Front page story in today's Financial Times is about "Emerging Markets inflation surges"
Latest China CPI inflation rate is 5.4%, India is 9%, US is 1.2%.
The article notes:
One side-effect of surging inflation is a stronger incentive for Beijing to let the renminbi rise in order to lower the price of imported commodities.
Saturday, April 16, 2011
Thursday, April 7, 2011
Energy stocks drop about 2% after EIA inventories only slightly more than expected. Signal investors are skittish
Yesterday's action in the oil and gas names was surprisingly negative considering how minor the apparent news behind the move was. Whenever the stock trading looks odd compared to the news flow, it can indicate an important shift in psychology. In other words, if the market fails to go down in the face of bad news, or it goes down despite good news, it can be a key clue.
Yesterday (4/6/11) there was widespread price weakness in equities in the energy sector and the closely-correlated agriculture sector. with a number of ETFs falling around 2%, while the overall market rose about 0.2%. This pullback took these equities back to where they were about a week earlier. The sector ought to be watched closely to see whether this is the beginning of a downtrend, or just a temporary overreaction to what seems like a minor bit of news. The trigger was a weekly report from the EIA on petroleum inventories, which came out as scheduled at 10:30 am Wednesday. The closely watched US commericial crude oil inventories (excluding the SPR) was up 2 million barrels to 357.7 million barrels from 355.7 the week before and 1.5 million barrels higher than the same week a year ago. That small change, up 0.6%, compares to an expected rise of 1.6 million. Thus the magnitude of the "surprise" was only 0.4 million barrels. That amount represents about half an hour of the the total petroleum and products used by the US economy at the current rate of 19 millions barrels per day. Below is a chart showing how the inventories have been trending, including this most recent data point for the week ended April 1.
Inventories are above the normal range now, but that is not new. They have been above, or at the high end of, the average range for the past two years. The EIA report did not seem to have much effect on oil prices themselves. Here is what the WSJ reported about futures prices.
"Light, sweet crude for May delivery was up 15 cents to $108.49 on the New York Mercantile Exchange following the report. Front-month May reformulated gasoline blendstock, or RBOB, dropped 1.25 cents to $3.1888 a gallon. May heating oil rose 0.97 cent to $3.1947 a gallon."Below is a chart from yesterday's EIA report of oil and product spot prices since January 2010. The green line is the WTI crude oil series that was $108.49 as noted in the WSJ report. In this chart, the scales are set so that the dollars per gallon on the left are exactly 1/42 of the dollars per barrel on the right hand scale, reflecting the fact that one barrel equals 42 gallons. Thus, gasoline (red line)and heating oil prices (red line), in dollars per gallon, can be viewed on the same chart as oil prices in dollars per barrel.
The equity markets had a much different reaction to the EIA data than the oil markets themselves. Starting right about 10:30 am eastern time, when the data were released, most equities tied to energy and agricultural companies weakened.
The first chart below shows a year of daily prices. The main index shown with candlestick bars is hte PowerShares S&P Small Cap Energy portfolio. The scale is % change so that multiple indexes can be compared. The lines represent a variety of energy and agriculture ETFs, and these have all been fairly closely correlated and have been moving up as oil prices have moved up. Note that the last point shows a small downtick on each of the series. This reflects the reaction to the EIA data. The symbols of the various ETFs shown in the chart, for thoese who care about details, are OIH,OSX, VDE, XLE, MOO, DBA, and XOI.
To get a clearer picture of the timing of the move down, we zoom in on the last 10 days of data shown in ten minute intervals in the chart below. The same PSCE ETF compared to the same 7 related ETFs are shown. Ignore the first two bars of the PSCE ETF for April 6 as there was some anomalous trading in the first half hour or so. Then, from 10:30 am Wednesday until about the middle of the day there was a significant drop in all of these price series that takes them back to about where they had been a week or so ago. There is a small rise in the second half of the trading day for most, but they close well below the prior day closes and the opening prices.
In conclusion, we saw energy equities, which have been rising strongly since about August 2010, hit with selling pressure in reaction to a seeminly very small surprise that crude oil inventories were higher than expected by about 30 minutes of all petroleum usage. Since the fundamentals of the situation do not match up with the technicals, something psychological seems to be going on. We know that investors can panic and the herd can move in a new direction even though the news cannot explain it. This bears watching.
