vania-janet/multiturn-rag-retrieval-data huggingface dataset:
If an accounting firm had constant errors and was wrong over 50% of the time about the books how would that go about?  As a former bond trader, I would rarely look at the ratings.... CDS was most useful when applicable, and if not spread to the closest benchmark/sectoral average.
Doesn't mean this user isn't right. My experiences in all stores lead me to believe that everyone in the business is freaking out. They are all trying to use data science to generate reliable sales. Costco is determined to fuck you over with their science. They have algo that says if you buy LMN there is a 90% probability you will by P and therefore we can rip you off on P. Don't forget most retailers know who you are know and they know when you visit and what you bought and for how much. I predict within 5 years the only thing sold on stores will be stuff with a very inelastic demand curve that you need right now. And can't wait a day for it to come in the mail.
The only way I've seen mezz work for more than one cycle is the hard money model.   Stay away from glory assets.  Maturities of a year or two max.  Have the stomach and wherewithal to foreclose quickly when the borrower defaults.  Keep the (C)LTV under 70, where the V is not aspirational.  Unitranche is generally better because it simplifies and, therefore, expedites things.    All that requires sitting on dry powder while your competitors are busy and then it will cost you relationships in your other funds/divisions when you ramp up. This is why most lenders succumb: they tie their managers' careers to the short term, which, to be fair, is much easier to measure.  This also applies the broader mezz market beyond RE.
"The formula you are looking for is pretty complicated. It's given here: http://itl.nist.gov/div898/handbook/eda/section3/eda3661.htm You might prefer to let somebody else do the grunt work for you. This page will calculate the probability for you: http://stattrek.com/online-calculator/normal.aspx. In your case, you'd enter mean=.114, standard deviation=.132, and ""standard score""= ... oh, you didn't say what you're paying on your debt. Let's say it's 6%, i.e. .06. Note that this page will give you the probability that the actual number will be less than or equal to the ""standard score"". Enter all that and click the magic button and the probability that the investment will produce less than 6% is ... .34124, or 34%. The handy rule of thumb is that the probability is about 68% that the actual number will be within 1 standard deviation of the mean, 95% that it will be within 2 standard deviations, and 99.7% that it will be within 3. Which isn't exactly what you want because you don't want ""within"" but ""less than"". But you could get that by just adding half the difference from 100% for each of the above, i.e. instead of 68-95-99.7 it would be 84-98-99.9. Oh, I missed that in a follow-up comment you say you are paying 4% on a mortgage which you are adjusting to 3% because of tax implications. Probability based on mean and SD you gave of getting less than 3% is 26%. I didn't read the article you cite. I assume the standard deviation given is for the rate of return for one year. If you stretch that over many years, the SD goes down, as many factors tend to even out.
Here is an article that claims to know something about it. Here are a selection of quotes: The IRS says there are several ways a return can be selected for audit and the first is via the agency's computer-scoring system known as Discriminant Information Function, or DIF. The IRS evaluates tax returns based on IRS formulas, and DIF is based on deductions, credits and exemptions with norms for taxpayers in each of the income brackets. The actual scoring formula to determine which tax returns are most likely to be in error is a closely guarded secret. But Nath, a tax attorney in the Washington, D.C., area, says it's no mystery the system is designed to screen for returns that could put more money in the government Treasury. So what is likely to trigger a discriminant information function red flag?
" I have no issue with faith-based religion, it actually stands to reason, but mortgages are numbers and there's order to them. If you want my $3500, you should know how your system works. Not one does, or they would know it was a scam. Nassim Taleb, author of ""The Black Swan"" offered up a wonderful quote, ""if you see fraud, and do not say 'fraud,' you are a fraud."" The site you link to isn't selling a product, but a fraudulent idea. What's most disturbing to me is that the math to disprove his assertion is not complex, not beyond grade school arithmetic. Update 2015 - The linked ""rule of thumb"" is still there. Still wrong of course. Another scam selling software to do this is now promoted by a spin off of UFirst, called Worth Unlimited. Same scam, new name."
