Essentially, Bloomberg was using the ratio y/x where y is the number of homicides and x the population to compare various cities. NYC has a ratio of 5.125 which is about one-tenth the y/x for Detroit and so on.
This is also amazingly what Wall Street does. If company A had a profit margin of 20% it is better than company B with a profit margin of 5%.
This is also what I have been trying to call attention to here with my Instablog posts. A few seem to be taking notice and that is a good sign. This few must grow into hundreds and thousands to make the difference. And, a difference must be made.
And, so I urge everyone to read my analysis of what Mayor Bloomberg did and what he should have done. What is the valid basis for comparing the homicides rate for various cities? What is apples to apples and what is apples to oranges? How do we tell apples from oranges?
The solution lies in studying the nature of the underlying x-y relation, not just the y/x ratios. When we use y/x ratios, we are more likely to get into apples and oranges comparisons. The x-y diagrams, on the other hand, tells us what is apples and apples and oranges and oranges.
If you understand this, you will also understand how to compare different companies (based on profit margin, EPS, etc.), airlines (based on their On-Time arrivals ratios, missed baggages ratio, the denied boarding, etc.) and countries (based on debt/GDP ratio, for example, unemployment rates, etc) and literally hundreds and thousands of other problems of interest to us, where we use simple y/x ratios to make sense of our empirical observations. Here's the link to the full article.
www.scribd.com/doc/147960590/Mayor-Bloom...-examined
My apologies again for doing this. Our forum here does not permit uploading of pdf files and I haven't quite figured out what to do with uploading of figures here using current posting tools.
]]>Essentially, Bloomberg was using the ratio y/x where y is the number of homicides and x the population to compare various cities. NYC has a ratio of 5.125 which is about one-tenth the y/x for Detroit and so on.
This is also amazingly what Wall Street does. If company A had a profit margin of 20% it is better than company B with a profit margin of 5%.
This is also what I have been trying to call attention to here with my Instablog posts. A few seem to be taking notice and that is a good sign. This few must grow into hundreds and thousands to make the difference. And, a difference must be made.
And, so I urge everyone to read my analysis of what Mayor Bloomberg did and what he should have done. What is the valid basis for comparing the homicides rate for various cities? What is apples to apples and what is apples to oranges? How do we tell apples from oranges?
The solution lies in studying the nature of the underlying x-y relation, not just the y/x ratios. When we use y/x ratios, we are more likely to get into apples and oranges comparisons. The x-y diagrams, on the other hand, tells us what is apples and apples and oranges and oranges.
If you understand this, you will also understand how to compare different companies (based on profit margin, EPS, etc.), airlines (based on their On-Time arrivals ratios, missed baggages ratio, the denied boarding, etc.) and countries (based on debt/GDP ratio, for example, unemployment rates, etc) and literally hundreds and thousands of other problems of interest to us, where we use simple y/x ratios to make sense of our empirical observations. Here's the link to the full article.
www.scribd.com/doc/147960590/Mayor-Bloom...-examined
My apologies again for doing this. Our forum here does not permit uploading of pdf files and I haven't quite figured out what to do with uploading of figures here using current posting tools.
]]>It appears to me that baseball fans, much more than Wall Street analysts, know the difference between the ratio y/x and the ratio ∆y/∆x. Here x is the number of At Bats (AB) and y the number of Hits (H). The ratio y/x = H/AB = BA is the batting average, one of the three categories in which a player must lead to win the Triple Crown, the other two being home runs (HR) and Runs Batted In (RBI). When Miguel Cabrera - with a batting log of (At Bats, Hits, HR) = (4, 4,3), (4, 1,1), (4, 2,1) (3, 2,1), over a four-game stretch from May 19 to May 23, 2013 - hit six home runs total, with a home run in each of the four consecutive games, no one was thinking about the ratio y/x. Everyone was just using the ratio ∆y/∆x = 9/15 = 0.600 and talking about the incredible stretch with a BA of 600. Here ∆y = 9 is the additional hits (9 = 4 + 1 + 2 + 2) and ∆x = 15 is the additional AB (15 = 4 + 4 + 4 + 3).
Rather surprisingly, as discussed in detail in an article I uploaded yesterday, the same logic is not being used to predict the end of the season RBI for Cabrera who is widely believed to be on pace to break the "untouchable" single-season RBI record of Hack Wilson, established in 1930 and also capture a second straight Triple Crown.