Yesterday (4/6/11) there was widespread price weakness in equities in the energy sector and the closely-correlated agriculture sector. with a number of ETFs falling around 2%, while the overall market rose about 0.2%. This pullback took these equities back to where they were about a week earlier. The sector ought to be watched closely to see whether this is the beginning of a downtrend, or just a temporary overreaction to what seems like a minor bit of news. The trigger was a weekly report from the EIA on petroleum inventories, which came out as scheduled at 10:30 am Wednesday. The closely watched US commericial crude oil inventories (excluding the SPR) was up 2 million barrels to 357.7 million barrels from 355.7 the week before and 1.5 million barrels higher than the same week a year ago. That small change, up 0.6%, compares to an expected rise of 1.6 million. Thus the magnitude of the "surprise" was only 0.4 million barrels. That amount represents about half an hour of the the total petroleum and products used by the US economy at the current rate of 19 millions barrels per day. Below is a chart showing how the inventories have been trending, including this most recent data point for the week ended April 1.
"Light, sweet crude for May delivery was up 15 cents to $108.49 on the New York Mercantile Exchange following the report. Front-month May reformulated gasoline blendstock, or RBOB, dropped 1.25 cents to $3.1888 a gallon. May heating oil rose 0.97 cent to $3.1947 a gallon."Below is a chart from yesterday's EIA report of oil and product spot prices since January 2010. The green line is the WTI crude oil series that was $108.49 as noted in the WSJ report. In this chart, the scales are set so that the dollars per gallon on the left are exactly 1/42 of the dollars per barrel on the right hand scale, reflecting the fact that one barrel equals 42 gallons. Thus, gasoline (red line)and heating oil prices (red line), in dollars per gallon, can be viewed on the same chart as oil prices in dollars per barrel.
The first chart below shows a year of daily prices. The main index shown with candlestick bars is hte PowerShares S&P Small Cap Energy portfolio. The scale is % change so that multiple indexes can be compared. The lines represent a variety of energy and agriculture ETFs, and these have all been fairly closely correlated and have been moving up as oil prices have moved up. Note that the last point shows a small downtick on each of the series. This reflects the reaction to the EIA data. The symbols of the various ETFs shown in the chart, for thoese who care about details, are OIH,OSX, VDE, XLE, MOO, DBA, and XOI.
To get a clearer picture of the timing of the move down, we zoom in on the last 10 days of data shown in ten minute intervals in the chart below. The same PSCE ETF compared to the same 7 related ETFs are shown. Ignore the first two bars of the PSCE ETF for April 6 as there was some anomalous trading in the first half hour or so. Then, from 10:30 am Wednesday until about the middle of the day there was a significant drop in all of these price series that takes them back to about where they had been a week or so ago. There is a small rise in the second half of the trading day for most, but they close well below the prior day closes and the opening prices.
In conclusion, we saw energy equities, which have been rising strongly since about August 2010, hit with selling pressure in reaction to a seeminly very small surprise that crude oil inventories were higher than expected by about 30 minutes of all petroleum usage. Since the fundamentals of the situation do not match up with the technicals, something psychological seems to be going on. We know that investors can panic and the herd can move in a new direction even though the news cannot explain it. This bears watching.
Wednesday, April 6, 2011
The Corn ETF
Teucrium, new since 2009, sponsors an ETF that trades on NYSE under the symbol CORN. It is a commodity pool that invests in a number of corn futures and is designe to mirror the daily percentage changes in corn futures.
It should be in my portfolio. Note that the history on CORN only goes back to 6/10/10 and so the chart does not show the 2008 peak in corn, which was June 2008, very close to the peak oil price in August 2008. The corn price has already surpassed its 2008 peak. Some 40% of the US corn crop is now going to make gasoline, and weather has lowered production, and inventories are unusually low.
The stochastics are saying today is not a good time to enter this trade. Daily chart shows it is overbought. Continue to monitor it for an entry on the long side. Looking for other ways to play agriculture.
Here is a brief profile of the corn market from the ETF propsectus, followed by a daily chart for the period January 2006 through December 2009.
It should be in my portfolio. Note that the history on CORN only goes back to 6/10/10 and so the chart does not show the 2008 peak in corn, which was June 2008, very close to the peak oil price in August 2008. The corn price has already surpassed its 2008 peak. Some 40% of the US corn crop is now going to make gasoline, and weather has lowered production, and inventories are unusually low.
