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Questions and Answers of Financial Reporting and Analysis

Recognize revenue when each performance obligation is satisfied. LO.1
Allocate the transaction price to the separate performance obligations. LO.1
Determine the transaction price. LO.1
Identify the separate performance obligations in the contract. LO.1
Identify the contract(s) with the customer. The proposed guidance indicates that the contracts may be written, oral, or implied. LO.1
The seller retains neither continuing management involvement associated with ownership nor effective control over the goods being sold. LO.1
The seller has transferred significant risks and rewards of ownership of the goods to the buyer. LO.1
Does Dot-com assume the risks of ownership such as possible losses from bad debts or returns? LO.1
Does Dot-com take title to the ticket? LO.1
Is Dot-com acting as a principal or as an agent/broker in the transaction? LO.1
Proposed changes that IASB and FASB are considering for contract-based revenue recognition? LO.1
Key differences between IFRS and U.S. GAAP rules for revenue recognition. LO.1
How error corrections and restatements of prior period financial statements are repor ted. LO.1
SEC guidance on revenue recognition designed to curb earnings management. LO.1
The various techniques used to manage earnings. LO.1
How the flexibility in GAAP for income determination invites managers to manipulate or manage earnings. LO.1
Specialized application of revenue recognition principles for franchise sales, sales with right of return, and“bundled” sales with multiple deliverable elements. LO.1
The procedures for recognizing revenue and adjusting associated asset values in three specific settings:long-term construction contracts, agricultural commodities, and installment sales. LO.1
The conditions under which it is appropriate to recognize revenues and profits either before or after the point of sale. LO.1
Why financial statements are valuable sources of information about companies. LO.1
How the demand for financial information comes from its ability to improve decision making and monitor managers’ activities. LO.1
How the supply of financial information is influenced by the costs of producing and disseminating it and by the benefits it provides. LO.1
How accounting rules are established, and why management can shape the financial information communicated to outsiders and still be within those rules. LO.1
Why financial repor ting philosophies and detailed accounting practices sometimes differ across countries. LO.1
Consider the monthly log stock returns, in percentages and including dividends, of Merck & Company, Johnson & Johnson, General Electric, General Motors, Ford Motor Company, and valueweighted index
The file m-excess-c10sp-9003.txt contains the monthly simple excess returns of ten stocks and the S&P 500 index. The threemonth Treasury bill rate on the secondary market is used to compute the
Again, consider the ten stock returns in m-excess-c10sp9003.txt. The stocks are from companies in three industrial sectors.ABT, LLY, MRK, and PFE are major drug companies, F and GM are automobile
Again, consider the ten excess stock returns in the file mexcess-c10sp-9003.txt. Perform a principal component analysis on the returns and obtain the scree plot. How many common factors are there?
Again, consider the ten excess stock returns in the file mexcess-c10sp-9003.txt. Perform a statistical factor analysis. How many common factors are there if the 5% significance level is used?Plot the
Consider the monthly log returns of the S&P composite index, IBM stock, and Hewlett-Packard (HPQ) stock from January 1962 to December 2003 for 504 observations. The log returns are in the file
Focus on the monthly log returns of IBM and HPQ stocks from January 1962 to December 2003. Fit a DVEC(1,1) model to the bivariate return series. Is the model adequate? Plot the fitted volatility
Build a constant-correlation volatility model for the three monthly log returns of the S&P composite index, IBM stock, and HPQ stock. Write down the fitted model. Is the model adequate?Why?
The file m-spibmge.txt contains the monthly log returns in percentages of the S&P 500 index, IBM stock, and General Electric stock from January 1926 to December 1999. The returns include dividends.
Focus on the monthly log returns in percentages of GE stock and the S&P 500 index. Build a time-varying correlation GARCH model for the bivariate series using a logistic function for the correlation
Focus on the monthly log returns in percentages of GE stock and the S&P 500 index. Build a time-varying correlation GARCH model for the bivariate series using the Cholesky decomposition.Check the
Consider the three-dimensional return series jointly. Build a multivariate time-varying volatility model for the data, using the Cholesky decomposition. Discuss the implications of the model and
An investor is interested in daily value at risk of his position on holding long $0.5 million of Dell stock and $1 million of Cisco Systems stock. Use 5% critical values and the daily log returns
Consider the ARMA(1,1) model yt − 0.8yt−1 = at + 0.4at−1 with at ~ N(0, 0.49). Convert the model into a state-space form using (a)Akaike's method, (b) Harvey's approach, and (c) Aoki's approach.
