On 10/03/2008, the $700 Billion bail out/rescue plan passed US Congress ( After the first attempt that failed a week earlier). The bail out probably passed since the issue at hand posed a double edged sword. The still open edge which is 'What has changed in average person's life?. Treasury buying the bad assets through tagging a price will help companies. While getting their books to some decent shape could have helped corporates goto 2001 level, the consumers are still stuck. With this, how can one expect a consumer based economy to change things unless some design has changed radically ?
From a global perspective, this is artificial inflation of the assets through additional pumping of fresh US dollars ( Thanks to non-public disclosure to M3 supply). This just means that dollar will go down in the long term and China and other Gulf nations get more opportunity to stack $700 billion more to their reserves. It will be interesting to see how the emerging markets behave for the next 4-5 months and their GDP levels with a distressed US. This will validate how the US's cold makes the the rest of the world to sneeze.
Saturday, October 4, 2008
Tuesday, September 23, 2008
Wall Street Turmoil and US Congress response
I planned to take a minute to recap on some of the recent happenings on the current Financial crisis hitting Wallstreet that is ubiquitous on internet, radio, TV and street talks.
To provide some background on why we are where we are :
The creative financial instruments invented by the Wall Street heads in terms of derivatives like MBS, ABS, CDO, CDS etc have started to expose their real face as the interest bearing debts collateralized by these assets don't meet the investment expectations. In simple words, as people holding mortgages can't make their payments on time or get into foreclosures, these derivatives which expect their returns through their payments take a direct hit. This leads to banks not able to pass through their payments to the investors who had bought those derivatives from the bank. In addition, those investors who have insured these risks through insurance companies have passed on these effects to those Insurance companies as well. The creative financial instruments masked the mortgages with low ratings through packaging in different facets thereby giving them an artificial uplift.
Where are we now:
As investors sense a risk to their expected returns due to missing mortgage payments, the confidence level in the underlying assets ( All the securitized derivatives mentioned above) goes down, thereby shorting them. With no cushion build up to resist this fall, this gets into a spiral downward movement. Aaha, the credit ratings , who is supposed to be leading the curve, now wake up and short their ratings on these companies as well paving way for the perfect storm. The companies who didnot see this coming but are burdened with these poor assets are at the brink of filing bankruptcy or closing their business. The trillions of dollars and the big institutions involved in this has the potential to crack the US Financial system which is the rockbed for the jaggernaut capitalistic system. Hence the government intervention to ponder a $700 billion bailout.
Root cause of these issues from my point of view:
1) The rate of these instruments/derivatives coming up were too fast for the credit rating agencies to keep up that ended up endorsing these.
2) The SEC and Govt regulations were not keeping up with these as well.
3) The institutions impacted by this turmoil were too busy finding new customers than take a second look at their risk management models. In addition, their models relied on the credit agencies' ratings.
4) People were either greedy or scared of the skyrocketing home appreciation that everyone wanted to be part of the crowd than taking time to calculate if they can meet their financial obligations.
To provide some background on why we are where we are :
The creative financial instruments invented by the Wall Street heads in terms of derivatives like MBS, ABS, CDO, CDS etc have started to expose their real face as the interest bearing debts collateralized by these assets don't meet the investment expectations. In simple words, as people holding mortgages can't make their payments on time or get into foreclosures, these derivatives which expect their returns through their payments take a direct hit. This leads to banks not able to pass through their payments to the investors who had bought those derivatives from the bank. In addition, those investors who have insured these risks through insurance companies have passed on these effects to those Insurance companies as well. The creative financial instruments masked the mortgages with low ratings through packaging in different facets thereby giving them an artificial uplift.
Where are we now:
As investors sense a risk to their expected returns due to missing mortgage payments, the confidence level in the underlying assets ( All the securitized derivatives mentioned above) goes down, thereby shorting them. With no cushion build up to resist this fall, this gets into a spiral downward movement. Aaha, the credit ratings , who is supposed to be leading the curve, now wake up and short their ratings on these companies as well paving way for the perfect storm. The companies who didnot see this coming but are burdened with these poor assets are at the brink of filing bankruptcy or closing their business. The trillions of dollars and the big institutions involved in this has the potential to crack the US Financial system which is the rockbed for the jaggernaut capitalistic system. Hence the government intervention to ponder a $700 billion bailout.