There is no thermodynamic law of the conservation of monetary value-- just as you and I created it by creating transferable debt, it can also be genuinely destroyed. (We'll get back to this in a minute, it gets interesting). - The second issue is that, in all probability, the whole town is not *just* trading apple-certificates. I could also issue promises to catch deer, the fisherman could issue promises of fish, and so on. This could get pretty messy, especially if you got the notion to issue more apple-certificates than you can grow: you could buy all kinds of stuff with self-issued debt that you could never repay, and the town wouldn't find out until harvest-time comes. Once again, value has been ""destroyed"" people worked and made stuff and gave you stuff in exchange for something that doesn't exist, and will never exist. All that stuff they made is gone, you consumed it, and there is nothing to show for it. The above two concerns are likely to become manifest in our village sooner or later, and probably sooner. This leads to the question of *credit*, which is, at its most basic, a measure of *credibility*. Every time you issue an apple-certificate, you are *borrowing*, with a promise to repay from future apple-harvests. After the first couple of town scandals, people will start taking a closer look at the credibility of the issuer. Let's say the town potato-farmer comes up with a scheme where his potato-certificates are actually issued by some credible third-party, say the town priest or whatever, who starts every growing season with a book of numbered certificates equal to the typical crop-yield and no more, and keeps half of the certificate on file, issuing the other half. Now there is an audit trail and a very credible system that is likely to earn the potato-grower a lot of credit, compared to other farmers in town. That means that the potato-grower can probably issue more notes at a better exchange rate than some murkier system. Similarly, the town drunk probably won't get much value for his certificates promising a ship of gold.
The formula for discounting a lump sum is: Present Value=Future Value* (1/(1+interest rate))^((# of periods)) The formula for discounting a stream of equal payments is: Present Value=(Single Payment)* (〖1-(1+i)〗^((-n))/i) (i = interest rate and n = number of periods) **cite investopedia So let’s look at how this would look in pricing the pretend Dell bond as a par bond. First, we discount the return of the $10,000 principal as (10,000 * (1 / 1.08)^5). That equals $6,807.82. Next we discount the 5 equal payments of $800 as (800* (3.9902)). I just plugged and chugged but you can do that yourself. That equals $3,192.18. You may get slightly different numbers with rounding. So you add the two together, and it says that you would be willing to pay ($6,807.82 + $3,192.18) = $10,000. Surprise! When the bond is a par bond you’re basically being compensated for the time value of money with the interest payments. You purchase the bond at the ‘face value’, which is the principal that will be returned at the end. If you worked through the math for a 6% discount rate on an 8% coupon bond, you would see that it’s “premium”, because you would pay more than the principal that is returned to obtain the bond [10,842.87 vs 10,000]. Similarly, if you work through the math for a 10% discount rate on an 8% coupon bond, it’s a ‘discount’ bond because you will pay less than the principal that is returned for the bond [9,241.84 vs 10,000]. It’s easy to see how an investor could hold our imaginary Dell bond for one year, collect the first interest payment, and then sell the bond on to another investor.
Transunion Official name: FICO Risk Score Classic 98 Common name: TU-98 Available directly to consumers: No Real-world score range: 336 to 843 (as shown on page 16 of this Transunion document) Equifax Official name: Equifax FICO Score 4 (also known as Equifax Beacon 96) Common name: EQ-98 This version appears to be seldom used, but a poster reported it used on a mortgage application in 2014. Available directly to consumers: No Experian Official name: Experian FICO Score 2 (also known as Experian FICO Risk Model v2) Common name: EX-98 Available directly to consumers: from myFICO when buying a product that includes all 19 available scores (as described here). Some credit unions such as PSECU provide it free each month to members. Real-world score range: 320 to 844 (as shown on this Experian document) Most mortgage lenders use FICO 04 for Equifax and Transunion, and FICO 98 for Experian. All three scores will normally be pulled and the middle score (not the average) will be used by the lender. Transunion Official name: Transunion FICO Score 4 (also known as Transunion FICO Risk Score Classic 04) Common name: TU-04 Available directly to consumers: from myFICO as described here. Real-world score range: 309 to 839 (as shown on page 16 of this Transunion document) Equifax Official name: Equifax FICO Score 5 (also known as Equifax Beacon 5.0) Common name: EQ-04 Available directly to consumers: from myFICO as described here. Also available from Equifax when buying FICO score (as a one-time purchase with the ""Score Power"" product available here, or as part of credit monitoring available here). Some credit unions such as DCU provide it free each month to members.
The numbers are presented by his underling. If they were baseless or intentionally false (like claims by several of his past secretaries) then you hold the executive accountable.  That's how management works.  I'm not asking him to be an expert, I'm asking for him to have a reliable staff.
