Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Finance
Reexamination Certificate
1999-10-08
2002-07-02
Hafiz, Tariq R. (Department: 2163)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Finance
Reexamination Certificate
active
06415268
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to the methods of recovering or extracting useful information from data containing noise or distorted by noise. More particularly, the present invention relates to recovering the real value of a stock from the stock pricing data by removing the noise associated with a stock in a stock market.
BACKGROUND OF THE INVENTION
The prior art in the field of the invention does not appear to disclose any method that is similar to the method of the present invention for recovering the real value of a stock from the stock pricing data.
The closest prior art appears to reside in the three following groups of approaches to and methods of stock valuation.
The first group of the stock valuation methods is usually associated with the value investment pioneered by Benjamin Graham. Its basic concept holds that the ability of a corporation to produce earnings determines the value that a stock market attributes to the stock of the corporation. The following is an interpretation of this concept by Nelson D. Schwartz (FORTUNE, Nov. 24, 1997). Aimed at general public, in order to make his point more vivid and convincing, he describes how Graham created a Mr. Market who comes and offers to buy a candy store. “Some days he's feeling up and offers wildly high prices; other days he offers wildly low prices. But regardless of Mr. Market's mood, the store's current business and its future prospects are unaffected. If you were the owner of the store, you'd value it on how much cash it was throwing off, and how much you expected it to throw off in the future. You'd sell to Mr. Market only if you believed that, after taking the proceeds and putting them into an alternative investment, you'd end up with more money—not just today, but all the way down the line. The trick, of course, is predicting the cash flow of your candy store out into the future, as well as the appropriate interest rate to use in discounting it”.
Given the assumption that the financial fundamentals of a corporation is the only determinant of its stock pricing in the market, the Graham's approach to stock valuation can be defined as deterministic valuation.
Accuracy of such valuation is low because it does not properly account for the random components in both stock pricing data and financial fundamentals of a corporation.
For example, consider how Jim Jubak, a well known analyst and writer, addresses this issue in his article “A Buying Panic” in the April's, 1997, WORTH magazine: “Fundamentals don't count for much. It does not matter if a stock is expensive (like Merck) or outrageously expensive (like Coca-Cola), as long as the price is rising. Professional investors are torn between a desperate fear of underperforming the indexes again and knowledge that the market could trash any stock tomorrow on the smallest signs that its upward price momentum might be slowing down—even if the fundamentals are still sound”.
Another example is by Chuck Clough, Merrill Lynch's top strategist: “Stock market valuation is imperfect science. I've been around a long time, and I've used every conceivable model known to man. There are valuation models that say the market's incredibly overvalued, and there are models that say it's undervalued”, cited in the above-mentioned article by Nelson D. Schwartz.
The second group of the methods of stock valuation and investment analysis can be classified as a pure probabilistic approach. Harry Markowitz initiated it in 1952. A Yale Professor William N. Goetzmann in the following way represent this concept in his Web course of investment theory: “Finance professor Harry Markowitz began a revolution by suggesting that the value of a security to an investor might best be evaluated by its mean, its standard deviation, and its correlation to other securities in the portfolio. This audacious suggestion amounted to ignoring a lot of information about the firm—its earnings, its dividend policy, its capital structure, its market, its competitors—and calculating a few simple statistics.
The Markowitz model was a brilliant innovation in the science of portfolio selection. With almost a disarming sleight-of-hand, Markowitz showed us that all the information needed to choose the best portfolio for any given level of risk is contained in three simple statistics: mean, standard deviation and correlation. It suddenly appeared that you didn't even need any fundamental information about the firm.”
However, the mathematical abstracts such as the mean and the standard deviation, unable to substitute or represent the real influence of corporate financial fundamentals on stock value and return on investment. Without such a link to the real world, this approach results in statistical processing of data containing both a regular (not random) component and a pure random component. Without first dividing these parts, it is impossible to distinguish between a change caused by a trend of appreciation of the value of a stock and a random change of its price.
More generally, such a true expanding system as a stock market can not be adequately represented or described by such simple statistics as the mean (the first moment of a probability distribution) and/or the standard deviation (the second moment of a probability distribution). By definition, an expanding system is the system whose main parameters and characteristics are evolving over time. Such a system has no fixed set of probabilities or a fixed probabilities distribution, the main requirements of applying to a system those standard probabilistic procedures. Such statistics as the mean value of a variable or its standard deviation in an expanding system are becoming outdated the moment they are calculated and thus meaningless without specifying how they are influenced by the expansion of the system. When these statistics nonetheless are applied to an expanding system, this brings about a distorted picture of the system, which a prominent English statesman, Benjamin Disraeli (1804-81) summarized in the following way: “There are three kinds of lies: lies, damned lies and statistics”.
A combination of deterministic and probabilistic approaches to stock valuation (often referred to as determining the potential of appreciation of a stock) is widely represented in the prior art. Both the pricing data of a stock and fundamental data of an underlying company are processed together for selecting stocks for a portfolio of stocks aimed at surpassing a select market index. There are more and more complex computing means involved in probabilistic processing of huge arrays of these kinds of data.
U.S. Pat. No. 5,761,442 to Dean S. Barr et al. discloses a data processing system involving an artificial neural network “for estimating the appreciation potential of each security in a capital market using both fundamental and technical information about the security”.
However, though being complex and sophisticated in terms of scope of processed data, fundamentally, such data processing systems do not depart from the above-mentioned pure probabilistic approach, as both the market pricing data and fundamental data are processed using similar statistical procedures and involving traditional statistics and statistical indicators such as the standard deviation, BETA, ALPHA and others. Though declared in the Summary of the above-mentioned invention that the use of neural networks “provides the capability to capture non-linear functional relationship among input variables”, there is no proof in the invention disclosure that this kind of result is achieved, as there is no expressions, formulas or graphs in the disclosure demonstrating an actually “captured” functional relationship between the system's variables, but a standard formula for calculating the average of the difference between two variables.
More generally, the main flaw of the systems of probabilistic processing of these kinds of arrays of data is that the processing itself is just changing the form of data representa
Hafiz Tariq R.
Meinecke-Diaz Susanna
LandOfFree
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