These videos are a good resource. Don't worry if you don't understand everything. It takes time.

I love you Aswath. I'll pray for a long and happy career for you to pioneer the world of valuation and corporate finance :)!

These videos are a great resource, thank you

Great explanation, thank you very much!

The fundamental Beta and Market implied equity risk premium are insightful, thanks

This was fantastic

Legend.

I agree, this is very helpful. However, I still think you want to have the regression beta as a perspective. I usually regress the beta over the last 5 or 10 years and then I run a 3 year rolling beta to capture the trend/direction of the beta throughout the time series I use.

@danielhinds2262

4 ай бұрын

Makes sense...but he did say that he still utilizes regression betas to derive 'bottom-up' betas @ 8:13

THANK YOU

Superb video

How do we get EV value of different businesses within a firm?

The calculations for average beta.. what betas to use.. are these from different indices with different companies or only same index different companies?

Bravo!

This reminds me a bit of elastic and nonelastic (elasticity) products a bit in regards to beta..

When are taking the beta from all the companies in the industry do u you include the beta for the business you are valuing 🤔

legend

Question on the bottom-up beta methodology. Problems of regression beta is that it reflects the historical average of financial leverage over the period of regression, yet you unlever the average of regression beta of the publicly traded firms in a given business with current debt to equity ratio? Wouldn't this be 'noisy' because if the industry have undergone a de-leveraging phase within the regression timeline, it would overestimate the unlevered beta of a given business?

@alex_8704

7 жыл бұрын

Exactly

@Holy_hand-grenade

5 жыл бұрын

Excellent point. I feel like you could control for that with fairly minimal research. Plot the average current period D/E of those companies you are regressing over the maximum regression period and narrow the window to match current D/E levels. Then de-lever and find the asset betas, then lever back up at the D/E of the subject asset.

@MegaParth2

2 жыл бұрын

One way could be to average out the D/E ratio for the firms. (beginning of the period and ending period) Should be more accurate with better weights. Eg - annual ratios etc

I have a question, for the EV/SalesValue Weights the "EV" refers to the enterprise value for the software industry or how did he come for both 3.25 and 2.00?

@lucaoliveira9246

3 жыл бұрын

3.25 is the measure for software and 2.00 for consulting sector

Hello Sir Damodaran, thank you so much for your explanations and help to let us understand better the company valuation processes. I have a question regarding Betas. If we do not have access to Bloomberg or other costly data websites, how can I come with an unlevered Beta for a Hungarian Private Company ? Thank you in advance

@dineshssairam

6 жыл бұрын

You usually take the average un-levered Beta for the public companies in the industry and then re-lever it using the D/E ratio of the private company in question. If all the companies in the industry are largely private, then bottom-up Beta is very difficult to calculate without access to more information.

@sheyrazpp

6 жыл бұрын

Please go to Damodaran website. U can find betas for different sectors.

Where to find unlevered beta? How to calculate it?

Table of calculation of unlevered Beta: Please skip to 12:17. I have not understood calculation of weight i.e. 17.23 and 4.40. How unlevered data for individual segments is calculated i.e. 1.30 and 1.05?

@anubhav_lal

4 жыл бұрын

I believe the table image at 12:15 is very confusing because he hasn't shown the calculations - the steps are followed as per the bottom-up betas approach. Step 2 hasn't been explained so I have been unable to get values of 1.30 and 1.05 as well. Step 3 (weights) : He has computed the multiple at which these companies were valued in the market (as in Market Value/Revenue). Then, he has equated this multiple of software companies to the Revenue of SAP software business to get an estimated value of the SAP software business, after which, he found multiple by dividing this estimated value by the revenue [same was done with the consulting part - this gave the values of 3.25 and 2 respectively]. This has then been multiplied by the revenue to get the weights (5.3*3.25 and 2.2*2). The weights gave an estimate of the % revenue from businesses (weight/sum of weights). These weights are then multiplied with individual unlevered beta of businesses to get the unlevered beta of the firm [(1.30*0.8 + 1.05*0.2)/(0.8+0.2) = 1.25 : this is step 4 of approach]

How to find unlevered beta if we don't have beta(s) for other publicly traded companies?

❤️👍🏻

You can only become truly accomplished at something you love. Don’t make money your goal. Instead, pursue the things you love doing, and then do them so well that people can’t take their eyes off you. --Maya Angelou,.,.,.

But what if you are dealing with companies that are not traded?

