Where or how can I model historical market purchases
Robert Shiller has an on-line page with links to download some historical data that may be what you want here. Center for the Research in Security Prices would be my suggestion for another resource here. This site should help you to accomplish what you are looking for: https://www.quantopian.com
"Unfortunately, there is very little data supporting fundamental analysis or technical analysis as appropriate tools to ""time"" the market. I will be so bold to say that technical analysis is meaningless. On the other hand, fundamental analysis has some merits. For example, the realization that CDOs were filled with toxic mortgages can be considered a product of fundamental analysis and hence provided traders with a directional assumption to buy CDSs. However, there is no way to tell when there is a good or bad time to buy or sell. The market behaves like a random 50/50 motion. There are many reasons for this and interestingly, there are many fundamentally sound companies that take large dips for no reason at all. Depending on your goal, you can either believe that this volatility will smooth over long periods and that the market has generally positive drift. On the other hand, I feel that the appropriate approach is to remain active. You will be able to mitigate the large downswings by simply staying small and diversifying  - not in the sense of traditional finance but rather looking for uncorrelated products. Remember, volatility brings higher levels of correlation. My second suggestion is to look towards products like options to provide a method of shaping your P/L - giving up upside by selling calls against a long equity position is a great example. Ground your trades with fundamental beliefs if need be, but use your tools and knowledge to combat risks that may create long periods of drawdown." The closest I can think of from the back of my head is http://finviz.com/map.ashx, which display a nice map and allows for different intervals. It has different scopes (S&P500, ETFs, World), but does not allow for specific date ranges, though. well there are many papers on power spot price prediction, for example. It depends on what level of methodology you would like to use. Linear regression is one of the basic steps, then you can continue with more advanced options. I'm a phd student studying modelling the energy price (electricity, gas, oil) as stochastic process. Regarding to your questions: 1. mildly speaking, it's really hard, due to its random nature! (http://www.dataversity.net/is-there-such-a-thing-as-predictive-analytics/)  2. well, i would ask what kind of measure of success you mean? what level of predicted interval one could find successful enough? 3. would you like me to send you some of the math-based papers on? 4. as i know, the method is to fully capture all main characteristics of the price. If it's daily power price, then these are mean-reversion effect, high volatility, spike, seasonality (weekly, monthly, yearly).  Would you tell me what kind of method you're using? Maybe we can discuss some shared ideas? Anna Some personal finance packages can track basis cost of individual purchase lots or fractions thereof. I believe Quicken does, for example. And the mutual funds I'm invested in tell me this when I redeem shares. I can't vouch for who/what would make this visible at times other than sale; I've never had that need. For that matter I'm not sure what value the info would have unless you're going to try to explicitly sell specific lots rather than doing FIFO or Average accounting. Robert Shiller published US Stock Market data from 1871.  Ken French also has historical data on his website.  Damodaran has a bunch of historical data, here is some historical S&P data. "To answer your question directly..  you can investigate by using google or other means to look up research done in this area.  There's been a bunch of it Here's an example of search terms that returns a wealth of information. effect+of+periodic+rebalancing+on+portfolio+return I'd especially look for stuff that appears to be academic papers etc, and then raid the 'references' section of those.  Look for stuff published in industry journals such as ""Journal of Portfolio Management"" as an example. If you want to try out different models yourself and see what works and what doesn't, this Monte Carlo Simulator might be something you would find useful The basic theory for those that don't know is that various parts of a larger market do not usually move in perfect lockstep, but go through cycles.. one year tech might be hot, the next year it's healthcare.  Or for an international portfolio, one year korea might be doing fantastic only to slow down and have another country perform better the next year.    So the idea of re-balancing is that since these things tend to be cyclic, you can get a higher return if you sell part of a slice that is doing well (e.g. sell at the high) and invest it in one that is not (buy at the low) Because you do this based on some criteria, it helps circumvent the human tendency to 'hold on to a winner too long' (how many times have you heard someone say 'but it's doing so well, why do I want to sell now""? presuming trends will continue and they will 'lose out' on future gains, only to miss the peak and ride the thing down back into mediocrity.)  