Overview
CANSLIM is an acronym for a stock market investment method developed by William O'Neil. O'Neil is the founder and chairman of Investor's Business Daily, a national business newspaper. He also heads an investment research organization, William O'Neil & Company, Inc.
Drawing from his study of the greatest money-making stocks from 1953 to 1985, O'Neil developed a set of common characteristics that each of these stocks possessed. The key characteristics to focus on are captured in the acronym CANSLIM.
C urrent quarterly earnings per share
A nnual earnings growth
N ew products, New Management, New Highs
S hares outstanding
L eading industry
I nstitutional sponsorship
M arket direction
Interpretation
The following text summarizes each of the seven components of the CANSLIM method.
Current Quarterly Earnings
Earnings per share ("EPS") for the most recent quarter should be up at least 20% when compared to the same quarter for the previous year (e.g., first quarter of 1993 to the first quarter of 1994).
Annual Earnings Growth
Earnings per share over the last five years should be increasing at the rate of at least 15% per year. Preferably, the EPS should increase each year. However, a single year set-back is acceptable if the EPS quickly recovers and moves back into new high territory.
New Products, New Management, New Highs
A dramatic increase in a stock's price typically coincides with something "new." This could be a new product or service, a new CEO, a new technology, or even new high stock prices.
One of O'Neil's most surprising conclusions from his research is contrary to what many investors feel to be prudent. Instead of adhering to the old stock market maxim, "buy low and sell high," O'Neil would say, "buy high and sell higher." O'Neil's research concluded that the ideal time to purchase a stock is when it breaks into new high territory after going through a two to 15 month consolidation period. Some of the most dramatic increases follow such a breakout, due possibly to the lack of resistance (i.e., sellers).
Shares Outstanding
More than 95% of the stocks in O'Neil's study of the greatest stock market winners had less than 25 million shares outstanding. Using the simple principles of supply and demand, restricting the shares outstanding forces the supply line to shift upward which results in higher prices.
A huge amount of buying (i.e., demand) is required to move a stock with 400 million shares outstanding. However, only a moderate amount of buying is required to propel a stock with only four to five million shares outstanding (particularly if a large amount is held by corporate insiders).
Leader
Although there is never a "satisfaction guaranteed" label attached to a stock, O'Neil found that you could significantly increase your chances of a profitable investment if you purchase a leading stock in a leading industry.
He also found that winning stocks are usually outperforming the majority of stocks in the overall market as well.
Institutional Sponsorship
The biggest source of supply and demand comes from institutional buyers (e.g., mutual funds, banks, insurance companies, etc). A stock does not require a large number of institutional sponsors, but institutional sponsors certainly give the stock a vote of approval. As a rule of thumb, O'Neil looks for stocks that have at least 3 to 10 institutional sponsors with better-than-average performance records.
However, too much sponsorship can be harmful. Once a stock has become "institutionalized" it may be too late. If 70 to 80 percent of a stock's outstanding shares are owned by institutions, the well may have run dry. The result of excessive institutional ownership can translate into excessive selling if bad news strikes.
O'Neil feels the ideal time to purchase a stock is when it has just become discovered by several quality institutional sponsors, but before it becomes so popular that it appears on every institution's hot list.
Market Direction
This is the most important element in the formula. Even the best stocks can lose money if the general market goes into a slump. Approximately seventy-five percent of all stocks move with the general market. This means that you can pick stocks that meet all the other criteria perfectly, yet if you fail to determine the direction of the general market, your stocks will probably perform poorly.
Market indicators are designed to help you determine the conditions of the overall market. O'Neil says, "Learn to interpret a daily price and volume chart of the market averages. If you do, you can't get too far off the track. You really won't need much else unless you want to argue with the trend of the market."
Thursday, March 13, 2008
CORRELATION ANALYSIS
Overview
Correlation analysis measures the relationship between two items, for example, a security's price and an indicator. The resulting value (called the "correlation coefficient") shows if changes in one item (e.g., an indicator) will result in changes in the other item (e.g., the security's price).
