Important tips after completing 1st Reading.

The first thing one should do after his or her initial read of the material is take at least three, three hour practice exams, grade the exams, and analyize his or her weaknesses. This is key because it will allow you to formulate an effective gameplan for review. While studying the material I recommend you highlight or mark important concepts and formulas, as well as material that you don't understand. Also, after completing practice problems, mark in the book which problems you got wrong (I marked each with a "W"), and also mark which problems you had a hard time with (I marked each with an "H"). That way when you begin reviewing you will know which problems to review. Also, after reading each chapter I rated the chapter on a scale of 1 to 5; five meaning I fully understand the concepts and will remember the material and one meaning I desperately need to review the chapter. This rating system will help you when constructing your review strategy.

After finishing each book, I would go back and reread the chapters (or parts of chapters) I marked as a "one" (remember that "one" indicates an extreme need for review). You may have the urge not to do this because after finishing an entire book you become eager to move onto the next. Fight this urge and conduct mini reviews after finishing each book. Your mini reviews may take a long time, sometimes 3 or more days, however, they will greatly help drive the concepts into your head.


It is important to note that I read these tips somewhere before I started off my preparation, noted them and followed them after I was done with my first reading. They were quite helpful & this is the sole reason why I have shared them here.

Autoregressive Conditional Heteroskedasticity models (ARCH)

When regression model has (conditional) heteroskedasticity i.e. variance of the error in a particular time-series model in one period depends on the variance of the error in previous periods, standard errors of the regression coefficients in AR, MA or ARMA models will be incorrect, and hypothesis tests would be invalid.


ARCH model: 
ARCH model must be used to test the existence of conditional heteroskedasticity. An ARCH (1) time series is the one in which the variance of the error in one period depends on  size of the squared error in the previous period i.e. if a large error occurs in one period, the variance of the error in the next period will be even larger.

To test whether time series is ARCH (1), the squared residuals from a previously estimated time-series model are regressed on the constant and first lag of the squared residuals



Decision Rule: If the estimate ofα1 is statistically significantly different from zero, the time series is ARCH (1). If a time-series model has ARCH (1) errors, then the variance of the errors in period t+1 can be predicted in period t.


Consequences of ARCH: 
• Standard errors for the regression parameters will not be correct.
• When ARCH exists, we can predict the variance of the error terms.



Generalized least squares or other methods that correct for heteroskedasticity must be used to estimate the correct standard error of the parameters in the time-series model.

Autoregressive model versus ARCH model:

• Using AR (1) model implies that model is correctly specified.
• Using ARCH (1) implies that model can not be correctly specified due to existence of conditional heteroskedasticity in the residuals; therefore, ARCH (1) model is used to forecast variance/volatility of residuals.

Video Tutorial - What is Fundamental Analysis?

Fundamental analysis is an important topic in Equity of the curriculum. The following video presents a quick overview on Introduction to Fundamental Analysis.

When should one finish the reading before the exam?

1. Those who have started the preparation 6 to 7 months before the exam must finish their preparation at least 2 months before the exam. It is worthwhile noting that these are group members who do not have any financial background.

2. People who have started the preparation around 5 and 1/2 months and 6 months before the exam they should complete their preparation around 1 and a half month before the exam.

3. People who have started preparation 5 months or 5 1/2 months before the exam should finish by latest 1 month or 3 weeks.

4. People who have started preparation around 4 months before the exam and having adequate knowledge or repeating for Level I should finish by latest 3 weeks of 2 weeks before the exam

Apple Inc.'s Long Lived Assets - Form 10K FY 2011

Below is the snapshot taken from Form 10K submitted by Apple Inc. for the year ended on 24th September, 2011.

Following is the extract from Reading 21 Long-lived Assets: Implications for Financial Statements & Ratios.

"Impairment of Long-lived Tangible Assets held for use 
IFRS:
• Impairment losses are recognized when:
Carrying amount of the asset > recoverable amount of the asset where,

Recoverable amount is higher of (Fair value - cost to sell) and value in use.
Value in use = discounted expected future cash flows
• Companies are required to assess at least annually whether there are any indications of impairment of an asset.
• Goodwill and identifiable intangible assets that are not amortized must be tested for impairment at least annually.