Root cause of these issues from my point of view:
1) The rate of these instruments/derivatives coming up were too fast for the credit rating agencies to keep up that ended up endorsing these.
2) The SEC and Govt regulations were not keeping up with these as well.
3) The institutions impacted by this turmoil were too busy finding new customers than take a second look at their risk management models. In addition, their models relied on the credit agencies' ratings.
4) People were either greedy or scared of the skyrocketing home appreciation that everyone wanted to be part of the crowd than taking time to calculate if they can meet their financial obligations.
Sunday, August 10, 2008
Good Youtube links
Over the years, I have learned that learning through Videos and Podcasts can be efficient and effective. The following is a good place to learn more on financial concepts and in depth security related subject. I plan to add more such Video links both for my reference as well as to save time for people like me. If you have more such links, please share them through comments.
http://www.youtube.com/user/bionicturtledotcom
Goto google and do a search for the following
Structured Finance, Lecture 1 - The Alphabet Soup of the Credit Crisis - 62 min - Jan 30, 2008
Above is from Krassimir Petrov, AUBG - http://home.aubg.bg/faculty/kpetrov/
http://www.youtube.com/user/bionicturtledotcom
Goto google and do a search for the following
Structured Finance, Lecture 1 - The Alphabet Soup of the Credit Crisis - 62 min - Jan 30, 2008
Above is from Krassimir Petrov, AUBG - http://home.aubg.bg/faculty/kpetrov/
Monday, July 28, 2008
GDP translated to private spending and govt spending
Rearranging components of GDP, ignoring net exports, one can write the formula as
Investment = GDP - Consumption - Govt spending
In addition,
Savings = GDP - Consumption - Govt spending
= (GDP - T - Consumption) + (T - Govt Spending)
= Private saving + Govt surplus.
Investment = GDP - Consumption - Govt spending
In addition,
Savings = GDP - Consumption - Govt spending
= (GDP - T - Consumption) + (T - Govt Spending)
= Private saving + Govt surplus.
Sunday, July 27, 2008
Std deviation vs Beta
The difference between Standard deviation vs Beta of a stock can be understood by starting asking few basic questions.
Let us say you have 2 choices, one to pick investment A and other B.
A is positioned such a way that the expected return, which is the probability of different expected returns due to various factors, is calculated as
E(A) = p(x) * E(x) + p(y) * E(y) + p(z) * E(z)
Note that sum of the above probabilities is 1.
A similar calculation can be done for E(B).
Any average person would pick either A or B, whichever is higher. An educated investor would like to find the risk he/she is taking.
Risk can be measured by calculating standard deviation. Mathematically, std dev is the square root of variance. If an investment's return varies like a roller-coster to provide the return calculated above, it more sounds like a gamble. On the other hand, if the investment has a smaller variance, it is a relatively stable investment where one can lay back and not worry too much on the odds.
How to calculate Std Dev/Risk?
Std dev = sqrt(Variance)
where
Variance = (p(x) * (E(x) - E(A))^2) + (p(y) * (E(y) - E(A))^2) + ( p(z) * (E(z) - E(A))^2
Okay, now that we have a better sense of what the risk is and what the corresponding return is, it makes life easy since the investment with higher return and lower risk wins. No Brainer !!
Let us assume that the return and risk for Investment A than those of B. Now it is logical that A being risky will yield higher return, but is that the best that one can choose. Since we have been comparing oranges and apples, its time to calculate risk for an unit of return. This is called Coefficient of variance.
Calculation is simple .. Just divide Std Dev (A) / E(A). Lets call it C(A).
The investor would pick C(A) or C(B), whichever is lower since the risk to earn an unit of return needs to be lower.
With the above detailed explanation of the basics, one should recall the standard deviation as the risk of a particular investment.
Shifting gears to Beta.
Beta of a stock/investment is the risk of the stock relative to its market risk. While standard deviation measures the standalone risk, Beta measures the relative risk to the market. Eg: Std dev of investment A could be lower than std Dev of B, however B's Beta could be better. To understand this better, let us explore the Beta concept further.