The stochastics are saying today is not a good time to enter this trade. Daily chart shows it is overbought. Continue to monitor it for an entry on the long side. Looking for other ways to play agriculture.
Here is a brief profile of the corn market from the ETF propsectus, followed by a daily chart for the period January 2006 through December 2009.
Monday, April 4, 2011
The next big thing: energy and ag
Looking back for about 16 years, which gets us back to 1995, the way to have made a lot of money in equity markets was to have identified and ridden the two bubbles over this period. The first was the dot.com bubble (from about 1995 to the peak in the NASDAQ index on March 10 2000), which ended with the "tech wreck" (2000 to 2002). The second was the housing bubble (2004-2007), which ended in the 2008-09 financial crisis. The Fed's overly expansive monetary policy was at least in retrospect a contributing factor to each bubble, and the Fed used monetary easing to lessen the negative impacts after eqch bubble burst. Now in spring 2011 the Fed is again easing, this time via QE2, scheduled to end in June 2011, and one naturally wonders what might be the next bubble?
I believe it will be a bubble in energy and agriculture, and the signs are already there that these related sectors are starting too "bubble". Oil prices and food prices have risen significantly from the post-financial crisis trough. [add some charts here to demonstrate] These sectors are connected in that oil is a significant input to growing crops (fertilizers, operating farm equipment, transporting foods to markets). There is even an influence going the other way in that rising food costs appear to have been a factor in the unrest in poorer oil producing countries like Libya.
Besides the evidence of rising oil and food costs, there is a great body of research on "Peak Oil" that I find compelling. A great deal of the economic growth of the past hundred years or so was because of the remarkable power of (cheap) oil. Despite all the inefficiencies inherent inthe process, the idea that is is possible to move a 2500 lb fully-loaded vehicle for 20 to 30 miles using a gallon of a fuel that costs about the same as a gallon of milk is astonishing when one thinks about it.
One compelling piece of evidence in favor of Peak Oil (withough having to get bogged down in estimates of supply, demand, proven reserves, inventories, probable reserves, unused capacity, etc.) is the spike in oil prices in August of 2008 to $147 per barrel. Unlike the spikes of 1973 and 1979, which were caused by supply disruptions in the Middle East (the Arab oil embargo in 1973,and the overthrow of Sha of Iran in 1979), the oil price rise from 2006 to 2008 was the result of demand outstripping supply. Even though there were no major supply-side disruptions in 2006-2008, and even though the incentives to produce were strong from rising prices, and the global system was operating full, prices rose enough to create a supply shock that was a major factor, along with the financial crisis in the wake of the financial system melt-down, in the world-wide major recession of 2008-2009.
From Wikipedia: Peak oil is the point in time when the maximum rate of global petroleum extraction is reached, after which the rate of production enters terminal decline. This concept is based on the observed production rates of individual oil wells, and the combined production rate of a field of related oil wells. The aggregate production rate from an oil field over time usually grows exponentially until the rate peaks and then declines—sometimes rapidly—until the field is depleted. This concept is derived from the Hubbert curve, and has been shown to be applicable to the sum of a nation’s domestic production rate, and is similarly applied to the global rate of petroleum production. Peak oil is often confused with oil depletion; peak oil is the point of maximum production while depletion refers to a period of falling reserves and supply.
[add more about the supply demand balance outlook in oil globally]
[add more about food crisi]
Just as the upcoming oil crisis was preceded by a similiar criss in 2006-2008, there was also an food crisis in 2007-2008.
Again, we turn to Wikipedia:
The years 2007–2008 saw dramatic increases in world food prices, creating a global crisis and causing political and economical instability and social unrest in both poor and developed nations. Systemic causes for the worldwide increases in food prices continue to be the subject of debate.
Initial causes of the late 2006 price spikes included droughts in grain-producing nations and rising oil prices. Oil price increases also caused general escalations in the costs of fertilizers, food transportation, and industrial agriculture. Root causes may be the increasing use of biofuels in developed countries (see also food vs fuel),[1] and an increasing demand for a more varied diet across the expanding middle-class populations of Asia.[2][3]
These factors, coupled with falling world-food stockpiles all contributed to the worldwide rise in food prices.[4] Causes not commonly attributed by mainstream views include structural changes in trade and agricultural production, agricultural price supports and subsidies in developed nations, diversions of food commodities to high input foods and fuel, commodity market speculation, and climate change.