The file aa-rv-20m.txt contains the realized daily volatility series of Alcoa stock returns from January 2, 2003 to May 7, 2004; see the example in Section 11.1.The volatility series is constructed
Consider the monthly simple excess returns of Pfizer stock and the S&P 500 composite index from January 1990 to December 2003.The excess returns are in m-pfesp-ex9003.txt witeh Pfizer stock returns
Consider the AR(3) modelwhere {et} and {at} are independent and the initial values of xj with j ≤ 0 are independent of et and at for t > 0.(a) Convert the model into a state-space form.(b) If
The file m-ppiaco.txt contains year, month, day, and U.S.Producer Price Index (PPI) from January 1947 to August 2004. The index is for all commodities and not seasonally adjusted. Let zt =ln(zt) −
Suppose that x is normally distributed with mean μ and variance 4. Assume that the prior distribution of μ is also normal with mean 0 and variance 25. What is the posterior distribution of μ given
Consider the linear regression model with time series errors in Section 12.5.Assume that zt is an AR(p) process (i.e., zt = φ1 zt−1 +··· + φpzt−p + at). Let φ = (φ1,…,φp)′ be the
Consider the linear AR(p) model in Section 12.6.1. Suppose that xh and xh+1 are two missing values with a joint prior distribution being multivariate normal with mean μo and covariance matrix
Consider the monthly log returns of General Motors stock from 1950 to 1999 with 600 observations: (a) build a GARCH model for the series, (b) build a stochastic volatility model for the series,
Build a stochastic volatility model for the daily log return of Cisco Systems stock from January 1991 to December 1999. You may download the data from the CRSP database or the file dcsco9199.txt. Use
Build a bivariate stochastic volatility model for the monthly log returns of General Motors stock and the S&P 500 index for the sample period from January 1950 to December 1999. Discuss the
Consider the daily stock returns of American Express (axp), Caterpillar (cat), and Starbucks (sbux) from January 1994 to December 2003. The data are simple returns given in the file d3stock.txt
Answer the same questions as Exercise 1.1 but using monthly stock returns for IBM, CRSP value-weighted index (VW), CRSP equal-weighted index (EW), and S&P composite index from January 1975 to
Consider the monthly stock returns of S&P composite index from January 1975 to December 2003 in Exercise 1.2.Answer the following questions:(a) What is the average annual log return over the data
Consider the daily log returns of American Express stock from January 1994 to December 2003 as in Exercise 1.1.Use the 5%significance level to perform the following tests. (a) Test the null
Daily foreign exchange rates (spot rates) can be obtained from the Federal Reserve Bank in Chicago. The data are the noon buying rates in New York City certified by the Federal Reserve Bank of New
Suppose that the simple return of a monthly bond index follows the MA(1) model Rt = at + 0.2at−1, σa = 0.025.Assume that a100 = 0.01.Compute the 1-step and 2-step ahead forecasts of the return at
Suppose that the daily log return of a security follows the model rt = 0.01 + 0.2rt−2 + at, where {at} is a Gaussian white noise series with mean zero and variance 0.02.What are the mean and
Consider the monthly U.S. unemployment rate from January 1951 to February 2004 in the file m-unemhelp.txt. The data are seasonally adjusted and obtained from the Federal Reserve Bank in St. Louis.
Consider the monthly simple returns of the Decile 1, Decile 5, and Decile 10 of NYSE/AMEX/NASDAQ based on market capitalization. The data span is from January 1960 to December 2003, and the data are
Consider the daily simple returns of IBM stock from 1962 to 2002 in the file d-ibmvwew6202.txt. Compute the first 100 lags of the ACF of the absolute daily simple returns of IBM stock. Is there
Consider the demand for electricity of a manufacturing sector in the United States. The data are logged, denote the demand of a fixed day of each month, and are inpower6.txt. Build a time series
Consider the daily simple return of CRSP equal-weighted index, including distributions, from January 1980 to December 1999 in file d-ew8099.txt (date, ew). Indicator variables for Mondays, Tuesdays,
As demonstrated by the prior exercise, daily returns of equalweighted index have some weekday effects. How about daily returns of S&P composite index? To answer this question, consider the daily
Now consider similar questions of the previous exercise for individual stock returns. We use the daily simple returns of Dell stock in this exercise.(a) Is there a Friday effect on the daily simple
Consider the monthly yields of Moody's AAA BAA seasoned bonds from January 1919 to March 2004. The data are obtained from the Federal Reserve Bank in St. Louis. Monthly yields are averages of daily
Consider the monthly AAA bond yields of the prior exercise.Build a time series model for the series.
Consider the monthly log returns of CRSP equal-weighted index from January 1962 to December 1999 for 456 observations.You may obtain the data from CRSP directly or from the file mew6299.txt on the
This problem is concerned with the dynamic relationship between the spot and futures prices of the S&P 500 index. The data file sp5may.dat has three columns: log(futures price), log(spot price), and
The quarterly gross domestic product implicit price deflator is often used as a measure of inflation. The file q-gdpdef.dat contains the data for the United States from the first quarter of 1947 to
Derive multistep ahead forecasts for a GARCH(1,2) model at the forecast origin h.
Derive multistep ahead forecasts for a GARCH(2,1) model at the forecast origin h.
Suppose that r1,…,rn are observations of a return series that follows the AR(1)–GARCH(1,1) modelwhere εt is a standard Gaussian white noise series. Derive the conditional log likelihood function
In the previous equation, assume that εt follows a standardized Student-t distribution with v degrees of freedom. Derive the conditional log likelihood function of the data.
