Methods for forecasting price?
"Assuming a price is set on an free market there are particular difficulties to pricing. A free market is one where the price is entirely determined by the willingness of people to buy and sell at a particular price point. What you perceive as price, is actually the ""tick"", i.e. the quote of the last transaction. The first and most serious major obstacle to pricing is a variation of the prisoners dilemma, a psychological phenomenon. For instance, bitcoin might be worth 4$ now, but you believe it will be worth 5$ in 3 days. Will you buy bitcoin? If acting only on your conviction, yes. But what if you consider what other people will do? Will others believe bitcoin will be worth 5$ in 3 days? Will they act on their conviction? Will the others believe that others believe that it wil be worth 5$ in 3 days, and will the others believe that the others who believe will act on their conviction? Will the others believe that others believe of still others who believe that they will act on their conviction? It goes on like this ad-infinitum. The actual behavior of any individual on the market is essentially chaotic and unpredictable (for the reason stated above and others). This is related to a phenomenon you call market efficiency. An efficient market always reflects the optimal price-point at any given time. If that is so, then you cannot win on this market, because at the time you would have to realize a competitive edge, everybody else has already acted on that information. Markets are not 100% efficient of course. But modern electronic markets can be very, very efficient (as say compared to stock markets fro 100 years ago, where you could get a competitive edge just by having access to a fast courier). What makes matters rather more difficult for price forecasting is that not only are humans engaging in the market, machines are as well. The machines may not be terribly good at what they do, but they are terribly fast. The machines that work well (i.e. don't loose much) will survive, and the ones that don't will die in short order. Since speed is one of the major benefits of the machines over humans, they tend to make markets even more efficient. Another phenomenon to price forecasting is that of information and entropy. Suppose you found a reliable method to predict a market at a given time. You act on this information and indeed you make a profit. The profit you will be able to achieve will diminish over time until it reaches zero or reverts. The reason for this is that you acted on private information, which you leaked out by engaging in a trade. The more successful you are in exploiting your forecast, the better you train every other market participant to react to their losses. Since for every trade you make successfully, there has to be somebody who lost. People or machines who lose on markets usually exit those markets in some fashion. So even if the other participants are not adjusting their behavior, your success is weeding out those with the wrong behavior. Yet another difficulty in pricing forecasts are black-swan events. Since information can have a huge impact on pricing, the sudden appearance of new information can throw a conservative forecast completely off the rails and incur huge losses (or huge unexpected benefits). You cannot quantify black-swan events in any shape or form. It is my belief that you cannot predict efficient and well working markets. You might be able to predict some very sub-optimal markets, but usually, hedge-funds are always on the hunt for inefficient markets to exploit, so by simple decree of market economics, the inefficient markets tend to be a perpetually dying species." 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 "It's not impossible to forecast the future price of a commodity. However, it's exactly that; an educated guess, much like the weather, and the further out that prediction is made, the higher the percentage error is expected. A lot of information is gathered by various instruments, spotters etc at a very high cost of time and money, to produce a prediction that starts breaking down after about five days and is no more than a wild guess after about ten. How accurately a price can be forecast depends on the commodity. There are seasonal and thus cyclical changes in many commodities, on top of which there is a general trend which is nearer term. A pretty decent prediction can thus be arrived at with a relatively simple seasonally-adjusted percentage change algorithm; take a moving average of the last few measurements, compute the percent change versus the same period last year (current minus last divided by last) and multiply it by last year's number for the current day or month to arrive at a pretty decent prediction for the current and near-future periods (up to about as far ahead as you have looked behind). Another thing you may need to do is normalize. Many price graphs are very jittery; the price of a stock may fluctuate many percentage points on a single day, and there's a lot of ""noise"" inherent in them. A common tool to normalize is a box-and-whisker plot, which for a given time period will aggregate all samples within that period, and give you a measurement of the lowest sample, highest sample, median, and quartiles (the range of each 25% of the full sample space). Box plots can also be plotted on the ""interquartile range"" or ""middle fifty""; this throws away the very noisy outliers and constructs a much more regular plot from the inner part of the bell curve. You can reverse-engineer a best-fit line connecting the elements of each box, and the closer two lines are, the more likely the real future data will be around that area (because the quartile between those to lines is very dense; 25% of the values are in a very small range meaning many samples occurred there). Lastly, there are outside factors that are not included in simple percentage growth. Big news must be taken into account by