If the entire market in which you trade stock consist of only 1 stock. Then the Beta of the stock will be same as the market risk, meaning when the stock return is of the same proportion and direction as the market return. In contrast, let us assume you have 5 stocks forming a market. When the market return goes up, let us say one of the stock goes down, then the stock is negatively correlated with the market.
Ideally if you have an equal weightage of all stocks forming the market, the unsystematic or stock risk can be nullified. The systematic risk, which is the market risk due to economy and non-company related factors affecting returns cannot be controlled by diversification or effective asset allocation of investments within a portfolio.
Let us say you have 2 choices, one to pick investment A and other B.
A is positioned such a way that the expected return, which is the probability of different expected returns due to various factors, is calculated as
E(A) = p(x) * E(x) + p(y) * E(y) + p(z) * E(z)
Note that sum of the above probabilities is 1.
A similar calculation can be done for E(B).
Any average person would pick either A or B, whichever is higher. An educated investor would like to find the risk he/she is taking.
Risk can be measured by calculating standard deviation. Mathematically, std dev is the square root of variance. If an investment's return varies like a roller-coster to provide the return calculated above, it more sounds like a gamble. On the other hand, if the investment has a smaller variance, it is a relatively stable investment where one can lay back and not worry too much on the odds.
How to calculate Std Dev/Risk?
Std dev = sqrt(Variance)
where
Variance = (p(x) * (E(x) - E(A))^2) + (p(y) * (E(y) - E(A))^2) + ( p(z) * (E(z) - E(A))^2
Okay, now that we have a better sense of what the risk is and what the corresponding return is, it makes life easy since the investment with higher return and lower risk wins. No Brainer !!
Let us assume that the return and risk for Investment A than those of B. Now it is logical that A being risky will yield higher return, but is that the best that one can choose. Since we have been comparing oranges and apples, its time to calculate risk for an unit of return. This is called Coefficient of variance.
Calculation is simple .. Just divide Std Dev (A) / E(A). Lets call it C(A).
The investor would pick C(A) or C(B), whichever is lower since the risk to earn an unit of return needs to be lower.
With the above detailed explanation of the basics, one should recall the standard deviation as the risk of a particular investment.
Shifting gears to Beta.
Beta of a stock/investment is the risk of the stock relative to its market risk. While standard deviation measures the standalone risk, Beta measures the relative risk to the market. Eg: Std dev of investment A could be lower than std Dev of B, however B's Beta could be better. To understand this better, let us explore the Beta concept further.
If the entire market in which you trade stock consist of only 1 stock. Then the Beta of the stock will be same as the market risk, meaning when the stock return is of the same proportion and direction as the market return. In contrast, let us assume you have 5 stocks forming a market. When the market return goes up, let us say one of the stock goes down, then the stock is negatively correlated with the market.
Ideally if you have an equal weightage of all stocks forming the market, the unsystematic or stock risk can be nullified. The systematic risk, which is the market risk due to economy and non-company related factors affecting returns cannot be controlled by diversification or effective asset allocation of investments within a portfolio.
Monday, February 18, 2008
CFA - Quantitative analysis.
Let me start by saying time value of money. A $ today is not worth the same $ tomorrow. Hence someone not spending that $ today invests in something that can bring a reasonable additional return tomorrow. The more the risk in investing the more expected, though not guaranteed, return.
This forms the fundamentals of the Quant section leading to Present value (PV) and Future value FV) of money.
Present Value and Future value:
-------------------------------
Few formula to calculate future value
FV = PV (1+i)^n
where
FV = Future value
PV = PResent value
i = Expected interest rate or RAte of return
n = Time period
On a basic level, the principal or initial amount is compounded by the interest received at the end of every time period.
eg: $1000 today at 10% interest rate for 5 years compounded annually will be
FV = 1000 * (1+ 0.1)^5
However if the above was compounded semi annually, it would be
FV = 1000 * ( 1 + 0.05) ^ 10
As the frequency of compounding increases, the net FV increases. If the amount is compounded continuously, the equation would become
FV = PV * (e) ^ nr
= PV * (e) ^ (0.1 * 5)
Though knowing the above concepts is good, from a CFA point of view, it is more important that one knows how to use the financial calculator. The key is to identify and make sure that the n values and r values to be input are correct based on the frequency of compounding. A lot of practice is needed so as to not go wrong in this simple area.
