vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Looked at the luv model quickly. The most likely reason is you calculated a FCF lower than the market. You have FCF decreasing due to shrinking margins over the model period. Your terminal value then has the FCF growing by 2.3% into perpetuity so that doesn't really coincide. I mean, it could but I wouldn't use it. I personally think the shrinking margin assumption going forward is a little much.   For your terminal value calc, don't you want that to be the final model year cash flow times 1 plus the terminal growth rate?    Also, I find it interesting that the risk free rate is the terminal growth rate. Any particular reasoning behind that?  It works now at the 2.3% but probably wouldn't in a different interest rate environment.
I assume that when this actually matters for calculations with money on the line (if there is a need to calculate the probability of hitting a knockout, getting called out of a position, stop loss, etc.) many of these people will use the actual formulas that will give them the correct answers.
"Of course, which is why you need to have a scoring function / utility function for the ""filters"", i.e. Are you going to value it by rate of accuracy hor by a metric where wins = +2, losses = -1, such that it uses a criteria like that to decide whether or not a filter adds value, (some even use a compound effect i.e. wins = 2+e^(1+w) where w is the consecutive wins).  A metric like the above would capture the trade off between predictive power and profit. Also some traders watch their Max DD very carefully so they may be very risk averse."
Here are a few things I've already done, and others reading this for their own use may want to try.  It is very easy to find a pattern in any set of data. It is difficult to find a pattern that holds true in different data pulled from the same population.  Using similar logic, don't look for a pattern in the data from the entire population. If you do, you won't have anything to test it against. If you don't have anything to test it against, it is difficult to tell the difference between a pattern that has a cause (and will likely continue) and a pattern that comes from random noise (which has no reason to continue). If you lose money in bad years, that's okay. Just make sure that the gains in good years are collectively greater than the losses in bad years. If you put $10 in and lose 50%, you then need a 100% gain just to get back up to $10.  A Black Swan event (popularized by Nassim Taleb, if memory serves) is something that is unpredictable but will almost certainly happen at some point. For example, a significant natural disaster will almost certainly impact the United States (or any other large country) in the next year or two. However, at the moment we have very little idea what that disaster will be or where it will hit.  By the same token, there will be Black Swan events in the financial market. I do not know what they will be or when they will happen, but I do know that they will happen. When building a system, make sure that it can survive those Black Swan events (stay above the death line, for any fellow Jim Collins fans). Recreate your work from scratch. Going through your work again will make you reevaluate your initial assumptions in the context of the final system. If you can recreate it with a different medium (i.e. paper and pen instead of a computer), this will also help you catch mistakes.
With all that being said, what I'm assuming you are interested in is either called a ""delta-gamma approximation"" or more generally ""Greek/sensitivities based profit and loss attribution"" (in case you wanted to Google some more about it). Here is an example that is relevant to your question. Let's say we had the following European call contract: Popping this in to BS formula gives you a premium of $4.01, delta of 0.3891 and gamma of 0.0217. Let's say you bought it, and the price of the stock immediately moves to 55 and nothing else changes, re-evaluating with the BS formula gives ~6.23. Whereas using a delta-gamma approximation gives: The actual math doesn't work out exactly and that is due to the fact that there are higher order Greeks than gamma but as you can see here clearly they do not have much of an impact considering a 10% move in the underlying is almost entirely explained by delta and gamma."
"Do yourself a favor and actually read the article posted; it directly addresses your claim. Here is a lovely excerpt -  ""The claim that climate models are unreliable is the 6th-most popular contrarian myth. The argument is generally based on the claim that climate models didn't predict the slowdown in global surface warming over the past 15 years. That's in large part because during that time, we've predominantly experienced La Niña conditions. Climate modelers couldn't predict that ahead of time, but the models that happened to accurately simulate those conditions also accurately predicted the amount of global surface warming we've experienced."""
Of course, if you find a fund that will reliably return 20% after expenses of 3%, it would probably make sense to choose that over one that returns 10% after expenses of 1%. But ""will reliably return"" is not the same as ""has returned over the past N years"", and the difference between the two phrases becomes greater and greater the smaller N is. When you find a fund that seems to have performed staggeringly well over some time period, you should be cautious; there is a good chance that the future holds some regression to the mean, and the fund will not continue to be so stellar. You may want to take a look at this question which asked about Morningstar fund ratings, which are essentially a measure of past performance. My answer references a study done by Morningstar comparing its own star ratings vs. fund expenses as a predictor of overall results. I'll repeat here the take-home message: How often did it pay to heed expense ratios? Every time. How often did it pay to heed the star rating? Most of the time, with a few exceptions. How often did the star rating beat expenses as a predictor? Slightly less than half the time, taking into account funds that expired during the time period. In other words, Morningstar's own study showed that its own star ratings (that is, past fund performance) are not as good at predicting success as simply looking at the expense ratios of the funds."
