Stock trading models that use fundamental analysis, e.g. PEG ratios?
"One idea: If you came up with a model to calculate a ""fair price range"" for a stock, then any time the market price were to go below the range it could be a buy signal, and above the range it could be a sell signal. There are many ways to do stock valuation using fundamental analysis tools and ratios: dividend discount model, PEG, etc. See Wikipedia - Stock valuation. And while many of the inputs to such a ""fair price range"" calculation might only change once per quarter, market prices and peer/sector statistics move more frequently or at different times and could generate signals to buy/sell the stock even if its own inputs to the calculation remain static over the period. For multinationals that have a lot of assets and income denominated in other currencies, foreign exchange rates provide another set of interesting inputs. I also think it's important to recognize that with fundamental analysis, there will be extended periods when there are no buy signals for a stock, because the stocks of many popular, profitable companies never go ""on sale"", except perhaps during a panic. Moreover, during a bull market and especially during a bubble, there may be very few stocks worth buying. Fundamental analysis is designed to prevent one from overpaying for a stock, so even if there is interesting volume and price movement for the stock, there should still be no signal if that action happens well beyond the stock's fair price. (Otherwise, it isn't fundamental analysis — it's technical analysis.) Whereas technical analysis can, by definition, generate far more signals because it largely ignores the fundamentals, which can make even an overvalued stock's movement interesting enough to generate signals." "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."
"LOL. Its gotten harder for those who dont want to change with the times. Tools like sentdex and PsychSignal provide a solid argument against the headline of the article. Its not harder, the methods have just changed. Most people dont understand the methods and thus: ""its gotten harder.""  Harder is entirely relative, for people like me, its gotten easier...  [This algorithm looks for a high volume of bullish tweets about penny stocks and returns 95% in a year.](https://www.quantopian.com/posts/psychsignal-machine-learning-and-penny-stocks-95-percent-in-a-year)    [This algorithm trades my favorite tech stocks using tweets to determine when to buy. 140% 2016-mid 2017.](https://www.quantopian.com/posts/psychsignal-machine-learning-models-and-tech-stocks)   I will say that in those algorithms, I throw out more than half the data. To be more correct, I assume half the data is bullshit (people mindlessly reposting, morons talking about shit they dont understand, etc.)   People, in general, are idiots. The moment you accept this, social media analysis becomes rather easy.   In my first 3 weeks of trading ever I was up 60% because of Bio Med stocks (i lost it all due to greed, I now always pull out early (lol) as opposed to too late). I didn't know ANYTHING about the bio med market - I let stock twits do all the work (in general, Twitter is actually more correct than stocktwits, which is rather ironic). Once I started trading with algorithms, I automated this process.  Have real analysts been squashed by the short segments they get on TV? sure. but if you are really getting your info from TV, as opposed to just reading it in 1/10th of the time it takes to watch a TV program, thats your fault. These days I feel its best to do the research yourself, read about it online somewhere, or use the high volume of data available with algorithmic trading." "The hardest part seems to be knowing exactly when to sell the stock.  Well yes, that's the problem with all stock investing. Reports come out all the time, sometimes even from very smart people with no motivation to lie, about expected earnings for this company, or for that industry. Whether those predictions come true is something you will only find out with time. What you are considering is using financial information available to you (and equally available to the public) to make investment choices. This is called 'fundamental analysis'; that is, the analysis of the fundamentals of a business and what it should be worth. It forms the basis of how many investment firms decide where to put their money.  In a perfectly 'efficient' market, all information available to the public is immediately factored into the market price for that company's stock. ie: if a bank report states with absolute certainty (through leaked documents) that Coca-Cola is going to announce 10% revenue growth tomorrow, then everyone will immediately buy Coca-Cola stock today, and then tomorrow there would be no impact. Even if PwC is 100% accurate in its predictions, if the rest of the market agrees with them, then the price at the time of IPO would equal the future value of the cashflows, meaning there would be no gain unless results surpassed expectations. So what you are proposing is to take one sliver of the information available to the public (have you also read all publicly available reports on those businesses and their industries?), and using that to make a high risk investment. Are you going to do better than the investment firms that have teams of researchers and years of experience in the investment world? You can do quite well by picking individual stocks, but you can also lose a lot of money if you do it haphazardly. Be aware that there is risk in doing any type of investing. There is higher than average risk if you invest in equities ('the stock market'). There is higher risk still, if you pick individual stocks. There is yet even higher risk, if you pick small startup companies.  