How to find trailing 5-year stock returns for 1980s?
I dont know if this data is available for the 1980s, but this response to an old question of mine discusses how you can pull stock related information from google or yahoo finance over a certain period of time. You could do this in excel or google spreadsheet and see if you could get the data you're looking for.  Quote from old post: 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.
A couple ideas: Use excel - it has an IRR (internal rate of return) that can handle a table of inputs as you describe, along with dates deposited to give you a precise number.  Go simple - track total deposits over the year, assume half of that was present in January. So, for example, your account started the year with $10k, ended with $15k, but you deposited $4k over the year. It should be clear the return (gain) is $1k, right? But it's not 10%, as you added during the year. I'd divide $1k/$12k for an 8.3% return. Not knowing how your deposits were structured, the true number lies between the 10% and 6.7% as extremes. You'll find as you get older and have a higher balance, this fast method gaining accuracy, as your deposits are a tinier fraction of your account and likely spread out pretty smoothly over the year anyway. "For free, 5 years is somewhat available, and 10 years is available to a limited extent on money.msn.com. Some are calculated for you. Gurufocus is also a treasure trove of value statistics that do in fact reach back 10 years. From the Gurufocus site, the historical P/E can be calculated by dividing their figure for ""Earnings per Share"" by the share price at the time.  It looks like their EPS figure is split adjusted, so you'll have to use the split adjusted share price. ""Free cash"", defined in the comments as money held at the end of the year, can be found on the balance sheet as ""Cash, Cash Equivalents, Marketable Securities""; however, the more common term is ""free cash flow"", and its growth rate can be found at the top of the gurufocus financials page." I haven't seen one of these in quite some time. Back in the 1970s, maybe the 1980s, stock brokers would occasionally send their retail clients a complimentary copy once in a while. Also, I remember the local newspaper would offer a year-end edition for a few dollars (maybe $3) and that edition would include the newspaper company's name on the cover. They were very handy little guides measuring 5 1/2 x 8 (horizontal) with one line devoted to each company. They listed hundreds of publicly traded companies and had basic info on each company. As you stated, for further info you needed to go to the library and follow-up with the big S&P and/or Moody's manuals. That was long before the internet made such info available at the click of a button on a home computer! Yes, assuming that your cash flow is constantly of size 5 and initial investment is 100, the following applies: IRR of 5% over 3 years: Value of CashFlows: 4.7619 + 4.5351 + 4.3192 = 13.6162 NPV: 100 - 13.6162 = 86.3838 Continuous compounding: 86.3838 * (1.05^3) = 100 The first issue is if the stock has returned 8% since you purchased it that could be either very good (8% in two days) or very bad (8% over 20 years).  Even just measured over the past year it could be relatively very good (up 8% and the market is down 5%) or very bad (up 8% and the market is up 16%). Either way, the good rule of thumb is that you shouldn't choose your positions using the returns of the stock in the past, but only on your view of the future returns of the stock.  For instance, if the stock has gone up 8% in two months, but you think it has another 8% to go in the next two months you probably shouldn't take your earnings. As for the $5k, at first glance that is not an unreasonable amount.  If you use a discount broker the fees shouldn't be so large that you will eat up any return on a $5k amount.  Also, from what you describe it is not such a large amount that mistakes will put your retirement in danger. "So, there is no truly ""correct"" way to calculate return.  Professionals will often calculate many different rates of return depending on what they wish to understand about their portfolio.  However, the two most common ways of calculating multi-period return though are time-weighted return and money-weighted return. I'll leave the details to this good Investopeadia article, but the big picture is time-weighted returns help you understand how the stock performed during the period in question independent of how you invested it it.  Whereas money-weighted return helps you understand how you performed investing in the stock in question.  From your question, it appears both methods would be useful in combination to help you evaluate your portfolio. Both methods should be fairly easy to calculate yourself in a spread sheet, but if you are interested there are plenty of examples of both in google docs on the web." "Do you recall where you read that 25% is considered very good? I graduated college in 1984 so that's when my own 'investing life' really began. Of the 29 years, 9 of them showed 25% to be not quite so good.  2013     32.42, 2009     27.11,  2003     28.72,  1998     28.73,  1997     33.67,  1995     38.02,  1991     30.95,  1989     32.00,  1985     32.24.  