Annuity:
--------
Annuity in simple terms mean a series of periodic payments. Unlike PV where the entire principal is a one time lump sum, Annuity is same amounts applied in equal time periods. Hence common sense is that, with all factors being equal, FV for sum of annuity amounts will be lower than the FV for the same amount when invested as a lump sum today.
Annuity normal or regular is when the payments are invested or applied at the end of every time period whereas Annuity due is when the payments are invested or applied at the begining of the time period. Use Begin mode to calculate PV or FV for Annuity due and End mode for ordinary annuity. A special case of Annuity is perpetuity where PV is CF/discount rate. Perpetuity in layman terms mean that the periodic payments run till end of one's life period.
Probability:
------------
Another area to focus is the chance of something happening. Typically in financial world, this is applied to the chance or probability that a particular asset/security/portfolio will result in a particular return for a given risk.
The Expected return for a security is given by the sum of (probability of something happening* expected return if that happens)
Eg:
If consumer sentiment optimistic, expected return for a particular stock is $1.25 per share
Let us say that the probability of that happening is 80%.
If consumer sentiment optimistic, expected return for a particular stock is $0.25 per share
Let us say that the probability of that happening is 20%.
From the above, if someone invests in the above stock, the expected return would be the weighted average, ie (0.8 * 1.25) + ( 0.2 * 0.25) = 1.05
Assuming there is another stock with the same return, but a different combination of probabilities and return attributes, then one needs to weigh in the risk of putting the money in one vs another. This is calculated through the Standard deviation, which is square root of variance.
In our example,
variance = (1.25-1.05)^2 * 0.8 + (0.25-1.05)^2 * 0.2
= .04*0.8 + (.64 * .2)
= .032 + .124
= .156
SD = sqrt(var) = .39
Hence the expected return can vary from .75 to 1.44 (Exp return +_ SD)
If we did a similar calculation for a different stock and end up with a range 1 to 1.1, then we know that the risk of the other stock is less since the expected returns oscillate in a narrow range.
The above analysis works fine if the expected returns are same but SD's are different. What if a stock with higher Expected return has more SD compared to another with lower return but a lower risk. To make apple to apple comparison, we calculate Coefficient of variation which is defined as risk per unit of return
Hence CV = Return/SD.
Hypothesis testing :
------------------
When someone tells you that they can earn you $5 dollars in 1 yr for every $ you invest, how can you weigh in on their claim. Well, hypothesis testing comes to the rescue. All you need is a sample of their performance that contains the number of sample, mean return of the sample and the SD of the sample. Using the above data, one can tell with a certain % of confidence that their claim is valid or not valid.
Formula:
CI = Mean(sample) +- t value * std error
If sample size small, t value need to be used. Note that degree of freedom should be used to look up t value. df = sample size - 1
std error = SD of sample/ sqrt(sample size)
This is a two tailed model since the Null hypothesis where claim = $5 needs to be rejected or not rejected and the alternative hypothesis is everything but that.
There may be cases when the claim could be >= $5 in which case, the analysis would be one-tailed analysis.
( All the above notes have been without looking at any notes. I will take additional time tomorrow to provide better examples and also revisit the sequence for better readability).
Done for the day :)
This forms the fundamentals of the Quant section leading to Present value (PV) and Future value FV) of money.
Present Value and Future value:
-------------------------------
Few formula to calculate future value
FV = PV (1+i)^n
where
FV = Future value
PV = PResent value
i = Expected interest rate or RAte of return
n = Time period
On a basic level, the principal or initial amount is compounded by the interest received at the end of every time period.
eg: $1000 today at 10% interest rate for 5 years compounded annually will be
FV = 1000 * (1+ 0.1)^5
However if the above was compounded semi annually, it would be
FV = 1000 * ( 1 + 0.05) ^ 10
As the frequency of compounding increases, the net FV increases. If the amount is compounded continuously, the equation would become
FV = PV * (e) ^ nr
= PV * (e) ^ (0.1 * 5)
Though knowing the above concepts is good, from a CFA point of view, it is more important that one knows how to use the financial calculator. The key is to identify and make sure that the n values and r values to be input are correct based on the frequency of compounding. A lot of practice is needed so as to not go wrong in this simple area.