&gt; “If the [Black-Scholes] formula is applied to extended time periods….it can produce absurd results.”  &gt; -Warren Buffett, 2008 Letter to Berkshire Hathaway Shareholders  I will give your question more thought, and come back with a quantitative solution. It may be most fruitful to apply a backward-induction options pricing model with detailed scenario-based discounted cash flow valuation models supported by pro forma financial statement and investment analysis.    Nonetheless, my initial reaction is inline with Warren Buffett's belief that in the long-run an assumption of the Black-Scholes options pricing model is invalid (see [here](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.613.1657&amp;rep=rep1&amp;type=pdf) and [here](http://www.elon.edu/docs/e-web/academics/business/economics/faculty/kurt/OptionValuationBuffetCritique_123113.pdf)). The perceived invalid assumption is that the distribution of expected future stock prices is *not* log-normal in the long-run. This non-log-normal view is especially true for a single company stock.   This invalid assumption results in over-valuation of options prices from Black-Scholes - which makes it much better to sell long-term options than to buy them if market participants are using Black-Scholes pricing models.  Again, without having done the math yet, my gut tells me if the option seller is using Black-Sholes pricing for long-dated options, you would be best to avoid buying them as the prices will be inflated compared to an economic reality fair price.
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.
This reminds me of that old story about a company that sends out pamphlets predicting the results of sports games, complete with ""strategies"" and ""data"" to back up the predictions. The company sends out several versions of the pamphlet every game, each predicting a different winner. Given a large enough sample size, by the end of the season, there are a few people who have received a pamphlet that accurately predicted the winner for every game and they're convinced the system is perfect. The others weren't so lucky, however. Relying on candlestick charts and TA patterns that are relics from the pre-computerized era is reassuring to some traders and gives them a sense of control and ""beating the market,"" but how long will chance remain on your side? This is why I maintain that visual tools like candlestick charts are a slowly dying medium. They certainly still add value to some trading firms, which is why Bloomberg terminals still ship with this functionality built in, but as more and more research shows, automated algorithms and statistical indicators can provide more value. It's also important to think about whether the majority of the value added by visual tools like candlestick charts comes in the form of profit or a sense of security to traders who learned the field using them over the past few decades. Finally, it's extremely important to realize that the actions of retail investors in the equities market cannot begin to represent the behaviors of the market as a whole. In the equities markets alone, trading firms and institutional investors dwarf retail investors, and the difference in scale is even more vastly pronounced in derivatives and currency markets. The fact that some retail investors use candlestick charts and the technical indicators they (hope) underlie them provides nothing but minor anecdotal evidence as to their effectiveness."
"The major bureaus use the Fair Isaac scoring model, for the most part. Here's an excerpt from a web site (Versions of the FICO scoring model) to explain: One of the first things a newcomer to this board learns is the difference between FICO and FAKO scores. FAKO refers to the non-FICO scores offered by various companies. FAKO scores have little value since few of them are used by lenders and they do not match closely to FICO scores. But even when you stick with FICO scores, confusion can ensue because FICO scores have many different editions, versions, and variations. On a single day, a consumer could theoretically have dozens of different FICO scores, depending on which version and credit agency is used to produce the score. This post provides a summary of the various FICO versions. Please offer any corrections or updates, and they will be edited in. The FICO scoring model with its familiar range of 300 to 850 was first introduced in 1989. Since then, FICO has released five major revisions: 1995, 1998, 2004, 2008, and 2014. Each ""edition"" uses a different formula and produces a different score. When a new FICO edition is released, many lenders continue using an older version for years before ""upgrading."" The 1995 revision is no longer in common use, but later editions are still used by lenders. Most FICO editions are commonly known by the year of introduction: FICO 98, FICO 04, and FICO 08 (although FICO now calls it FICO Score 8, without the zero). The most recent edition is FICO Score 9 introduced in 2014. As of 2014, FICO Score 8 is the most commonly used. However, most mortgage lenders use FICO 04 for Equifax and Transunion, and FICO 98 for Experian. In addition to the ""classic"" version, FICO offers ""Industry Option"" versions customized for auto loans, credit cards, installment loans, personal finance loans, and insurance. These have a score range of 250 to 900, so the scores are not fully comparable with ""classic"" versions. As of 2015, Auto and Bankcard scores are available from myFICO as described here.