U.S. GAAP: 
• Under U.S. GAAP, there is a two step test which is used to determine whether the asset is impaired.
1) Recoverability test:  Asset’s carrying amount is not recoverable when:

Carrying amount of the asset > Undiscounted expected future cash flows
2) Impairment loss measurement: When asset’s carrying amount is not recoverable, then Impairment loss = carrying amount of an asset –  Fair value of an asset (or discounted value of expected future cash flows)
• Companies must undertake an impairment test only when “events or circumstances indicate that carrying value of an asset is not recoverable on permanent basis”.
• Goodwill and identifiable intangible assets that are not amortized must be tested for impairment at least annually.

Examples of indicators of impairment test include:
• Significant adverse change in asset’s physical condition,
• Significant adverse change in legal or economic factors, or
• Significant decrease in the market price among others.
Effects of Impairment loss: 
• It reduces the carrying  amount of the asset on balance sheet.
• It reduces net income on the income statement.
• Impairment loss is a non-cash item and thus, it does not affect operating cash flows.
• Debt-to-total assets and fixed asset turnover ratios increase due to impairment loss because it reduces the carrying amount of fixed assets and thus total assets."

In previous posts I have been mentioning the practical application of the knowledge learned and the skills developed through the CFA curriculum. It is worthwhile to note that financial statements are key sources of information which help in inspecting the position and performance of any business. Understanding financial statements and the financial lingo used in them critical in analyzing them. For me figuring out that what I am studying is adding value to me is for sure a strong motivating factor.

Video Tutorial - Credit Default Swaps (Introduction)

When to start studying - CFA Level I

1. If you have no prior knowledge of the material and you don't have a technical background (Engineering, quantitative sciences), allow yourself 6-7 months of studying
2. If you have minimal knowledge of the material. i.e., you took micro or macro economics and statistics in college or you use them lightly at work, but you don't have a finance or accounting background. Allow yourself around 5 and 1/2 months to 6 months.
3. You have a solid knowledge of the material. i.e., you have seen statistics, economics, and either accounting or finance in school or at work. Allow yourself 5 to 6 months.
4. You have a vast knowledge of the material. i.e., you are an undergraduate student who has taken or is taking economics, statistics, corporate finance, and accounting or you've thoroughly studied for and already taken level 1 and you are taking it again. Allow yourself around 4 months. 

News Pick - Grubb & Ellis considers reverse stock split to avoid delisting

Stock splits & reverse stock splits where maintain the total wealth of a shareholder to be same, adjust increase in no. of stocks by proportionate decrease in the price and decrease in no. of stocks by proportionate increase in the stock price. Companies in order to avoid de-listing or increase shareholder confidence sometimes do stock splits or reverse stock splits. The article, link provided at the bottom, discusses one such strategy which has been explained in the CFA curriculum. It tells how reverse stock split can increase the stock price for Grubb & Ellis and can maintain its listing on NYSE.

Read the article here: "http://www.crainsdetroit.com/article/20111220/FREE/111229994"

Video Tutorial - Collaterized Debt Obligation

Below is the video tutorial of CDOs. An overview was uploaded before. This is a detailed tutorial.

Seasonality in Time Series - Level II Quantitative Methods

When a time series variable exhibit a repeating patterns at regular intervals over time, it is known as seasonality e.g. sales in Dec > sales in Jan. A time series with seasonality also has a non-constant mean and thus is not covariance stationary.


Detecting seasonality:
In case of seasonality in the data, autocorrelation in the model differ by season. For example, in case of quarterly sales data of a company, if the fourth autocorrelations of the error term differ significantly from 0  → This is a sign of seasonality in the model.


Decision Rule:
When t-statistic of the fourth lag of autocorrelations of the error > critical t-value → reject null hypothesis that fourth autocorrelations is 0.  Thus, there is seasonality problem.


Correcting Seasonality: This problem can be solved by adding seasonal lags in an AR model i.e. after including a seasonal lag in case of quarterly sales data, the AR model becomes:

xt = b0 + b1x (t-1) + b2x(t-4) + et

NOTE: R(square) of the model without seasonal lag will be less than the R(square) of the model with seasonal lag. This implies that when time series exhibit seasonality, including a seasonal lag in the model improves the accuracy of the model.