Interpretation
When comparing the correlation between two items, one item is called the "dependent" item and the other the "independent" item. The goal is to see if a change in the independent item (which is usually an indicator) will result in a change in the dependent item (usually a security's price). This information helps you understand an indicator's predictive abilities.
The correlation coefficient can range between ±1.0 (plus or minus one). A coefficient of +1.0, a "perfect positive correlation," means that changes in the independent item will result in an identical change in the dependent item (e.g., a change in the indicator will result in an identical change in the security's price). A coefficient of -1.0, a "perfect negative correlation," means that changes in the independent item will result in an identical change in the dependent item, but the change will be in the opposite direction. A coefficient of zero means there is no relationship between the two items and that a change in the independent item will have no effect in the dependent item.
A low correlation coefficient (e.g., less than ±0.10) suggests that the relationship between two items is weak or non-existent. A high correlation coefficient (i.e., closer to plus or minus one) indicates that the dependent variable (e.g., the security's price) will usually change when the independent variable (e.g., an indicator) changes.
The direction of the dependent variable's change depends on the sign of the coefficient. If the coefficient is a positive number, then the dependent variable will move in the same direction as the independent variable; if the coefficient is negative, then the dependent variable will move in the opposite direction of the independent variable.
You can use correlation analysis in two basic ways: to determine the predictive ability of an indicator and to determine the correlation between two securities.
When comparing the correlation between an indicator and a security's price, a high positive coefficient (e.g., move then +0.70) tells you that a change in the indicator will usually predict a change in the security's price. A high negative correlation (e.g., less than -0.70) tells you that when the indicator changes, the security's price will usually move in the opposite direction. Remember, a low (e.g., close to zero) coefficient indicates that the relationship between the security's price and the indicator is not significant.
Correlation analysis is also valuable in gauging the relationship between two securities. Often, one security's price "leads" or predicts the price of another security. For example, the correlation coefficient of gold versus the dollar shows a strong negative relationship. This means that an increase in the dollar usually predicts a decrease in the price of gold.
Correlation analysis measures the relationship between two items, for example, a security's price and an indicator. The resulting value (called the "correlation coefficient") shows if changes in one item (e.g., an indicator) will result in changes in the other item (e.g., the security's price).
Interpretation
When comparing the correlation between two items, one item is called the "dependent" item and the other the "independent" item. The goal is to see if a change in the independent item (which is usually an indicator) will result in a change in the dependent item (usually a security's price). This information helps you understand an indicator's predictive abilities.
The correlation coefficient can range between ±1.0 (plus or minus one). A coefficient of +1.0, a "perfect positive correlation," means that changes in the independent item will result in an identical change in the dependent item (e.g., a change in the indicator will result in an identical change in the security's price). A coefficient of -1.0, a "perfect negative correlation," means that changes in the independent item will result in an identical change in the dependent item, but the change will be in the opposite direction. A coefficient of zero means there is no relationship between the two items and that a change in the independent item will have no effect in the dependent item.
A low correlation coefficient (e.g., less than ±0.10) suggests that the relationship between two items is weak or non-existent. A high correlation coefficient (i.e., closer to plus or minus one) indicates that the dependent variable (e.g., the security's price) will usually change when the independent variable (e.g., an indicator) changes.
The direction of the dependent variable's change depends on the sign of the coefficient. If the coefficient is a positive number, then the dependent variable will move in the same direction as the independent variable; if the coefficient is negative, then the dependent variable will move in the opposite direction of the independent variable.
You can use correlation analysis in two basic ways: to determine the predictive ability of an indicator and to determine the correlation between two securities.
When comparing the correlation between an indicator and a security's price, a high positive coefficient (e.g., move then +0.70) tells you that a change in the indicator will usually predict a change in the security's price. A high negative correlation (e.g., less than -0.70) tells you that when the indicator changes, the security's price will usually move in the opposite direction. Remember, a low (e.g., close to zero) coefficient indicates that the relationship between the security's price and the indicator is not significant.
Correlation analysis is also valuable in gauging the relationship between two securities. Often, one security's price "leads" or predicts the price of another security. For example, the correlation coefficient of gold versus the dollar shows a strong negative relationship. This means that an increase in the dollar usually predicts a decrease in the price of gold.
