vania-janet/multiturn-rag-retrieval-data huggingface dataset:
But it has failed to catch on with lenders because its score range of 150 to 950 is incompatible with the familiar 300 to 850 range, requiring lenders to recalculate cutoff scores and revise many rules and policies. Only a small percentage of lenders reportedly use NextGen. Transunion Official name: Precision Available directly to consumers: No Equifax Official name: Pinnacle Available directly to consumers: In 2014, Pentagon Federal Credit Union (PenFed) began to provide this score free to its credit card holders, as discussed in this post. Experian Official name: FICO Advanced Risk Score Available directly to consumers: No I included all of this to make the point that there are many variations of the scoring models, and all of them are customized to one degree or another by each of the major bureaus as a means of giving their models more credibility, as far as they're concerned. To your question about coming up with a ""fair"" scoring model, can you propose what makes current scoring models unfair? I think it's a safe assumption to make that the financial community has already had a substantial amount of input into how the current scoring models work. To think otherwise implies that the credit bureaus are just kinda ""winging it"" with whatever they think is best. Their models are designed to give their client creditors the best scoring model possible based on what those creditors have stated is important to them. There isn't a unified single scoring model out there, and the bureaus definitely won't share the details of their modifications. You can always come up with your own custom model, but how it compares to what's widely used, that's anyone's guess. I hope this helps. Good luck!"
"Generally if you are using FIFO (first in, first out) accounting, you will need to match the transactions based on the number of shares. In your example, at the beginning of day 6, you had two lots of shares, 100 @ 50 and 10 @ 52.  On that day you sold 50 shares, and using FIFO, you sold 50 shares of the first lot.  This leaves you with 50 @ 50 and 10 @ 52, and a taxable capital gain on the 50 shares you sold. Note that commissions incurred buying the shares increase your basis, and commissions incurred selling the shares decrease your proceeds.  So if you spent $10 per trade, your basis on the 100 @ 50 lot was $5010, and the proceeds on your 50 @ 60 sale were $2990.  In this example you sold half of the lot, so your basis for the sale was half of $5010 or $2505, so your capital gain is $2990 - 2505 = $485. The sales you describe are also ""wash sales"", in that you sold stock and bought back an equivalent stock within 30 days.  Generally this is only relevant if one of the sales was at a loss but you will need to account for this in your code.  You can look up the definition of wash sale, it starts to get complex. If you are writing code to handle this in any generic situation you will also have to handle stock splits, spin-offs, mergers, etc. which change the number of shares you own and their cost basis.  I have implemented this myself and I have written about 25-30 custom routines, one for each kind of transaction that I've encountered.  The structure of these deals is limited only by the imagination of investment bankers so I think it is impossible to write a single generic algorithm that handles them all, instead I have a framework that I update each quarter as new transactions occur."
My 2 cents:As I understand it, stochastic math doesn't seem to cut it for pricing derivatives. Ito's Lemma and Feynman-Kac SDE solutions both impose constraints on higher order moments of the PDF. So you can't just drop in your forecasted return distribution and get a price. All the fixes seem somewhat ad hoc. Shit gets real when you move from options to their first cousin: term structure of interest rates. The Heath-Jarrow-Morton framework was intractable in the original paper, but there are some promising simplifying assumptions. I think there's real money there for someone who works that out and comes up with new arbitrage possibilities. On the other hand, you get some phenomenally bad math like the Gaussian copula that was used to value the subprime tranches and bring them a mind-boggling AAA rating. A lot of folks here do fancy nonlinear time series analysis to forecast the 1st, 2nd &amp; higher moments of asset returns. Not a lot of theoretical basis (well neuroscience), but if you can do it better you win.
"Much more than that.  Last I looked at this, I saw the DJI have fat tails (i.e. non-Gaussian) to the order of hundreds of days.  The effect becomes less pronounced the larger the interval, and the curves show that it becomes more Gaussian.  If you plot the return distributions for 1-day, 2-days, etc, on a semilog plot you end up with curves that look like leftmost figure of figure 1.3 (edit - sorry, I got the figure wrong):  http://fedc.wiwi.hu-berlin.de/xplore/ebooks/html/csa/node235.html  The Gaussian curve on a semilog plot comes out as a parabola, i.e. the black curve.  Non-Gaussian fat tails ""fan out"" like the green, red and blue plots.  The fanning out occurs even when dealing with returns over a couple of hundred days, but the effect becomes less pronounced, suggesting a convergence to Gaussian."
There is always some fine print, read it. I doubt there is any product out there that can guarantee an 8% return.  As a counter example - a 70 yr old can get 6% in a fixed immediate annuity. On death, the original premium is retained by the insurance company.  Whenever I read the prospectus of a VA, I find the actual math betrays a salesman who misrepresented the product. I'd be really curious to read the details for this one.
