Tuesday, August 07, 2007
NEWPORT BEACH, CA--(MARKET WIRE)--Aug 7, 2007—Biomerica, Inc. (OTC BB:BMRA.OB - News) announced that it received CE mark approval for its 15-minute home diagnostic test for detecting Helicobacter pylori (H. pylori), a bacterium responsible for over 85% of ulcers. The one step test can now be marketed directly to consumers through drug stores in the European Union.
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Epidemiological studies have shown that the H. pylori infection is widespread, affecting over one billion people worldwide. It has been shown that nearly all duodenal ulcer patients and over 80% of stomach ulcer patients are infected with the bacterium. The disease affects both men and women. In addition to peptic ulcer disease, H. pylori infection has also been shown to be a significant risk factor for stomach cancer.
“This regulatory approval allows us to now sell our product to consumers throughout Europe,” said Zackary Irani, CEO Biomerica. “We are already in discussions with several significant distributors.”
Biomerica’s home test, which will be marketed under the trade name “FORTEL H.P. Ulcer Test,” is a simple fifteen-minute test that individuals can perform in the privacy of their own home. The test utilizes an advanced technology that requires only a drop of a patient’s blood.
It is estimated that the worldwide anti-ulcer market is over $9.5 billion. The FORTEL H.P. Ulcer Test is a part of BIOMERICA’s line of easy to use tests designed to detect diseases before they become catastrophic, incurable or costly to treat.
About Biomerica (OTC BB:BMRA.OB - News)
Biomerica, Inc. (http://www.biomerica.com) is a global medical technology company, based in Newport Beach, CA. The Company manufactures and markets advanced diagnostic products used at home, in hospitals, and in physicians’ offices for the early detection of medical conditions and diseases.
The Private Securities Litigation Reform Act of 1995 provides a “safe harbor” for forward-looking statements. Certain information included in this press release (as well as information included in oral statements or other written statements made or to be made by Biomerica) contains statements that are forward-looking; such as statements relating to intended launch dates, sales potential, significant benefits, market size, growth of business, favorable positions, expansion, expected orders, leading market positions, anticipated future revenues or production volume of the Company, success of product and new product offerings. Such forward-looking information involves important risks and uncertainties that could significantly affect anticipated results in the future, and accordingly, such results may differ materially from those expressed in any forward-looking statements made by or on behalf of Biomerica. The potential risks and uncertainties include, among others, fluctuations in the Company’s operating results due to its business model and expansion plans, downturns in international and or national economies, the Company’s ability to raise additional capital, the competitive environment in which the Company will be competing, and the Company’s dependence on strategic relationships. The Company is under no obligation to update any forward-looking statements after the date of this release.
Contact:
Contact :
Zackary Irani
Tel: 949-645-2111
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Source: Biomerica
Report Page 3
MBRA Forum
Friday, August 03, 2007
Company Profile
Since 1971, Biomerica has developed, manufactured, and distributed medical diagnostic products for the early detection and monitoring of chronic diseases and certain medical conditions.
The Company’s test kits and devices are sold in three markets:
· Clinical laboratories
· Physician’s offices
· Over-the-counter (pharmacies)
Biomerica is proud of its history of product innovation spanning more than three decades in several major clinical areas. It was the first Company to manufacture and market tests for:
· Myoglobin (Cardiac)
· H. Pylori (Digestive Disease)
· Histamine (Allergy)
· Self test for Colon Disease (Digestive)
· Early detection of Diabetes (Diabetes)
All of our products are CE marked for European sales and manufactured under EN ISO 13485 certification.
The Biomerica main campus occupies an area of over 25,000 square feet in beautiful Newport Beach, California. This complex houses the company’s headquarters, including administrative offices, laboratories, and FDA registered manufacturing facilities.
Biomerica, Inc. also operates a 10,000 square foot production facility in Mexicali, Mexico. Like the US headquarters, the Mexico facility is both EN ISO 13485 and FDA certified.
Stocks Reporter.com has received no compensation in the form of cash or securities for coverage and has no position of MBRA shares.
Report Page 2
MBRA Forum
Learning to Trade: The Psychology of Expertise part1
When people hear that I am an active trader and a professional psychologist, they naturally want to hear about techniques for mastering emotions in trading. That is an important topic to be sure, and later in this article I will even have a few things to say about it. But there is much more to psychology and trading than “trading psychology”, and that is the ground I hope to cover here. Specifically, I would like to address a surprisingly neglected issue: How does one gain expertise as a trader?
