Putting the gordon equation into practice
The Gordon equation does not use inflation-adjusted numbers. It uses nominal returns/dividends and growth rates. It really says nothing anyone would not already know.  Everyone knows that your total return equals the sum of the income return plus capital gains.   Gordon simply assumes (perfectly validly) that capital gains will be driven by the growth of earnings, and that the dividends paid will likewise increase at the same rate. So he used the 'dividend growth rate' as a proxy for the 'earnings growth rate' or 'capital gains rate'. You cannot use inflation-removed estimates of equity rates of return because those returns do not change with inflation.  If anything they move in opposite directions.  Eg in the 1970's inflation the high market rates caused people to discount equity values at larger rates --- driving their values down -- creating losses.
"Despite having a math degree, I basically only use basic algebra/probability/calculus on a day to day basis as my career has gone a different direction away from the modelling/quanty stuff.  Some fun reading:  * The SABR Model - [SABR/LIBOR Model](http://www.amazon.com/SABR-LIBOR-Market-Model-Interest-Rate/dp/0470740051) * Shevre's Stochastic Calculus for Finance - [Book 1](http://www.amazon.com/Stochastic-Calculus-Finance-Binomial-Textbooks/dp/0387249680/) &amp; [Book 2](http://www.amazon.com/Stochastic-Calculus-Finance-Continuous-Time-Textbooks/dp/144192311X/)  One of the big 'hard' problems is calibrating a swap curve w/ what's known as the 3s6s Basis. As a number of true quants have said to me it is a ""non-trivial problem"". Its basically trying to match two curves with different compounding over a number of different knot points.  SABR Model, listed above, is all about calibrating and figuring out how the current rate enviroment is behaving, is it normal or lognormal? What is the blend between the two, how do you know when you are in a different environment etc. Can rates go negative?" I'm not sure of another other forums, maybe WSO? For subreddits, perhaps r/Economics can at least guide you better.  Thanks for sharing the papers! I wish I had some I could share with you but I don't know any. Maybe you can ask your profs, or even profs at other institutions? I'm sure they will appreciate the initiative and help you out.  I really don't know much about any of that yet unfortunately. Wish I could be of more help! I don't expect you to remember the area for the surface of a sphere; I do expect you to be able to take a derivative of a volume. Especially if you are a PhD. Believe it or not, it IS faster than looking it up on the internet. And abilities to do quick back of the napkin estimations are very handy in my field (CS).  Also, I noticed that meetings with people who are in command of basic facts are usually faster and more productive than with people who are constantly looking things up on the internet. But an axiomatic approach only works in a deterministic environment, it does not work in a probabilistic environment. By your own arguments, because humans have free will so economics cannot be equated with a deterministic branch like physics. At this point now you are just contradicting yourself over and over.   And it depends entirely on how accurate the axioms are. If observations do not match equation outcomes, then while the mathematics of the equation might be sound, the obvious conclusion is that the axioms themselves are faulty. "Wow, this turns out to be a much more difficult problem than I thought from first looking at it. Let's recast some of the variables to simplify the equations a bit. Let rb be the growth rate of money in your bank for one period. By ""growth rate"" I mean the amount you will have after one period. So if the interest rate is 3% per year paid monthly, then the interest for one month is 3/12 of 1% = .25%, so after one month you have 1.0025 times as much money as you started with. Similarly, let si be the growth rate of the investment. Then after you make a deposit the amount you have in the bank is pb = s. After another deposit you've collected interest on the first, so you have pb = s * rb + s. That is, the first deposit with one period's growth plus the second deposit. One more deposit and you have pb = ((s * rb) + s) * rb + s = s + s * rb + s * rb^2. Etc. So after n deposits you have pb = s + s * rb + s * rb^2 + s * rb^3 + ... + s * rb^(n-1). This simplifies to pb = s * (rb^n - 1)/(rb - 1). Similarly for the amount you would get by depositing to the investment, let's call that pi, except you must also subtract the amount of the broker fee, b. So you want to make deposits when pb>pi, or s*(ri^n-1)/(ri-1) - b > s*(rb^n-1)/(rb-1) Then just solve for n and you're done! Except ... maybe someone who's better at algebra than me could solve that for n, but I don't see how to do it. Further complicating this is that banks normally pay interest monthly, while stocks go up or down every day. If a calculation said to withdraw after 3.9 months, it might really be better to wait for 4.0 months to collect one additional month's interest. But let's see if we can approximate. If the growth rates and the number of periods are relatively small, the compounding of growth should also be relatively small. So an approximate solution would be when the difference between the interest rates, times the amount of each deposit, summed over the number of deposits, is greater than the fee. That is, say the investment pays 10% per month more than your bank account (wildly optimistic but just for example), the broker fee is $10, and the amount of each deposit is $200. Then if you delay making the investment by one month you're losing  10% of $200 = $20. This is more than the broker fee, so you should invest immediately. Okay, suppose more realistically that the investment pays 1% more per month than the bank account. Then the first month you're losing 1% of $200 = $2. The second month you have $400 in the bank, so you're losing $4, total loss for two months = $6. The third month you have $600 in the bank so you lose an additional $6, total loss = $12. Etc. So you should transfer the money to the investment about the third month. Compounding would mean that losses on transferring to the investment are a little higher than this, so you'd want to bias to transferring a little earlier. Or, you could set up a spreadsheet to do the compounding calculations month by month, and then just look down the column for when the investment total minus the bank total is greater than the broker fee. Sorry I'm not giving you a definitive answer, but maybe this helps." "Using the following equations from the book a stab at the correlation can be made.   