As I explore investment ideas based on this theme, the first thing I looked at is the ETF from Power Shares S&P for small cap energy. PSCE is the symbol. Bought a small amount on 4 5 11. I wanted to download crude oil price data so that I could compare it to the equity stock prices of PSCE or other individual equities. The futures price data is apparently not readily available for download for free on the Internet. I guess the futures exchanges keep the data to themselves unlike the equity exchanges.
I did manage to find daily prices from the EIA. They are for Cushing OK delivery. Here is the link:
http://www.eia.doe.gov/dnav/pet/pet_pri_fut_s1_d.htm
What is noteworthy is how quickly oil prices have rebounded even though economies around the world are still well below the levels of 2008 when prices hit their prior peak.
I believe it will be a bubble in energy and agriculture, and the signs are already there that these related sectors are starting too "bubble". Oil prices and food prices have risen significantly from the post-financial crisis trough. [add some charts here to demonstrate] These sectors are connected in that oil is a significant input to growing crops (fertilizers, operating farm equipment, transporting foods to markets). There is even an influence going the other way in that rising food costs appear to have been a factor in the unrest in poorer oil producing countries like Libya.
Besides the evidence of rising oil and food costs, there is a great body of research on "Peak Oil" that I find compelling. A great deal of the economic growth of the past hundred years or so was because of the remarkable power of (cheap) oil. Despite all the inefficiencies inherent inthe process, the idea that is is possible to move a 2500 lb fully-loaded vehicle for 20 to 30 miles using a gallon of a fuel that costs about the same as a gallon of milk is astonishing when one thinks about it.
One compelling piece of evidence in favor of Peak Oil (withough having to get bogged down in estimates of supply, demand, proven reserves, inventories, probable reserves, unused capacity, etc.) is the spike in oil prices in August of 2008 to $147 per barrel. Unlike the spikes of 1973 and 1979, which were caused by supply disruptions in the Middle East (the Arab oil embargo in 1973,and the overthrow of Sha of Iran in 1979), the oil price rise from 2006 to 2008 was the result of demand outstripping supply. Even though there were no major supply-side disruptions in 2006-2008, and even though the incentives to produce were strong from rising prices, and the global system was operating full, prices rose enough to create a supply shock that was a major factor, along with the financial crisis in the wake of the financial system melt-down, in the world-wide major recession of 2008-2009.
From Wikipedia: Peak oil is the point in time when the maximum rate of global petroleum extraction is reached, after which the rate of production enters terminal decline. This concept is based on the observed production rates of individual oil wells, and the combined production rate of a field of related oil wells. The aggregate production rate from an oil field over time usually grows exponentially until the rate peaks and then declines—sometimes rapidly—until the field is depleted. This concept is derived from the Hubbert curve, and has been shown to be applicable to the sum of a nation’s domestic production rate, and is similarly applied to the global rate of petroleum production. Peak oil is often confused with oil depletion; peak oil is the point of maximum production while depletion refers to a period of falling reserves and supply.
[add more about the supply demand balance outlook in oil globally]
[add more about food crisi]
Just as the upcoming oil crisis was preceded by a similiar criss in 2006-2008, there was also an food crisis in 2007-2008.
Again, we turn to Wikipedia:
The years 2007–2008 saw dramatic increases in world food prices, creating a global crisis and causing political and economical instability and social unrest in both poor and developed nations. Systemic causes for the worldwide increases in food prices continue to be the subject of debate.
Initial causes of the late 2006 price spikes included droughts in grain-producing nations and rising oil prices. Oil price increases also caused general escalations in the costs of fertilizers, food transportation, and industrial agriculture. Root causes may be the increasing use of biofuels in developed countries (see also food vs fuel),[1] and an increasing demand for a more varied diet across the expanding middle-class populations of Asia.[2][3]
These factors, coupled with falling world-food stockpiles all contributed to the worldwide rise in food prices.[4] Causes not commonly attributed by mainstream views include structural changes in trade and agricultural production, agricultural price supports and subsidies in developed nations, diversions of food commodities to high input foods and fuel, commodity market speculation, and climate change.