There are some specific interesting side-elements with your proposal to purchase stock about to have an IPO - those are better dealt with in a separate question if you want more information; search this site for 'IPO' and you should find a good starting point. In short, the company about to go public will hire a firm of analysts who will try to calculate the best price the public will accept for an offering of shares. Stock often goes up after IPO, but not always. Sometimes the company doesn't even fill its full IPO order, adding a new type of risk to a potential investor, that the stock will drop on day 1. Consider an analogy outside the investing world:  Let's say Auto Trader magazine prints an article that says ""all 2015 Honda Civics are worth $15,000 if they have less than 50,000 Miles."" Assume you have no particular knowledge about cars. If you read this article, and you see an ad in the paper the next day for a Honda Civic with 40k miles, should you buy it for $14k? The answer is not without more research. And even if you determine enough about cars to find one for $14k that you can reasonably sell for $15k, there's a whole world of mechanics out there who buy and sell cars for a living, and they have an edge both because they can repair the cars themselves to sell for more, and also because they have experience to spot low-offers faster than you. And if you pick a clunker (or a stock that doesn't perform even when everyone expected it would), then you could lose some serious money. As with buying and selling individual stocks, there is money to be made from car trading, but that money gets made by people who really know what they're doing. People who go in without full information are the ones who lose money in the long run." "I have my ""safe"" money in index funds but like to dabble in individual stocks. My criteria and thought process are usually like this, let's use SBUX as an example: Understand what the company does. Also paraphrased as ""buy what you know"". A profitable/growing business doesn't need to be complicated. Open stores. Sell coffee. For SBUX, my decision process literally started inside a store: ""Rocky, why are you standing in line to overpay for coffee? Wow, look at all these people! Hmmm. I wonder if this is a good stock to buy?"" Check out their fundamentals. Are they profitable? P.E.ratio, book value, and PEG are helpful, and I tend to use them as a gauge for whether I think the stock is overpriced or not. I compare those values to others in the industry. SBUX right now has a PE of ~30, which looks about average for its peers (PEP, KKD, GMCR). So far so good. Does it pay a dividend? This isn't necessarily good or bad, just useful to know. I like dividend-paying stocks, even if it means the stock price might not grow as aggressively. Also, a company that pays a dividend is naturally confident in its ability to turn a profit and generate cash. So it's a safer pick, in my opinion. SBUX pays a dividend, a small one, but that's a plus for me. Am I willing to watch the stock? With my index funds, I buy and forget. With my stocks, I keep an eye on the situation, read the news, and have to make a buy/sell decision regularly. With SBUX, I don't watch all that closely, I just keep up with the news. IMO, it's still a buy based on all the above criteria. And I feel less silly now standing in line to overpay for coffee." Your question is a bit odd in that you are mixing long-term fundamental analysis signals which are generally meant to work on longer time frames with medium term trading where these fundamental signals are mostly irrelevant.  Generally you would buy-and-hold on a fundamental signal and ride the short-term fluctuations if you believe you have done good analysis. If you would like to trade on the 2-6 month time scale you would need a signal that works on that sort of time scale.  Some people believe that technical analysis can give you those kind of signals, but there are many, many, many different technical signals and how you would trade using them is highly dependent on which one you believe works. Some people do mix fundamental and technical signals, but that can be very complicated.  Learning a good amount about technical analysis could get you started.  I will note, though, that studies of non-professionals continuously show that the more frequently people trade the more on they underperform on average in the long term when compared with people that buy-and-hold. An aside on technical analysis: michael's comment is generally correct though not well explained.  Say Bob found a technical signal that works and he believes that a stock that costs $10 dollars should be $11.  He buys it and makes money two months later when the rest of the market figures out the right price is $11 and he sells at that price.  