It turns out that there are two broad answers to this question, focusing upon quantitative and qualitative insights into the markets. We can dub these research expertise and pattern-recognition expertise, respectively. These perspectives are much more than academic, theoretical issues. How we view knowledge and learning in the markets will shape the strategies we employ and—quite likely—the results we will obtain. In this article, I will summarize these two positions and then offer a third, unique perspective that draws upon recent research in the psychology of learning. I believe this third perspective, based on implicit learning, has important, practical implications for our development as traders.
Developing Expertise Through Research
The research answer to our question says that we gain trading expertise by performing superior research. We collect a database of market behavior and then we research variables (or combinations of variables) that are significantly associated with future price trends. This is the way of mechanical trading systems, as in the trading strategies developed with TradeStation and the systems featured on the FuturesTruth.com site. We become expert, the mechanical system trader would argue, by building a better mousetrap—finding the system with the lowest drawdown, least risk, greatest profit, etc.
A variation of the research answer can be seen in traders who rely on data-mining strategies. The data-miner questions whether there can be a single system appropriate for all markets or appropriate for all time frames. To use a phrase popularized by Victor Niederhoffer, the market embodies “ever-changing cycles”. The combination of predictors that worked in the bull market of 2000 may be disastrous a year later. The data-miner, therefore, engages in continuous research: modeling and remodeling the markets to capture the changing cycles. Tools for data mining can be found at kdnuggets.com.
There are hybrid strategies of research, in which an array of prefabricated mechanical systems are defined and then applied, data-mining style, to individual stocks to see which ones have predictive value at present. This is the approach of “scanning” software, such as Nirvana Systems’ OmniTrader. By scanning a universe of stocks and indices across an array of systems, it is possible to determine which systems are working best for which instruments.
As most traders are aware, the risk of research-based strategies is that of over fitting. If you define enough parameters and time periods, eventually you’ll find a combination that predicts the past very well—by complete chance. It is not at all unusual to find an optimized research strategy that performs poorly going forward. Reputable researchers develop and test their systems on independent data sets, so as to demonstrate the reliability of their findings.
Can quantitative, research-based strategies capture market expertise? I believe the answer is an unequivocal “Yes!” A perusal of the most successful hedge funds reveals a predominance of “quant shops”. Several research-based stock selection strategies, such as Jon Markman’s seasonal patterns (MoneyCentral.com) and the Value Line system, exhibit long-term track records that defy mere chance occurrence.
And yet it is also true that many successful traders neither rely upon mechanical systems nor data-mining. Indeed, one of Jack Schwager’s most interesting findings in his Market Wizards interviews was that the expert traders employed a wide range of strategies. Some were highly quantitative; others relied solely upon discretionary judgment. Several of the most famous market participants—Warren Buffet and Peter Lynch, for example—employed research in their work, but ultimately based their decisions upon their judgment: their personal synthesis of this research. Quantitative strategies can capture market expertise, but it would appear that all market expertise cannot be reduced to numbers.
Developing Expertise Through Pattern Recognition
The second major answer to the question of trading expertise is that of pattern recognition. The markets display patterns that repeat over time, across various time-scales. Traders gain expertise by acquiring information about these patterns and then learning to recognize the patterns for themselves. An analogy would be a medical student learning to diagnose a disease, such as pneumonia. Each disease is defined by a discrete set of signs and symptoms. By running appropriate tests and making proper observations of the patient, the medical student can gather the information needed to recognize pneumonia. Becoming an expert doctor requires seeing many patients and gaining practice in putting the pieces of information together rapidly and accurately.
The clearest example of gaining trading expertise through pattern recognition is the large literature on technical analysis. Most technical analysis books are like the books carried by medical students. They attempt to group market “signs” and “symptoms” into identifiable patterns that help the trader “diagnose” the market. Some of the patterns may be chart patterns; others may be based upon the identification of cycles, configurations of oscillators, etc. Like the doctor, the technical analyst cultivates expertise by seeing many markets and learning to identify the patterns in real time.
Note how the pattern recognition answer to the question of expertise leads to a very different approach to the training of traders. In the research perspective, traders learn to improve their trading by conducting better research. This means learning to use more sophisticated tools, gather more data, uncover better predictors, etc. From a pattern recognition vantage point, however, trading success will not come from doing more research. Rather, direct instruction from experts and massed practice leads to the development of competence (again, like medical school, where the dictum is “See one, do one, teach one”).
