Is it possible to quantify the probability of sudden big movements for a high-volume stock?
"Certainly no one knows in advance how much a stock is going to swing around. However, there are measures of how much it has swung around in the past, and there are people who will estimate the probability. First of all, there's a measure of an individual stock's volatility, commonly referred to as ""beta"". A stock with a beta of 1 tends to rise and fall about as much as the market at large. A stock with a beta of 2, in the meantime, would rise 10% when the market is up 5%. These are, of course, historical averages. See Wikipedia: http://en.wikipedia.org/wiki/Beta_(finance) Secondly, you can get an implied measure of volatility expectations by looking at options pricing. If a stock is particularly volatile, the chance of a big price move will be baked into the price of the stock options. (Note also that other things affect options pricing, such as the time value of money.) For an options-based measure of the volatility of the whole market, see the Volatility Index aka the ""Fear Gauge"", VIX.  Wikipedia: http://en.wikipedia.org/wiki/VIX Chart: http://finance.yahoo.com/q?s=%5EVIX Looking at individual stocks as a group (and there's an oxymoron for you), individual stocks are definitely much more likely to have big moves than the market. Besides Netflix, consider the BP oil spill, or the Tokyo Electric Power Company's Fukushima incident (yow!). I don't have any detailed statistics on quantitatively how much, mind you, but in application, a standard piece of advice says not to put more than 5% of your portfolio in a single company's stock. Diversification protects you. (Alternatively, if you're trying to play Mr. Sophisticated Stock-Picker instead of just buying an index fund, you can also buy insurance through stock options: hedging your bets. Naturally, this will eat up part of your returns if your pick was a good one)." The P/E is currently 20. In hindsight, it's easy to see that when it was 50, not long ago, it was very overpriced. They were not adding customers or increasing revenue as they should have to sustain that P/E level.  Probability? I suppose this can happen with any company that has both a high P/E and non-diversified business. Why did you think this company was large and stable? Their marketing blunders simply pricked the bubble level pricing these guys had.  (Disclaimer - I am actually a happy customer of Netflix. For $8/mo, I get 6-8 DVDs and neither spend gas nor time to get them. Others who grew used to free streaming feel otherwise) "This is a classic correlation does not imply causation situation.  There are (at least) three issues at play in this question: If you are swing- or day-trading then the first and second issues can definitely affect your trading.  A higher-price, higher-volume stock will have smaller (percentage) volatility fluctuations within a very small period of time. However, in general, and especially when holding any position for any period of time during which unknowns can become known (such as Netflix's customer-loss announcement) it is a mistake to feel ""safe"" based on price alone. When considering longer-term investments (even weeks or months), and if you were to compare penny stocks with blue chip stocks, you still might find more ""stability"" in the higher value stocks.  This is a correlation alone — in other words, a stable, reliable stock probably has a (relatively) high price but a high price does not mean it's reliable. As Joe said, the stock of any company that is exposed to significant risks can drop (or rise) by large amounts suddenly, and it is common for blue-chip stocks to move significantly in a period of months as changes in the market or the company itself manifest themselves. The last thing to remember when you are looking at raw dollar amounts is to remember to look at shares outstanding.  Netflix has a price of $79 to Ford's $12; yet Ford has a larger market cap because there are nearly 4 billion shares compared to Netflix's 52m." "In general, when companies are regarded as ""hot"" growth stocks, they are expected to keep up an accelerated level of growth for a good long time. That accelerated growth justifies a high PE relative to a slow-growth stock. When companies that are supposed to grow miss expectations or (worse) lose money, the markets punish the stock severely... Particularly if the company doesn't make analysts aware of problems early on. Netflix is a great example of a company bungling a few different business problems, creating a much bigger one in the process. A poorly conceived rate hike killed the reliable cash flow of the company, and that crazy Quixter thing just confused everyone. Now nobody trusts the management. BlackBerry is another example of a high performing company that just screwed up, damaging shareholders in the process. We're living in a very challenging era today, but growth stocks are always risky by nature -- growing a company rapidly is very difficult."