The implications of using the rate of change h = ∆y/∆x, or the derivative, dy/dx, of the mathematical function relating these three quantities is discussed here (and in two companion articles, click here and here). The broader applications of such an analysis to many problems in the so-called "soft sciences" (economics, finance, business, social and political sciences, etc.) is also discussed, briefly.
See link here www.scribd.com/doc/144798463/What-is-Wro...
]]>It appears to me that baseball fans, much more than Wall Street analysts, know the difference between the ratio y/x and the ratio ∆y/∆x. Here x is the number of At Bats (AB) and y the number of Hits (H). The ratio y/x = H/AB = BA is the batting average, one of the three categories in which a player must lead to win the Triple Crown, the other two being home runs (HR) and Runs Batted In (RBI). When Miguel Cabrera - with a batting log of (At Bats, Hits, HR) = (4, 4,3), (4, 1,1), (4, 2,1) (3, 2,1), over a four-game stretch from May 19 to May 23, 2013 - hit six home runs total, with a home run in each of the four consecutive games, no one was thinking about the ratio y/x. Everyone was just using the ratio ∆y/∆x = 9/15 = 0.600 and talking about the incredible stretch with a BA of 600. Here ∆y = 9 is the additional hits (9 = 4 + 1 + 2 + 2) and ∆x = 15 is the additional AB (15 = 4 + 4 + 4 + 3).
Rather surprisingly, as discussed in detail in an article I uploaded yesterday, the same logic is not being used to predict the end of the season RBI for Cabrera who is widely believed to be on pace to break the "untouchable" single-season RBI record of Hack Wilson, established in 1930 and also capture a second straight Triple Crown.
The implications of using the rate of change h = ∆y/∆x, or the derivative, dy/dx, of the mathematical function relating these three quantities is discussed here (and in two companion articles, click here and here). The broader applications of such an analysis to many problems in the so-called "soft sciences" (economics, finance, business, social and political sciences, etc.) is also discussed, briefly.
See link here www.scribd.com/doc/144798463/What-is-Wro...
]]>It is shown here that an understanding of the significance of the high batting average in this four game stretch will also lead to a better understand of many other complex problems in the business world, and in the so-called "soft sciences", where we now use simple y/x ratios to make sense of our (x, y) observations. However, this focus of the behavior of the y/x ratio has led to a general lack of appreciation of the nature of the underlying x-y relation, which can be either linear (of the type y= hx +c, as in many commonly observed in many problems) or nonlinear (y = m*x^n*exp(-ax) as in the traffic fatality problems). The reason for the often bewildering variation in the y/x ratio can be understood if we pay attention to the nonzero intercept c which appears in many problems, as we can appreciate from an analysis of the baseball batting stats. This nonzero intercept is shown to be related to the missing hits in baseball and is also related to the work function conceived by Einstein to explain the phenomenon known as photoelectricity.
For full article, please see http://www.scribd.com/doc/143727444/Trust-Me-The-Financial-World-will-Change-Forever-if-Wall-Street-Starts-Analyzing-Financial-Data-like-we-do-Baseball-Stats-Miguel-Cabrera?post_id=1189058830_10200472544754685#_=_
]]>It is shown here that an understanding of the significance of the high batting average in this four game stretch will also lead to a better understand of many other complex problems in the business world, and in the so-called "soft sciences", where we now use simple y/x ratios to make sense of our (x, y) observations. However, this focus of the behavior of the y/x ratio has led to a general lack of appreciation of the nature of the underlying x-y relation, which can be either linear (of the type y= hx +c, as in many commonly observed in many problems) or nonlinear (y = m*x^n*exp(-ax) as in the traffic fatality problems). The reason for the often bewildering variation in the y/x ratio can be understood if we pay attention to the nonzero intercept c which appears in many problems, as we can appreciate from an analysis of the baseball batting stats. This nonzero intercept is shown to be related to the missing hits in baseball and is also related to the work function conceived by Einstein to explain the phenomenon known as photoelectricity.
For full article, please see http://www.scribd.com/doc/143727444/Trust-Me-The-Financial-World-will-Change-Forever-if-Wall-Street-Starts-Analyzing-Financial-Data-like-we-do-Baseball-Stats-Miguel-Cabrera?post_id=1189058830_10200472544754685#_=_
]]>Ratio versus Rate of Change
Is Einstein's Work Function Observed in this Problem?