"If i do this, I would assume I have an equal probability to make a profit or a loss. The ""random walk""/EMH theory that you are assuming is debatable. Among many arguments against EMH, one of the more relevant ones is that there are actually winning trading strategies (e.g. momentum models in trending markets) which invalidates EMH.  Can I also assume that probabilistically speaking, a trader cannot do worst than random? Say, if I had to guess the roll of a dice, my chance of being correct can't be less than 16.667%. It's only true if the market is truly an independent stochastic process. As mentioned above, there are empirical evidences suggesting that it's not.  is it right to say then that it's equally difficult to purposely make a loss then it is to purposely make a profit?  The ability to profit is more than just being able to make a right call on which direction the market will be going. Even beginners can have a >50% chance of getting on the right side of the trades. It's the position management that kills most of the PnL."
"I'd like to know if there is any reliable research on the subject. Intuitively, this must be true, no? Is it? First, is it even possible to discover the correlation, if one exists? Dave Ramsey is a proponent of ""Proven study that shows you will spend 10% more on a credit card than with cash."" Of course, he suggests that the study came from an otherwise reliable source, Dun & Bradstreet. A fellow blogger at Get Rich Slowly researched and found - Nobody I know has been able to track down this mythical Dun and   Bradstreet study. Even Dun and Bradstreet themselves have been unable   to locate it. GRS reader Nicole (with the assistance of her trusty   librarian Wendi) contacted the company and received this response:   “After doing some research with D&B, it turns out that someone made up   the statement, and also made up the part where D&B actually said   that.” In other words, the most cited study is a Myth.  In fact, there are studies which do conclude that card users spend more. I think that any study (on anything, not just this topic. Cigarette companies buy studies to show they don't cause cancer, Big Oil pays to disprove global warming, etc.) needs to be viewed with a critical eye. The studies I've seen nearly all contain one of 2 major flaws - My own observation - when I reviewed our budget over the course of a year, some of the largest charges include - I list the above, as these are items whose cost is pretty well fixed. We are not in the habit of ""going for a drive,"" gas is bought when we need it. All other items I consider fixed, in that the real choice is to pay with the card or check, unlike the items some claim can be inflated. These add to about 80% of the annual card use. I don't see it possible for card use to impact these items, and therefore the ""10% more"" warning is overreaching.  To conclude, I'll concede that even the pay-in-full group might not adhere to the food budget, and grab the $5 brownie near the checkout, or over tip on a restaurant meal. But those situations are not sufficient to assume that a responsible card user comes out behind over the year for having done so.  A selection of the Studies I am referencing -"
I wrote one to check against the N3 to N6 bonds: http://capitalmind.in/2011/03/sbi-bond-yield-calculator/ Things to note:
"Regarding SPY: ""One SPDR unit is valued at approximately 1/10 of the value of the S&P 500. Dividends are distributed quarterly, and are based on the accumulated stock dividends held in trust, less any expenses of the trust.""  (source)  These are depository receipts, not the actual stocks. Regarding IVV: ""The component stocks are weighted according to the total float-adjusted market value of their outstanding shares. The Fund invests in sectors, such as energy, information technology, industrials, financials, consumer staples, healthcare, telecom services, consumer discretionary and materials.""  (more here) VOO is the Vanguard S&P 500 ETF. The tracking error seems pretty small to me.  I went to Google Finance and plotted the percent change for all four on one chart.  They lie pretty much on top of one another.  The actual dollar value of each one doesn't matter nearly as much as the fact that they move up and down almost in lock-step.  There may be a larger difference going farther out, but for three separate financial products, the agreement is still remarkably good."
Finance is complex because it's complex. There's no great conspiracy here - the authors of article like this need to recognise that they cant understand everything - physicists don't invent complexity in their theories to keep the public stupid, and neither to finance experts.
