Why Google Finance puts to two decimal places for the trading volumes?
Many brokerages offer automatic dividend reinvestment. It is very infrequent that these dividends are exactly a whole share. So, if you have signed up for automatic dividend reinvestment, many brokerages will reinvest your dividends and assign to you a fractional share. I can't speak for how these shares work with regards to voting, but I can say that the value of these fractional holdings does change with stock price as if one genuinely could hold a fraction of a share.
"Volume is measured in the number of shares traded in a given day, week, month, etc. This means that it's not necessarily a directly-comparable measure between stocks, as there's a large difference between 1 million shares traded of a $1 stock ($1 million total) and 1 million shares traded of a $1000 stock ($1 billion total). Volume as a number on its own is lacking in context; it often makes more sense to look at it as an overall dollar amount (as in the parentheses above) or as a fraction of the total number of shares in the marketplace. When you see a price quoted for a particular ticker symbol, whether online, or on TV, or elsewhere, that price is typically the price of the last trade that executed for that security. A good proxy for the current fair price of an asset is what someone else paid for it in the recent past (as long as it wasn't too long ago!). So, when you see a quote labeled ""15.5K @ $60.00"", that means that the last trade on that security, which the service is using to quote the security's price, was for 15500 shares at a price of $60 per share. Your guess is correct. The term ""institutional investor"" often is meant to include many types of institutions that would control large sums of money. This includes large banks, insurance companies, pooled retirement funds, hedge funds, and so on." weird holdover from the bad old days when you had to do arithmetic by hand I would guess.  Stocks used to trade in 1/8ths, so bonds trading in even smaller increments makes sense.  Also (and I am unsure if this is still true) U.S. bonds trade on a 360 day year (or used to anyway) for the same reason... 360 divides well into months and quarters (for easier math) whereas 365 is considerably harder.  Most of the world now trades in decimals and 365/365 years so I am unsure why the U.S. doesn't.  Institutional inertia I would guess. "I have traded options, but not professionally. I hadn't come across this terminology, but I expect it counts how far in-the-money, as an ordinal, an option is relative to the distinct strike prices offered for the option series — a series being the combination of underlying symbol, expiration date, and option type (call/put); e.g., all January 2015 XYZ calls, no matter the strike. For instance, if stock XYZ trades today at $11 and the available January 2015 XYZ calls have strike prices of $6, $8, $10, $12, $14, and $16, then I would expect the $10 call could be called one strike in the money, the $8 two strikes in the money, etc. Similarly, the $12 and $14 calls would be one and two strikes out of the money, respectively. However, if tomorrow XYZ moves to $13, then the $10 previously known as one strike in the money would now be two strikes in the money, and the $12 would be the new one strike in the money. Perhaps this terminology arose because many option strategies frequently involve using options that are at- or near-the-money, so the ""one strike in"" (or out) of the money contracts would tend to be those employed frequently? Perhaps it makes it easier for people to describe strategies in a more general sense, without citing specific examples. However, the software developer in me dislikes it, given that the measurement is relative to both the current underlying price (which changes quickly), and the strike prices available in the given option series. Hence, I wouldn't use this terminology myself and I suggest you eschew it, too, in favor of something concrete; e.g. specify your contract strikes in dollar terms — especially when it matters." "Market makers (shortened MM) in an exchange are generally required to list both a bid and ask price to allow both buyers and sellers to trade and keep the market moving.  However, a more general idea of a MM may includes companies off an exchange (say large banks acting as broker/dealers in an over-the-counter market) are not required to give a simultaneous bid/ask, but often will on request.  So, it might depend on where you are getting this data but likely the bid/ask was quoted simultaneously. An exchange, like the NASDAQ for instance, may have multiple MMs for a given market.  The ""market"" spread will be from the highest bid to the lowest ask over all the MMs.  The highest bid and lowest ask may come from different MMs and any particular MM often will have a larger spread. The size of the spread gives a rough idea of how much a MM is trying to make off of a ""round trip"" trade (buying than immediately selling to someone else or selling than immediately buying from someone else).  Of course, immediate round-trip trades are not always possible and there are many other complications.  However, half the spread is a rough indicator of how much they hope to make off of a single trade." IESC has a one-time, non-repeatable event in its operating income stream.  It magnifies operating income by about a factor of five.  It impacts both the numerator and the denominator.  Without knowing exactly how the adjustments are made it would take too much work for me to calculate it exactly, but I did get close to their number using a relatively crude adjustment rule. Basically, Yahoo is excluding one-time events from its definitions since, although they are classified as operating events, they distort the financial record.  I teach securities analysis and have done it as a profession.  If I had to choose between Yahoo and Marketwatch, at least for this security, I would clearly choose Yahoo. The last column in the source data is volume (the number of stocks that was exchanged during the day), and it also has a value of zero for that day, meaning that nobody bought or sold the stocks on that day. And since the prices are prices of transactions (the first and the last one on a particular day, and the ones with the highest/lowest price), the prices cannot be established, and are irrelevant as there was not a single transaction on that day. Only the close price is assumed equal to its previous day counterpart because this is the most important value serving as a basis to determine the daily price change (and we assume no change in this case). Continuous-line charts also use this single value. Bar and candle charts usually display a blank space for a day where no trade occurred. Log-returns are very commonly used in financial maths, especially quantitative finance.  