EFFICIENT MARKET THEORY
The Efficient Market Theory says that security prices correctly and almost immediately reflect all information and expectations. It says that you cannot consistently outperform the stock market due to the random nature in which information arrives and the fact that prices react and adjust almost immediately to reflect the latest information. Therefore, it assumes that at any given time, the market correctly prices all securities. The result, or so the Theory advocates, is that securities cannot be overpriced or underpriced for a long enough period of time to profit therefrom.
The Theory holds that since prices reflect all available information, and since information arrives in a random fashion, there is little to be gained by any type of analysis, whether fundamental or technical. It assumes that every piece of information has been collected and processed by thousands of investors and this information (both old and new) is correctly reflected in the price. Returns cannot be increased by studying historical data, either fundamental or technical, since past data will have no effect on future prices.
The problem with both of these theories is that many investors base their expectations on past prices (whether using technical indicators, a strong track record, an oversold condition, industry trends, etc). And since investors expectations control prices, it seems obvious that past prices do have a significant influence on future prices.
The Theory holds that since prices reflect all available information, and since information arrives in a random fashion, there is little to be gained by any type of analysis, whether fundamental or technical. It assumes that every piece of information has been collected and processed by thousands of investors and this information (both old and new) is correctly reflected in the price. Returns cannot be increased by studying historical data, either fundamental or technical, since past data will have no effect on future prices.
The problem with both of these theories is that many investors base their expectations on past prices (whether using technical indicators, a strong track record, an oversold condition, industry trends, etc). And since investors expectations control prices, it seems obvious that past prices do have a significant influence on future prices.
Play with Technicals
Preferring simplicity in nearly all areas of my life, when it comes to using indicators in my trading I also try to avoid becoming overwhelmed with daunting algorithms and a plethora of lines and squiggles overpowering my trading screens. As such, in this three-part series on Fibonacci, I shall be speaking to you in layman's terms, so that even those with the most basic of technical analysis skills shall come away with a core skill set to immediately apply to improve market timing, and in the process, the most sought-after goal of consistent profitability. In this week?s segment, I will begin by laying the foundation for understanding the development and theories leading to this popular tool used extensively by technical analysts.
Leonardo Pisano, an Italian mathematician born in Pisa during the 12th century, was renowned as one of the most talented mathematicians of his day. He is most prominently recognized for his publication of the modern numbering sequence called Fibonacci series. The name Fibonacci itself was a nickname given to Leonardo. It was derived from his grandfather?s name and means son of Bonaccio.
Although Leonardo was not responsible for discovering the number sequence, it was his publication of Liber Abaci in 1202 which introduced it to the West. In his book, he used the sequence to suggest a solution to the hypothetical growth of a population of rabbits (assuming they never die, of course!). While many claims for the prevalence of Fibonacci series in nature are poorly substantiated, it does appear in many biological settings, such as in the progression of branches on a tree.
The Fibonacci numbering sequence, which can be traced as far back as the 2nd century BC in India, is created by first beginning with 0 and adding 1. At that point, each new number in the sequence is the sum of the previous two numbers. For instance, 0+1 = 1, 1+1=2, 1+2=3, and so on. The sequence of numbers hence looks like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, to infinity.
Although this numbering series is studied quite extensively in terms of its relevance to growth patterns in nature, it is when we take this series a step further that its applications become even more widespread. The relationship of each two adjacent numbers within this series yields a predictable ratio.
When you divide the former number by the latter after the first 3 pairs, beginning with 8 divided by 13, it yields approximately 0.618.
34/55 = .618181 ~ 0.618
55/89 = .617977 ~ 0.618
89/144 = .618055 ~ 0.618
Dividing the latter number by the former number after the first 3 pairs also results in another relationship from the sequence. This relationship yields approximately 1.618.
55/34 = 1.617647 ~ 1.618
89/55 = 1.618181 ~ 1.618
144/89 = 1.617977 ~ 1.618
The dimensional properties adhering to the 1.618 ratio occur throughout nature and the ratio is most referred to as The Golden Ratio. The uncurling of a fern and the patterns found on various mollusk shells are commonly cited examples of this ratio.