introducing more subjective guesses about future data. If you see an active hurricane season coming (or a hurricane bearing down on Galveston/Houston) then it's reasonable to assume that the price of oil and/or refined oil products (like gas and jet fuel) will skyrocket. A cyclical growth model will not predict these events, but you can factor in the likelihood of a big change with a base onto which you add last year's numbers, and onto that you add regular growth. Conversely, when a huge spike happens due to a non-cyclical event like a natural disaster, you must smooth it out by reducing the readings to fit in the curve, otherwise your model for next year will expect the same anomaly at the same time and so it will be wrong. These adjustments are necessary, but the more of them you make, the less the graph reflects real history and the more it reflects what you think it should have been."
"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." you could try [FRED](http://research.stlouisfed.org/fred2/graph/?g=HO7), or maybe try the CME and ICE's websites for some decent data.. haven't looked just suggestions - pretty sure the symbol for the Libor futures is EM, you could approximate from that so long as it's not a doctoral thesis Assuming you were immersed in math with your CS degree, the book **'A Non-Random Walk Down Wall Street' by Andrew Lo** is a very interesting book about the random walk hypothesis and it's application to financial markets and how efficient markets might not necessarily imply complete randomness.    Lots of higher level concepts in the book but it's an interesting topic if you are trying to branch out into the quant world.  The book isn't very specific towards algorithmic trading but it's good for concept and ideas.  Especially for general finance, that will give you a good run down about markets and the way we tackle modern finance.    **A Random Walk Down Wall Street** (which the book above is named after) by **Burton Malkiel** is also supposed to be a good read and many have suggested reading it before the one I listed above, but there really isn't a need to do so.    For investing specifically, many mention **'The Intelligent Investor' by Benjamin Graham** who is the role model for the infamous Warren Buffet.  It's an older book and really dry and I think kind of out dated but mostly still relevant.  It's more specifically about individual trading rather than markets as a whole or general markets.  It sounds like you want to learn more about markets and finance rather than simply trading or buying stocks.  So I'd stick to the Andrew Lo book first.    ---  Also, since you might not know, it would be a good idea to understand the capital asset pricing model, free cash flow models, and maybe some dividend discount models, the last of which isn't so much relevant but good foundations for your finance knowledge.  They are models using various financial concepts (TVM is almost used in every case) and utilizing them in various ways to model certain concepts.  You'd most likely be immersed in many of these topics by reading a math-oriented Finance book.  Try to stay away from those penny stock trading books, I don't think I need to tell a math major (who is probably much smarter than I am) that you don't need to be engaging in penny stocks, but do your DD and come to a conclusion yourself if you'd like.  I'm not sure what career path you're trying to go down (personal trading, quant firm analyst, regular analyst, etc etc) but it sounds like you have the credentials to be doing quant trading.    ---  Check out www.quantopian.com.  It's a website with a python engine that has all the necessary libraries installed into the website which means you don't have to go through the trouble yourself (and yes, it is fucking trouble--you need a very outdated OS to run one of the libraries).  It has a lot of resources to get into algorithmic trading and you can begin coding immediately.  You'd need to learn a little bit of python to get into this but most of it will be using matplotlib, pandas, or some other library and its own personal syntax. Learning about alpha factors and the Pipeline API is also moderately difficult to get down but entirely possible within a short amount of dedicated time.     Also, if you want to get into algorithmic trading, check out Sentdex on youtube.  He's a python programmer who does a lot of videos on this very topic and has his own tool on quantopian called 'Sentiment Analyzer' (or something like that) which basically quantifies sentiment around any given security using web scrapers to scrape various news and media outlets.  Crazy cool stuff being developed over there and if you're good, you can even be partnered with investors at quantopian and share in profits.    You can also deploy your algorithms through the website onto various trading platforms such as Robinhood and another broker and run your algorithms yourself.    Lots of cool stuff being developed in the finance sector right now.  Modern corporate finance and investment knowledge is built on quite old theorems and insights so expect a lot of things to change in today's world.  ---  With a math degree, finance should be like algebra I back in the day.  You just gotta get familiar with all of the different rules and ideas and concepts.  There isn't that much difficult math until you begin getting into higher level finance and theory, which mostly deals with statistics anyways like covariance and regression and other statistic-related concepts.  Any other math is simple arithmetic. "I'll give the TLDR answer. 