When you are gathering data for betas, how do you find the dividend info?

@alex_8704

7 жыл бұрын

The historical closing price data at finance.yahoo.com has two columns: regular (nominal closing price) and adjusted. The adjusted closing price incorporates dividend distributions. You may use it to calculation beta.

@ahadahsan1

3 жыл бұрын

This entire lecture we kept going on about inaccurate historical beta and you want dividend info?!

Haven’t taken statistics, bust isn’t average only work make sense for a normal distribution ? Left and right skew would give bias average

can anyone tell me when calculating levered beta why debt / equity ratio is multiplied (1-tax%)?

what is average risk investment?

sir in finance the betas are inversely proportional to revenue(weights) then how come yours is not?

9:57: is the nominator standard error or should it be standard deviation??

@alex_8704

7 жыл бұрын

I think you're right. I believe this formula to predict the location of the target company's beta appears to be misleading. Because the only thing it estimates is the location of the population mean of the industry beta, while the target company's beta can be anywhere with the probability that can be potentially estimated based on the population distribution. As an example, I took the "sample" of the U.S. listed pharma companies with the current market cap higher than $ 4B. The "sample" included: RDY, LLY, TARO, NVO, TEVA, PRGO, JNJ, AZN, NVS, SNY, MRK, BMY, PFE, GSK, ZTS, AGN, MYL, ABBV, JAZZ, MNK, SHPG, TSRO, ALKS, ACAD. Their betas (from finance.yahoo) were: 0.08, 0.2, 0.35, 0.49, 0.54, 0.6, 0.68, 0.68, 0.71, 0.76, 0.94, 0.96, 1.04, 1.04, 1.22, 1.28, 1.45, 1.46, 1.54, 1.7, 1.71, 2.31, 2.41, 4.54, respectively, with the mean 1.20, median 1.00, mode in the area of 0.68 (0.6-0.8) with the 2nd, lower peak at 1.4-1.6. The distribution doesn't seem to be normal, no bell-shaped (there are two peaks). Its (unbiased) SD is 0.93. According to Chebyshev's inequality (which applies to any distribution), we may expect that the probability the unknown beta of the target company falls outside the range of 1.20±1.10 (i.e., 0.10-2.30) is up to 1/(k^2)*100%=1/((1.1/0.93)^2)*100%=72%. If we want to be sure the confidence interval includes the target's company beta with the probability of at least 95% (up to 5% probability it is outside the CI), we need that "k" in the Chebyshev's inequality = (1/(5%))^(1/2)=4.47. Thus, the beta with at least 95% probability will lie within the range of −2.96-+5.36. However, this estimate depends on the assumption that the sample SD equals the population SD, which most probably isn't true. Although the sample unbiased SD equals the expected population SD, the latter can deviate from this expected value. Therefore, if the true population SD is bigger than that of our sample, the 95% CI for the target company's beta will be even wider. If the population distribution were normal, we could use chi square statistic to estimate the 95% (or whatever) range of the population SD. However, the chi square test is not robust, and when the population distribution isn't normal, it produces totally unreliable results.

11:53 how did u find 1.25 (unlevered beta ) didn't get it well

@daiweitang4152

3 жыл бұрын

the wighted average:1.3*0.8+1.05*0.2

Maybe you should call it top-down-beta

I’m takin a bbbreak😅

1.4% is a high debt to equity ratio, i think.

@ashutoshlipan1055

3 жыл бұрын

No, it's low. He meant 1.4%, not 1.4 times.(1.4% translates to 0.014 times).

@rakeshpanwar6382

3 жыл бұрын

@@ashutoshlipan1055 thanks

Man he hates beta

I was enjoying the series up to here, but CAPM Beta is so well disproven as a measure of risk and acknowledged as being a useless and infact misleading metric. I had to skip this one, but it makes me question a lot of his other work if he is still peddling betas as a relative risk measure.

@mansoorshar9204

9 жыл бұрын

galumay gurruwiwi if you watch the rest of it he actually says he hasn't used regression/CAPM betas for 15 years and favors Bottom-up betas..

@xXTodo1Xx

6 жыл бұрын

wtf just watch the full video

@caetano1998

5 жыл бұрын

Doubting Damodaran... Very pretentious of you

@alexandervalladares2501

5 жыл бұрын

caetano brito ... you were nice... I would say how stupid

@joaquinsardon7305

4 жыл бұрын

im also doubting einsteins relativity. does anyone have his email?