Immediately following the publication of the 2013 Forbes billionaire list, another article appeared, in Slate magazine with a ranking of countries based 'per capita' calculations. We all understand that these are pretty ridiculous rankings since per capita means we are dealing with the ratio y/x, where y is the number of billionaires and x is the population. The ratio will obviously have a high value for countries with a small population. Even so, such per capita lists are routinely prepared. India, it was noted, had 55 billionaires, a respectable number the article said, but not really if you consider that Germany, with only 6% the Indian population, has 58 billionaires.
This got me interested in analyzing the relationship between the number of billionaires and the population of a country. As in many other complex problems of interest to us, the empirical observations suggest a remarkably simple and linear relation, of the type y = hx + c, where x is the population and y the number of billionaires, is observed when we consider the Forbes data for the countries with the lowest populations. In other words, the number of billionaires increases at a fixed rate h = ∆y/dx with increasing population. Here ∆x and ∆y are, respectively, the changes in population and the number of billionaires when we consider the data for different countries. The per capita ratio y/x = m = h + (c/x) is thus affected by both the size of the population x and the nonzero intercept c.
The linear law observed here can be compared to Einstein's photoelectric law. The nonzero intercept c is exactly similar to the work function W introduced into physics by Einstein. The extension of this important idea of a nonzero work function and Planck's ideas, outside physics, to economics and the social sciences is also discussed briefly in the article I have uploaded (with references being made to earlier articles by the present author, for completeness); see
Table 1: Per capita rank and number of billionaires
Rank (per capita ratio) | Country | Population, x | Billionaires, y |
6 | Cyprus | 1.1 | 3 |
9 | Kuwait | 2.8 | 5 |
8 | Singapore | 5.2 | 10 |
10 | Switzerland | 7.9 | 13 |
7 | Israel | 7.8 | 17 |
4 | Hong Kong | 7.1 | 39 |
Source: Slate magazine. This data is analyzed in what follows here. The key figures are included here without discussion which may be found in the article cited.
Figure 1: The linear law y = hx + c illustrated here with data for six of the top 10 in the per capita list. Hong Kong is an outlier but the other five reveal a nice linear relationship between the number of billionaires y and the population x. This increases at a fixed rate of about 1.8 per million.
Figure 2: The linear law y = hx + c illustrated here with data for four of the top 10 in the per capita list. Cyprus and Kuwait fall on the lower line A with slope h = 1.176 and Singapore and Switzerland fall on the roughly parallel line B with a slope h = 1.111. The difference in the intercept c and the also the population x thus affects the ratio y/x = m = h + (c/x).
Five Countries from Forbes list with Population and Number of Billionaires
Country, No. | 1 | 2 | 3 | 4 | 5 |
Billionaires, y | 3 | 5 | 10 | 13 | 17 |
Population, x | 1.1 | 2.8 | 5.2 | 7.9 | 7.8 |
Planck's distribution of energy from the 1900 paper
Particle No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
Energy units, ε | 7 | 38 | 11 | 0 | 9 | 2 | 20 | 4 | 4 | 5 |
The sum of the values in bottom row equals 100. In Planck problem the average energy of N particles is U and the total energy is UN = NU = Pε where P and N are very large integers. Here N = 10 and P = 100 for illustration. It appears that the same ideas can be extended to many other problems outside physics to economics and the social sciences. We are determining the "average" value of some property of interest in a very complex system, such as described by various distributions, simplified above.
]]>Ratio versus Rate of Change
Is Einstein's Work Function Observed in this Problem?
Immediately following the publication of the 2013 Forbes billionaire list, another article appeared, in Slate magazine with a ranking of countries based 'per capita' calculations. We all understand that these are pretty ridiculous rankings since per capita means we are dealing with the ratio y/x, where y is the number of billionaires and x is the population. The ratio will obviously have a high value for countries with a small population. Even so, such per capita lists are routinely prepared. India, it was noted, had 55 billionaires, a respectable number the article said, but not really if you consider that Germany, with only 6% the Indian population, has 58 billionaires.
This got me interested in analyzing the relationship between the number of billionaires and the population of a country. As in many other complex problems of interest to us, the empirical observations suggest a remarkably simple and linear relation, of the type y = hx + c, where x is the population and y the number of billionaires, is observed when we consider the Forbes data for the countries with the lowest populations. In other words, the number of billionaires increases at a fixed rate h = ∆y/dx with increasing population. Here ∆x and ∆y are, respectively, the changes in population and the number of billionaires when we consider the data for different countries. The per capita ratio y/x = m = h + (c/x) is thus affected by both the size of the population x and the nonzero intercept c.
