Home Depot Financial Analysis Essay

Home Depot Inc: Senior Management Report

Data was collected from CRSP daily observations for Home Depot starting January 1993 and ending December 2004. Observations for S&P and Home Depot were matched, and also for the T-Bill composite which is used as a substitute for the risk free rate. No unusual data patterns were observed during the work-up. After having done the Event Check, no large differences in the slopes of the data in the periods before and after 911 were discovered and both periods are used. The data matches the usual modeling assumptions and thus, results are to be expected to be interpreted without contradictions.

HD' Market Rating Analysis (MRA):
Jensen's Alpha ( ) was largely in the positive range. Therefore, Home Depot return was greater than the S&P return and HD
outperformed the market. Beta ( ) was in the range of [1,1275959 to 1,0648879] and thus contained 1,0. So HD's risk/return profile is not much different from the market. (See appendix, p. 1)
The Market SPI was higher than the HD's Sharpe ratio, and therefore the market return is higher relative to total risk and the investor is better compensated for risk taken.
On the other side, the TPI of the market was lower than Home Depots' Treynor ratio and thus, the company gives higher return relative to non-diversifiable risk (See Appendix, p. 1).
The BH-L Relative Unique Risk (RUR) was 47,8% which offers a moderate return for investors for risk taking (See Appendix, p.
2).
For the -group the 95% Confidence Interval on the mean contained zero and the Standard Deviation Group is all in the negative zone. In the Peer comparison Home Depot did better than its peers, but was below the average in the standard deviation grouping (See Appendix, p. 2-3).

A general assessment of the company will help in evaluating the future performance of the stock. H.D. is in an advanced expansion stage, with strong growth in the last years through an aggressive growth policy. That is why it is likely that the company's domestic growth will loose speed. Thus H.D. has to compensate its growth by reorientation and expansion to new markets, abroad. Expansionary projects include the international growth on the Mexican as well as the on Asian Market, e.g. China. On the US market several innovative changes have been implemented, such as new formatted urban Home Depot stores, Internet shopping and increased customer service. This creates new market potential and revenue opportunities.
In the IBR, PriceTarget Research (PTR) evaluates HD as an upper level performer, giving the company the high rating of A.
This rating is based upon PTR main measurements – a high appreciation score (89) which represents the potential price change to target, and a good power rating (74) which states the likelihood of a favorable performance. Home Depot Inc. has a high historical profitability, high forecast profitability, a good price/ asset ratio and a high financial strength. According to PTR, HD is expected to continue to create value for investors since returns exceed capital costs. PTR values Home Depot Inc. as highest investment quality and is likely to achieve a good performance in the
future.
We confirm PTR's evaluation of Home Depot Inc. and rate the company as an above average market performer, thus recommending investors to buy.

Technical Report

Data Work up of Home Depot

The first step was to include the T-Bills into the company data set. They both differed in the dates listed. To do the windowing and all the other preceding steps we had to create the Excess return columns for HD and S&P. For that we used a formula in JMP:

and

In the following we set up a window, to limit the data. To do this we determined the Standard Deviation of HD Return Excess which is 0.0228377. HD Excess Return is used here because we need it to generate Jensen's α and β later.
The window is: 0.0228377 x 2.25 = 0,051384825
The adjusted, rounded window is then: thus we are able to create the Excess Return Screened columns for the HD, S&P, Beta and the Standard Deviation data columns.
This requires us to use another formula in JMP, which is as following: In the following the Excess Return Screened data is used to do the outlier analysis. A look at the CC-Plot, The Box Plot and The M-Plot gives enough evidence that there are a quite an amount of outliers which seem critical for the ongoing analysis. I used the M-Plot to lasso out 195 outliers above the blue line.
The next step in the Data Work-up procedure is an Event Check. I decided on doing on 911.
To figure out whether to eliminate any of the two periods (pre and post) there are 3 things we have to watch for.
First, we look at the CC-Plot and look for any trend changes. A comparison of the two graphs shows that there are no obvious changes.
Second, we compare the both slopes of both periods and see if the change in the slope is more than 15%.

In our case it is:
911-Post
Linear Fit:
HD Excess Return Screened = 0,000736 + 1,0569741 S&P Excess ReturnScreened
911-Pre
Linear Fit:
HD Excess Return Screened = 0,0000915 + 1,1169993 S&P Excess ReturnScreened

Change in Slope= (Largest Slope less Smallest Slope)/ Largest Slope
(1,1169993 - 1,0569741) / (1,1169993) = 0,053737903

So the change in the slope is about 5% which is smaller than 15%.
Third, there is no sign change in the slopes and the slope test is statistically not .
Since none of the assumptions holds, we leave both periods in. The final data set consists of
N= 2802 daily market observations.
Kappa p- value
Bivariate Fit of HD Excess Return Screened By S&P Excess ReturnScreened
Whole regression model

To figure out whether the Regression model is appropriate we are doing an Analysis on the Residuals in the JMP program, doing a Spectral Density Analysis, which gives the Kappa p-value. The Fisher's Kappa p-value is 0,45375. The value is in the zone (0,1 to 1) which tells us that the model is about as good as it gets and the model statistics are reliable estimates of the true parameters.