The second chapter of Competitive Strategy looks at three approaches in "coping with the five competitive forces" outlined in the previous chapter, which are industry competitors, potential entrants, substitutes, suppliers, and buyers. The three strategies include overall cost leadership, differentiation, and focus. While they are not mutually exclusive, it is "rarely possible" to pursue multiple approaches.
Overall cost leadership entails "aggressive construction of efficient-scale facilities" to minimize cost, which provides "high margins which can be reinvested." This strategy is usually associated with high volume and market share. Differentiation sacrifices volume for perception of uniqueness industry-wide. Finally, focus strategy is "built around serving a particular target very well." Honed onto a particular segment only, focus can pursue either low cost, high differentiation position, or both.
The chapter notes that a "firm failing to develop its strategy in at least one of these three directions ... is in an extremely poor strategic situation" and "is almost guaranteed low profitability." These firms lose both the high-volume customers looking for low costs, and the high-margin business that depends on differentiation. While each of these strategies has its set of risks, these three generic competitive strategies give an intuitive explanation of the U-shaped relationship between profitability and market share, as observed in some industries.
Sunday, June 23, 2013
Thursday, June 20, 2013
Backward and Forward Integration
The first chapter of Competitive Strategy talks about structural analysis of industries, which aims to relate "a company to its environment." Among the five competitive forces surrounding a company are industry competitors, potential entrants, substitutes, suppliers, and buyers. A buyers or supplier can exert competitive force with credible threat of backward or forward integration, respectively.
Buyer can engage in backward integration by purchasing the parts it typically procures from suppliers. This poses competitive force on the suppliers. On the other hand, suppliers can engage in forward integration by expanding activity "to include control of the direct distribution of its products," posing competitive force on the buyers typically involved in the distribution. The same company can be both a buyer or supplier in different markets. A manufacturing company, for example, may be the buyer in the market for raw materials, but becomes the supplier in the market of the assembled goods to other companies.
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Buyer can engage in backward integration by purchasing the parts it typically procures from suppliers. This poses competitive force on the suppliers. On the other hand, suppliers can engage in forward integration by expanding activity "to include control of the direct distribution of its products," posing competitive force on the buyers typically involved in the distribution. The same company can be both a buyer or supplier in different markets. A manufacturing company, for example, may be the buyer in the market for raw materials, but becomes the supplier in the market of the assembled goods to other companies.
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Monday, June 17, 2013
Look Into Historical Unemployment vs. CPI Data
Bureau of Labor Statistics (BLS) publishes and archives monthly unemployment and consumer price index data. The CPI data is stored as 1-month percentage change, indicative of the monthly inflation rate. The current data archive goes back to 2003 for each. A quick data manipulation procedure in Excel gets each tabular output into a series output for juxtaposition.
The Phillips Curve is a historical inverse relationship between unemployment and inflation rate. The data from the 10 years here demonstrates a very strenuous inverse relationship. Here the slope of the best-fit line is -0.02135, but the R^2 value was merely 1.29%.
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The Phillips Curve is a historical inverse relationship between unemployment and inflation rate. The data from the 10 years here demonstrates a very strenuous inverse relationship. Here the slope of the best-fit line is -0.02135, but the R^2 value was merely 1.29%.
Sources:
Growing Sectors From June BLS Data
On the first Friday of every money, Bureau of Labor Statistics publishes employment reports for the previous month in the United States. In the report published on June 7th, total nonfarm payroll increased by 175,000 in May, putting the unemployment rate at 7.6%. The growth is above the 172,000 figure, which has been the monthly average over the past 12 months. The report highlights that professional and business services added 57,000 jobs in May.
A look into Table B-1, which breaks down the nonfarm payrolls by industry sector and selected industry detail, reveals which sectors have shown the highest consecutive monthly growth. At the levels of detail provided by the report, only 4 selected entries illustrated 0.5% monthly growth for both March to April, and April to May: employment services, temporary help services, business support services, and home health care services. Employment services have seen 1.47% increase from seasonally-adjusted payroll of 3.286 million in March to 3.334 million. Compared to last May, it has climbed by 6.44% during the past year. Temporary help services claim the honor of seeing the highest year-over-year payroll growth at 7.46%.
Sources:
A look into Table B-1, which breaks down the nonfarm payrolls by industry sector and selected industry detail, reveals which sectors have shown the highest consecutive monthly growth. At the levels of detail provided by the report, only 4 selected entries illustrated 0.5% monthly growth for both March to April, and April to May: employment services, temporary help services, business support services, and home health care services. Employment services have seen 1.47% increase from seasonally-adjusted payroll of 3.286 million in March to 3.334 million. Compared to last May, it has climbed by 6.44% during the past year. Temporary help services claim the honor of seeing the highest year-over-year payroll growth at 7.46%.