"The simple answer for the so-called 'Rule of X' would be found by: In your case: Update:  If you want an approximation closer to the nominal ""Rule of 72"" value, use this equation that incorporates a better approximation for the natural log. The ""Rule of 72"" fits this for an interest rate of 7.79% for a growth multiple of 2: The rule of 72 comes from by approximating the natural logarithms as such: The 2 is the multiple of growth. The rate r here is not in percent, so to change to percent (say, R) you have to multiply by 100: The number 72 is often used because it is easier to divide evenly than 69.3 and is a better fit approximation for the natural log and common interest rates.  If you need more, you can find all this on Wikipedia: https://en.wikipedia.org/wiki/Rule_of_72#Derivation"
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
"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.
Well, one can easily have rates below -100%.  Suppose I start with $100, and end up with $9 after a year. What was my rate of return? It could be -91%, -181%, -218%, or -241%, or something else, depending on the compounding method.  We always have that the final amount equals the initial amount times a growth factor G, and we can express this using a rate r and a day count fraction T. In this case, we have T = 1, and B(T) = B(0) * 0.09, so: So, depending on how we compound, we have a rate of return of -91%, -181%, -218%, or -241%. This nicely illustrates that:
"Again, there is nothing in the world that is ""totally safe"". Drink enough water and you will die. So let's not set the bar so high that it cannot be met by any standard.  Yes, I am aware of what confirmation bias is. I find that most research-denial and data-denial comes from the anti-Monsanto crowd who continually disregard decades of mounting evidence that there is no link between glyphosate and cancer.  ""After extensive reviews, most regulatory agencies—the US Environmental Protection Agency, the European Food Safety Authority, and those of many other nations—have come to the conclusion that it does not cause cancer.""  EDIT: more  ""One of the largest and most highly regarded studies to examine effects of pesticide use in real life is the Agricultural Health Study, a prospective investigation of about 89,000 agricultural workers, farmers and their families in Iowa and North Carolina. Since the early 1990s, it has gathered and analysed detailed information on the health of participants and their families, and their use of pesticides, including glyphosate.  AHS researchers have published numerous studies from their data. One paper looking at glyphosate and possible links with cancers was published in 2005. It concluded that “glyphosate exposure was not associated with cancer incidence overall.” Since then, more data has been collected, adding statistical power to subsequent AHS analyses.""  http://www.reuters.com/investigates/special-report/glyphosate-cancer-data/"
"Currently, when ""implied volatility"" is spoken, the Black-Scholes-Merton model is implied.  This model has been shown to be deficient, thus the Variance Gamma Model should be used. However, as nearly no one uses VG, it can be assumed that BS is still being implied. The BS formula has multiple variables.  Some are external to the underlying in question.  The rest are internal. When all but one variable is known or assumed, the last variable can be calculated, so if one has the price of the underlying and all else except the volatility, the volatility can be calculated thus implied. If one selects an implied volatility, and all variables except the underlying price is known, the underlying price can be calculated. For the present, one uses the current price of the underlying to calculate the implied volatility.  For future option prices, one assumes an implied volatility at a later date to calculate a possible price. For prices not at the money, the BS model is extremely imprecise.  The VG model can better determine a potential future price."
No one takes Roubini seriously because he's always predicting failure. If I constantly predicted failure, it's kind of dump to trumpet it as a success when failure occurs once every 5-7 years.  Oh look, we're just about 5 years past the last failure, time to bring out the old corpse again.
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?"". That formula is PV = FV / (1+R)N where all the terms mean the same thing, except that R in this form is typically called the ""discount rate"", because its purpose here is to lower (discount) a future amount of money to show what it's worth to you now. Now, the discount rate (or yield rate) used in these calculations isn't always the actual yield rate that the investment promises or has been shown to have over time. Investors will calculate the discount rate for a stock or other investment based on the risks they see in the company's financial numbers or in the market as a whole. The models used by professional investors to quantify risk are rather complex (the people who come up with them for the big investment banks are called ""quants"", and the typical quant graduates with an advanced math degree and is hired out of college with a six-figure salary), but it's typically enough for the average investor to understand that there is an inherent risk in any investment, and the longer the time period, the higher the chance that something bad will happen that reduces the return on your investment. This is why the 30-year Treasury note carries a higher interest rate than the 10-year T-note, which carries higher interest than the 6-month, 1-year and 5-year T-bills. In most cases, you as an individual investor (or even an institutional investor like a hedge fund manager for an investment bank) cannot control the rate of return on an investment.