Of course this is only in hindsight, and the returns I list are for the S&P index. Even with these great 9 years, the CAGR (compound annual growth) of the S&P from 1985 till the end of 2013 was 11.32%  Most managed funds (i.e. mutual funds) do not match the S&P over time. Much has been written on how an individual investor's best approach is to simply find the lowest cost index and use a mix with bonds (government) to match their risk tolerance.  ""my long term return is about S&P less .05%"" sounds like I'm announcing that I'm doing worse than average. Yes, and proud of it. Most investors (85-95% depending on survey) lag by far more than this, many percent in fact)" "Below I will try to explain two most common Binomial Option Pricing Models (BOPM) used. First of all, BOPM splits time to expiry into N equal sub-periods and assumes that in each period the underlying security price may rise or fall by a known proportion, so the value of an option in any sub-period is a function of its possible values in the following sub period. Therefore the current value of an option is found by working backwards from expiry date through sub-periods to current time. There is not enough information in the question from your textbook so we may assume that what you are asked to do is to find a value of a call option using just a Single Period BOPM. Here are two ways of doing this: First of all let's summarize your information: Current Share Price (Vs) = $70 Strike or exercise price (X) = $60 Risk-free rate (r) = 5.5% or 0.055 Time to maturity (t) = 12 months Downward movement in share price for the period (d) = $65 / $70 = 0.928571429 Upward movement in share price for the period (u) = 1/d = 1/0.928571429 = 1.076923077 ""u"" can be translated to $ multiplying by Vs => 1.076923077 * $70 = $75.38 which is the maximum probable share price in 12 months time. If you need more clarification here - the minimum and maximum future share prices are calculated from stocks past volatility which is a measure of risk. But because your textbook question does not seem to be asking this - you probably don't have to bother too much about it yet. Intrinsic Value: Just in case someone reading this is unclear - the Value of an option on maturity is the difference between the exercise (strike) price and the value of a share at the time of the option maturity. This is also called an intrinsic value.  Note that American Option can be exercised prior to it's maturity in this case the intrinsic value it simply the diference between strike price and the underlying share price at the time of an exercise. But the Value of an option at period 0 (also called option price) is a price you would normally pay in order to buy it. So, say, with a strike of $60 and Share Price of $70 the intrinsic value is $10, whereas if Share Price was $50 the intrinsic value would be $0. The option price or the value of a call option in both cases would be fixed.  So we also need to find intrinsic option values when price falls to the lowest probable and rises to the maximum probable (Vcd and Vcu respectively) (Vcd) = $65-$60 = $5 (remember if Strike was $70 then Vcd would be $0 because nobody would exercise an option that is out of the money) (Vcu) = $75.38-$60 = $15.38 1. Setting up a hedge ratio:  h = Vs*(u-d)/(Vcu-Vcd) h = 70*(1.076923077-0.928571429)/(15.38-5) = 1 That means we have to write (sell) 1 option for each share purchased in order to hedge the risks. You can make a simple calculation to check this, but I'm not going to go into too much detail here as the equestion is not about hedging. Because this position is risk-free in equilibrium it should pay a risk-free rate (5.5%). Then, the formula to price an option (Vc) using the hedging approach is: (Vs-hVc)(e^(rt))=(Vsu-hVcu) Where (Vc) is the value of the call option, (h) is the hedge ratio, (Vs) - Current Share Price, (Vsu) - highest probable share price, (r) - risk-free rate, (t) - time in years, (Vcu) - value of a call option on maturity at the highest probable share price. Therefore solving for (Vc): (70-1*Vc)(e^(0.055*(12/12))) = (75.38-1*15.38) => (70-Vc)*1.056540615 = 60 => 70-Vc = 60/1.056540615 => Vc = 70 - (60/1.056540615)  Which is similar to the formula given in your textbook, so I must assume that using 1+r would be simply a very close approximation of the formula above. Then it is easy to find that Vc = 13.2108911402 ~ $13.21 2. Risk-neutral valuation: Another way to calculate (Vc) is using a risk-neutral approach. We first introduce a variable (p) which is a risk-neutral probability of an increase in share price. p = (e^(r*t)-d)/(u-d) so in your case: p = (1.056540615-0.928571429)/(1.076923077-0.928571429) = 0.862607107 Therefore using (p) the (Vc) would be equal: Vc = [pVcu+(1-p)Vcd]/(e^(rt)) => Vc = [(0.862607107*15.38)+(0.137392893*5)]/1.056540615 => Vc = 13.2071229185 ~ $13.21 As you can see it is very close to the hedging approach. I hope this answers your questions. Also bear in mind that there is much more to the option pricing than this. The most important topics to cover are: Multi-period BOPM Accounting for Dividends Black-Scholes-Merton Option Pricing Model" Back of the envelope calculation: 30K limit, 5 year draw, 5 year post draw payback. Int 6% a year, 3% minimum payment. Borrow 5K at the start of the draw period, make the minimum payment for the next 5 years. At the end of 5 years still owe approximately $1123, and have paid ~775 in interest. Borrow 5K at the start of the draw period, make the minimum payment for the next 5 years but borrow the money from the line of credit. At the end of 5 years still  owe approximately $6711, and have paid ~1711 in interest. The bank loves you. The balance grows instead of decreases. That growing balance become pur profit. Of course you are good for it, because you never came anywhere near the maximum limit of 30K.  