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Friday, May 24, 2013
Strengthening Australian Dollar and Ford Shutting Down Production
It was reported this week that after nine decades of operating in Australia, Ford will shut down production in the country due to the strength of the Australia dollar and high costs, particularly wages. Ford reported that production costs in Australia "are twice that of Europe and nearly four times higher than in Asia." The factories are planned to close within 3 years.
Strength of the Australian dollar would make it more costly to operate there. How exactly has the Australian dollar fared against other major currencies? The AUD-USD is currently around 0.97. This is actually on the lower end of the 52-week range, and further down from the peak of around 1.10 reached in mid-2011. However looking at the 5 year chart, AUD-USD was as low as 0.6 in late 2008. Since the start of 2011, it has been steadily strong at above 1. Looking at AUD-JPY, again a similar strengthening trend can be observed. After hovering around 80 for most of mid-2009 to mid-2012, it has surpassed 100 this year and currently is around 97.78. Finally, looking at AUD-EUR, the pattern can be seen as it has strengthened from around 0.5 in late 2008 to currently around 0.75, although it was as high as 0.85 in mid-2012.
Sources:
Strength of the Australian dollar would make it more costly to operate there. How exactly has the Australian dollar fared against other major currencies? The AUD-USD is currently around 0.97. This is actually on the lower end of the 52-week range, and further down from the peak of around 1.10 reached in mid-2011. However looking at the 5 year chart, AUD-USD was as low as 0.6 in late 2008. Since the start of 2011, it has been steadily strong at above 1. Looking at AUD-JPY, again a similar strengthening trend can be observed. After hovering around 80 for most of mid-2009 to mid-2012, it has surpassed 100 this year and currently is around 97.78. Finally, looking at AUD-EUR, the pattern can be seen as it has strengthened from around 0.5 in late 2008 to currently around 0.75, although it was as high as 0.85 in mid-2012.
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Thursday, May 23, 2013
Mixed Headlines Regarding Housing Market
There were mixed headlines regarding the house market today. Sales of new homes in the United States climbed to an annualized rate of 454,000 in April. This was the second highest monthly figure since July 2008. Year-over-year, the median selling price increased 14.9% to $271,600, which is the highest on record. Partially fueling the rising demand are the low interest rates. This week the average 30-year fixed mortgage rate was 3.59%.
Despite these news, limitation on home supply is put by the lack of equity to sell homes. In a report released by Zillow (NASDAQ: Z), a real estate information provider with a "database of more than 110 million U.S. homes," keeping the property listing tight are the "about 22 million Americans ... [who] lack enough home equity to move." The report stated that 44% of homeowners with mortgages had less than 20% equity in their home, and 20% is usually required to purchase a new home. This figure includes 13 million Americans, or a quarter of homeowners with mortgages, who were underwater, or owed more than what their homes are worth. Zillow projects that by Q1-2014, only about 1.4 million homeowners will regain positive equity.
Sources:
Despite these news, limitation on home supply is put by the lack of equity to sell homes. In a report released by Zillow (NASDAQ: Z), a real estate information provider with a "database of more than 110 million U.S. homes," keeping the property listing tight are the "about 22 million Americans ... [who] lack enough home equity to move." The report stated that 44% of homeowners with mortgages had less than 20% equity in their home, and 20% is usually required to purchase a new home. This figure includes 13 million Americans, or a quarter of homeowners with mortgages, who were underwater, or owed more than what their homes are worth. Zillow projects that by Q1-2014, only about 1.4 million homeowners will regain positive equity.
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Tuesday, May 21, 2013
A Look into Venture Capital Firms Behind Tumblr
Yahoo's acquisition of Tumblr was a boon for many, including Union Square Ventures and Spark Capital, the two venture capital firms who made initial investments into Tumblr during its infancy. The combined investments of $10 million translated to 47% ownership of the company, giving them a 50-fold return in the recent deal with Yahoo that valued Tumblr at $1.1 billion. The deal was also a spark for the New York start-up community, as Tumblr represents the "largest venture-backed purchase for a New York company."
Union Square Ventures, based in New York, describes itself on its website as a "small collegial partnership that manages $450,000,000 across three funds" and invests in companies that "create services that have the potential to fundamentally transform important markets." Spark Capital, based in Boston, manages about $1.5 billion. Common across both funds include Twitter.