This works a bunch of times and he now publishes how the signal works on Stack Exchange to show everyone how awesome he is.  Next time, Bob's signal finds a different stock at $10 that should be $11, but Anna just wrote a computer program that checks that signal Bob published faster than he ever could.  The computer program buys as much as it can in milliseconds until the price is $11.  Bob goes to buy, but now it is too late the price is already $11 and he can't make any money. Eventually, people learn to anticipate/adjust for this signal and even Anna's algorithms don't even work anymore and the hunt for new signals starts again. Why wouldn't you expect a long-term profit?  Say you buy 100 shares of company X, selling for$1/share today.  You hold it for 20 years, after which it's worth $10/share (in inflation-adjusted dollars).  So you've made a profit, only making two trades (buy & sell).  What the algorithmic traders have done with short-term trades during those 20 years is irrelevant to you. Now expand the idea.  You want some diversification, so instead of one stock, you buy a bit of all the stocks on whatever index interests you, and you just hold them for the same 20 years.  How has what the short-term traders done in the intervening time affected you? I have been looking into CMEs trading tool. I might just play around with futures on it. You make a good point on that though. I am reading Hull's book on options, futures and derivatives, and so far so good. Only thing I would want to test is options on futures, which is missing :( . "That characterisation of arbitrage-free pricing sounds a bit like the ""relative vs. fundamental"" approaches to asset pricing that Cochrane outlines (in his text, *Asset Pricing*). Rebonato also makes this distinction with regard to term structure models in *Volatility and Correlation*.   On one extreme you have CAPM-style models in which asset prices are completely determined by investors' risk preferences; on the other extreme, you would have something like a SABR-Libor Market Model where you take everything up to and including the volatility surface as given.   What's interesting to me is the way in which these different classes of models get used in various parts of the financial industry. So, buy side firms tend to rely a lot more on equilibrium-style models, since they ultimately care about things like how the equity risk premium or the bond risk premium affect asset prices. In contrast, derivatives quants working at a big sell-side bank who are pricing exotics don't care about what the ""fundamental"" value of their underlying assets is; they just take that as given and price the exotic accordingly." "While derivative pricing models are better modeling reality as academia invests more into the subject, none sufficiently do. If, for example, one assumes that stock returns are lognormal for the purposes of pricing options like Black Scholes does, the only true dependent variable becomes log-standard deviation otherwise known as ""volatility"", producing the infamous ""volatility smile"" which disappears in the cases of models with more factors accounting for other mathematical moments such as mean, skew, and kurtosis, etc.  Still, these more advanced models are flawed, and suffer the same extreme time mispricing as Black Scholes.  In other words, one can model anything however one wants, but the worse the model, the stranger the results since volatility for a given expiration should be constant across all strikes and is with better models. In the case of pricing dividends, these can be adjusted for the many complexities of taxation, but the model becomes ever more complex and extremely computationally expensive for each eventuality.  Furthermore, with more complexity in any model, the likelihood of discovering a closed form in the short run is less. For equities in a low interest rate, not high dividend yield, not low volatility, low dividend tax environment, the standard swap pricing models will not provide results much different from one where a single low tax rate on dividends is assumed. If one is pricing a swap on equity outside of the bounds above, the dividend tax rate could have more of an effect, but for computational efficiency, applying a single assumed dividend tax rate would be optimal with D*(1-x), instead of D in a formula, where D is the dividend paid and x is the tax rate. In short, a closed form model is only as good as its assumptions, so if anomalies appear between the actual prices of swaps in the market and a swap model then that model is less correct than the one with smaller anomalies of the same type.  In other words, if pricing equity swaps without a dividend tax rate factored more closely matches the actual prices than pricing with dividend taxes factored then it could be assumed that pricing without a dividend tax factored is superior.  This all depends upon the data, and there doesn't seem to be much in academia to assist with a conclusion. If equity swaps do truly provide a tax advantage and both parties to a swap transaction are aware of this fact then it seems unlikely swap sellers wouldn't demand some of the tax advantage back in the form of a higher price.  A model is no defense since volatility curves persist despite what Black Scholes says they should be."