The important property is that they're symmetrical around 0 with respect to addition. This property makes it possible to talk about an average return. For instance, if a stock goes down 20% over a period of time, it has to gain 25% to be back where you started.  For the log-return on the other hand the numbers are 0.223 down over a period of time, and 0.223 up to get you back to square 1. In this sense, you can simply take an arithmetic average and it makes sense.  You can freely add up or subtract values on the log-return scale, like log-interest rates or log-inflation rates.  Whereas the arithmetic mean of (non-log) returns is simply meaningless: A stock with returns -3% and +3% would have 0% on average, when in fact the stock has declined in price? The correct approach on direct price-returns would be to take a different mean (e.g. geometric) to get a decent average.  And yet it will be hard to incorporate other information, like subtracting the risk-free rate or the inflation rate to get rate-adjusted average returns. In short: Log-returns are easier to handle computationally, esp. in bulk, but non-log-returns are easier to comprehend/imagine as a number of their own. Here's another attempt at explanation: it's basically because parabolas are flat at the bottom. Let me explain.  As you might know, the variance of the log stock price in Black Scholes is vol^2 * T, in other words, variance of the log stock price is linear in time to expiry. Now, that means that the standard deviation of your log stock price is square root in time. This is consequential.  For normally distributed random variables, in 68% of cases we end up within one standard deviation. So, basically, we expect our log stock price to be within something something times square root of T. So, if your stock has a vol of 16%, it'll be plus/minus 32% in 4 years, plus/minus 16% for one year, plus/minus 8% for 3m, plus/minus 4% for 3-ish weeks, and plus/minus 1% for a business day. As you see, the decay is slow at first, but much more rapid as we get closer.  How does the square root function look? It's a sideways parabola. As we come closer to zero, the slope of the square root function goes to infinity. (That is related to the fact that Brownian motion is almost surely no-where differentiable - it just shoots off with infinite slope, returning immediately, of course :-) Another way of looking at it is the old traders rule of thumb that an at-the-money option is worth approximately S * 0.4 * vol * sqrt(T). (Just do a Taylor expansion of Black Scholes). Again, you have the square root of time to expiry in there, and as outlined above, as we get closer to zero, the square root drops slowly at first, and then precipitously. First - Google's snapshot -  Then - Yahoo -  I took these snapshots because they will not exist on line after the market opens, and without this context, your question won't make sense. With the two snapshots you can see, Yahoo shows the after hours trades and not just the official market close for the day. The amount it's down is exactly tracked from the close shown on Google. Now you know. Profit after tax can have multiple interpretations, but a common one is the EPS (Earnings Per Share).  This is frequently reported as a TTM number (Trailing Twelve Months), or in the UK as a fiscal year number.  Coincidentally, it is relatively easy to find the total amount of dividends paid out in that same time frame. That means calculating div cover is as simple as:  EPS divided by total dividend.  (EPS / Div). It's relatively easy to build a Google Docs spreadsheet that pulls both values from the cloud using the GOOGLEFINANCE() function.  I suspect the same is true of most spreadsheet apps.  With a proper setup, you can just fill down along a column of tickers to get the div cover for a number of companies at once. Previously, Google had a delayed update for their stock prices (15 minutes I believe). That change enabled users of Google Finance to see updates to stock prices in real-time. Simply because forex brokers earn money from the spread that they offer you. Spread is the difference between buyers and sellers. If the buy price is at 1.1000 and the sell price is at 1.1002 then the spread is 2 pips. Now think that this broker is getting spread from its liquidity cheaper (for example 1 pip spread). As you can understand this broker makes a profit of 1 pip for each trade you place... Now multiply 1 pip X huge volume, and then you will understand why most forex brokers don't charge commissions. "In the US, stocks are listed on one exchange but can be traded on multiple venues.  You need to confirm exactly what your data is showing: a) trades on the primary-listed exchange; or b) trades made at any venue. Also, the trade condition codes are important.  Only certain trade condition codes contribute towards the day's open/high/low/close and some others only contribute towards the volume data.  The Consolidated Tape Association is very clear on which trades should contribute towards each value - but some vendors have their own interpretation (or just simply an erroneous interpretation of the specifications). It may surprise you to find that the majority of trading volume for many stocks is not on their primary-listed exchange.  For example, on 2 Mar 2015, NASDAQ:AAPL traded a total volume across all venues was 48096663 shares but trading on NASDAQ itself was 12050277 shares. Trades can be cancelled.  Some data vendors do not modify their data to reflect these busted trades.   Some data vendors also ""snapshot"" their feed at a particular point in time of the data.  Some exchanges can provide data (mainly corrections) 4-5 hours after the closing bell.  By snapshotting the data too early and throwing away any subsequent data is a typical cause of data discrepancies.   Some data vendors also round prices/volumes - but stocks don't just trade to two decimal places. So you may well be comparing two different sets of trades (with their own specific inclusion rules) against the same stock.  You need to confirm with your data sources exactly how they do things. Disclosure: Premium Data is an end-of-day daily data vendor." That is called a 'volume chart'. There are many interactive charts available for the purpose. Here is clear example.  (just for demonstration but this is for India only) 1) Yahoo Finance 2) Google Finance 3) And many more Usually, the stock volume density is presented together (below it) with normal price vs time chart. Note: There is a friendly site about topics like this. Quant.stackexchange.com. Think of checking it out. 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.