Why is the volume highest at the beginning and end of a trading day?
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 One of the fundamental of technical analysis suggests that holding a security overnight represents a huge commitment.  Therefore it would follow that traders would tend to close their positions prior to market close and open them when it opens.
There are several ways that the issuers profit from CFDs. If the broker has trades on both sides (buy and sell) they can net the volumes off against each other and profit off the spread whilst using the posted margins to cover p&l from both sides. Because settlement for most securities is not on the same day that the order is placed they can also buy the security with no intention of taking delivery and simply sell it off at the end of day to pass delivery on to someone else. Here again they profit from the spread and that their volumes give them really low commissions so their costs are much lower than the value of the spread. If they have to do this rather than netting the position out the spreads will be wider.  Sometimes that may be forced to buy the security outright but that is rare and the spreads will be even wider so that they can make a decent profit. "It depends on how the program is run. If the company runs the program out of treasury stock (shares that are authorized, but not issued), then there aren't any shares being purchased on the open market.   Because of that, the share price wouldn't be affected.    If you look in your employer's annual report, you will probably find how the program is run and how many shares are issued annually under that program. By comparing that to the daily trading volume of the company's stock you can gauge whether there's any likelihood of the share price being affected by the employee purchases. That is, of course, if shares are being purchased on the open market.  For example, here is Books-A-Million's program, as described in their 2011 annual report: Employee Stock Purchase Plan   The Company maintains an employee stock purchase plan under which shares of the Company’s common stock are reserved   for purchase by employees at 85% of the fair market value of the common stock at the lower of the market value for the   Company’s stock as of the beginning of the fiscal year or the end of the fiscal year. On May 20, 2010, the stockholders of the   Company approved an additional 200,000 shares available for issuance under the plan, bringing the aggregate number of   shares that may be awarded to 600,000. Of the total reserved shares, 391,987, 373,432 and 289,031 shares have been   purchased as of January 29, 2011, January 30, 2010 and January 31, 2009, respectively. This describes an instance of the employee purchase program being run from unissued stock, not open market purchases. From it, we can tell 18,555 shares were issued during the past fiscal year.  As their average daily volume is ~40,000 shares, if the program were run from a single open market purchase, it would have potential to ""move the market"".  One would think, though, that a company running it from open market purchases would spread the purchases over a period of time to avoid running up the price on themselves." Quote driven markets are the predecessors to the modern securities market. Before electronic trading and HFTs specifically, trading was thin and onerous.  Today, the average investor can open up a web page, type in a security, and buy at the narrowest spread permitted by regulators with anyone else who wants to take the other side. Before the lines between market maker and speculator became blurred to indistinction, a market maker was one who was contractually obligated to an exchange to provide a bid and ask for a given security on said exchange even though at heart a market maker is still simply a trader despite the obligation.  A market maker would simultaneously buy a large amount of securities privately and short the same amount to have no directional bias, exposure to the direction of the security, and commence to making the market.  The market maker would estimate its cost basis for the security based upon those initial trades and provide a bid and ask appropriate for the given level of volume.  If volumes were high, the spread would be low and vice versa. Market makers who survived crashes and spikes would forgo the potential profit in always providing a steady price and spread, ie increased volume otherwise known as revenue, to maintain no directional bias.  In other words, if there were suddenly many buyers and no sellers, hitting the market maker's ask, the MM would raise the ask rapidly in proportion to the increased exposure while leaving the bid somewhere below the cost basis.  Eventually, a seller would arise and hit the MM's bid, bringing the market maker's inventory back into balance, and narrowing the spread that particular MM could provide since a responsible MM's ask could rise very high very quickly if a lack of its volume relative to its inventory made inventory too costly.  This was temporarily extremely costly to the trader if there were few market makers on the security the trader was trading or already exposed to. Market makers prefer to profit from the spread, bidding below some predetermined price, based upon the cost basis of the market maker's inventory, while asking above that same predetermined cost basis.  Traders profit from taking exposure to a security's direction or lack thereof in the case of some options traders. Because of electronic trading, liquidity rebates offered by exchanges not only to contractually obligated official market makers but also to any trader who posts a limit order that another trader hits, and algorithms that become better by the day, market making HFTs have supplanted the traditional market maker, and there are many HFTs where there previously were few official market makers.  This speed and diversification of risk across many many algorithmically market making HFTs have kept spreads to the minimum on large equities and have reduced the same for the smallest equities on major exchanges. Orders and quotes are essentially identical.  