As I explore investment ideas based on this theme, the first thing I looked at is the ETF from Power Shares S&P for small cap energy. PSCE is the symbol. Bought a small amount on 4 5 11. I wanted to download crude oil price data so that I could compare it to the equity stock prices of PSCE or other individual equities. The futures price data is apparently not readily available for download for free on the Internet. I guess the futures exchanges keep the data to themselves unlike the equity exchanges.
I did manage to find daily prices from the EIA. They are for Cushing OK delivery. Here is the link:
http://www.eia.doe.gov/dnav/pet/pet_pri_fut_s1_d.htm
What is noteworthy is how quickly oil prices have rebounded even though economies around the world are still well below the levels of 2008 when prices hit their prior peak.
Saturday, February 26, 2011
Techncial Tradnig Course by Barry Burns of Top Dog Trading
My efforts to learn how to day trade or swing trade using technical analysis have taken me to a second course (the first being OTA or Online Trading Academy). As I don't think I posted here on OTA before, I found the instructor--Chris Muldoon-- to be good, but the infrastructure or delivery mechanism of OTA to be seriously deficient. Also, the 7 day course was simply not enough to launch one into being able to trade. More later on OTA. (Reminders to self: cover the flawed use of TradeStation, the poor quality of the written material, the mess-up over my retake, the badly designed website, the stupid sales efforts.)
This post is about the second course I tried, which is provided by Barry Burns (BB hereafter). His web-based selling process can be annoying, but what convinced me to buy it was his free 5-video series n which he lays out some basic concepts so that you can see how he teaches. About $300 cost for a series of videos, pdf documents, and Word files. The downloading of these and unlocking of them using security codes takes awhile, maybe an hour. The materials are not very well organized. It seeems that he threw in additional materials to fill in holes in the initial material, and called them "special bonuses" instead of "bug fixes". His delivery is very clear and easy to listen to/follow in the videos. Much of the material is "filler"-not really telling you anything new, just repeating what he has already presented. This course is considerably cheaper than most. It is also very much a "one-man show". These are his personal favorite ideas, rather than the product of an organization that has done research and testing. BB said to me in an email (one good thing is he does make himself available for emails in reply to questions) that he is not a believer in the value ofbacktesting. He also admitted in another email that he has no knowledge of Excel, which I found hard to believe.
He begins with Trends (covered in course 1 of 2), which he defines based on a 50 period moving average rather than the classic "higher highs, higher lows; lower highs lower lows" definition. (I think a 50 period moving average is a better way to measure a trend than relying only on the "outliers" that constitute extreme highs and lows. But there is still some ambiguity that arises from the possibility of a 50 period MA becoming flat or nearly flat.) A trend ends when the 50 MA goes flat or becomes close to flat. BB says that if the trend looks close to flat, one should default to calling it flat. An trend is not necessarily followed by a trend in the opposite direction. A downtrend could be followed by another downtrend. These would be treated as two separate downtrends, rather than a single downtrend. Once a trend has been identified, the trader is to count "waves", which are straight lines connecting cycle highs and cycle lows. The cycles are analagous to breathing. A typical trend lasts for five waves, but could be three or more than 5. The purpose of counting the waves is to try to identify whether the price is at an early part of the trend, the middle, or the end. The goal is to trade the stock in the middle of the trend, after it is far enough along so that it will likely continue, but not so far along that it will be likely to reverse. The logic is ok, but I wonder whether it is really possible to predict how long a trend is going to last from the trading activity alone.
He uses a 15 period EMA in addition to the 50 SMA. Not sure exactly why.
The second major concept is momentum, covered in couse 2 of 2. (Example of quality problem--the manual for course two does not have page numbers whereas course 1 manual does.)
This post is about the second course I tried, which is provided by Barry Burns (BB hereafter). His web-based selling process can be annoying, but what convinced me to buy it was his free 5-video series n which he lays out some basic concepts so that you can see how he teaches. About $300 cost for a series of videos, pdf documents, and Word files. The downloading of these and unlocking of them using security codes takes awhile, maybe an hour. The materials are not very well organized. It seeems that he threw in additional materials to fill in holes in the initial material, and called them "special bonuses" instead of "bug fixes". His delivery is very clear and easy to listen to/follow in the videos. Much of the material is "filler"-not really telling you anything new, just repeating what he has already presented. This course is considerably cheaper than most. It is also very much a "one-man show". These are his personal favorite ideas, rather than the product of an organization that has done research and testing. BB said to me in an email (one good thing is he does make himself available for emails in reply to questions) that he is not a believer in the value ofbacktesting. He also admitted in another email that he has no knowledge of Excel, which I found hard to believe.
