DJIA components multipliers
You can create something like that by: You'll have to determine the PE ratio manually from the financial statements.  To get the PE ratio for each company, you can try the Edgar database, though I doubt it goes as far back as 1950. This blog has a graph of the DJIA PE ratio from 1929 - 2009.
As others have pointed out, the value of Apple's stock and the NASDAQ are most likely highly correlated for a number of reasons, not least among them the fact that Apple is part of the NASDAQ. However, because numerous factors affect the entire market, or at least a significant subset of it, it makes sense to develop a strategy to remove all of these factors without resorting to use of an index. Using an index to remove the effect of these factors might be a good idea, but you run the risk of potentially introducing other factors that affect the index, but not Apple. I don't know what those would be, but it's a valid theoretical concern.  In your question, you said you wanted to subtract them from each other, and only see an Apple curve moving around a horizontal line. The basic strategy I plan to use is similar but even simpler. Instead of graphing Apple's stock price, we can plot the difference between its stock price on business day t and business day t-1, which gives us this graph, which is essentially what you're looking for:  While this is only the preliminaries, it should give you a basic idea of one procedure that's used extensively to do just what you're asking. I don't know of a website that will automatically give you such a metric, but you could download the price data and use Excel, Stata, etc. to analyze this.  The reasoning behind this methodology builds heavily on time series econometrics, which for the sake of simplicity I won't go into in great detail, but I'll provide a brief explanation to satisfy the curious. In simple econometrics, most time series are approximated by a mathematical process comprised of several components: In the simplest case, the equations for a time series containing one or more of the above components are of the form that taking the first difference (the procedure I used above) will leave only the random component. However, if you want to pursue this rigorously, you would first perform a set of tests to determine if these components exist and if differencing is the best procedure to remove those that are present. Once you've reduced the series to its random component, you can use that component to examine how the process underlying the stock price has changed over the years. In my example, I highlighted Steve Jobs' death on the chart because it's one factor that may have led to the increased standard deviation/volatility of Apple's stock price. Although charts are somewhat subjective, it appears that the volatility was already increasing before his death, which could reflect other factors or the increasing expectation that he wouldn't be running the company in the near future, for whatever reason. My discussion of time series decomposition and the definitions of various components relies heavily on Walter Ender's text Applied Econometric Time Series. If you're interested, simple mathematical representations and a few relevant graphs are found on pages 1-3. Another related procedure would be to take the logarithm of the quotient of the current day's price and the previous day's price. In Apple's case, doing so yields this graph:  This reduces the overall magnitude of the values and allows you to see potential outliers more clearly. This produces a similar effect to the difference taken above because the log of a quotient is the same as the difference of the logs The significant drop depicted during the year 2000 occurred between September 28th and September 29th, where the stock price dropped from 26.36 to 12.69. Apart from the general environment of the dot-com bubble bursting, I'm not sure why this occurred. Another excellent resource for time series econometrics is James Hamilton's book, Time Series Analysis. It's considered a classic in the field of econometrics, although similar to Enders' book, it's fairly advanced for most investors.  I used Stata to generate the graphs above with data from Yahoo! Finance: There are a couple of nuances in this code related to how I defined the time series and the presence of weekends, but they don't affect the overall concept. For a robust analysis, I would make a few quick tweaks that would make the graphs less appealing without more work, but would allow for more accurate econometrics. Transform your business today with the help of I-Thrive. Through their online course called the Small Business Multiplier, you can learn a lot of strategies and techniques that will ultimately change the way you run your business. Waste no more of your time and get in touch with them right now! Visit their website at https://www.i-thrive.co.uk/ for more information. To add technical detail to other answers, your (and some commenters') estimates of storing that data is woefully (many orders of magnitude) off. Let's take your 10MB of transaction data per user. You're only estimating text records like in Quicken. Now add on the volume of storing