What time period is used by yahoo finance to calculate beta
Citing the Yahoo Finance Help page, Beta: The Beta used is Beta of Equity. Beta is the monthly price   change of a particular company relative to the monthly price change of   the S&P500. The time period for Beta is 3 years (36 months) when   available. Regarding customised time periods, I do not think so.
This is a useful metric in that it gives you a trust factor on how reliable the beta is for future expectations   It is akin to velocity and acceleration  First  and second order derivatives of distance / time.  Erratic acceleration implies the velocity is less trustworthy  Same idea for beta In regards to your title, it's based on product. Rates based products had a late 2016 early 2017 run. It's now summer time and the fed is acting as expected. Clients have already positioned themselves going into the slow season. Distressed bonds and HY loans are still moving. After the latest fed increase and the yield curve flattening, HY loans took a hit. Par loans were trading at a discount. The market has moved back to paying a premium. However, HY bonds have been slowing down since June. New issue has dropped off, and equities have slowed as well. It's summer time. I wouldn't say that traders have it tough as the tittle suggests, it's just that it was a very active first quarter and now volatility has subsided. It's just the quiet season. "Calculating beta is finding the correlation between the dependent variable, MSCI world benchmark, and the independent variable, your companies. If you know how to run liner regression models, run each company as the independent variable with the dependent MSCI. You can use Excel to gather this result (Y = MSCI price change at closing hour while X = company stock price closing prince). Running the regression will give you the Beta (and alpha when doing portfolios); which (from linear algebra) is the ""m"" in y = mx + b" I looked at this a little more closely but the answer Victor provided is essentially correct.  The key to look at in the google finance graph is the red labled SMA(###d) would indicate the period units are d=days.  If you change the time axis of the graph it will shift to SMA(###m) for period in minutes or SMA(###w) for period in weeks.  Hope this clears things up! Identify the market and time period. Use the [capital asset pricing model](http://en.wikipedia.org/wiki/Capital_asset_pricing_model) to determine the market beta(http://en.wikipedia.org/wiki/Beta_(finance) for your given stock and interpret the results (if your stock plots above the security market line, it means you are getting higher return for your risk, with consideration of the affects of market risk).  Maybe give a more detailed question? You might simply need to compute a modified [Sharpe Ratio](http://en.wikipedia.org/wiki/Sharpe_ratio) using the market (during the time you've decided is the recession) as the risk free rate. Tough to give a good answer to such a general/non-specific question.  EDIT: link formatting - can't get the beta page to link because of '( )' in url Should be noted that pacoverflow's answer is wrong. Yahoo back-adjusts all the previous (not current or future) values based on a cumulative adjustment factor. So if there's a dividend ex-date on December 19, Yahoo adjusts all the PREVIOUS (December 18 and prior) prices with a factor which is: 1 - dividend / Dec18Close "Probably the best way to investigate this is to look at an example. First, as the commenters above have already said, the log-return from one period is log(price at time t/price at time t-1) which is approximately equal to the percentage change in the price from time t-1 to time t, provided that this percentage change is not big compared to the size of the price. (Note that you have to use the natural log, ie. log to the base e -- ln button on a calculator -- here.) The main use of the log-return is that is a proxy for the percentage change in the price, which turns out to be mathematically convenient, for various reasons which have mostly already been mentioned in the comments. But you already know this; your actual question is about the average log-return over a period of time. What does this indicate about the stock? The answer is: if the stock price is not changing very much, then the average log-return is about equal to the average percentage change in the price, and is very easy and quick to calculate. But if the stock price is very volatile, then the average log-return can be wildly different to the average percentage change in the price. Here is an example: the closing prices for Pitchfork Oil from last week's trading are: 10, 5, 12, 5, 10, 2, 15. The percentage changes are: -0.5, 1.4, -0.58, 1, -0.8, 6.5 (where -0.5 means -50%, etc.) The average percentage change is 1.17, or 117%. On the other hand, the log-returns for the same period are -0.69, 0.88, -0.88, 0.69, -1.6, 2, and the average log-return is about 0.068. If we used this as a proxy for the average percentage change in the price over the whole seven days, we would get 6.8% instead of 117%, which is wildly wrong. The reason why it is wrong is because the price fluctuated so much. On the other hand, the closing prices for United Marshmallow over the same period are 10, 11, 12, 11, 12, 13, 15. The average percentage change from day to day is 0.073, and the average log-return is 0.068, so in this case the log-return is very close to the percentage change. And it has the advantage of being computable from just the first and last prices, because the properties of logarithms imply that it simplifies to (log(15)-log(10))/6. Notice that this is exactly the same as for Pitchfork Oil. So one reason why you might be interested in the average log-return is that it gives a very quick way to estimate the average return, if the stock price is not changing very much. Another, more subtle reason, is that it actually behaves better than the percentage return. When the price of Pitchfork jumps from 5 to 12 and then crashes back to 5 again, the percentage changes are +140% and -58%, for an average of +82%. That sounds good, but if you had bought it at 5, and then sold it at 5, you would actually have made 0% on your money. The log-returns for the same period do not have this disturbing property, because they do add up to 0%.  What's the real difference in this example? Well, if you had bought $1 worth of Pitchfork on Tuesday, when it was 5, and sold it on Wednesday, when it was 12, you would have made a profit of $1.40. If you had then bought another $1 on Wednesday and sold it on Thursday, you would have made a loss of $0.58. Overall, your profit would have been $0.82. This is what the average percentage return is calculating. On the other hand, if you had been a long-term investor who had bought on Tuesday and hung on until Thursday, then quoting an ""average return"" of 82% is highly misleading, because it in no way corresponds to the return of 0% which you actually got!  