On Tuesday, the front page of Union Square Ventures highlights the Tumblr acquisition, which characterizes Tumblr as "world's greatest platforms for self expression." Tumblr has 108 million blogs across its network, and generates about $13 million in revenue annually, which is the amount Yahoo generates daily. Nevertheless, it is the younger audience and mobile users whom Yahoo seeks to attract through this acquisition.
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Union Square Ventures, based in New York, describes itself on its website as a "small collegial partnership that manages $450,000,000 across three funds" and invests in companies that "create services that have the potential to fundamentally transform important markets." Spark Capital, based in Boston, manages about $1.5 billion. Common across both funds include Twitter.
On Tuesday, the front page of Union Square Ventures highlights the Tumblr acquisition, which characterizes Tumblr as "world's greatest platforms for self expression." Tumblr has 108 million blogs across its network, and generates about $13 million in revenue annually, which is the amount Yahoo generates daily. Nevertheless, it is the younger audience and mobile users whom Yahoo seeks to attract through this acquisition.
Sources:
Saturday, May 11, 2013
Column Generation Illustration
This example illustrates the first few steps of using column generation method to solve an integer program. In particular, this is a cutting beam example, where given beams of length L = 100, determine the minimum number of beams to satisfy demand of size n_i for length d_i: n1 = 21, d1 = 13, n2 = 39, d2 = 31, n3 = 61, d3 = 36, n4 = 14, d4 = 45
The initial objective function is to minimize x1 + x2 + x3 + x4, given the following constraints:
Objective value is 135. This is the dummy case where no cutting takes place. The dual solution is (1, 1, 1, 1), so the next LP to solve is: max p1 + p2 + p3 + p4 subject to 13*p1 + 31*p2 + 36*p3 + 45*p4 <= 100. The solution is (7, 0, 0, 0), and since the objective value of 7 is greater than 1, continue. Add this column in the next run. The pattern generated here (7, 0, 0, 0) means that 7 pieces of length 13 will be cut from the stock, whose length is 100.
Now with the addition of a new variable, minimize x1 + x2 + x3 + x4 + x5, given the following constraints:
The objective value has decreased to 117, and the dual solution is (1/7, 1, 1, 1), so the next LP to solve is: max 1/7*p1 + p2 + p3 + p4 subject to 13*p1 + 31*p2 + 36*p3 + 45*p4 <= 100. The constraint is the same as the earlier maximization problem. The solution is (0, 3, 0, 0), so this would be the next column to generate, given that the the objective value of 3 is still greater than 1. Continue this process.
The initial objective function is to minimize x1 + x2 + x3 + x4, given the following constraints:
| 1 | 0 | 0 | 0 | >= | 21 |
| 0 | 1 | 0 | 0 | >= | 39 |
| 0 | 0 | 1 | 0 | >= | 61 |
| 0 | 0 | 0 | 1 | >= | 14 |
Objective value is 135. This is the dummy case where no cutting takes place. The dual solution is (1, 1, 1, 1), so the next LP to solve is: max p1 + p2 + p3 + p4 subject to 13*p1 + 31*p2 + 36*p3 + 45*p4 <= 100. The solution is (7, 0, 0, 0), and since the objective value of 7 is greater than 1, continue. Add this column in the next run. The pattern generated here (7, 0, 0, 0) means that 7 pieces of length 13 will be cut from the stock, whose length is 100.
Now with the addition of a new variable, minimize x1 + x2 + x3 + x4 + x5, given the following constraints:
| 1 | 0 | 0 | 0 | 7 | >= | 21 |
| 0 | 1 | 0 | 0 | 0 | >= | 39 |
| 0 | 0 | 1 | 0 | 0 | >= | 61 |
| 0 | 0 | 0 | 1 | 0 | >= | 14 |
The objective value has decreased to 117, and the dual solution is (1/7, 1, 1, 1), so the next LP to solve is: max 1/7*p1 + p2 + p3 + p4 subject to 13*p1 + 31*p2 + 36*p3 + 45*p4 <= 100. The constraint is the same as the earlier maximization problem. The solution is (0, 3, 0, 0), so this would be the next column to generate, given that the the objective value of 3 is still greater than 1. Continue this process.
Thursday, March 21, 2013
Interest Rate Swap and Swaption
Interest rate swap is also known as plain vanilla swap, or fixed and fixing swap. It is the exchange of cash flows on a notional value (say $1 million) given a fixed interest rate (say 3%), for the cash flows from on the same notional value given a floating interest rate (say LIBOR + 0.5%). The notable aspect is that the notional value never gets traded, and thus is rather arbitrary. Over the life of the swap, the payments are made to the counterparty at intervals that are usually 6 months. The swap benefits entities that may want more exposure or mitigate exposure to interest rate risk.
