where to get stock price forecast
"First, stock prices forecasts are usually pretty subjective so in the following resources you will find differing opinions. The important thing is to read both positive and negative views and do some of your additional research and form your own opinion. To answer your question, some analysts don't provide price targets, some just say ""Buy"", ""Sell"", ""Hold"", and others actually give you a price target. Yahoo provides a good resource for collecting reports and giving you a price target. http://screener.finance.yahoo.com/reports.html" There's only one real list that states what people think stock prices should be, and that's the stocks order book. That lists the prices at which stock owners are willing to buy stocks now, and the price that buyers are willing to pay.  A secondary measure is the corresponding options price. Anything else is just an opinion and not backed by money. "I believe you are looking for price forecasts from analysts. Yahoo provides info in the analyst opinions section: here is an example for Apple the price targets are located in the ""Price Target Summary"" section."
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. "Assuming that you accept the premise that technical analysis is legitimate and useful, it makes sense that it might not work for a small market, or at the very least that it wouldn't be the same for a small market as it is for a large market.  The reason for this is that a large stock market like the U.S. stock market is as close to a perfect market as you will find: Compare this to a small market in a small country. Market information is harder to get, because there are not as many media outlets covering the news. There aren't as many participants. And possibly it might be more expensive to participate in, and there might be more regulatory intervention than with the large market.  All of these things can affect the prices. The closer you get to a perfect market, the closer you get to a point where the prices of the stocks reflect the ""true value"" of the companies, without external forces affecting prices." "What you're thinking of is more market making kind of activity, HFT algo's thrive on this; having information faster than anyone else. This type of activity could also likely be lumped into what is considered top-down analysis as opposed to bottom-up (which is what most mutual fund equity research involves). Again, the more important aspect is, what does the company you are applying to use!  Top-down analysis means that you are forecasting the revenue drivers for a company using macro-economic analysis. For example, let's say I'm investing in Chinese cement manufacturer's, what implications does Chinese interest rate policy have on infra-structure expansion and how does that drive revenue for this specific company. I might then look at margins, etc. to get an EPS estimate. Part of this could fall into secular  investing, too. Let's say I like LCD panel glass because of this consortium, I might take a look at 5 companies and then find the ones I think would benefit most from this. The problem with top-down is it tends not to be as much of a deep-dive, and its hard to pick individual companies because of it. Bottom-up tends to be more analytical and is what most pitches would be based around. The most important thing I'm not saying one is right or wrong, they are just different, and every investor has their own style.  Bottom-up analysis, which would be closer to what an equity research analyst would be doing on the sell-side, is analyzing what bottom-line indicators drive revenue and how are those expanding. For example, lets say I'm looking at search providers (i.e. Baidu, Google, Yahoo, etc.) I'd be looking at Cost-Per-Thousand-Clicks (CPTC) and number of clicks on the website. Multiply the two and I get revenue (very simplified version) for clicks business. I might then also forecast other revenue driving segments and try to understand how they are growing/pricing at an individual segment level (i.e. business services or mobile advertising). I'd then break down costs/margins for each segment and forecast those out. I could then get a forward EPS, get a range of multiples I believe it could trade in (i.e. I think the multiple will trade up/down), to get a target price. Also, I would likely do a DCF analysis on forward earnings to get a ""fair market value,"" and then try to triangulate a price. I would also be looking at stuff like management teams and industry trends, too, but bottom line, I'm pitching a company because I think it is undervalued and will outperform competitors **in the long run**. This type of work tends to be more research oriented and is what most (not all) mutual funds use when analyzing companies. Since mutual funds tend to have longer holding periods (2-10 years), as opposed to short-term, it's harder to justify investing in a company only because it has a short-term catalyst. Anecdotally, it's also easier to present in a written thesis because the numbers tend to be more concrete and easier to forecast than top-down (which have wider target ranges).  Your thought process that catalyst + industry context = market beating returns isn't wrong, it's just that every company thinks about investing differently, and it's important to tailor the report to that group's style." I don't know what restrictions are put on the average employee at your company. In my case, we were told we were not permitted to either short the stock, or to trade in it options.  That said, I was successful shorting the exact number of shares I'd be buying at the 6 month close, the same day the purchase price would be set. I then requested transfer of the stock purchase to my broker where the long and short netted to zero.  The return isn't 15%, it's  100/85 or 17.6% for an average 3 months they have your money. So do the math on APR. (Higher if the stock has risen over 6 months and you get the lower price from 6 months prior.) My method was riskless, as far as I am concerned. I did this a dozen times. The stock itself was +/- 4% by the time the shares hit, so in the end it was an effort, mostly to sleep better. I agree with posts suggesting the non-zero risk of a 20% 4 day drop. 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 "Forecasts of stock market direction are not reliable, so you shouldn't be putting much weight on them.  Long term, you can expect to do better in stocks, but obtaining this better expected return has the danger of ""buying in"" to the market at a particularly bad moment, leading to a substantially lower return.  So mitigate that risk while moving in a big piece of cash by ""dollar cost averaging"".  An example would be to divide your cash hoard (conceptually) into say six pieces, and invest each piece in the index fund two months apart.  After a year you will have invested the whole sum at about the average of the index for the year." I get a subscription to WSJ through work and I use it everyday.  What is the price of the subscription though?  I find it useful and Barrons has a lot regarding business and the stock market.  A lot of professionals have subscriptions to the WSJ.  I prefer the WSJ over other services and I really like the market data they provide. I've recently discovered that Morningstar provides 5yr avgs of a few numbers, including dividend yield, for free.  For example, see the right-hand column in the 'Current Valuation' section, 5th row down for the 5yr avg dividend yield for PG: http://financials.morningstar.com/valuation/price-ratio.html?t=PG&region=usa&culture=en-US Another site that probably has this, and alot more, is YCharts.  But that is a membership site so you'll need to join (and pay a membership fee I believe.)   YCharts is supposedly pretty good for long-term statistical information and trend graphs for comparing and tracking stocks.