Both are double sided auction markets with impermenant bids and asks.  The difference lies in that non-market makers, specialists, etc. orders are not shown to the rest of the market, providing an informational advantage to MMs and an informational disadvantage to the trader.  Before electronic trading, this construct was of no consequence since trader orders were infrequent.  With the prevalence of HFTs, the informational disadvantage has become more costly, so order driven markets now prevail with much lower spreads and accelerated volumes even though market share for the major exchanges has dropped rapidly and hyperaccelerated number of trades even though the size of individual trades have fallen. The worst aspect of the quote driven market was that traders could not directly trade with each other, so all trades had to go between a market maker, specialist, etc.  While this may seem to have increased cost to a trader who could only trade with another trader by being arbitraged by a MM et al, paying more than what another trader was willing to sell, these costs were dwarfed by the potential absence of those market makers.  Without a bid or ask at any given time, there could be no trade, so the costs were momentarily infinite. In essence, a quote driven market protects market makers from the competition of traders.  While necessary in the days where paper receipts were carted from brokerage to brokerage, and the trader did not dedicate itself to round the clock trading, it has no place in a computerized market.  It is more costly to the trader to use such a market, explaining quote driven markets' rapid exit. "Pay to play? You mean they pay for beefier data feeds that other firms don't need, sure they do. But everyone can trade on the US exchanges now (NYSE, NASDAQ, BATS, etc) which was not true 20 years ago when NYSE had the specialist monopoly, so yes the markets are more democratic than they've ever been.  Side note, the exchanges do directly profit from increased trading volume because they take a small % of every trade, so I'm not sure what you mean they don't directly profit from it.  Also I get the feeling you don't understand the scope of HFT activity, HFT peak profits were on the order of 7 billion during the highest volume time of the last decade (2005-2010). They are now around 1B. Compared to the trillions of dollars that change hands in the exchange per year, this is chump change, hardly a ""free money faucet"".  Also Katsayuma opened yet another dark pool that caters to high volume clients (your goldmans and merrils). The only difference in IEX is it got a free marketing campaign to entice clients. Seriously, IEX is nothing more than the existing fixed cross dark pools, which btw screws over retail investors more than the lit exchanges like NASDAQ.   Katsayuma got steamrolled in his executions because he couldn't keep up with the time and then hit the lottery jackpot by getting Michael Lewis to paint him as a ""hero"", honestly I'm confounded at how lucky that dude got.  BTW broker dealers get preferential treatment in IEX, meaning they get to cut in front of the line in front of retail investors. Why are you so opinionated about this, did you make a few bad trades on eTrade and need a scapegoat?" Opening - is the price at which the first trade gets executed at the start of the trading day (or trading period). High - is the highest price the stock is traded at during the day (or trading period). Low - is the lowest price the stock is traded at during the day (or trading period). Closing - is the price at which the last trade gets executed at the end of the trading day (or trading period). Volume - is the amount of shares that get traded during the trading day (or trading period). For example, if you bought 1000 shares during the day and another 9 people also bought 1000 shares each, then the trading volume for the day would be 10 x 1000 = 10,000. For starters, that site shows the first 5 levels on each side of the book, which is actually quite a bit of information.  When traders say the top of the book, they mean just the first level.  So you're already getting 8 extra levels. If you want all the details, you must subscribe to the exchange's data feeds (this costs thousands of dollars per month) or open an account with a broker who offers that information. More important than depth, however, is update frequency.  The BATS site appears to update every 5 seconds, which is nowhere near frequently enough to see what's truly going on in the book.  Depending on your use case, 2 levels on each side of the book updated every millisecond might be far more valuable than 20 levels on each side updated every second. "Just read the book Flash Boys by Michael Lewis.  It describes this process in detail, albeit a bit more dramatically than it has to.  Basically what ""HFTs"" (high frequency traders) do is they set up their line to the exchange so its microseconds faster than everyone else.  Then they test out the market with tiny orders, seeing how fast it's getting filled - if these are getting filled immediately, it probably means there's a big order coming in from an investor.  So the algorithm - and it's all algo-based obviously because no human can remotely hope to catch this - will detect that as soon as there's a spike in volume, it will buy all of the volume at the current price, and sell it back for higher, forcing the big investor to take on higher prices.    Another case is that some HFTs can basically buy the entire trade book from an exchange like Nasdaq.  So every time someone places a market order for 200 shares of a 6.5 stock, the HFT will see it, buy up all the current stock from say 6.5-6.6 and sell it back at 6.7.  Not rocket science if you already get info about the trade coming in, in fact this is basically market making but performed by an ""evil HFT"" instead of a ""trustworthy bank.""    But honestly there are a lot more ways to make money from HFT than front-running, which isn't even possible anymore because exchanges no longer sell their books to HFTs." "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."