Swaption is an option on the swap, whereby the writer has the option but not the obligation to enter into a swap agreement by a certain date. But furthermore, the contract specifies whether the buyer will be the recipient of the fixed or floating rate cash flows.
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Swaption is an option on the swap, whereby the writer has the option but not the obligation to enter into a swap agreement by a certain date. But furthermore, the contract specifies whether the buyer will be the recipient of the fixed or floating rate cash flows.
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Tuesday, March 19, 2013
Simulation of Simplified Version of Showcase Showdown Using MATLAB
See previous related post: Showcase Showdown Analysis: Circular Reasoning and Oscillating Nash Equilibrium
In the show The Price Is Right, contestants spin the Big Wheel, which has 20 sections randomly distributed, from 5 cents to $1.00 in 5-cent increments. The objective is to get the highest amount without going over $1, with one initial spin and an optional second spin. Clearly the first contestant is at a disadvantage, as the later contestants simply opt for the second optional spin if the first spin is not enough. For the first contestant, there needs to be a strategy consisting of a critical value for the first spin, at or above which the second spin is not preferred, as the probability of going over is too high. It's also a discrete case, as the critical value can only be from 5 to 100, in increments of 5.
In the actual show, there are three contestants. For the sake for simplicity, only two contestants are assumed in this simulation. In the case of tie, rerun the trial. For each possible critical value from 5 to 100 cents, 1 million trials are run in this Monte Carlo simulation to determine the winning percentage of player 1 under that strategy.
Here's the graph with the critical point on the x-axis and the winning percentage on the y-axis:
The maximum occurs at critical value of 55, corresponding with a winning percentage of 45.55%. At a value of 50, the winning percentage is up there at 45.22%. Even closer is 45.52% at a value of 60. The winning percentage gradually tapers off. Even at a value of 70, the winning percentage is still at 44.25%. Here's the MATLAB script to generate the data and graph:
In the code above, simply vary the value of n to change the number of trials at each strategy. Of course, this optimal strategy will be different if the third contestant is taken into consideration in the actual game.
In the show The Price Is Right, contestants spin the Big Wheel, which has 20 sections randomly distributed, from 5 cents to $1.00 in 5-cent increments. The objective is to get the highest amount without going over $1, with one initial spin and an optional second spin. Clearly the first contestant is at a disadvantage, as the later contestants simply opt for the second optional spin if the first spin is not enough. For the first contestant, there needs to be a strategy consisting of a critical value for the first spin, at or above which the second spin is not preferred, as the probability of going over is too high. It's also a discrete case, as the critical value can only be from 5 to 100, in increments of 5.
In the actual show, there are three contestants. For the sake for simplicity, only two contestants are assumed in this simulation. In the case of tie, rerun the trial. For each possible critical value from 5 to 100 cents, 1 million trials are run in this Monte Carlo simulation to determine the winning percentage of player 1 under that strategy.
Here's the graph with the critical point on the x-axis and the winning percentage on the y-axis:
The maximum occurs at critical value of 55, corresponding with a winning percentage of 45.55%. At a value of 50, the winning percentage is up there at 45.22%. Even closer is 45.52% at a value of 60. The winning percentage gradually tapers off. Even at a value of 70, the winning percentage is still at 44.25%. Here's the MATLAB script to generate the data and graph:
x = zeros(1,20);
y = zeros(1,20);
for crit = 1:20
x(crit) = 5*crit;
n = 1000000;
win = zeros(1,n);
for i = 1:n
done = false;
while ~done
firstSpin = ceil(20*rand)*5;
if firstSpin >= x(crit)
playerOne = firstSpin;
else
playerOne = firstSpin + ceil(20*rand)*5;
end
if playerOne > 100
playerOne = 0;
end
playerTwo = ceil(20*rand)*5;
if playerTwo < playerOne
playerTwo = playerTwo + ceil(20*rand)*5;
end
if playerTwo > 100
playerTwo = 0;
end
if playerOne > playerTwo
win(i) = 1;
done = true;
elseif playerOne < playerTwo
done = true;
end
end
end
y(crit) = sum(win)/n;
end
plot(x,y); grid on; xlabel('player 1 critical point'); ylabel('player 1 winning percentage')
In the code above, simply vary the value of n to change the number of trials at each strategy. Of course, this optimal strategy will be different if the third contestant is taken into consideration in the actual game.
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