He begins with Trends (covered in course 1 of 2), which he defines based on a 50 period moving average rather than the classic "higher highs, higher lows; lower highs lower lows" definition. (I think a 50 period moving average is a better way to measure a trend than relying only on the "outliers" that constitute extreme highs and lows. But there is still some ambiguity that arises from the possibility of a 50 period MA becoming flat or nearly flat.) A trend ends when the 50 MA goes flat or becomes close to flat. BB says that if the trend looks close to flat, one should default to calling it flat. An trend is not necessarily followed by a trend in the opposite direction. A downtrend could be followed by another downtrend. These would be treated as two separate downtrends, rather than a single downtrend. Once a trend has been identified, the trader is to count "waves", which are straight lines connecting cycle highs and cycle lows. The cycles are analagous to breathing. A typical trend lasts for five waves, but could be three or more than 5. The purpose of counting the waves is to try to identify whether the price is at an early part of the trend, the middle, or the end. The goal is to trade the stock in the middle of the trend, after it is far enough along so that it will likely continue, but not so far along that it will be likely to reverse. The logic is ok, but I wonder whether it is really possible to predict how long a trend is going to last from the trading activity alone.
He uses a 15 period EMA in addition to the 50 SMA. Not sure exactly why.
The second major concept is momentum, covered in couse 2 of 2. (Example of quality problem--the manual for course two does not have page numbers whereas course 1 manual does.)
Thursday, February 10, 2011
Fundamentals versus Technicals
Further evidence of the huge gap between fundamentals and technicals hit me as I tried to understand the dialect (or maybe it is even a different LANGUAGE than English) surrounding a branch of technical analysis that was new to me. Market Profile charts display price on the vertical axis, just the same as conventional charts, but for the horizontal axis they use volume over the couse of a specified trading period. In addition, numbers or letters are used to indicate at what time of day (in blocks of time) the trading happened. What different language/dialect?
Peter Steidlmeyer or James Dalton is credited with the concept. The CBOT trademarked the term Market Profile. Value area. Dual aucton process. Acceptance or rejection of price. TPO (time-price opportunity). Establishing parameters. Advantageous versus fair prices. Pioneer range. Price reoccurence. Different kinds of days (normal, trend, netural, normal variation). Distribute. Balanced and imbalanced markets. Control price.
Thinkorswim just rolled out a new charting feature they call "Monkey Bars" (in what seems like an attempt to be playful, but probably actually to avoid infringing the CBOT trademark).
Peter Steidlmeyer or James Dalton is credited with the concept. The CBOT trademarked the term Market Profile. Value area. Dual aucton process. Acceptance or rejection of price. TPO (time-price opportunity). Establishing parameters. Advantageous versus fair prices. Pioneer range. Price reoccurence. Different kinds of days (normal, trend, netural, normal variation). Distribute. Balanced and imbalanced markets. Control price.
Thinkorswim just rolled out a new charting feature they call "Monkey Bars" (in what seems like an attempt to be playful, but probably actually to avoid infringing the CBOT trademark).
Thursday, November 11, 2010
Two kinds of Fundamental Analysis
The term "fundamental analysis" gets thrown by around professional and amateur investors in ways that create unnecessary misunderstandings and debates. I have noticed that there are (at least) two different methods or approaches that users of the term may be referring to. I will call them "modeling" and "screening."
"Modeling" better captures what traditional fundamental analysis involves. It is time-consuming, difficult, and requires substantical experience. It usually refers to analysis of a company, but could also be applied to an industry or an economy. The phrase "take it apart and put it back together" describes what goes on with this type of fundamental analysis. The analyst takes the three of four financial statements (usually just the income statement, balance sheet, and cash flows, but sometimes also the statement of shareholders' equity) and studies them. By examining historical trends in the company's numbers and comparing them to other indicators like economic and industry data, he looks for a few key relationships that capture most of the variability in the data. Can revenues be explained as the product of a few volume and price series? Are there a few cost items that can explain much of the movement in expenses? Is there a measure of market size that can be used to track a company's revenues as the product of market share and market size? Can a company's capacity be measured and related to its capital spending and its production? After analyzing these questoins, the analyst endeavours to create a model, a highly simplified version of reality that condenses the available reported data and estimates of some values that may not be reported into a spreadsheet that shows the relationships between inputs and outputs. The model can then be used to project financial results into the future. As each quarterly financial report is released, the analyst compares the acutal results with the predicted results and looks for clues about what has been happening and adjusts the model to incorporate the new information or understanding. A typical senior, experienced "sell-side" analyst may cover 10 to 20 companies with this type of analysis, doing it full-time. It is not something that the typical retail investor has the time or the training to do.