"John Bensin's answer covers the math, but I like the plain-English examples of the theory from William Bernstein's fine book, The Intelligent Asset Allocator. At the author's web site, you can find the complete chapter 1 and chapter 2, though not chapter 3, which is the one with the ""multiple coin toss"" portfolio example I want to highlight. I'll summarize Bernstein's multiple coin toss example here with some excerpts from the book. (Another top user, @JoeTaxpayer, has also written about the coin flip on his blog, also mentioning Bernstein's book.) Bernstein begins Chapter 1 by describing an offer from a fictitious ""Uncle Fred"": Imagine that you work for your rich but eccentric Uncle Fred. [...] he decides to let you in on the company pension plan. [...] you must pick ahead of time one of two investment choices for the duration of your employment: Certificates of deposit with a 3% annualized rate of return, or, A most peculiar option: At the end of each year Uncle Fred flips a coin. Heads you receive a 30% investment return for that year, tails a minus 10% (loss) for the year. This will be hereafter referred to as ""Uncle Fred’s coin toss,"" or simply, the ""coin toss."" In effect, choosing option 2 results in a higher expected return than option 1, but it is certainly riskier, having a high standard deviation and being especially prone to a series of bad tosses. Chapters 1 and 2 continue to expand on the idea of risk, and take a look at various assets/markets over time. Chapter 3 then begins by introducing the multiple coin toss example: Time passes. You have spent several more years in the employ of your Uncle Fred, and have truly grown to dread the annual coin-toss sessions. [...] He makes you another offer. At the end of each year, he will divide your pension account into two equal parts and conduct a separate coin toss for each half [...] there are four possible outcomes [...]: [...]
As you point out, the moving average is just MA(k)t = (Pt-1 + … + Pt-k )/k and is applied in technical analysis (TA) to smooth out volatile (noise) price action. If it has any logic to it, you might want to think in terms of return series (Pt - Pt-1 / Pt-1) and you could hypothesize that prices are in fact predictable and will oscillate below and above a running moving average.  Below is a link to a study on MA trading rules, published in the Journal of Finance, with the conclusion of predictive power and abnormal returns from such strategies. As with any decision made upon historical arguments, one should be aware of structural changes and or data mining.  Simple  technical trading rules and the stochastic properties of stock  returns Brock, W., J. Lakonishok and B. Le Baron, 1992, Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, 1731-64. MA rules betterthan chance in US stock market, 1897-1986 I don't know whether you are new to TA or not, but a great commercial site, with plenty of computer-generated signals is FinViz.
It's explained in the article that you need a critical mass to reach profitability. Not sure what you're arguing here.   Edit: I'm sorry. I got this confused with [another article](https://www.google.ca/url?sa=t&amp;source=web&amp;rct=j&amp;url=/amp/s/techcrunch.com/2017/05/31/uberpool-sf-buzzfeed-documents-burn-rate/amp/&amp;ved=0ahUKEwjI8IqTuKDUAhUByoMKHT7zCHsQFggwMAQ&amp;usg=AFQjCNFMBLxrbnKD1QDka1w0Jm99Xs6OFg&amp;sig2=-efzgIxdqrDmb0j7oAiPyw) I had just read on Uber. Sorry for the attitude.
Rating agencies should be paid on accuracy, i.e. each bond issued pays x $ into a pool, and the bond companies whom are accurate over the maturity get a share of the pool.  Those whom are not, must add money to the pool. (Like an accuracy market!)
Since you are using the percentage method to determine the home/business use split, I would think that under most circumstances the distance driven to get your car from the dealership to home, and from home to mechanic and back would be less than 1% of the total miles driven. This is an acceptable rounding error. When refueling, I typically do that on my way to another destination and therefore it's not something I count separately.  If your miles driven to attend to repair/refueling tasks are more than 1% of the total miles driven, split them as you feel comfortable in your above examples. I'd calculate the B/P percentages as total miles less maintenance miles, then apply that split to maintenance miles as well.
Expecting accurate accounting in a war-zone is unrealistic. Money is used to buy loyality that would be lost if it got out that they had accepted infidel invader's money. Secrets are a characteristic of all wars.  Expecting accurate accounting in a wallstreet investment firm is completely reasonable.
To be flippant: it is illegal because it is against the law; there is no considered involved, it just is. To elaborate, part of the illusion of the stock exchange and other market-like entities is that of (apparent) fairness. If I think a stock will go up because it is involved in a growing industry, that is generally public information.  Conversely if I have a dim view of a particular company because of its track record of product launches, that is similarly out in the open. A secret formula is something that I invented or discovered, not (presumably) something that I stole from someone else. To stretch that further: If I notice that Company X stock always moves with Company Y stock, that is indeed something that I have found, that I can try to profit from.  It is secret to me, but not particularly dependent upon information not available to others, just that my interpretation is better. So trading on information in the public domain is fine, as it preserves the principal of fairness I mentioned, whereas inside knowledge breaks that principal.