"If it could, it seems yet to be proven.  Long Term Capital Management was founded by a bunch of math whizzes and they seem to have missed something.  I'd never suggest that something has no value, but similar to the concept that ""if time travel were possible, why hasn't anyone come back from the future to tell us"" I'd suggest that if there were a real advantage to what you suggest, someone would be making money from it already.  In my opinion, the math is simple, little more than a four function calculator is needed."
The formula is FV = PV * (1+R)N, where FV is the future value (how much you'll have later), PV is the present value (how much you'll have now), R is the periodic rate of return (the percentage that your money will grow in each unit period of time, say a month or a year), and N is the number of unit periods of time in the overall time span. Now, you asked what ""compounding"" is. The theory is very simple; if you put an amount of money (the ""principal"") into an investment that pays you a rate of return (interest), and don't touch the account (in effect reinvesting the interest you earn in the account back into the same account), then after the first period during which interest is calculated and paid, you'll earn interest on not just the original principal, but the amount of interest already earned. This allows your future value to grow faster than if you were paid ""simple interest"", where interest is only ever paid on the principal (for instance, if you withdrew the amount of interest you earned each time it was paid). That's accounted for in the future value formula using the exponent term; if you're earning 8% a year on your investment, then after 1 year you'll have 108% of your original investment, then after two years you'll have 1.082 = 116.64% (instead of just 116% which you'd get with simple interest). That .64% advantage to compounding doesn't sound like much of an advantage, but stay tuned; after ten years you'll have 215.89% (instead of 180%) of your original investment, after 20 you'll have 466.10% (instead of 260%) and after 30 your money will have grown by over 1000% as opposed to a measly 340% you'd get with simple interest. The present value formula is based on the same fundamental formula, but it's ""solved"" for the PV term and assumes you'll know the FV amount. The present value formula answers questions like ""how much money would I have to invest now in order to have X dollars at a specific future time?"".
"Eh, for the most part US labeling laws are pretty good. There are exceptions, but most of the time there's a reasonable argument at least. In the above example, it would need to be ""made with 100% chicken,' as opposed to ""100% chicken."" That seems fair to me. Not that misbranding never occurs, because mistakes happen, and people do also break the law, but misbranding is taken seriously. Even with no safety threat that's still a type three recall."
"Yes, the ""effective"" and ""market"" rates are interchangeable. The present value formula will help make it possible to determine the effective interest rate. Since the bond's par value, duration, and par interest rate is known, the coupon payment can be extracted. Now, knowing the price the bond sold in the market, the duration, and the coupon payment, the effective market interest rate can be extracted.  This involves solving large polynomials. A less accurate way of determining the interest rate is using a yield shorthand. To extract the market interest rate with good precision and acceptable accuracy, the annual coupon derived can be divided by the market price of the bond."
In addition to other answers consider the following idea. That guy could have invented say one thousand formulas many years ago and been watching how they all perform then select the one that happened to be beat the market.
While it is true that this formula may have historically outperformed the market you have to keep one important thing in mind: once the formula is out in the open, the market inefficiency will disappear. Here is what I mean. Historically there have always been various inefficiencies in the market structure. Some people were able to find these and make good money off them. Invariably these people tend to write books about how they did it. What happens next is that lots of people get in on the game and now you have lots of buyers going after positions that used to be under-priced, raising demand and thus prices for these positions. This is how inter-exchange arbitrage disappeared. Its how high frequency trading is running itself into the ground. If enough demand is generated for an inefficiency, the said inefficiency disappears or the gains get so small that you can only make money off it with large amounts of capital. Keep in mind, as Graham said, there is no silver bullet in the stock market since you do not hold any data that is unavailable to everyone else.
Don't take it so literally. 100 is close to 98, so if your formula calls for 98, buy 100.
Thank you very much for this thoughtful response.  In my opinion the judges care more about the why behind your valuation rather than a how.  Anyone can use a formula, but it takes so much more to understand why to use the formula.    Personally, the 'why' is going to be the toughest part for me understand and wrap my head around.  Once again thank you for the advice and the tip.