Depending on the volatility of the specific market, and the various slices, using re balancing can get you a pretty reasonable 'lift' above the market average, for relatively low risk. generally the more volatile the market, (such as say an emerging markets portfolio) the more opportunity for lift. I looked into this myself a number of years back, the concensus I came was that the most effective method was to rebalance based on 'need' rather than time.  Need is defined as one or more of the 'slices' in your portfolio being more than 8% above or below the average.  So you use that as the trigger. How you rebalance depends to some degree on if the portfolio is taxable or not.  If in a tax deferred account, you can simply sell off whatever is above baseline and use it to buy up the stuff that is below.   If you are subject to taxes and don't want to trigger any short term gains, then you may have to be more careful in terms of what you sell.  Alternatively if you are adding funds to the portfolio, you can alter how your distribute the new money coming into the portfolio in order to bring up whatever is below the baseline (which takes a bit more time, but incurs no tax hit)  The other question is how will you slice a given market?  by company size?  by 'sectors' such as tech/finance/industrial/healthcare, by geographic regions?" I don't see balance sheet in what you're looking at, and I'd definitely suggest learning how to read a balance sheet and looking at it, if you're going to buy stock in a company, unless you know that the recommendations you're buying on are already doing that and you're willing to take that risk.  Also, reading past balance sheets and statements can give you an idea about how accurate the company is with their predictions, or if they have a history of financial integrity. Now, if you're going the model portfolio route, which has become popular, the assumption that many of these stock buyers are making is that someone else is doing that for them.  I am not saying that this assumption is valid, just one that I've seen; you will definitely find a lot of skeptics, and rightly so, about model portfolios.  Likewise, people who trade based on what [Person X] does (like Warren Buffett or David Einhorn) are assuming that they're doing the research.  The downside to this is if you follow someone like this.  Yeah, oops. I should also point out that technical analysis, especially high probability TA, generally only looks at history.  Most would define it as high risk and there are many underlying assumptions with reading the price movements by high probability TA types. You need a source of delisted historical data. Such data is typically only available from paid sources. According to my records, Lawson Software Inc listed on the NASDAQ on 7 Dec 2001 and delisted on 6 Jul 2011.  Its final traded price was $11.23.  It was taken over by Infor who bid $11.25 per share. Source: Symbol LWSN-201107 within Premium Data US delisted stocks historical data set available from http://www.premiumdata.net/products/premiumdata/ushistorical.php Disclosure: I am a co-owner of Norgate / Premium Data. "The effect of making a single purchase, of size and timing described, would not cause market disequilibrium, it would only hurt you (and your P&L). As @littleadv said, you would be unlikely to get your order filled.  You asked about making a ""sudden"" purchase. Let's say you placed the order and were willing to accept a series of partial fills e.g. in 5,000 or 10,000 share increments at a time, over a period of hours. This would be a more moderate approach. Even spread out over the span of a day, this remains unwise.  A better approach would be to buy small lots over the course of a week or month. But your transaction fees would increase. Investors make money in pink sheets and penny stocks due to increases in share price of 100% (on the low end), with a relatively small number of shares. It isn't feasible to earn speculator profits by purchasing huge blocks (relative to number of shares outstanding) of stock priced < $1.00 USD and profit from merely 25%  price increases on large volume." You can easily build a Google Sheet spreadsheet to track what you want as Sheet has a 'googlefinance()' function to look-up the same prices and data you can enter and track in a Google Finance portfolio, except you can use it in ways you want. For example, you can track your purchase price at a fixed exchange rate, track the current market value as the product of the stock's price times the floating exchange rate, and then record your realized profit and loss using another fixed exchange rate.  You don't have to record the rates either, as googlefinance() func is able to lookup prices as of a particular date. You can access Google Sheet through a web browser or Android app. "Yeah, you could literally just set up a fixed amount of states, set up a simple AR model based on normal components, find the optimal parameters for the state spaces and the AR model and you're done. Or you could set up a VAR model with student's t components instead which makes it more complicated by a couple of magnitudes. With student's t components the regression is structurally complicated so I've found I need to apply copluas (or is it copulae? or copuli? I have no damn idea lmao) instead to be able to sample properly. I might not be very bright but I wouldn't say that's basic math. Also the regression for state spaces is a bit more complicated as well.  