Regardless of your thoughts on what was happening in late 2012, financial prices should not be used as the sole barometer of a recovery...this is why the Fed uses tons of macroeconomic data, not just that BAC has been up 20% in the last 6 months.   My point is if you are going to tout what seems to be a market timing model, where the authors are proud of the fact that the entire thing is data mined,  you need strong out of sample evidence (over a few business cycles) that it works.  Nothing here comes close
There are those who would suggest that due to the Efficient Market Hypothesis, stocks are always fairly valued. Consider, if non-professional posters on SE (here) had a method that worked beyond random chance, everyone seeking such a method would soon know it. If everyone used that method, it would lose its advantage. In theory, this is how stocks' values remain rational.  That said, Williams %R is one such indicator. It can be seen in action on Yahoo finance -  In the end, I find such indicators far less useful than the news itself. BP oil spill - Did anyone believe that such a huge oil company wouldn't recover from that disaster? It recovered by nearly doubling from its bottom after that news. A chart of NFLX (Netflix) offers a similar news disaster, and recovery.  Both of these examples are not quantifiable, in my opinion, just gut reactions. A quick look at the company and answer to one question - Do I feel this company will recover? To be candid - in the 08/09 crash, I felt that way about Ford and GM. Ford returned 10X from the bottom, GM went through bankruptcy. That observation suggests another question, i.e. where is the line drawn between 'investing' and 'gambling'? My answer is that buying one stock hoping for its recovery is gambling. Being able to do this for 5-10 stocks, or one every few months, is investing.
"To calculate the balance (not just principal) remaining, type into your favorite spreadsheet program: It is important that the periods for ""Periods"" and ""Rate"" match up.  If you use your annual rate with quarterly periods, you will get a horribly wrong answer. So, if you invest $1000 today, expect 6% interest per year (0.5% interest per month), withdraw $10 at the end of each month, and want to know what your investment balance will be 2 years (24 months) from now, you would type: And you would get a result of $872.84. Or, to compute it manually, use the formula found here by poster uart:   This is often taught in high-school here as a application of geomentric series. The derivation goes like this.  Using the notation :  r = 1 + interest_rate_per_term_as_decimal p = present value a = payment per term eot1 denotes the FV at end of term 1 etc.  eot1: rp + a   eot2: r(rp + a) + a = r^2p + ra + a   eot3: r(r^2p + ra + a) + a = r^3p + r^2a + ra + a ...   eotn: r^np + (r^(n-1) + r^(n-2) + ... 1)a = p r^n + a (r^n - 1)/(r-1)  That is, FV = p r^n + a (r^n - 1)/(r-1).   This is precisely what exel [sic] computes for the case of payments made at the end of each term (payment type = 0). It's easy enough to repeat the calculations as above for the case of payments made at the beginning of each term. This won't work for changing interest rates or changing withdrawal amounts.  For something like that, it would be better for you (if you don't want online calculators) to set up a table in a spreadsheet so you can adjust different periods manually."
no that's not what i'm saying.  I've been making the reports, running the stress tests and Movar for the past 2 risk managers.  I have a BS in Economics and Business Administration so i'm not really a hard core finance guy.  like our first RM was one of the people in the 70s that basically created futures for oil.  our last guy was a french dude from EDF.  I just feel that i lack their experience and i really don't wanna fuck up so im asking people with more experience perhaps to point the way for me.  books, programs, classes etc.   Also i think you mistaken my comment about having no idea was on the Crystal Ball sofware.  Im mainly a VBA and SQL guy, but i guess to run monte carlo sims i need to learn crystal ball
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. Citibank provides the Equifax FICO 8 Bankcard score free each month to credit cards holders. Each credit agency (Transunion, Equifax, and Experian) uses a customized version of each FICO edition. As a result, a consumer's FICO scores from each agency may differ even when all credit information is identical among the agencies. Because there are many FICO versions, when a score is received, it's helpful to know which version it is. If a lender provides a credit score, ask for details such as which credit agency was used, which FICO edition was used, and whether the score is an Industry Option version. The lender may not always be willing or able to provide the answers, but it doesn't hurt to ask. 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.
P of borrowing power = F / (1+R2)^x = A1 { (1+R2-R1)/(1+R2) }^x Now, let's put some specific values in. Say that you can borrow $300k with your good credit history and this applies for the next 25 years, after which you retire. The borrowing rate is 7% and the time-value of money to you is 10%. I would then calculate: P of borrowing power = $58 k < $100 k This indicates that it would be more economical to default. Of course, some people might point out that it will be removed from your record after 7 years. If you plug 7 years instead of 25 years into the equation, almost no assumptions about rates will lead to the option of keeping your house being preferable. So in a nutshell, the value of your credit is probably less than $100k in a purely mathematical sense. But there are other factors too. If you don't have that borrowing ability maybe you wouldn't be able to borrow money to start the business of your dreams. If you are a rock star entrepreneur, then time-value of money to you could be 1,000% yield, sure, then maybe you could make the above numbers work (to favor keeping the house). I've also neglected ethics. As other people point out, it would be like stealing from the bank.