Contributing factors to historical increase in trading volume
Prior to 1975, commissions for trading stock on the NYSE were fixed at 1% of the amount of the trade.  In 1975, the SEC made fixed commission rates illegal, giving rise to discount brokers offering much reduced commission rates.  Simultaneously, Electronic Communications Networks (ECNs) gained market share as alternative venues for executing trades.  The increased competition led to further declines in commissions.  Finally, as technology was widely adopted on Wall Street and human beings were largely taken out of the order execution process, commissions fell further.  This had the effect of both drawing in new participants and increasing the rate of transactions of the existing participants (see Day Trading, which was largely unheard of prior to the technology revolution of the 1990s).  Most recently, the exchanges themselves have shifted their business model to depend on high frequency traders, and the proportion of trades accounted for by HFT firms ballooned from under 10% in the early 2000s to over 50% today. It's not primarily more people investing. In the 1980s stock exchanges went from open outcry trading floors where all trades involved actually exchanging pieces of paper to electronic trading. Once that happened, it wasn't long before most trades were executed by computer programs rather than human beings, turning stocks over rapidly for very short-term profits rather than long-term investment, greatly increasing  the number of trades (and also increasing liquidity for the actual investors; it's by no means all bad).
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. Large volume just means a lot of market participants believe they know where the stock price will be (after some amount of time).  The fact that the price is not moving just means that about 50% of those really confident traders think the stock will be moving up, and about 50% of those really confident traders think the stock will be moving down. You should not look at volume in isolation but look at it together with other indicators and/or the release of news (good or bad). When there is lower than average volume this could be an indication that the stock is in a bit of a holding pattern, possibly waiting for some company or economic news to come out (especially when accompanied by small changes in price). It could also mean that trading in a certain direction is drying up and the trend is about to end (this could be accompanied with a large move in price). When there is higher than average volume (2 to 3 times more or higher), this could be due to the release of company results, company or economic news, or the start or end of a trend (especially if accompanied by a gap). A large increase in volume accompanied by a large fall in price (usually a gap down) may also be an indication the stock has gone ex-dividend. There could be a range of reasons for variations in volume to the average volume. That is why you need to look at other indicators, company reporting and news, and economic news in combination with the volume changes to get a grasp of what is really happening. This would be a nice Raspberry Pi project for Mathematica, which comes bundled free on the Raspbian OS. You can program it up and leave it running.  It's not expensive and doesn't use much power. A program to monitor stock prices or volume could be written as simply as :-  This checks the volume of trades of Oct 2014 US crude oil futures every 30 seconds and sends an email if the volume jumps by more than 100. The financial data in this example is curated from Yahoo.  If specific data is not available or not updated frequently enough, if you can find an alternative online data source it's usually possible read the data in.  For example, this is apparently real-time data :- {Crude Oil, 92.79, -0.67, -0.71%} After leaving the above program running while writing this the volume of trades has risen like so :-  Edit I just set this running on a Raspberry Pi.  I had to use gmail for the email setup as described in this post: Configuring Mathematica to send email from a notebook.  Anyway, it's working.  Hope I don't get inundated with emails. ;-) "Both explanations are partly true. There are many investors who do not want to sell an asset at a loss.  This causes ""resistance"" at prices where large amounts of the asset were previously traded by such investors.  It also explains why a ""break-through"" of such a ""resistance"" is often associated with a substantial ""move"" in price. There are also many investors who have ""stop-loss"" or ""trailing stop-loss"" ""limit orders"" in effect.  These investors will automatically sell out of a long position (or buy out of a short position) if the price drops (or rises) by a certain percentage (typically 8% - 10%). There are periods of time when money is flowing into an asset or asset class.  This could be due to a large investor trying to quietly purchase the asset in a way that avoids raising the price earlier than necessary.  Or perhaps a large investor is dollar-cost-averaging.  Or perhaps a legal mandate for a category of investors has changed, and they need to rebalance their portfolios.  This rebalancing is likely to take place over time.  Or perhaps there is a fad where many small investors (at various times) decide to increase (or decrease) their stake in an asset class.  Or perhaps (for demographic reasons) the number of investors in a particular situation is increasing, so there are more investors who want to make particular investments.  All of these phenomena can be summarized by the word ""momentum"". Traders who use technical analysis (including most day traders and algorithmic speculators) are aware of these phenomena.  They are therefore more likely to purchase (or sell, or short) an asset shortly after one of their ""buy signals"" or ""sell signals"" is triggered.  This reinforces the phenomena. There are also poorly-understood long-term cycles that affect business fundamentals and/or the politics that constrain business activity.  For example: Note that even if the markets really were a random walk, it would still be profitable (and risk-reducing) to perform dollar-cost-averaging when buying into a position, and also perform averaging when selling out of a position.  But this means that recent investor behavior can be used to predict the near-future behavior of investors, which justifies technical analysis." Mostly, when an equity's price rises, its statistical and implied volatilities fall and vice versa. The reason why is a mathematical phenomenon mixed with the reality that a unceasingly falling asset price will soon not exist, skewing the results with survivorship bias.   