Annuity:
--------
Annuity in simple terms mean a series of periodic payments. Unlike PV where the entire principal is a one time lump sum, Annuity is same amounts applied in equal time periods. Hence common sense is that, with all factors being equal, FV for sum of annuity amounts will be lower than the FV for the same amount when invested as a lump sum today.
Annuity normal or regular is when the payments are invested or applied at the end of every time period whereas Annuity due is when the payments are invested or applied at the begining of the time period. Use Begin mode to calculate PV or FV for Annuity due and End mode for ordinary annuity. A special case of Annuity is perpetuity where PV is CF/discount rate. Perpetuity in layman terms mean that the periodic payments run till end of one's life period.
Probability:
------------
Another area to focus is the chance of something happening. Typically in financial world, this is applied to the chance or probability that a particular asset/security/portfolio will result in a particular return for a given risk.
The Expected return for a security is given by the sum of (probability of something happening* expected return if that happens)
Eg:
If consumer sentiment optimistic, expected return for a particular stock is $1.25 per share
Let us say that the probability of that happening is 80%.
If consumer sentiment optimistic, expected return for a particular stock is $0.25 per share
Let us say that the probability of that happening is 20%.
From the above, if someone invests in the above stock, the expected return would be the weighted average, ie (0.8 * 1.25) + ( 0.2 * 0.25) = 1.05
Assuming there is another stock with the same return, but a different combination of probabilities and return attributes, then one needs to weigh in the risk of putting the money in one vs another. This is calculated through the Standard deviation, which is square root of variance.
In our example,
variance = (1.25-1.05)^2 * 0.8 + (0.25-1.05)^2 * 0.2
= .04*0.8 + (.64 * .2)
= .032 + .124
= .156
SD = sqrt(var) = .39
Hence the expected return can vary from .75 to 1.44 (Exp return +_ SD)
If we did a similar calculation for a different stock and end up with a range 1 to 1.1, then we know that the risk of the other stock is less since the expected returns oscillate in a narrow range.
The above analysis works fine if the expected returns are same but SD's are different. What if a stock with higher Expected return has more SD compared to another with lower return but a lower risk. To make apple to apple comparison, we calculate Coefficient of variation which is defined as risk per unit of return
Hence CV = Return/SD.
Hypothesis testing :
------------------
When someone tells you that they can earn you $5 dollars in 1 yr for every $ you invest, how can you weigh in on their claim. Well, hypothesis testing comes to the rescue. All you need is a sample of their performance that contains the number of sample, mean return of the sample and the SD of the sample. Using the above data, one can tell with a certain % of confidence that their claim is valid or not valid.
Formula:
CI = Mean(sample) +- t value * std error
If sample size small, t value need to be used. Note that degree of freedom should be used to look up t value. df = sample size - 1
std error = SD of sample/ sqrt(sample size)
This is a two tailed model since the Null hypothesis where claim = $5 needs to be rejected or not rejected and the alternative hypothesis is everything but that.
There may be cases when the claim could be >= $5 in which case, the analysis would be one-tailed analysis.
( All the above notes have been without looking at any notes. I will take additional time tomorrow to provide better examples and also revisit the sequence for better readability).
Done for the day :)
Wednesday, January 23, 2008
Sensex volatility
The last 2 days of heavy selling seen globally, especially in India, has been portrayed as
"India’s investors lack sophistication"
My take on the above:
Indian fundamentals are strong in the long run, however, there is still a heavy biding to the US economy. With substantial percentage of Indian GDP still relying on Indian exports, with US dominating that pie, it ultimately means that the Sensex is subjected to volatility in the days ahead until we see US consumer confidence.
From a long term view, within a span of 2 years, the Indian economy will play a dominant role following the output of FDI investments currently at work.
"India’s investors lack sophistication"
My take on the above:
Indian fundamentals are strong in the long run, however, there is still a heavy biding to the US economy. With substantial percentage of Indian GDP still relying on Indian exports, with US dominating that pie, it ultimately means that the Sensex is subjected to volatility in the days ahead until we see US consumer confidence.
From a long term view, within a span of 2 years, the Indian economy will play a dominant role following the output of FDI investments currently at work.
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