutral investor uses a computer software to rank thousands of stocks according to how over- or underpriced they are. The investor buys a collection of the underpriced stocks and “neutralizes” these purchases by short-selling an equal dollar amount of the overpriced stocks. These strategies make money in a bull market if the owned stocks gain more than the short sales lose.
Each market-neutral manager puts a different spin on the basic strategy. One might screen for stocks using price/earnings ratios while another prefers earnings-growth rates. Some managers try to balance their purchases and short sales within individual sectors and market caps. Others might make small sector bets, overweighing financial stocks, for example, and shorting technology issues. One of these strategies is a correlation/convergence trading strategy (also called statistical arbitrage) designed to make profits without taking significant directional risk. Specifically, this strategy is a trade idea based on the principle that when the price of a stock (or stock portfolio) significantly deviates from its long-term trend, it will sooner or later converge (move back) back to its original trend. For example, a trading strategy that generates a buy signal at every dip of the stock price in a general up trend is a convergence trading strategy.
The convergence strategy primarily focuses on liquid price signals that are significantly strongly correlated. The correlation is defined as the linear regression correlation coefficient of a stock portfolio (or a single stock) and a benchmark over a certain period of time. For example, one can compute the correlation coefficient of IBM stock with respect to the S&P 500 index over the past 6 months. The meaning of correlation coefficient can be simply put this way: if the index moves up, and the correlation coefficient is X, then X percent of the time the stock also moves up.
Basically, the majority of market-neutral strategies work because the market prices of stocks may not have a random, chaotic character, as seems to be the case by following memory-less stochastic or Markov processes. These are the processes in which the future distribution of a variable depends only on the variable's current value. Stock prices are widely assumed to follow a Markov process. However, as illustrated in FIG.
1
and widely accepted in the investment industry, the typical stock price may be represented as a combination of a random or memory less market signal
10
and an oscillating or swinging rhythmical signal
12
of relatively small amplitude. Further, it has been noted that similar stocks, such as stocks of similar companies in the same industry, are usually well correlated.
Considering an individual stock, it may be noticed that the main component of the price signal is the memory-less signal
10
, as shown in FIG.
1
. It is because of this overwhelming influence of the random signal, a mean reverting price signal
14
calculated as a sum of the two components of the rice E signal may not be satisfactory reliable. As a result, it is difficult to consistently make profits. In order to enhance a chance of making profits, it was proposed to in trade two or more stocks simultaneously in order to minimize the memory-less
1
component and, at the same time, to rely on the oscillating rhythmic component in the price differential of at least two stocks. In other words, a group of stocks is selected such that the stocks' random components in the overall trading position are cancelled out, leading to a well defined oscillating and mean reverting price signal. This is illustrated in
FIG. 2
, showing two related stocks A and B as if they are connected by a spring. The difference between these stocks is an oscillating and mean reverting price signal C, which can be easily traded on: buy lows and sell highs.
In practical terms, as has been explained above, this market neutral portfolio management strategy is based on a classic hedge: a manager looks at stocks in pairs, buying the one he expects to perform best and selling short the one he expects to underperform. The concept has been generalized to accommodate long and short portfolios with different performance expectations.
As ideally as it sounds, the common practice in utilizing the neutral market strategy may not be entirely satisfactory. First of all, while analyzing related stocks, investors typically use the level or daily change correlation coefficient “r” as a measure of correlation strength between two stocks.
The correlation coefficient is a measure (ranging in value from −1 to 1) of the association between a dependent variable and one or more independent variables. If one variable's values are higher than its average value when another variable's values are higher than its average value, their correlation will be positive. By contrast, if one variable's values are lower than its average value when another variable's values are higher than its average value, their correlation will be negative. Thus, this coefficient is not necessarily a measure of causality, but it does indicate the strength of a relationship. A correlation coefficient of 1 implies that the variables move perfectly in lockstep; a correlation coefficient of 1 implies that the variables move inversely in lockstep; a correlation coefficient of 0 implies that the variables, as calibrated, are uncorrelated. Applying this measure to a pairtrade, it may not exclude fortuitous high correlation. For example, if two totally unrelated stocks or even loosely related stgcks both increase their prices in last few months, a high level of correlation will indicate this trend. Yet, this similarity may rather be explained by the fact that the economy as a whole experiences unprecedented growth, which indiscriminately positively affects a great majority of stocks.
Further, assuming that stock pairs have been “correctly” selected, the question arises as to how many shares of an overpriced stock should be shorted versus how many shares of a related underpriced stock should be acquired. For example, if two stocks are weakly correlated, the conventional linear regression slope will be very slightly inclined or near zero. This tells the investor to buy one stock without short selling any shares of the second stock, because shorting the second stock is not going to reduce the total variance of the two-stock portfolio. However, in reality, as mentioned before, if the market suddenly drops significantly, all stocks most likely will tend to drop proportionally. As a consequence, the investor is going to loose a golden opportunity of making profits on an overpriced stock, shares of which he could have sold but did not. In order to avoid this possibility, many financial institutions determine a market neutral hedge. The determination first involves calculating a conventional regression slope “x” (
FIG. 5
) of the time series of the prices of one of the stocks S
1
of the selected pair
St. Onge Steward Johnston & Reens LLC
Trammell James P.
Westport Financial LLC
Winter John
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