Since volatility is standard deviation of price indexes, a security that changes in price by the same amount every day will have lower volatility, so a rising price will have lower implied volatility because its mostly experiencing positive daily price change while a recently falling price will have higher volatility because factored together with the positive price changes, the negative price changes will widen the standard deviation of the securities price index. Quantitatively, any change, in or out of one's favor, is a risk because change is uncertain, and any uncertainty is a risk. This quantitative interpretation while valid runs almost totally counter to the value opinion, that a lower price relative to value is a lower risk than a higher price relative to value, but both have their place in time. Over long time periods, it's best to use the value interpretation, quantitative for shorter.  Using the opposite has hastily destroyed many a fund manager. There are multiple factors at play that drive stock price movements, but one that can be visualized is that stocks can be priced relative to other (similar stocks).  When one stock price goes up without fundamental changes (i.e. daily market noise/movements), it becomes slightly more expensive relative to it's peers.  Opportunists will sell the now slightly more expensive stock, while others may buying the slightly cheaper (relatively) peer stock.    Imagine millions of transactions like this happening throughout each hour, downward selling pressure on a stock that rises too fast in value relative to it's peers or the market, and upward buying pressure on stocks that are relatively cheaper than it's peers.  It pushes two stocks towards an equilibrium that is directionally the same.    Another way to think of it is lets say tomorrow there is $10B in net new dollars moved into buy orders for stocks that comes from net selling of bonds (this is also partly why bonds/stocks are generally inversely related).  That money goes to buys stocks ABC, XYZ, EFG.  As the price of those goes up with more buying pressure, stocks JKL, MNO, PQR are relatively a tad cheaper, so some money starts to flow into there.  Repeat until you get a large majority of the stocks that buyers are willing to buy and you can see why stocks move in the same direction a lot of times. GDP growth is one of several components of nominal equity returns; the (probably not comprehensive) list includes:  Real GDP/earnings growth  Inflation  Dividend payouts and share buybacks  Multiple expansion (the market willing to pay more per dollar of earnings)  Changes in interest rate expectations  As other comments mention you could also see larger companies tending to deliver higher returns as for any number of reasons related to M&amp;A, expansion into foreign markets, etc. HFT doesn't increase correlations nor do hedge funds, If you look at the euro crisis, correlations have skyrocketed, then in late december jan and feb during the rally the correlations started subsiding and specific risk started taking over, crisis mode increases correlations. "Do not reinvent the wheel! Historical data about stock market returns and standard deviations suffer from number of issues such as past-filling and mostly survivorship bias -- that the current answers do not consider at all. I suggest to read the paper ""A Century of Global Stock Markets"" by Philippe Jorion (UC Irvine) and William Goetzmann (Yale), here. William Bernstein comments the results here, notice that rebalancing is sometimes a good option but not always, his non-obvious finding where the low SD did not favour from rebalancing:  Look at the final page of the paper,  ""geometric returns -- represent returns to a buy-and-hold strategy"" and the ""arithmetic averages -- give equal weight to each observation interval."", where you can find your asked ""historical effect of Rebalancing on Return and Standard Deviation"". The paper nicely summarizes the results to this table:  The results in the table are from the interval 1921-1996, it is not that long-time but even longer term data has its own drawbacks. The starting year 1921 is interesting choice because it is around the times of social-economical changes and depressing moments, historical context can be realized from books such as Grapes Of Wrath (short summary here, although fiction to some extent, it has some resonance to the history). The authors have had to ignore some years because of different reasons such as political unrest and wars.  Instead of delving into marketed spam as suggested by one reply, I would look into this search here. Look at the number of references and the related papers to judge their value.  P.s. I encourage people to attack my open question here, hope we can solve it!" "There tends to be high volume around big changes in stock price. The volume of a stock does not remain constant and the term ""fat fingers"" can influence price.--> http://www.bloomberg.com/news/2014-10-01/that-japanese-fat-finger-can-absolutely-happen-in-u-s-.html That being said keshlam is 99% right when it comes to a stock moving when their is no news or earnings announcements. Check out these papers. http://onlinelibrary.wiley.com/doi/10.1111/j.1475-6803.2010.01285.x/full They do a time series analysis to try and predict future prices off of past demand during news events. They forecast using auto-regressive models.  google ""forecasting autoregressive model"" and the upenn lecture will be helpful. I would post another link but I cannot because I do not have enough rep/ This is more of a quant question. Hope this helps. JL" I hovered over the label for trading volume and the following message popped up: Volume / average volume  Volume is the number of shares traded on the   latest trading day. The average volume is measured over 30 days. While volume per trade is higher at the open and to a lesser extent at the close, the overall volume is actually lower, on average. Bid ask spreads are widest at the open and to a lesser extent at the close. Generally, bid ask spreads are inversely proportional to overall volumes. Why this is the case hasn't been sufficiently clearly answered by academia yet, but some theories are that The volume required to significantly move the price of a security depends completely on the orderbook for that particular security. There are a variety of different reasons and time periods that a security can be halted, this will depend a bit on which exchange you're dealing with. This link might help with the halt aspect of your question: https://en.wikipedia.org/wiki/Trading_halt You can use trade volume for divergence and convergence studies