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."
Any accounting software should be able to handle this.  When you invoice them, set the invoice date to the date of the event.  Then receive a partial payment against that invoice.  This will cause your accounting software to display the service income in the correct period as well.  So if you sent them invoice for August 7, 2014 event on May 5th, 2014 and they gave you $500 due, you would see this Income in August ($500 on Cash basis, $1000 on Accrual basis.  When you received the other $500 in August, you would see $1000 for both methods).  You would not see any income in May, when you created the invoice.  This is better for revenue matching with the correct period. When you send them same invoice (say 30 days before the event), Set the software to show payments already received  (it seems that most online accounting software will do this by default).  Here is an example in Freshbooks.    Here is an example in Xero:  Seems they both display information on when you can expect payment on the their respective dashboards.   In the Desktop version of Quickbooks (which I use a lot), it will not show the balance of the customer by default on an invoice.  You will have to modify the invoice template.  There are more details on that here.  In Desktop version of Quickbooks, you can look at Cash Flow Forecast report to see the expected amount coming in.   I hope that helps and good luck. First off, some companies do something like this.  Microsoft for example was well-known for consistently hitting earnings estimates every quarter - nearly never missed them, and most of the time didn't exceed by much either.  In order to do this and not be prosecuted for accounting fraud, you typically have to be a service or nontangible good company (like Microsoft used to be) where you can manipulate the amount of product on hand and move costs fairly easily from one quarter to the next.  A company like, say, Home Depot or Caterpillar - both of which have tangible goods they're either retailing or producing - has less flexibility there, although they will still try to move profits around to match earnings estimates more closely. However, you have to be consistently doing well to be able to do this.  You can't manufacture additional total revenue; so if you have one 'down' quarter, you have to either have moved some revenue into it from the previous quarter, or you have to be able to move some into it from the next quarter.  That obviously doesn't work consistently unless you're a fast-growing company, or have an extremely stable base.  It's also hard to do this in a legal-seeming fashion - technically this sort of manipulation is illegal, so decisions have to be justifiable.  Companies (like Microsoft) that are expanding can also do things to encourage slightly lower expectations.  A company in need of a stock price bump issues press releases touting its inventions and products as amazing things that will drive profits through the roof and an aggressive profit forecast - just as easy to issue a press release with a conservative forecast, meaning the bar will be lower to hit.   It's also not really necessary to manipulate earnings to have a consistently well-performing stock.  This article for example shows that companies who miss earnings estimates don't really suffer much (when controlling for their actual earnings changes, of course) in the long run.  Your price might drop a bit, but if your company is otherwise sound, it will recover. Finally, companies do sometimes come out with information ahead of earnings that cause expectations to be lowered.  7-Eleven for example just lowered its earnings expectations due to various reasons.  Some companies choose to do this in order to dilute the effect on the market.  I'm not sure if this is ever required, but it seems to me that some companies are much quicker to restate earnings expectations than others. &gt; corporate strategy vs. corporate finance vs business development?  Broadly speaking there are different functional roles (regardless of title) that are involved in:  - Financial Planning and Analysis (FPA).  Forecasting, variance analysis (demand, supply etc.), pricing strategy, etc.  Depending on your skill set, they can be viewed as basic excel work or more complex optimization problems.  - Strategic planning, M&amp;A.  Build or buy analysis.  Market analysis, etc.  This area is more on the capital allocation side in terms of whether or not a company should buy a competitor or build their own product line/service in a given area, geography, etc.  Investment banking backgrounds are helpful here.  - Communication.  There is a side of the business involved in presenting the company's business strategy to outside parties whether that be creditors or investors (stock holders).  The 'marcom / IR' (marketing communications, investor relations) side of the business involves presenting the company's strategy, forecast and results to outside parties.  This could also include the board of directors or senior management.  If you have a strong quantitative background at the engineering level, your ability to take differential equations and translate that to forecasting/econometric time series won't be difficult.  But for a lot of people that will be overkill and they only want basic Excel skills and understanding of finance/accounting. I like Pocketsmith for simple cashflow forecasting. I use Moneycenter for more complex tracking. "Their algorithm may be different (and proprietary), but how I would to it is to assume that daily changes in the stock are distributed normally (meaning the probability distribution is a ""bell curve"" - the green area in your chart).  I would then calculate the average and standard deviation (volatility) of historical returns to determine the center and width of the bell curve (calibrating it to expected returns and implied volaility based on option prices), then use standard formulas for lognormal distributions to calculate the probability of the price exceeding the strike price. So there are many assumptions involved, and in the end it's just a probability, so there's no way to know if it's right or wrong - either the stock will cross the strike or it won't." "All of these frameworks, Markowitz, Mean/CVaR, CARA, etc  sit inside a more general framework which is that ""returns are good"" and ""risk/lack of certainty in the returns is bad"", and there's a tradeoff between the two encoded as some kind of risk aversion number.  You can measure ""lack of certainty in returns"" by vol, CVaR, weighted sum of higher moments, but even sector/region concentration.  Similarly do I want more ""returns"" or ""log returns"" or ""sqrt returns"" in the context of this tradeoff?  You don't need any formal notion of utility at that point - and I don't know what formal ideas of utility beyond ""I want more returns and less risk"" really buys you. The Sharpe ratio only really gets its meaning because you've got some formal asset-pricing notion of utility.   In my view the moment that you're putting constraints on the portfolio (e.g. long only, max weights, don't deviate too much from the benchmark ...) - really you're operating in this more general framework anyway and you're not in ""utility-land"" anymore." We have custom software written in mostly C# for the long term strategies. Day trading is done on multiple platforms. Currently using ToS scripts for some futures and equities strategies to great success, and sierra charts for a few futures exclusively. I just moved into a position to work with day trading so I'm still learning more about the systems he uses I really like Value Investing by Bruce Greenwald. It's not a textbook so you can probably pick it up for about $20. While it is dense, I think with some patience you might be able to understand it at the undergrad level. The process outlined in the VI book is very different from the conventional corp finance way of valuing a company.  A typical corp finance model would probably have you model cash flows 5 or 10 years out and then assume some sort of terminal growth. The VI book argues that it's nearly impossible to predict things that far out accurately so build your valuation on what we know. Start with the balance sheet. Then look at this year's earnings. Is that sustainable?  This is a simplification of course but I describe it only so you can get the idea. I think it's definitely a worthwhile read.