Another way of stating this is that the research answer treats trading as a science. We gain knowledge by uncovering new observations and patterns. The pattern recognition answer treats trading as a performance activity. We gain proficiency through mentoring and constant practice. This is the way of the athlete, the musician, and the craftsperson.
Can expertise be acquired by learning patterns from others and then gaining experience identifying them on one’s own? It would seem so: this is traditionally how chess champions and Olympic athletes develop. There are also examples of such expertise development in trading: Linda Raschke’s chatroom (http://www.mrci.com/lbr) is an excellent example of a learning device that takes the pattern recognition approach. Users of the site can “listen in” as Linda—a Market Wizard trader herself—identifies market patterns in real time. My conversations with traders who have enrolled in this service leave me with little doubt that they have acquired profitable skills, eventually moving on to becoming successful independent traders. Richard Dennis’ experiment with the “Turtles” is perhaps the most famous example of how expertise (in this case, a pattern-based trading system) can be modeled for people with little market background and yield winning results.
And yet there are nagging doubts about the actual value of the patterns typically described in market books and tapes. A comprehensive investigation of technical analysis strategies ____ found very little evidence for their effectiveness. An attempt to quantify technical analysis patterns by Andrew Lo at MIT found that they did, indeed, contain information about future market moves, but hardly as much as is generally portrayed. Because pattern recognition entails a healthy measure of judgment, it is very difficult to demonstrate its efficacy outside of the expert’s hands. In other words, the expert trader may be utilizing more information in trading than he or she can verbalize. This is certainly the case for chess experts and athletes. While they can describe what they are doing, it is clear that their proficiency extends well beyond the application of a limited set of rules or patterns.
This phenomenon has been the subject of extensive study in psychotherapy research. It turns out that there really is a difference in results between expert therapists and novices. But it also turns out that there is a difference between what expert therapists say they do and what they actually do in their sessions. This was noted as far back as the days of Freud. While he advocated a set of strict therapeutic procedures to be followed, his own published cases deviated from these significantly. What appears to work in therapy is not what the therapists focus on—their behavioral techniques, psychoanalytic methods, etc.—but the ways in which these are employed. Using any techniques in a sensitive way that gains the client’s trust and fits with the client’s understandings is more important than the procedures embodied by any of the techniques.
So it may well be with trading. Expert traders may describe their work in terms of price-volatility patterns, momentum divergences, short-skirt patterns, or a nesting of cycles, but it might be the ways in which these patterns are employed that makes for the expertise. Great traders may be able to identify patterns in their work, but it is not clear that their greatness lies in their patterns.
Implicit Learning: A New Perspective
The term implicit learning began with the research of Brooklyn College’s Arthur Reber in the mid 1960s. Since that time, it has been an active area of investigation, producing numerous journal articles and books.
Implicit learning can be contrasted with the research and pattern recognition perspectives described above, in that the latter are examples of explicit learning. By conducting research or by receiving instruction in market patterns, we are learning in a conscious, intentional fashion. The implicit learning research suggests that much of the expertise we acquire is the result of processes that are neither conscious nor intentional.
A simple example drawn from Reber’s work will illustrate the idea. Suppose I invent an artificial “grammar”. In this grammar, there are rules that determine which letters can follow given letters and which cannot. If I use a very simple grammar such as
MQTXG, then every time I show a subject the letter M, it should be followed by a Q; every time I flash a T, it should be followed by an X.
The key in the research is that subjects are not told the rules behind the grammar in advance. They are simply shown a letter string (QT, for example) and asked whether it is “grammatical” or not. If they get the answer wrong, they are given the correct answer and then shown another string. This continues for many trials.
Interestingly, the subjects eventually become quite proficient at distinguishing the grammatical strings from the ungrammatical ones. If they are shown a TX, they know this is right, but that TG is not. Nevertheless, if you ask the subjects to describe how they know the string is grammatical or not, they cannot verbalize any set of cogent rules. Indeed, many subjects insist that the letter arrangements are random—even as they sort out the grammatical ones from the ungrammatical ones with great skill.
Brett N. Steenbarger, Ph.D. is Associate Professor of Psychiatry and Behavioral Sciences at SUNY Upstate Medical University in Syracuse, NY and a daily trader of the stock index futures markets. Author of The Psychology of Trading (Wiley, 2003) and coeditor of the forthcoming The Art and Science of the Brief Psychotherapies (American Psychiatric Press, Inc., 2004), Dr. Steenbarger archives his trading psychology articles and daily trading strategies at his website, http://www.brettsteenbarger.com.