To take these relationships further, if you skip a number and then divide, the result is 0.382. This number, when added to 0.618, equals 1.
The ratios created using the Fibonacci series found their way into the financial mainstream during the late bull market of the 90s. Although futures traders had been using them for quite some time, it was not until the advent of real-time charting software was invented, which manually calculated the Fibonacci levels, that it became more readily available as a tool for the general public. The levels created by the Fibonacci series are now widely popular in all markets, although still most widely followed in the futures.
The main Fibonacci retracement levels which I use in the markets are the 138.2%, 100%, 61.8%, 50%, 38.2%, 0%, and -38.2% levels. Two other numbers often used by other traders of my acquaintance include 0.786 and 1.27. These are the square roots of 0.618 and 1.618. As I said earlier, however, I prefer to keep things simple and have never been compelled enough to add these to my chart analysis. The practical uses of Fibonacci ratios in technical analysis are as a means of projecting upcoming price corrections or retracement levels, and can be used both in terms of price projections, as well as time projections.
Leonardo Pisano, an Italian mathematician born in Pisa during the 12th century, was renowned as one of the most talented mathematicians of his day. He is most prominently recognized for his publication of the modern numbering sequence called Fibonacci series. The name Fibonacci itself was a nickname given to Leonardo. It was derived from his grandfather?s name and means son of Bonaccio.
Although Leonardo was not responsible for discovering the number sequence, it was his publication of Liber Abaci in 1202 which introduced it to the West. In his book, he used the sequence to suggest a solution to the hypothetical growth of a population of rabbits (assuming they never die, of course!). While many claims for the prevalence of Fibonacci series in nature are poorly substantiated, it does appear in many biological settings, such as in the progression of branches on a tree.
The Fibonacci numbering sequence, which can be traced as far back as the 2nd century BC in India, is created by first beginning with 0 and adding 1. At that point, each new number in the sequence is the sum of the previous two numbers. For instance, 0+1 = 1, 1+1=2, 1+2=3, and so on. The sequence of numbers hence looks like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, to infinity.
Although this numbering series is studied quite extensively in terms of its relevance to growth patterns in nature, it is when we take this series a step further that its applications become even more widespread. The relationship of each two adjacent numbers within this series yields a predictable ratio.
When you divide the former number by the latter after the first 3 pairs, beginning with 8 divided by 13, it yields approximately 0.618.
34/55 = .618181 ~ 0.618
55/89 = .617977 ~ 0.618
89/144 = .618055 ~ 0.618
Dividing the latter number by the former number after the first 3 pairs also results in another relationship from the sequence. This relationship yields approximately 1.618.
55/34 = 1.617647 ~ 1.618
89/55 = 1.618181 ~ 1.618
144/89 = 1.617977 ~ 1.618
The dimensional properties adhering to the 1.618 ratio occur throughout nature and the ratio is most referred to as The Golden Ratio. The uncurling of a fern and the patterns found on various mollusk shells are commonly cited examples of this ratio.
To take these relationships further, if you skip a number and then divide, the result is 0.382. This number, when added to 0.618, equals 1.
The ratios created using the Fibonacci series found their way into the financial mainstream during the late bull market of the 90s. Although futures traders had been using them for quite some time, it was not until the advent of real-time charting software was invented, which manually calculated the Fibonacci levels, that it became more readily available as a tool for the general public. The levels created by the Fibonacci series are now widely popular in all markets, although still most widely followed in the futures.
The main Fibonacci retracement levels which I use in the markets are the 138.2%, 100%, 61.8%, 50%, 38.2%, 0%, and -38.2% levels. Two other numbers often used by other traders of my acquaintance include 0.786 and 1.27. These are the square roots of 0.618 and 1.618. As I said earlier, however, I prefer to keep things simple and have never been compelled enough to add these to my chart analysis. The practical uses of Fibonacci ratios in technical analysis are as a means of projecting upcoming price corrections or retracement levels, and can be used both in terms of price projections, as well as time projections.
Subscribe to:
Posts (Atom)