1) You can't forecast the price direction. If you get it right you got lucky. If you think you get it right consistently you are either a statistical anomaly or a victim of confirmation bias. Countless academic studies show that you can not do this. 2) You reduce volatility and, importantly, left-tail risk by going to an index tracking ETF or mutual fund. That is, Probability(Gigantic Loss) is MUCH lower in an index tracker. What's the trade off? The good thing is there is NO tradeoff. Your expected return does not go down in the same way the risk goes down! 3) Since point (1) is true, you are wasting time analysing companies. This has the opportunity cost of not earning $ from doing paid work, which can be thought of as a negative return. ""With all the successful investors (including myself on a not-infrequent basis) going for individual companies directly"" Actually, academic studies show that individual investors are the worst performers of all investors in the stock market." "Strategy would be my top factor.  While this may be implied, I do think it helps to have an idea of what is causing the buy and sell signals in speculating as I'd rather follow a strategy than try to figure things out completely from scratch that doesn't quite make sense to me. There are generally a couple of different schools of analysis that may be worth passing along: Fundamental Analysis:Fundamental analysis of a business involves analyzing its financial statements and health, its management and competitive advantages, and its competitors and markets. When applied to futures and forex, it focuses on the overall state of the economy, interest rates, production, earnings, and management. When analyzing a stock, futures contract, or currency using fundamental analysis there are two basic approaches one can use; bottom up analysis and top down analysis. The term is used to distinguish such analysis from other types of investment analysis, such as quantitative analysis and technical analysis. Technical Analysis:In finance, technical analysis is a security analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. Behavioral economics and quantitative analysis use many of the same tools of technical analysis, which, being an aspect of active management, stands in contradiction to much of modern portfolio theory. The efficacy of both technical and fundamental analysis is disputed by the efficient-market hypothesis which states that stock market prices are essentially unpredictable. There are tools like ""Stock Screeners"" that will let you filter based on various criteria to use each analysis in a mix.  There are various strategies one could use.  Wikipedia under Stock Speculator lists: ""Several different types of stock trading strategies or approaches exist including day trading, trend following, market making, scalping (trading), momentum trading, trading the news, and arbitrage.""  Thus, I'd advise research what approach are you wanting to use as the ""Make it up as we go along losing real money all the way"" wouldn't be my suggested approach. There is something to be said for there being numerous columnists and newsletter peddlers if you want other ideas but I would suggest having a strategy before putting one's toe in the water." id like to start by saying youre still doing this yourself, and i dont actually have all the info required anyway, dont send it but  &gt;[3] Descriptive Statistical Measures: Provide a thorough discussion of the meaning and interpretation of the four descriptive &gt;statistical measures required in your analysis: (1) Arithmetic Mean, (2) Variance, (3) Standard Deviation and (4) Coefficient of &gt;Variation. For example, how are these measures related to each other? In order to develop this discussion, you may want to &gt;consult chapters 2 and 3 of your textbook. This topic is an important part of your report. can be easily interpreted, im guessing the mean is simply just the observed (and then projected stock price for future models) the standard deviation determines the interval in which the stock price fluctuates. so you have like a curve, and then on this curve theirs a bunch of normal distributions modeling the variance of the price plotted against the month  also the coefficient of variation is just r^2 so just read up on that and relate it to the meaning of it to the numbers you have  actually my stats are pretty rusty so make sure you really check into these things  but otherwise the formulas for part 4 is simple too. you can compare means of a certain month using certain equations, but there are different ones for certain situations  you can test for significance by comparing the differences of the means and if its outside of your alpha level then it probably means your company is significantly different from the SP index. (take mu of SP - mu of callaway)  you can also find more info on interpreting the two different coefficients your given if you look up comparing means of linear regression models or something You may refer to project http://jstock.sourceforge.net. It is open source and released under GPL. It is fetching data from Yahoo! Finance, include delayed current price and historical price. you can check google scholar for some research reports on it.  depends how complex you want to get...  it is obviously a function of the size of the portfolio of each type of asset. do you have a full breakdown of securities held? you can get historical average volumes during different economic periods, categorized by interest rates for example, and then calculate the days required to liquidate the position, applying a discount on each subsequent day. Also, this would be a sick way to predict the weather. What if there were options on rain that had week long maturities? One could back out from the price and different maturities the probability of it raining on a certain day. "No. Some grocery stores may discount specific products based on inventory to drive sales using ""loss leaders"" where the product is intentionally priced as a loss for the business. While commodity futures may impact some prices, I'm not sure one can easily extract the changes solely due to futures shifts." "First, to mention one thing - better analysis calls for analyzing a range of outcomes, not just one; assigning a probability on each, and comparing the expected values. Then moderating