Calculating the residual volatilities from equation 2.4 The correlation of stock A with stock B is 0.378 and stock B has the higher residual volatility. However, the correlation is given as a ""simple model"", which may suggest that it is an approximation.  If I have applied it correctly, some testing shows that it is only approximate. Also of interest" Think of it this way: C + (-P) = forward contract. Work it out from there. Anyways, this stack is meant for professionals, not students, I think. "Both models understand that the value of a company is the sum total of all cashflows in the future, discounted back to the present. They vary in their definition of ""cash"". The Gordon Growth model uses dividends as a proxy for cashflow, under the assumption that this is the only true cash received by shareholders. (In theory, counting cash is meaningless if there's no eventual end-game where the accumulated cash is divvied up amongst the owners.) The Gordon model is best used to value companies that have an established, reliable dividend. The company should be stable, and the payout ratio high. GG will underestimate the value of firms that consistently maintain a low payout ratio, and instead accumulate cash. There are multiple DCF models. A firm valuation measures all cash available to both equity and debt holders. A traditional equity valuation measures all cash that can be claimed by shareholders. While the latter seems most intuitive and pertinent to a shareholder, the former is very good at showing what a company can do regardless of their choice of capital structure. A small add-on to a firm valuation is the concept of EVA, or economic value add, where the return on capital (all capital -- both debt and equity) is compared to the blended cost of capital. The DCF model is more flexible (optimistic?) than the Gordon in its approach to cash. The approach can be applied to many types of companies, at every stage in their maturity, even if they don't pay a dividend. A simplistic, or single-stage DCF is similar to the Gordon. The assumption is that the company is fully mature, growing at a rate perhaps just slightly above inflation, forever. For younger companies a multi-stage DCF can be employed, where you forecast fairly confident numbers for the next 3-5 years, then 3-5 years beyond that the forecast is less certain, but assumed to be slowing growth, and a generally maturing, stabilizing company. And then the steady-state stage is tacked on to the end. You'll want to check out Professor Aswath Damodaran's website: http://pages.stern.nyu.edu/~adamodar/ . He addresses all this and so much more, and has a big pile of spreadsheets freely downloadable to get you started. I also highly recommend his book ""Investment Valuation"". It's the bible on the topic." This is a useful metric in that it gives you a trust factor on how reliable the beta is for future expectations   It is akin to velocity and acceleration  First  and second order derivatives of distance / time.  Erratic acceleration implies the velocity is less trustworthy  Same idea for beta I would use neither method.  Taking a short example first, with just three compounding periods, with interest rate 10%.  The start value y0 is 1. So after three years the value is 1.331, the same as y0 (1 + 0.1)^3. Depreciating (like inflation) by 10% (to demonstrate) gets us back to y0 = 1 Appreciating and depreciating by 10% cancels out: Appreciating by 10% interest and depreciating by 3% inflation: This is the same as y0 (1 + 0.1)^3 (1 + 0.03)^-3 = 1.21805 So for 50 years the result is y0 (1 + 0.1)^50 (1 + 0.03)^-50 = 26.7777 Note You can of course use subtraction but the not using the inflation figure directly.  E.g. (edit: This appears to be the Fisher equation.) 2nd Note Further to comments, here is a chart to illustrate how much the relative performance improves when inflation is accounted for.  The first fund's return is 6% and the second fund's return varies from 3% to 6%.  Inflation is 3%. The details of the DJIA methodology is outlined in the official methodology document on their website.  In addition, you will need their index mathematics document, which gives the nitty-gritty details of any type of adjustments that must be made. Between the two you should have the complete picture in as fine a detail as you want, including exactly what is done in response to various corporate actions like splits and structural changes. "What are you trying to do Exactly?  What is a covariance ""profile""? (Cov Matrix)?   Also lose the jargon.  From my understanding you want the same covariance in the portfolio, however it doesn't work like that as covariance is an observed matrix. It's hard enough to target variance in a portfolio (see GMV) and to target covariance would be even more difficult. You can attempt to use the weights you have right now in terms of exposures, but LEAPS may give you unwanted theta.   Are you long short or long only? if you're long only the interplay between the positions is rather irrelevent, as right now all that matters are the weights." There are entire 5-person teams dedicated to forecasting *individual products* at major companies that roll up to product class teams, which then roll up to balance sheet forecasting. Since all of those companies have major capital markets operations, these things get huge. The real forecasters are spending 10-14 hours a day, year-round using massive models to come up with competent forecasts. I did this for a few years, it's leaps and bounds more than applying multiples.   I hope you get a lot of participation, but you have to see the flaw in this dataset. I think this would be better done by honing your focus in on what forecasting you're talking about. It's a very, very broad field. I just used the formula in below link and did some math.  I have that book too but haven't looked at it yet really.  Lots of maths to have fun with.  Let me know if this is correct or needs fixing. Source: http://wiki.fool.com/How_to_Calculate_Beta_From_Volatility_%26_Correlation "I'm reasonably familiar with the field for a non-specialist, certainly with the 14 qubit example and the D-Wave stuff.  I've experimented with quantum lambda calculi, which relate to my own interests.  But an article much like this could have been written back in the mid-90s.    The fact is we don't know yet what limitations, hard or soft (e.g. economic), will be encountered.  Although the article admitted that, it just felt too hyped to me overall.    To be fair to the author, it's not just him: e.g. he quotes Seth Lloyd saying that quantum computing ""allow us to understand the universe in its own language"".  Which, btw, is nonsense when you consider that imposing qubits on some underlying substrate is actually just encoding a specific approach to computation, and has nothing to do with the universe's ""own language"", whatever that means."