It'd be like me saying that valuating exotic derivatives is ""literally just solving PDE's with simple numerical methods."" It's a *little* bit more work to it." "Ugh... yes, you have to tell us what information you have available. It would be a completely different answer if, for example, you had a balance sheet for a prior period and an income statement for the current period and had to estimate the working capital accounts. If you can't be bothered to ""want to give the problem"" nobody is going to be bothered to help you with it.   Inventory days = days of COGS in inventory. (15 / 360 times cogs). AR is 35 days of sales in AR. 35/360* sales. Vendor credit is accounts payable -- 40 /360 * COGS.   If your sales and COGs are given by operating cycle rather than annually, use 50 instead of 360. (for whatever reason, convention says use 360 instead of 365)." It would be difficult, but it's a statistical task, and you'd need to refer to a competent statistician to really get a sense of what sort of certainty could be derived from the available data.  I believe that you'd start by looking over your state-by-state data on a granular level to try to find if there was any persistent correlation between Amazon's market penetration in a particular area and employment data from the retail industry.  With regards to Ma &amp; Pa's complaint, you're sort of wrong and sort of right.  Obviously they have no direct knowledge that online retail was responsible for the decline in sales that they saw.  In terms of sustainability and mismanagement, however, they can show you their books.  If the business had been established from some time it would be easy to see whether it had indeed been a sustainable business model in prior years.  Sustainability and mismanagement, however, are Scotsmen when it comes to reasoning about causes.  In measuring the effect of Amazon's entry into the market on local businesses, we can just as easily use a model that assumes a perfect market, that inefficiencies on the part of Ma^1 &amp; Pa^1 would lead them to be displaced by Ma^2 &amp; Pa^2, and that on average Ma^x &amp; Pa^x manage their business sufficiently well to extract an optimal return on effort.    If circumstances are such that the role of vendor is not fungible, and the supply of Mas and Pas does not respond to the demand for family stores, then I don't actually know how to do the math, but on the other hand I do recognize a smoking gun. I couldn't find historical data either, so I contacted Vanguard Canada and Barclays; Vanguard replied that  This index was developed for Vanguard, and thus historical information is available as of the inception of the fund. Unfortunately, that means that the only existing data on historical returns are in the link in your question. Vanguard also sent me a link to the methodology Barclay's uses when constructing this index, which you might find interesting as well.  I haven't heard from Barclays, but I presume the story is the same; even if they've been collecting data on Canadian bonds since before the inception of this index, they probably didn't aggregate it into an index before their contract with Vanguard (and if they did, it might be proprietary and not available free of charge). "How/when does my employer find out? Do they get a report from their   bank stating that ""check 1234 for $1212.12 paid to John Doe was never   deposited"" or does it manifest itself as an eventual accounting   discrepancy that somebody has to work to hunt down? The accounting department or the payroll company they use will report that the check was not deposited. The bank has no idea that a check was written, but the accounting deportment will know.  The bank reports on all the checks that were cashed. Accounting cares because the un-cashed check for $1212.12 is a liability. They have to keep enough money in the bank to pay all the liabilities. It shouldn't be hard for them to track down the discrepancy, they will know what checks are outstanding. Can my employer punish me for refusing the money in this way? Do they   have any means to force me to take what I am ""owed?"" They can't punish you. But at some time in the future they will will tell their bank not to honor the check. They will assume that it was lost or misplaced, and they will issue a new one to you.  When tax time comes, and I still have not accepted the money, would it   be appropriate to adjust my reported income down by the refused   amount? You can't decide not to report it. The company knows that in year X they gave you a check for the money. They are required to report it, since they also withheld money for Federal taxes, state taxes, payroll taxes, 401K, insurance. They also count your pay as a business expense.  If you try and adjust the numbers on the W-2  the IRS will note the discrepancy and want more information. Remember the IRS get a copy of every W-2.  The employer has to report it because some people who aren't organized may not have cashed a December check before the company has to generate the W-2 in late January. It would confuse everything if they could skip reporting income just because a check wasn't cashed by the time they had to generate the W-2."