Consider the monthly simple returns of Intel stock from 1973 to 2003 in m-intc7303.txt. Transform the returns into log returns. Build a GARCH model for the transformed series and compute 1-step to
The file m-3m4603.txt contains two columns. They are date and the monthly simple return for 3M stock. Transform the returns to log returns.(a) Is there any evidence of ARCH effects in the log
Revisit the file d-gmsp9303.txt. However, we shall investigate the value of using market volatility in modeling volatility of individual stocks. Convert the two simple return series into percentage
Again, consider the percentage daily log returns of GM stock and the S&P 500 index from 1993 to 2003 as before, but we shall investigate whether the volatility of GM stock has any contribution in
Consider the daily simple returns of Johnson and Johnson stock from January 1990 to December 2003. The data are in the file d-jnj9003.txt or can be obtained from CRSP. Convert the returns into log
Consider the monthly simple returns of General Electric (GE)stock from January 1926 to December 2003. You may download the data from CRSP or use the file m-ge2603.txt on the Web. Convert the returns
Suppose that the monthly log returns of GE stock, measured in percentages, follow a smooth threshold GARCH(1,1) model. For the sampling period from January 1926 to December 1999, the fitted model
Suppose that the monthly log returns, in percentages, of a stock follow the following Markov switching model:where the transition probabilities areSuppose that , and s100 = 2 with probability
Consider the monthly simple returns of GE stock from January 1926 to December 2003. Use the last three years of data for forecasting evaluation.(a) Using lagged returns rt−1,rt−2,rt−3 as input,
Because of the existence of inverted yield curves in the term structure of interest rates, the spread of interest rates should be nonlinear. To verify this, consider the weekly U.S. interest rates of
Let rt be the log return of an asset at time T. Assume that {rt } is a Gaussian white noise series with mean 0.05 and variance 1.5.Suppose that the probability of a trade at each time point is 40%
Let pt be the observed market price of an asset, which is related to the fundamental value of the asset via Eq. (5.09). Assume thatΔ = < − p*t−1 forms a Gaussian white noise series with mean
The file mmm9912-dtp.txt contains the transactions data of the stock of 3M Company in December 1999. There are three columns:day of the month, time of transaction in seconds from midnight, and
To gain experience in analyzing high-frequency financial data, consider the trade data of GE stock from December 1 to December 5, 2003 in the file taq-t-ge-dec5.txt. The file has four major
Again, consider the high-frequency data of GE stock from December 1 to December 5, 2003 and ignore the transactions outside normal trading hours. Construct an intraday 5-minute return series. Note
Consider the same problem as in Exercise 5.8, but use 10-minute time intervals. See filetaq-ge-dec5-10m.txt.
Again, consider the high-frequency data of GE stock and ignore transactions outside normal trading hours. Compute the percentage of consecutive transactions without price change in the sample.
Assume that the log price pt = ln(Pt) follows a stochastic differential equation dpt = γ dt + σ dwt, where wt is a Wiener process. Derive the stochastic equation for the price pt.
Considering the forward price F of a nondividend-paying stock, we have Ft, T=pt er(T −t),where r is the risk-free interest rate, which is constant, and pt is the current stock price. Suppose pt
Assume that the price of IBM stock follows Ito process dPt= μPt dt + σPt dwt, where μ and σ are constant and wt is a standard Brownian motion.Consider the daily log returns of IBM stock in 1997.
Suppose that the current price of a stock is $120 per share with volatility σ =50% per annum. Suppose further that the risk-free interest rate is 7% per annum and the stock pays no dividend. (a)What
Derive the limiting marginal effects of the five variables K, pt, T −t, σ, and r on a European put option contingent on a stock.
A stock price is currently $60 per share and follows the geometric Brownian motion dPt = μPt dt + σPt dt. Assume that the expected return μ from the stock is 20% per annum and its volatility is
A stock price is currently $60 per share and follows the geometric Brownian motion dPt = μPt dt + σPt dt. Assume that the expected return μ from the stock is 20% per annum and its volatility is
Suppose that the current price of stock A is $70 per share and the price follows the jump diffusion model in Eq. (6.26). Assume that the risk-free interest rate is 8% per annum, the stock pays no
Consider the daily returns of GE stock from July 3, 1962 to December 31, 1999. The data can be obtained from CRSP or the file d-ge6299.txt Convert the simple returns into log returns. Suppose that
The file d-csco9199.txt contains the daily simple returns of Cisco Systems stock from 1991 to 1999 with 2275 observations. Transform the simple returns to log returns. Suppose that you hold a long
Use Hill's estimator and the data d-csco9199.txt to estimate the tail index for daily log returns of Cisco stock.
The file d-hwp3dx8099.txt contains dates and the daily simple returns of Hewlett-Packard, the CRSP value-weighted index, equalweighted index, and the S&P 500 index from 1980 to 1999. The returns

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