The moral is that it may be better to look at the log-returns if you are a buy-and-hold type of investor, because log-returns cancel out when prices fluctuate, whereas percentage changes in price do not. But the flip-side of this is that your average log-return over a period of time does not give you much information about what the prices have been doing, since it is just (log(final price) - log(initial price))/number of periods. Since it is so easy to calculate from the initial and final prices themselves, you commonly won't see it in the financial pages, as far as I know.  Finally, to answer your question: ""Does knowing this single piece of information indicate something about the stock?"", I would say: not really. From the point of view of this one indicator, Pitchfork Oil and United Marshmallow look like identical investments, when they are clearly not. Knowing the average log-return is exactly the same as knowing the ratio between the final and initial prices." "At the bottom of Yahoo! Finance's S & P 500 quote Quotes are real-time for NASDAQ, NYSE, and NYSE MKT. See also delay   times for other exchanges. All information provided ""as is"" for   informational purposes only, not intended for trading purposes or   advice. Neither Yahoo! nor any of independent providers is liable for   any informational errors, incompleteness, or delays, or for any   actions taken in reliance on information contained herein. By   accessing the Yahoo! site, you agree not to redistribute the   information found therein. Fundamental company data provided by Capital IQ. Historical chart data   and daily updates provided by Commodity Systems, Inc. (CSI).   International historical chart data, daily updates, fund summary, fund   performance, dividend data and Morningstar Index data provided by   Morningstar, Inc. Orderbook quotes are provided by BATS Exchange. US   Financials data provided by Edgar Online and all other Financials   provided by Capital IQ. International historical chart data, daily   updates, fundAnalyst estimates data provided by Thomson Financial   Network. All data povided by Thomson Financial Network is based solely   upon research information provided by third party analysts. Yahoo! has   not reviewed, and in no way endorses the validity of such data. Yahoo!   and ThomsonFN shall not be liable for any actions taken in reliance   thereon. Thus, yes there is a DB being accessed that there is likely an agreement between Yahoo! and the providers." If you do not need it for a day or a week or something like that, an easy thing to do to get the beta of a security is to use wolframalpha.  Here is a sample query: BETA for AAPL Calculating beta is an important metric, but it is not a be all end all, as there are ways to hedge the beta of your portfolio. So relying on beta is only useful if it is done in conjunction with something else. A high beta security just means that overall the security acts as the market does with some multiplier effect. For a secure portfolio you want beta as close to zero as possible for capital preservation while trying to find ways to exploit alpha. "Google Docs spreadsheets have a function for filling in stock and fund prices.  You can use that data to graph (fund1 / fund2) over some time period. Syntax: =GoogleFinance(""symbol"", ""attribute"", ""start_date"", ""num_days|end_date"", ""interval"") where: This analysis won’t include dividends or distributions. Yahoo provides adjusted data, if you want to include that." "Beta is the correct answer.  It is THE measure of the risk relationship of a stock with the broad market. R squared is incorrect unless you mean something very odd by ""co-efficiency.""  A stock that goes up each time the market goes down has very low co-efficiency (negative risk as you have defined it) but very high R squared.  A stock that goes the same direction as the market but twice as far (with a lot of noise) has a very low R squared but contains a lot of market risk.  A stock that always goes in the same direction as the market but only a 100th as far is very safe but has a very high R squared. You can calculate beta using ""slope"" in excel or doing a regression, but the easiest thing is just to look up the beta in yahoo finance or elsewhere.  You don't need to calculate it for yourself normally." Yahoo Finance has this now, the ticker is CL=F. The Money Chimp site lets you choose two points in time to see the return. i.e. you give it the time (two dates) and it tells you the return.  One can create a spreadsheet to look at multiple time periods and answer your question that way, but I've not seen it laid out that way in advance. For what it's worth, I am halfway to my retirement number. I can tell you, for example that at X%, I hit my number in Y years. 8.73% gets me 8/25/17 (kid off to college) 3.68% gets me 8/25/21 (kid graduates), so in a sense, we're after the same type of info. With the long term return being in the 10% range, you're going to get 3 years or so as average, but with a skewed bellish curve when run over time. My level of analysis is not quite that advanced. Can you share what that would show and why that particular measure is the one to use?  I've run regression on prices between the two. VIX prices have no correlation to the s&amp;p500 prices. Shouldn't true volatility result in the prices (more people putting options on the VIX during the bad times and driving that price up) correlate to the selloff that occurs within the S&amp;P500 during recessions and other events that would cause significant or minor volatility? My r2 showed no significance within a measurement of regression within Excel.  But, *gasp* I could be wrong, but would love to learn more about better ways of measurement :) "Would still be affected by energy prices, labor, weather, and any other input they don't have full control over. Labor and weather can never be controlled.  Other users of beef may have a derivative hedge. A derivative hedge would likely provide more direct (maybe short term) protection than a vertical integration hedge.  With a financial hedge all of the secondary risk factors are ""incorporated"" while with vertical integration you are still left with the risk of each and every input to the final product that you do not control.  Vertical integration is done for a lot of reasons and it doesn't always result in lower than market costs, especially over any given period."