The linear law observed here can be compared to Einstein's photoelectric law. The nonzero intercept c is exactly similar to the work function W introduced into physics by Einstein. The extension of this important idea of a nonzero work function and Planck's ideas, outside physics, to economics and the social sciences is also discussed briefly in the article I have uploaded (with references being made to earlier articles by the present author, for completeness); see
Table 1: Per capita rank and number of billionaires
Rank (per capita ratio) | Country | Population, x | Billionaires, y |
6 | Cyprus | 1.1 | 3 |
9 | Kuwait | 2.8 | 5 |
8 | Singapore | 5.2 | 10 |
10 | Switzerland | 7.9 | 13 |
7 | Israel | 7.8 | 17 |
4 | Hong Kong | 7.1 | 39 |
Source: Slate magazine. This data is analyzed in what follows here. The key figures are included here without discussion which may be found in the article cited.
Figure 1: The linear law y = hx + c illustrated here with data for six of the top 10 in the per capita list. Hong Kong is an outlier but the other five reveal a nice linear relationship between the number of billionaires y and the population x. This increases at a fixed rate of about 1.8 per million.
Figure 2: The linear law y = hx + c illustrated here with data for four of the top 10 in the per capita list. Cyprus and Kuwait fall on the lower line A with slope h = 1.176 and Singapore and Switzerland fall on the roughly parallel line B with a slope h = 1.111. The difference in the intercept c and the also the population x thus affects the ratio y/x = m = h + (c/x).
Five Countries from Forbes list with Population and Number of Billionaires
Country, No. | 1 | 2 | 3 | 4 | 5 |
Billionaires, y | 3 | 5 | 10 | 13 | 17 |
Population, x | 1.1 | 2.8 | 5.2 | 7.9 | 7.8 |
Planck's distribution of energy from the 1900 paper
Particle No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
Energy units, ε | 7 | 38 | 11 | 0 | 9 | 2 | 20 | 4 | 4 | 5 |
The sum of the values in bottom row equals 100. In Planck problem the average energy of N particles is U and the total energy is UN = NU = Pε where P and N are very large integers. Here N = 10 and P = 100 for illustration. It appears that the same ideas can be extended to many other problems outside physics to economics and the social sciences. We are determining the "average" value of some property of interest in a very complex system, such as described by various distributions, simplified above.
]]>Couldn't help it folks, had to post this!
Like many others, after the recent negative press received by Tesla Motors Inc. (as a result of the negative review of the Model S performance by a NYT reporter), I took interest in the company for the first time and started analyzing its financial data using a new methodology that I have described in several articles uploaded at scribd.com. At least on my computer, I can adjust the three parameters, "m", "n", and "a", in the mathematical equation, y = m*(x^n)*exp (-ax), which describes the costs-revenues data for Tesla Motors and extrapolate to produce any desired level of profits! Here x is revenues and y is costs and x^n means x raised to the exponent n. If n = 1/2, we have the square root of x, for n = 2, we have square of x, if n = 3, we have the cube and so on for any general value of n.
If we consider the cumulative revenues and costs for 2012 (these are the 3 month, 6 month, 9 month, and full year values), we find that costs have decreased dramatically in the fourth quarter of 2012, see Figure 1, although the company has not yet reported a profit since it became a public company in 2012. This nonlinear decrease in the costs, with increasing revenues, can be modeled using the mathematical equation give above, where the numerical values of the constants m, n, and a can be deduced, as I have shown, from the financial data. This is discussed in detail in two articles that interested readers can find, see links given below (or click here and here).
I have posted several short posts on my Facebook page describing these findings. The simpler power-law model, y = m(x^n) is seen to offer an adequate description for the costs-revenues data. Although costs exceed revenues and the company has not reported a profit, the power-law tells us that the rate of increase of costs, with increasing revenues, is actually decreasing. Also, if we examine the data carefully, we see that the data at the higher revenues begins to deviate from the power-law. The power-exponential law, with the exponential term exp (-ax), then becomes a more appropriate model.
I have also found the value of the parameter "a", holding m and n constant (to match with 2012 cumulative quarterly data) so that Tesla Motors could start making a profit once they exceed sales (or deliveries as they put it, a car is made only after there is a commitment by the customer by advanced reservations) of 15,000 units per year.
Current production capability is 20,000 units per year. This means the last 5000 units produce a profit. The first 15,000 units help them breakeven (recover fixed costs and the variable costs which increase as N, the number of units sold, increases). After that it is all profits. So, one plant can produce a profit of 5000 times $100,000 per unit (the current average price for Tesla's Model S) or $500 million in profits. The profits per plant could potentially even go up to $ 1 billion --- at least on my computer!