I feel like there is a lot of media and confirmation bias involved. I'm not necessarily disagreeing with you but it's easy to point out examples where moves to cut costs have ended badly. It's unlikely to make the news if cost cutting moves are successful. For example let's say with the Schlitz example, the new process they used was in fact successful and didn't result in a loss of quality. That wouldn't be as big in the news as screwing up would be.
Or the far less nefarious response that nobody could have accurate predicted the changes in the cost basis and the financing that has come about for panels 10 years ago.  Also, as mentioned, production estimates weren't off by nearly as much.  This is like laughing about someone getting an internet expansion estimation wrong in 96, it was clear it was going to go way up, but the errors in just a few percentage points of growth for explosive growth lead to massive differences in outcome.
I forgot to respond to this comment. WorldQuant is nothing like MIT, Harvard, or the CFA institute.   You only need to take a look at their courses to see that. Where Compare their syllabus with the top 20 MFE programs:  https://www.quantnet.com/mfe-programs-rankings/  Where are their math courses? (Look for page 38)  https://wqu.org/wp-content/uploads/2017/01/WorldQuant-University-Catalog-Volume-2-1_10_17.pdf?v=76cb0a18730b%E2%80%9D  Compare with:  [University of Chicago's Financial Mathematics](https://finmath.uchicago.edu/page/five-quarter-track)  [NYU's Mathematics in Finance](https://math.nyu.edu/financial_mathematics/academics/courses/)  [Columbia Universities Financial Engineering](http://ieor.columbia.edu/concentration/259)  It's nowhere as rigorous as these programs. Key components of education in risk, derivatives modeling, stochastic calculus and more mathematics is completely missing. Not to mention there isn't even C++ as a requirement which is a must know for financial engineers.
"Yes if and only if you properly use P/Y and C/Y.   For your familiarity, I recommend also trying it with I = 7.56% / 12, and P/Y and C/Y = 1.  I like to think of P/Y and C/Y as ""per period"" and prefer to divide the interest rate by the number of periods per year, but so long as you keep everything coherent in the solver, you will get the correct answer with both approaches."
Yes E[x] is expected value of x. E[x|y] = expected value of x, given y. c, k are some constants Let E[s_{n+1}|s_n=c] = c, but if  E[s_{n+1}|s_n,s_{n-1},...,s_{n-m}] ->some constant k as m->\infty (call this equation 1) then rebalancing makes sense. Notes:
So the actual price you'd pay is expressed thus: {resale value of GOOG before settlement payoff = X} + ( {expectation that settlement payoff will pay 100% of difference = S5} * {expected nominal difference between GOOG and GOOGL = D} ) + ({S4} * {80% D}) + ({S3} * {60% D}) + ({S2} * {40% D}) + ({S1} * {20% D}) + ({S0} * {0% D}) = {price willing to pay for Class C GOOG = P} Plus you'd technically have to present value the whole thing for the time horizon, since the payoff is in a year. Note that I've shunted any voting/dividend analysis into X. It's reasonable to thing that S5, S4, S3, and maybe S2 are nearly zero, given the open arbitrage opportunity. And we know that S0 times 0% of D is zero. So the real analysis, again ignoring PV, is thus: P = X + (S1*D) Which is a long way of saying: what are the odds that GOOG will happen to be worth no more than 99% of GOOGL on the payoff determination date?
&gt; ES_F June today the most recent 5 min bar has 809 volume. while the september has 10616 volume on the same bar.  on my rolled over contract, the ratio based adjustment has happened last week (9 bus days prior to expiration)  &gt; 1. Commision p/l should be subtracted as actual actual.  absolutely BUT, initially, it is interesting to see if there's a good positive bias in the strategy. then we check whether comm/slippage eats it all. if so we try to play it in an index/etf whatever that charges less c&amp;s/.  agree ?
"That's true.  But you post a picture of a kitten to Google, and 99.99% of the time Google will identify it as a kitten.  You'll never reach 100%.  That's why it's an ignorant argument.  No model is 100% accurate.  No one argues that.  But when people say ""correlation doesn't imply causation"" they are really just saying ""no model is 100% accurate"".  And any idiot could say that."
All other agencies is complete hyperbole. The WHO didn't take into account one unpublished, not yet peer reviewed study and that means Round-up is totally safe?  I mean, the first paragraph admits there is conflicting information and links to another story they did that casts doubt on the claim that Round-up doesn't cause cancer.  Are you aware of what confirmation bias is?