"Maria, there are a few questions I think you must consider when considering this problem. Do fundamental or technical strategies provide meaningful information? Are the signals they produce actionable? In my experience, and many quantitative traders will probably say similar things, technical analysis is unlikely to provide anything meaningful. Of course you may find phenomena when looking back on data and a particular indicator, but this is often after the fact. One cannot action-ably trade these observations. On the other hand, it does seem that fundamentals can play a crucial role in the overall (typically long run) dynamics of stock movement. Here are two examples, Technical: suppose we follow stock X and buy every time the price crosses above the 30 day moving average. There is one obvious issue with this strategy - why does this signal have significance? If the method is designed arbitrarily then the answer is that it does not have significance. Moreover, much of the research supports that stocks move close to a geometric brownian motion with jumps. This supports the implication that the system is meaningless - if the probability of up or down is always close to 50/50 then why would an average based on the price be predictive? Fundamental: Suppose we buy stocks with the best P/E ratios (defined by some cutoff). This makes sense from a logical perspective and may have some long run merit. However, there is always a chance that an internal blowup or some macro event creates a large loss.  A blended approach: for sake of balance perhaps we consider fundamentals as a good long-term indication of growth (what quants might call drift). We then restrict ourselves to equities in a particular index - say the S&P500. We compare the growth of these stocks vs. their P/E ratios and possibly do some regression. A natural strategy would be to sell those which have exceeded the expected return given the P/E ratio and buy those which have underperformed. Since all equities we are considering are in the same index, they are most likely somewhat correlated (especially when traded in baskets). If we sell 10 equities that are deemed ""too high"" and buy 10 which are ""too low"" we will be taking a neutral position and betting on convergence of the spread to the market average growth. We have this constructed a hedged position using a fundamental metric (and some helpful statistics). This method can be categorized as a type of index arbitrage and is done (roughly) in a similar fashion. If you dig through some data (yahoo finance is great) over the past 5 years on just the S&P500 I'm sure you'll find plenty of signals (and perhaps profitable if you calibrate with specific numbers). Sorry for the long and rambling style but I wanted to hit a few key points and show a clever methods of using fundamentals." "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." It may have some value!  Investopedia has a well-written quick article on how stock holders may still get some portion of the liquidated assets.  While there is generally little left for common shareholders if the price of those shares is tiny and some money does come back to shareholders there can still be significant profit to be made. As to why the trading volume is so high... there are many firms and hedge funds that specialize in calculating the value of and buying distressed debt and stock.  They often compete with each other to by the stock/debt that common shareholders are trying to get rid of.  In this particular case, there is a lot of popular interest, intellectual property at stake and pending lawsuits that probably boosts volume. 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. 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. Nothing is guaranteed - candlesticks are not crystal balls nor is any part of technical analysis. Candlestick patterns used correctly and in combination with other western technical indicators can increase the probability of a trade going into the derived direction, but they are not a guarantee - which is why you should always use stop losses with your candlestick or any trading. In saying that, another candlestick pattern that can provide high probability trades is the Doji, or a combination of Dojis in a row at a market extreme. Note that both Engulfing patterns and Dojis work best at price extremes (highs and lows) and in combination with other technical indicators such as an overbought momentum indicator at a market high, or an oversold momentum indicator at a market low. EDIT - An Example Here is a sample trade I placed on the 17th October and am currently 15.6% in profit on. See the chart below as it shows taking the trade on the open of the following day after a bullish engulfing pattern appeared at the bottom of a downtrend on the 16th in combination with the Slow Stochastic crossing over in the oversold region (below 20%). I would consider this a high probability trade and have placed an initial stop loss at 10% below my open price in case the trade went against me. As the price moved up I moved the 10% stop loss up as a trailing stop loss. My profit target is set at 25% or $4.00. I've never heard of such a thing, but seems like if such a product existed it would be easily manipulated by the big trading firms - simply bet that trading volume will go up, then furiously buy and sell shares yourself to artificially drive up the volume. The fact that it would be so easily manipulated makes me think that no such product exists, but I could be wrong. I think if you are only trading stocks with average volume greater than 1M you should not have any trouble entering a 10,000 size trade. If you are you can try a couple of things: Change your order from a market order to a limit order, however this may potentially reduce the number of shares that are actually traded on that day, and you may miss out on some or all of your order. Limit your trading to more liquid stocks, say average daily volumes above 10M or 100M. Apart from that you might have to just put up with some extra slippage and incorporate it into your trading plan. That is you can reduce your R multiple to allow some slippage.