For the typical retail investor, fundamental analysis takes the form of what could be called "screening". Companies can be ranked on sales growth, gross margin, EPS growth, dividend yield, return on equity, or any number of similar variables. The consensus of analyst-predicted EPS estimates can be used. This type of fundamental analysis allows the user to process data on thousands of companies in contrast to the dozen or so companies that the "modeling" analyst is limited to. Sometimes the screening will be done is a series of steps, or phases, initially screening a universe of companies to select a smaller number for further study, and then applying other criteria to the names that "pass" the previous screen. The phrase "a mile wide and an inch deep" comes to mind.
To illustrate the differences between the two approaches consider the P/E ratio. For the modeler, the P/E ratio is the price divided by the projected EPS (current year or next four quarters). The P/E is then compared to the P/E ratios of other companies and a judgment may be made about whether the P/E is higher or lower than it "should" be. It is a measure of the market's expectations about future growth.
For the screener, the P/E ratio is a measure of value. A high P/E means the stock might be overvalued, and a low P/E means it might be undervalued. The screener does not much care about whether the E is past or projected. He wants to use an E that is defined the same way for all the companies. The screener is just trying to reduce thousands of companies down to a more manageable number.
"Modeling" better captures what traditional fundamental analysis involves. It is time-consuming, difficult, and requires substantical experience. It usually refers to analysis of a company, but could also be applied to an industry or an economy. The phrase "take it apart and put it back together" describes what goes on with this type of fundamental analysis. The analyst takes the three of four financial statements (usually just the income statement, balance sheet, and cash flows, but sometimes also the statement of shareholders' equity) and studies them. By examining historical trends in the company's numbers and comparing them to other indicators like economic and industry data, he looks for a few key relationships that capture most of the variability in the data. Can revenues be explained as the product of a few volume and price series? Are there a few cost items that can explain much of the movement in expenses? Is there a measure of market size that can be used to track a company's revenues as the product of market share and market size? Can a company's capacity be measured and related to its capital spending and its production? After analyzing these questoins, the analyst endeavours to create a model, a highly simplified version of reality that condenses the available reported data and estimates of some values that may not be reported into a spreadsheet that shows the relationships between inputs and outputs. The model can then be used to project financial results into the future. As each quarterly financial report is released, the analyst compares the acutal results with the predicted results and looks for clues about what has been happening and adjusts the model to incorporate the new information or understanding. A typical senior, experienced "sell-side" analyst may cover 10 to 20 companies with this type of analysis, doing it full-time. It is not something that the typical retail investor has the time or the training to do.
For the typical retail investor, fundamental analysis takes the form of what could be called "screening". Companies can be ranked on sales growth, gross margin, EPS growth, dividend yield, return on equity, or any number of similar variables. The consensus of analyst-predicted EPS estimates can be used. This type of fundamental analysis allows the user to process data on thousands of companies in contrast to the dozen or so companies that the "modeling" analyst is limited to. Sometimes the screening will be done is a series of steps, or phases, initially screening a universe of companies to select a smaller number for further study, and then applying other criteria to the names that "pass" the previous screen. The phrase "a mile wide and an inch deep" comes to mind.
To illustrate the differences between the two approaches consider the P/E ratio. For the modeler, the P/E ratio is the price divided by the projected EPS (current year or next four quarters). The P/E is then compared to the P/E ratios of other companies and a judgment may be made about whether the P/E is higher or lower than it "should" be. It is a measure of the market's expectations about future growth.
For the screener, the P/E ratio is a measure of value. A high P/E means the stock might be overvalued, and a low P/E means it might be undervalued. The screener does not much care about whether the E is past or projected. He wants to use an E that is defined the same way for all the companies. The screener is just trying to reduce thousands of companies down to a more manageable number.
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