That's good news Tesla Motors can use, I think, after some of the beating they have received from other experts here who have been dealing with such problems for much longer than I have been.
Figure 1: The dramatic change in the slope of the cumulative costs versus cumulative revenues in 2012 for Tesla Motors, Inc. The rate of increase of costs, as measured by the slope of the graph has decreased.
Finally, the meaning of the term "costs" is clarified in both the articles above. The "breakeven" analysis can only be performed if all the costs of operation are properly accounted for. The reader is urged to review this point. Also, although this might all seem academic to many here who are interested in more practical matters such as making money, even the ideas such as fixed costs and variable costs, and the three constants (a, b, p) which appear in the classical breakeven analysis (see discussion in Ref.[1]) are academic concepts. To date, I am not aware of any attempts to quantify the three constants (a, b, p) in the classical breakeven analysis. As discussed in the articles cited, Tesla Motors offers a unique test case to study the meaning of these real "fundamentals" about a company. As new operational data on revenues, profits, and costs become available in the near future, we will be able to gain deeper insights into the meanings of these theoretical concepts. More importantly, we might actually start "designing" companies, like scientists and engineers design our cars, airplanes, rockets, cell phones, computers, washing machines, refrigerators, etc. Call me a fool, if you like. Cheers. :)
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
]]>Couldn't help it folks, had to post this!
Like many others, after the recent negative press received by Tesla Motors Inc. (as a result of the negative review of the Model S performance by a NYT reporter), I took interest in the company for the first time and started analyzing its financial data using a new methodology that I have described in several articles uploaded at scribd.com. At least on my computer, I can adjust the three parameters, "m", "n", and "a", in the mathematical equation, y = m*(x^n)*exp (-ax), which describes the costs-revenues data for Tesla Motors and extrapolate to produce any desired level of profits! Here x is revenues and y is costs and x^n means x raised to the exponent n. If n = 1/2, we have the square root of x, for n = 2, we have square of x, if n = 3, we have the cube and so on for any general value of n.
If we consider the cumulative revenues and costs for 2012 (these are the 3 month, 6 month, 9 month, and full year values), we find that costs have decreased dramatically in the fourth quarter of 2012, see Figure 1, although the company has not yet reported a profit since it became a public company in 2012. This nonlinear decrease in the costs, with increasing revenues, can be modeled using the mathematical equation give above, where the numerical values of the constants m, n, and a can be deduced, as I have shown, from the financial data. This is discussed in detail in two articles that interested readers can find, see links given below (or click here and here).
I have posted several short posts on my Facebook page describing these findings. The simpler power-law model, y = m(x^n) is seen to offer an adequate description for the costs-revenues data. Although costs exceed revenues and the company has not reported a profit, the power-law tells us that the rate of increase of costs, with increasing revenues, is actually decreasing. Also, if we examine the data carefully, we see that the data at the higher revenues begins to deviate from the power-law. The power-exponential law, with the exponential term exp (-ax), then becomes a more appropriate model.
I have also found the value of the parameter "a", holding m and n constant (to match with 2012 cumulative quarterly data) so that Tesla Motors could start making a profit once they exceed sales (or deliveries as they put it, a car is made only after there is a commitment by the customer by advanced reservations) of 15,000 units per year.
Current production capability is 20,000 units per year. This means the last 5000 units produce a profit. The first 15,000 units help them breakeven (recover fixed costs and the variable costs which increase as N, the number of units sold, increases). After that it is all profits. So, one plant can produce a profit of 5000 times $100,000 per unit (the current average price for Tesla's Model S) or $500 million in profits. The profits per plant could potentially even go up to $ 1 billion --- at least on my computer!
That's good news Tesla Motors can use, I think, after some of the beating they have received from other experts here who have been dealing with such problems for much longer than I have been.
Figure 1: The dramatic change in the slope of the cumulative costs versus cumulative revenues in 2012 for Tesla Motors, Inc. The rate of increase of costs, as measured by the slope of the graph has decreased.