the choice based on risk tolerance. But now, just look at the outcome or scenario of 3% and time frame of 2 days. Let's assume your investable capital is exactly $1000 (multiply everything by 5 for $5,000, etc.). A. Buy stock: the value goes to 103; your investment goes to $1030; net return is $30, minus let's say $20 commission (you should compare these between brokers; I use one that charges 9.99 plus a trivial government fee). B. Buy an call option at 100 for $0.40 per share, with an expiration 30 days away (December 23). This is a more complicated. To evaluate this, you need to estimate the movement of the value of a 100 call, $0 in and out of the money, 30 days remaining, to the value of a 100 call, $3 in the money, 28 days remaining. That movement will vary based on the volatility of the underlying stock, an advanced topic; but there are techniques to estimate that, which become simple to use after you get the hang of it. At any rate, let's say that the expected movement of the option price in this scenario is from $0.40 to $3.20. Since you bought 2500 share options for $1000, the gain would be 2500 times 2.8 = 7000. C. Buy an call option at 102 for $0.125 per share, with an expiration 30 days away (December 23). To evaluate this, you need to estimate the movement of the value of a 102 call, $2 out of the money, 30 days remaining, to the value of a 102 call, $1 in the money, 28 days remaining. That movement will vary based on the volatility of the underlying stock, an advanced topic; but there are techniques to estimate that, which become simple to use after you get the hang of it. At any rate, let's say that the expected movement of the option price in this scenario is from $0.125 to $ 1.50. Since you bought 8000 share options for $1000, the gain would be 8000 times 1.375 = 11000. D. Same thing but starting with a 98 call. E. Same thing but starting with a 101 call expiring 60 days out. F., ... Etc. - other option choices. Again, getting the numbers right for the above is an advanced topic, one reason why brokerages warn you that options are risky (if you do your math wrong, you can lose. Even doing that math right, with a bad outcome, loses). Anyway you need to ""score"" as many options as needed to find the optimal point. But back to the first paragraph, you should then run the whole analysis on a 2% gain. Or 5%. Or 5% in 4 days instead of 2 days. Do as many as are fruitful. Assess likelihoods. Then pull the trigger and buy it. Try these techniques in simulation before diving in! Please! One last point, you don't HAVE to understand how to evaluate projected option price movements if you have software that  does that for you. I'll punt on that process, except to mention it. Get the general idea? Edit P.S. I forgot to mention that brokers need love for handling Options too. Check those commission rates in your analysis as well." Standard Markowitz's portfolio optimization takes trend into account, not mean reversion. Otherwise, since a portfolio is a linear combination of your individual assets, you could 1st model them separately and than establish a second-layer criteria for weighting. For the 1st layer, mean reversion (as well as trend) of returns can be captured with a ARIMA model, and for the 2nd layer, you could use the Kelly criterion, for instance. A more direct approach to mean-reversion portfolio selection is working with pairs trading. I'm not linking any materials as those topics are plentiful on the web. ...If that's still not the answer you're looking for... The problem with predicting economic cycles is that, they are long, and we hardly have a sufficient measured history to forecast anything reliable. In order to predict the mean reversion of a stock or bond market cycle, you've got to measure their long-term mean first. And there you'll have disagreements right on the start... Some researchers (see Jeremy Siegel) have tried to measure the long-term mean of returns for various asset classes. Some argue that stocks are the long-run winners and that CAPM explains that, but others say that's just questionable, since measurements go just as far as the western countries (US, UK, etc.) have thrived. Other countries have much more recent economic records. "You can interpret prices in any way you wish, but the commonly quoted ""price"" is the last price traded. If your broker routes those orders, unlikely because they will be considered ""unfair"" and will probably be busted by the exchange, the only way to drive the price to the heights & lows in your example is to have an overwhelming amount of quantity relative to the order book. Your orders will hit the opposing limit orders until your quantity is exhausted, starting from the best price to the worst price. This is the functional equivalent to a market order." Use the Black-Scholes formula. If you know the current price, an options strike price, time until expiration, and risk-free interest rate, then knowing the market price of the option will tell you what the market's estimation of the volatility is. This does rely on a few assumptions, such as Gaussian random walk, but those are reasonable assumptions for most stocks. You can also get a list of past stock prices, put them in Excel, and ask Excel to calculate the standard deviation with stdev.s(), but that gives you the past volatility. The market's estimate of future volatility is more relevant. "When structures recur at different scales, they're called ""fractals"", and there is something called the ""fractal markets hypothesis"" which attempts to analyse stock market movements as fractals and in terms of (related) chaos theory. Whether you can profit from it I have no idea.  If it was easy, everyone would be doing it.  Many of the non-academic pages linked in the search results (previous link) remind me of technical analysis/chartist stuff (which - to me - always seems to be a lot better at explaining things after the event than actually predicting things)."