This is a variation of somebody tapping the line of credit to invest it, then discovering that it is hard to make enough money to make it worthwhile.   Detailed scenario: "Please note that the following Graham Rating below corresponds to five years: Earnings Stability (100% ⇒ 10 Years): 50.00% Benjamin Graham - once known as The Dean of Wall Street - was a scholar and financial analyst who mentored legendary investors such as Warren Buffett, William J. Ruane, Irving Kahn and Walter J. Schloss. Buffett describes Graham's book - The Intelligent Investor - as ""by far the best book about investing ever written"" (in its preface). Graham's first recommended strategy - for casual investors - was to invest in Index stocks.  For more serious investors, Graham recommended three different categories of stocks - Defensive, Enterprising and NCAV - and 17 qualitative and quantitative rules for identifying them.  For advanced investors, Graham described various ""special situations"". The first requires almost no analysis, and is easily accomplished today with a good S&P500 Index fund. The last requires more than the average level of ability and experience. Such stocks are also not amenable to impartial algorithmic analysis, and require a case-specific approach. But Defensive, Enterprising and NCAV stocks can be reliably detected by today's data-mining software, and offer a great avenue for accurate automated analysis and profitable investment. For example, given below are the actual Graham ratings for International Business Machines Corp (IBM), with no adjustments other than those for inflation. Defensive Graham investment requires that all ratings be 100% or more. Enterprising Graham investment requires minimum ratings of - N/A, 75%, 90%, 50%, 5%, N/A and 137%. International Business Machines Corp - Graham Ratings Sales | Size (100% ⇒ $500 Million): 18,558.60% Current Assets ÷ [2 x Current Liabilities]: 62.40% Net Current Assets ÷ Long Term Debt: 28.00% Earnings Stability (100% ⇒ 10 Years): 100.00% Dividend Record (100% ⇒ 20 Years): 100.00% Earnings Growth (100% ⇒ 30% Growth): 172.99% Graham Number ÷ Previous Close: 35.81% Not all stocks failing Graham's rules are necessarily bad investments. They may fall under ""special situations"". Graham's rules are also extremely selective. Graham designed and backtested his framework for over 50 years, to deliver the best possible long-term results. Even when stocks don't clear them, Graham's rules give a clear quantifiable measure of a stock's margin of safety. Thank you." The author is using the simple Dietz method, (alternatively the modified Dietz), with the assumption that the net cash-flow occurs halfway through the time period.  Let's say the time period is one year for illustration, so the cash-flow would be at the end of the second quarter. The money-weighted method gives a more accurate return, but has to be solved by trial-and-error or using a computer.  The money-weighted return is 11.2718 % and the simple or modified Dietz return is 11.2676 %.  When the sums are done backwards to check, the Dietz is half a dollar out with a final value of $11,999.50 while the money-weighted return recalculates exactly $12,000. It is worth pointing out that the return changes if the cash-flow is not in the middle of the time period.  A case with the cash-flow at the end of Q3 is added to illustrate. "Total Return is the percent change in value (including andy dividends) of an instrument.  The ""trailing 12-month"" means that your starting point is the value 12 months ago.  So the formula is: where V is the value of the instrument on the reference date, V0 is the value of the instrument 12 months prior to the reference date, and D is the amount of dividends paid between the two dates." If annualized rate of return is what you are looking for, using a tool would make it a lot easier.   In the post I've also explained how to use the spreadsheet.   Hope this helps. "The formula for standard deviation is fairly simple in both the discrete and continuous cases. It's mostly safe to use the discrete case when working with adjusted closing prices. Once you've calculated the standard deviation for a given time period, the next task (in the simplest case) is to calculate the mean of that same period. This allows you to roughly approximate the distribution, which can give you all sorts of testable hypotheses. Two standard deviations (σ) away from the mean (μ) is given by:  It doesn't make any sense to talk about ""two standard deviations away from the price"" unless that price is the mean or some other statistic for a given time period. Normally you would look at how far the price is from the mean, e.g. does the price fall two or three standard deviations away from the mean or some other technical indicator like the Average True Range (an exponential moving average of the True Range), some support level, another security, etc.  