everye check's image. That's 100K (if not 500Kb depending on resolution of the scan) per check. If you have 100 checks per year (not unrealistic, if you pay all utility/morgage bills by check, as well as purchases), you now have 10Mb/year to 50MB/year. Now you're asking for 10 years of this, so you have 100-500MB per customer. NOT 10MB-70MB as you initially assumed. Let's take a mid-range figure, 300 MB. You were estimating using consumer grate cheap-o storage (which Facebook can afford for their data, as they don't store transaction data). Now let's up that to enterprise server hard drives. Your storage costs just rose 2x-5x. Now, typically you'd have RAID. So 2x more. Most large financial institutions have multiple data centers. You typically store all data's copies in those data centers for DR purposes. Your multiplier added another 2x-4x Most production data servers have multiple copies (Write DB server + one or more read-only copies). Multiply by 2x-4x With some rare exceptions, most banks don't just have one central database server. Each major app  / business line would have its own DB, so you multiply that by 2x-20x depending on the bank, especially if it's arrived at its size by merging with other banks and has dozens of inherited legacy systems. multiple backups. Regulatory backup requirements means you don't just back up your data once a year. You do it daily, till the data is purged from DB. Meaning, you don't store ONE copy of your transaction in backup. You stored, say, 10*365 copies, assuming 10 year retention) So, at the low end, your cost estimates are 30*2*2*2*2*2 = 900 times off (3 orders of magnitude) just for live database storage, and 3500 times off for backup costs. At the high end, they could be 50*5*2*4*4*20=16,000 times off (4-5 orders of magnitude) At this range, no, it isn't worth it for the bank to keep your transactions available in DB and online any longer than bare-bones absolutely critically necessary. If you check out China Stock Markets Web provides details on all things that trade on there. It covers the Hang Seng Index, SSE Index, and SSE Component Index. There is also tons of information for investors on the exchange website here. Ultra Torq is a major supplier of high quality bolt tensioners and tools for pipe cutting and beveling. Providing innovative production-boosting solutions in Eastern Canada.Oil refineries, mechanical construction or wind energy plants, you can count on us for highly efficient bolting, tensioning, pipe beveling, pipe cutting and tube removal &amp; installation tools from recognized names How would they make money from it?   They sell you the software for $100 (US example; could as easily be 100 Euros or 10,000 Japanese Yen).  You use it to make recommendations on your blog.  Your blog becomes rich from advertising.  They sold $100 worth of software.  If they spent $1 million in labor developing it, they're way behind.   Another problem is that the software would stop working and need adjusted periodically.  This is easy to do on a server but annoying on a PC.  And who pays for the adjustments?   Put both those things together, and it's a lot easier to do on a server.  Another advantage is that a server can get a better data feed as well.  Pay a premium for the detailed information rather than relying on public sources.  And people are used to renting server access where they expect to buy software once.   Another issue is that they are unlikely to beat the market this way.  Yes, AIs have done so.  But that's the latest AI, constantly adjusted.  This is going to be a previous generation AI.  It's more likely to match the market.  And we already have a way to match the market:  an index fund.   If someone had a brilliant AI, the best use would probably be to sell it to a fund manager.  The fund manager could then use the AI to find opportunities for its existing investors.  Note that a $10 billion fund with a 10% return that gives a .1% commission would be paying $1 million.  And that has no marketing or packaging overhead.  Think $10 billion is a lot?  Fidelity has $2 trillion. So if someone would invest 14000 credits on 1st April 2016, he'd get monthly dividend = ((14000 ÷ 14) × 0.0451) × (1 - 1.42 ÷ 100) = 44.459 credits, right? One would get ((14000 ÷ 14) × 0.0451) = 45.1 is what you would get. The expenses are not to be factored. Generally if a scheme has less expense ratio, the yield is more. i.e. this has already got factored in 0.0451. If the expense ratio was less, this would have been 0.05 if expense ration would have been more it would have been 0.040. Can I then consider the bank deposit earning a higher income per month than the mutual fund scheme? As the MIP as classified as Hybrid funds as they invest around 30% in equities, there is no tax on the income. More so if there is a lock-in of 3 years. In Bank FD, there would be tax applicable as per tax brackets. Calculation. To calculate the DJIA, the sum of the prices of all 30 stocks is divided by a divisor, the Dow Divisor. The divisor is adjusted in case of stock splits, spinoffs or similar structural changes, to ensure that such events do not in themselves alter the numerical value of the DJIA.