Finally, the meaning of the term "costs" is clarified in both the articles above. The "breakeven" analysis can only be performed if all the costs of operation are properly accounted for. The reader is urged to review this point. Also, although this might all seem academic to many here who are interested in more practical matters such as making money, even the ideas such as fixed costs and variable costs, and the three constants (a, b, p) which appear in the classical breakeven analysis (see discussion in Ref.[1]) are academic concepts. To date, I am not aware of any attempts to quantify the three constants (a, b, p) in the classical breakeven analysis. As discussed in the articles cited, Tesla Motors offers a unique test case to study the meaning of these real "fundamentals" about a company. As new operational data on revenues, profits, and costs become available in the near future, we will be able to gain deeper insights into the meanings of these theoretical concepts. More importantly, we might actually start "designing" companies, like scientists and engineers design our cars, airplanes, rockets, cell phones, computers, washing machines, refrigerators, etc. Call me a fool, if you like. Cheers. :)
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
]]>This is a follow-up on my first Instablog here on Trulia (posted yesterday 9/21/2012).
After Trulia's successful debut on the NYSE, its profits-revenues data were analyzed to predict both the profits and revenues for the fiscal year ending December 31, 2012.
Trulia's stock is being compared to Zillow, its main competition. Unlike Trulia, which has yet to report a profit, Zillow has reported quarterly profits for 4 out of 14 quarters, and a profit for three consecutive quarters starting Q42011. It has also reported a profit for the full year 2011.
I have now completed the analysis of the profits and revenues data for Zillow (from 2006 to 2011, annual basis and Q1 2009 to Q2 20102, quarterly basis). The analysis again reveals a simple linear law y = hx + c = h(x - x_{0}) relating the revenues x and profits y, regardless of whether we consider the quarterly, cumulative quarterly, or the annual data. In all cases, as revenues increase, losses decrease and eventually turn into profits at higher revenues.
Thus, the intercept x_{0} = - c/h made by the straight line on the x-axis (revenue axis) equals the cut-off or "breakeven" revenue, the minimum revenue needed before the company can report a profit. The slope h then gives the rate at which additional revenues are converted into profits.
On a quarterly basis h = 0.518 and on an annual basis h = 0.574. In other words, Zillow has the potential to convert about 52% of its quarterly revenues into profits, or about 57% of its annual revenues into profits.
However, costs (as revealed by the increasing values of x_{0}) have also been increasing and consistent delivery of profits will require paying attention to three fundamental constants (a, b, p) which appear in the classical breakeven analysis for the profitability of any company. Here "a" is the fixed cost, "b" the unit variable cost and "p" the unit price.
A full article with accompanying graphs has been uploaded earlier today at scribd.com, do see link given below.
www.scribd.com/doc/106626486/A-Look-at-Z...
Look forward to your comments. These two Instablogs, with the referenced articles, show that a simple approach based on the analysis of the graphical trends in the profits-revenues diagram can serve as a valuable tool for investors.
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
]]>This is a follow-up on my first Instablog here on Trulia (posted yesterday 9/21/2012).
After Trulia's successful debut on the NYSE, its profits-revenues data were analyzed to predict both the profits and revenues for the fiscal year ending December 31, 2012.
Trulia's stock is being compared to Zillow, its main competition. Unlike Trulia, which has yet to report a profit, Zillow has reported quarterly profits for 4 out of 14 quarters, and a profit for three consecutive quarters starting Q42011. It has also reported a profit for the full year 2011.
I have now completed the analysis of the profits and revenues data for Zillow (from 2006 to 2011, annual basis and Q1 2009 to Q2 20102, quarterly basis). The analysis again reveals a simple linear law y = hx + c = h(x - x_{0}) relating the revenues x and profits y, regardless of whether we consider the quarterly, cumulative quarterly, or the annual data. In all cases, as revenues increase, losses decrease and eventually turn into profits at higher revenues.
Thus, the intercept x_{0} = - c/h made by the straight line on the x-axis (revenue axis) equals the cut-off or "breakeven" revenue, the minimum revenue needed before the company can report a profit. The slope h then gives the rate at which additional revenues are converted into profits.
On a quarterly basis h = 0.518 and on an annual basis h = 0.574. In other words, Zillow has the potential to convert about 52% of its quarterly revenues into profits, or about 57% of its annual revenues into profits.
However, costs (as revealed by the increasing values of x_{0}) have also been increasing and consistent delivery of profits will require paying attention to three fundamental constants (a, b, p) which appear in the classical breakeven analysis for the profitability of any company. Here "a" is the fixed cost, "b" the unit variable cost and "p" the unit price.
A full article with accompanying graphs has been uploaded earlier today at scribd.com, do see link given below.
www.scribd.com/doc/106626486/A-Look-at-Z...
Look forward to your comments. These two Instablogs, with the referenced articles, show that a simple approach based on the analysis of the graphical trends in the profits-revenues diagram can serve as a valuable tool for investors.
Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
]]>