Payment The excel formula for Payment is =PMT(Rate, NPER, PV, FV). FV is usually zero. So in cell F3, type the formula =PMT(C3, 60, E3, 0). Obviously if you're really doing a 48 month (4 year) loan then you'll need to change the 60 to 48. You should be able to copy the result from cell F3 to N3 and the formula will update itself. For the 60 months, I'm showing the 12K car/10.8K loan has a pmt of $203.81. The 20K/18K loan has a pmt of 339.68. Interest The easiest way to calculate the interest is as =E3*C3. That's (Outstanding Loan Balance) x (Periodic Interest Rate). Put this in cell G4, since you don't actually owe any interest at Period 0. Principal If you pay PMT each month and X goes to interest, then the amount to principal is ""PMT - X"". So in H4 type =-F3 - G3. The 'minus' in front of F3 is because excel's PMT function returns a negative amount. If you want to, feel free to type ""=-PMT(...)"" for the formula that's actually in F3. It's your call. I get 159 for the amount to principal in period 1. Accumulated Equity As I mentioned in the comment, your ""Equity"" comes from your initial Loan-to-Value and the accumulated principal payments. So the formula in this cell should reflect that. There are a variety of ways to do this... the easiest is just to compare your car's expected value to your loan balance every time. In cell I3, type =(D3-E3). That's your initial equity in the car before making any payments. Copy that cell and paste it to I4. You'll see it updates to =(D4-E3) automatically. (Right now that is zero...
"Dan's link (he deleted his answer, BTW) is fine, it showed the components of the score FICO offers. Each input has data behind it, a bell curve of the behaviors and risk of the person behind it. For example, we've discussed utilization many time here. The ideal utilization is not 0%, but 1-19%. This does not mean paying interest, or carrying charges from month to month. Say I had just one card with a $10K limit. I'd want to be sure I never ran a bill above $2000. If I did, I'd see a slight drop in my score, and next month, it would go back to normal. In my case, I have enough available credit that going over 20% is rare, and if it happened, I'd pay the bill down before the bill was issued, just make a mid-cycle payment. FICO decided that those who go over 20% have a higher risk of default. And it gets higher as it goes up. Same with every aspect of the score's components. You are comparing US to non US use. In the US, it seems far more common to use our cards. In my family's case, we use very little cash, and run most of our spending through our cards. As far as The David is concerned, one should separate those who carry a balance from those who pay in full. The pay in full users are better off for their habits and responsible card use. In the US, it's not easy to rent a car or book a hotel with no card. Cards offer a cash rebate that adds up fast, and purchase protection from fraudulent vendors. They also offer extended warranty coverage. The David, and others, claim that ""studies prove those using cards spend 10-15% more than cash buyers."" This is a proven fact from scientific studies. Only they don't exist. The best I've seen proves that college kids given a $20 bill spend more carefully than those given a $20 credit card. This doesn't extrapolate to a family budget, and never will. But that quote has a way of being repeated as fact.
One might ask, what rate of return would the stock market need to yield to make it worth taking the loan? The APR proposed by the loan can be calculated. The present value of a loan is equal to the sum of the payments discounted to present value. I.e. with ∴ by induction So by comparing the $2495 lump sum payment with $997 over 3 x monthly instalments the interest rate implied by the loan can be found. Solving for r If you could obtain 9.64431% per month on the stock market the $x cash in hand required would be calculated by This is equal to the lump sum payment, so the calculated interest is comparable to the stock market rate of return. If you could gain more than 9.64431% per month on the stock market it would be better to invest and take the loan. Recurrence Form Solving the recurrence form shows the calculation is equivalent to the loan formula, e.g. becomes v[m + 1] = (1 + y) v[m] - p where v[0] = pv where In the final month v[final] = 0, i.e. when m = 3 Compare with the earlier loan formula: s = (d - d (1 + r)^-n) / r They are exactly equivalent, which is quite interesting, (because it wasn't immediately obvious to me that what the lender charges is the mirror opposite of what you gain by investing). The present value can be now be calculated using the formula. Still assuming the 8% stock market return is per month. If the stock market yield is 8% per annum effective rate and if it is given as a nominal annual yield, 8% compounded monthly
I agree, Malcolm has good points and interesting theories, but flawed to certain extents, but he does make disclaimers. For instance he said in an AMA that the 10,000 hour rule doesn't always apply, and thought he made it clear when he wrote it in his book.
Yes, it is bullshit. I get paid by a health care provider to make sure each claim is submitted in a manner most advantageous to the provider.  And I can assure you that at the end of the day the only things changed are several formulas in a program.  Internally it's called Diagnostic Recategorization and it has been blessed by the government as a fair practice.  Believe what you like.
A statistical measurement is still more honest than no measurements at all. For economics we don't have a reference frame for the absolute truth. There is no way to judge the absolute accuracy of a theory. Given that, at least a theory that matches real observational data is more *real* than something that does not match observations.   And quantitative easing has been in many ways one of the largest economic experiments. There were competing theories on what it would do, and one thing was certain - monetary velocity is inversely related to wealth. Even Kansas was an experiment. Something was predicted by Gov. Brown back, and that something turned out to be wrong. In the light of observations, I find it astonishing to hold on to a theory whose predictions have just not panned out.  Monetary velocity may still be wrong, but on a relative scale of wrongness, it is still much less inaccurate than the Laffer curve.