For most of this answer, I'll assume we're using the mean for the chosen time period as a base. However, the answer is still more complicated than many people realize. As I said before, to calculate the standard deviation, you need to decide on a time period. For example, you could use S&P 500 data from Yahoo Finance and calculate the standard deviation for all adjusted closing prices since January 3, 1950. Downloading the data into Stata and applying the summarize command gives me: As you can probably see, however, these numbers don't make much sense. Looking at the data, we can see that the S&P 500 hasn't traded close to 424.4896 since November 1992. Clearly, we can't assume that this mean and standard deviation as representative of current market conditions. Furthermore, these numbers would imply that the S&P 500 is currently trading at almost three standard deviations away from its mean, which for many distribution is a highly improbable event. The Great Recession, quantitative easing, etc. may have changed the market significantly, but not to such a great extent.  The problem arises from the fact that security prices are usually non-stationary.. This means that the underlying distribution from which security prices are ""drawn"" shifts through time and space. For example, prices could be normally distributed in the 50's, then gamma distributed in the 60's because of a shock, then normally distributed again in the 70's. This implies that calculating summary statistics, e.g. mean, standard deviation, etc. are essentially meaningless for time periods in which prices could follow multiple distributions.  For this and other reasons, it's standard practice to look at the standard deviation of returns or differences instead of prices. I covered in detail the reasons for this and various procedures to use in another answer. In short, you can calculate the first difference for each period, which is merely the difference between the closing price of that period and the closing price of the previous period. This will usually give you a stationary process, from which you can obtain more meaningful values of the standard deviation, mean, etc.  Let's use the S&P500 as an example again. This time, however, I'm only using data from 1990 onwards, for the sake of simplicity (and to make the graphs a bit more manageable). The summary statistics look like this: and the graph looks like this; the mean is the central horizontal red line, and the top and bottom lines indicate one standard deviation above and below the mean, respectively.   As you can see, the graph seems to indicate that there were long periods in which the index was priced well outside this range. Although this could be the case, the graph definitely exhibits a trend, along with some seemingly exogenous shocks (see my linked answer).  Taking the first difference, however, yields these summary statistics: with a graph like this:  This looks a lot more reasonable. In periods of recession, the price appears much more volatile, and it breaches the +/- one standard deviation lines indicated on the graph.  This is only a simple summary, but using first differencing as part of the wider process of detrending/decomposing a time series is a good first step.  For some technical indicators, however, stationary isn't as relevant. This is the case for some types of moving averages and their associated indicators. Take Bollinger bands for instance. These are technical indicators that show a number of standard deviations above and below a moving average. Like any calculation of standard deviation, moving average, statistic, etc. they require data over a specified time period. The analyst chooses a certain number of historical periods, e.g. 20, and calculates the moving average for that many previous periods and the moving/rolling standard deviation for those same periods as well. The Bollinger bands represent the values a certain number of standard deviations away from the moving average at a given point in time. At this given point, you can calculate the value two standard deviations ""away from the value,"" but doing so still requires the historical stock price (or at least the historical moving average). If you're only given the price in isolation, you're out of luck.  Moving averages can indirectly sidestep some of the issues of stationarity I described above because it's straightforward to estimate a time series with a process built from a moving average (specifically, an auto-regressive moving average process) but the econometrics of time series is a topic for another day.  The Stata code I used to generate the graphs and summary statistics:" Simple math. Take the sale proceeds (after trade expenses) and divide by cost. Subtract 1, and this is your return. For example, buy at 80, sell at 100, 100/80 = 1.25, your return is 25%. To annualize this return, multiply by 365 over the days you were in that stock. If the above stock were held for 3 months, you would have an annualized return of 100%.  There's an alternative way to annualize, in the same example above take the days invested and dive into 365, here you get 4. I suggested that 25% x 4 = 100%. Others will ask why I don't say 1.25^4 = 2.44 so the return is 144%/yr. (in other words, compound the return, 1.25x1.25x...) A single day trade, noon to noon the next day returning just 1%, would multiply to 365% over a year, ignoring the fact there are about 250 trading days. But 1.01^365 is 37.78 or a 3678% return. For long periods, the compounding makes sense of course, the 8%/yr I hope to see should double my money in 9 years, not 12, but taking the short term trades and compounding creates odd results of little value.