Rate of change of beta
"This is (almost) a question in financial engineering. First I will note that a discussion of ""the greeks"" is well presented at https://en.wikipedia.org/wiki/Greeks_(finance) These measures are first, second and higher order derivatives (or rate of change comparisons) for information that is generally instantaneous. (Bear with me.) For example the most popular, Delta, compares prices of an option or other derived asset to the underlying asset price. The reason we are able to do all this cool analysis is because the the value of the underlying and derived assets have a direct, instantaneous relationship on each other. Because beta is calculated over a large period of time, and because each time slice covered contributes equally to the aggregate, then the ""difference in Beta"" would really just be showing two pieces of information: Summarizing those two pieces of information into ""delta beta"" would not be useful to me. For further discussion, please see http://www.gummy-stuff.org/beta.htm specifically look at the huge difference in calculation of GE's beta using end-of-month returns versus calculation using day-before-end-of-month returns." 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 If you do not need it for a day or a week or something like that, an easy thing to do to get the beta of a security is to use wolframalpha.  Here is a sample query: BETA for AAPL Calculating beta is an important metric, but it is not a be all end all, as there are ways to hedge the beta of your portfolio. So relying on beta is only useful if it is done in conjunction with something else. A high beta security just means that overall the security acts as the market does with some multiplier effect. For a secure portfolio you want beta as close to zero as possible for capital preservation while trying to find ways to exploit alpha.
"&gt; Let's just say the insurance industry knows a lot more about underwriting than you do.  I'm sorry, but that is a meaningless statement. I work in insurance (first as a consultant, before 'retiring' to work in insurance distribution a few years ago), and I know that our industry frequently uses flawed or outdated methodologies due to the simple fact that insurance companies are very conservative and *very* resistant to change when it comes to changes to their core business. Unless you can show a direct, negative impact on the bottom line caused by the currently used method, you are unlikely to make any changes at all. In this case, if the entire US car insurance industry is using the same flawed system, it won't affect a single company if they also stick to it.   Until 1996, before the current system was introduced, insurance companies in Germany used to rate liability insurance for cars (almost) exclusively by engine power output. The industry had known that this method was fundamentally flawed since at least the late 1980s (comprehensive and partial coverage had been rated by the 'new' system for a few years at this point, which had also taken years to work out), but it took additional years of planning, negotiations and cooperation by the entire industry to change to the new system for liability.   So please, do not ever assume that ""the insurance companies are the experts, they know what they are doing!"". It might very well be the case that they are stuck with flawed/outdated systems simply because there is no sufficiently strong impulse to change what they do.   The current Tesla rate adjustment situation is a wonderful example of this - it apparantly took AAA *5 years* since the Model S first came out to realise that their initial estimate was wrong (it seems unlikely that the accident rate or repair costs have suddenly changed over the last year) and take appropriate actions. By late 2013, there were easily enough Teslas on the road (about 20,000) to get realiable data, yet nothing happened for nearly another 4 years." "Certainly no one knows in advance how much a stock is going to swing around. However, there are measures of how much it has swung around in the past, and there are people who will estimate the probability. First of all, there's a measure of an individual stock's volatility, commonly referred to as ""beta"". A stock with a beta of 1 tends to rise and fall about as much as the market at large. A stock with a beta of 2, in the meantime, would rise 10% when the market is up 5%. These are, of course, historical averages. See Wikipedia: http://en.wikipedia.org/wiki/Beta_(finance) Secondly, you can get an implied measure of volatility expectations by looking at options pricing. If a stock is particularly volatile, the chance of a big price move will be baked into the price of the stock options. (Note also that other things affect options pricing, such as the time value of money.) For an options-based measure of the volatility of the whole market, see the Volatility Index aka the ""Fear Gauge"", VIX.  Wikipedia: http://en.wikipedia.org/wiki/VIX Chart: http://finance.yahoo.com/q?s=%5EVIX Looking at individual stocks as a group (and there's an oxymoron for you), individual stocks are definitely much more likely to have big moves than the market. Besides Netflix, consider the BP oil spill, or the Tokyo Electric Power Company's Fukushima incident (yow!). I don't have any detailed statistics on quantitatively how much, mind you, but in application, a standard piece of advice says not to put more than 5% of your portfolio in a single company's stock. Diversification protects you. (Alternatively, if you're trying to play Mr. Sophisticated Stock-Picker instead of just buying an index fund, you can also buy insurance through stock options: hedging your bets. Naturally, this will eat up part of your returns if your pick was a good one)." This is going to be a bit of a shameless plug, but I've build a portfolio tracking website to track your portfolio and be able to share it (in read-only mode) as  well. It is at http://frano.carelessmusings.com and currently in beta. Most portfolio trackers are behind a login wall and thus will lack the sharing function you are looking for. Examples of these are: Yahoo Finance, Google Finance, Reuters Portfolios, MorningStart Portfolios, and many others. Another very quick and easy solution (if you are not trading too often) is a shared google docs spreadsheet. Gdocs has integration with google finance and can retrieve prices for stocks by symbol. A spreadsheet can contain the following: Symbol, Quantity, Avg. Buy Price, Price, P/L, P/L% and so on. The current price and P/L data can be functions that use the google finance API. Hope this helps, and if you check out my site please let me know what you think and what I could change. Amazon Go! was the beta and it was really cool (I work for Amazon and got to Beta test). Whole Foods is about to become the most technologically advanced supermarket and will be trimming serious staff overhead when it comes to checkouts. I can't wait to see what comes of this. "A kid can lose everything he owns in a crap shoot and live. But a senior citizen might not afford medical treatment if interest rates turn and their bonds underperform. In modern portfolio theory, risk/""aggression"" is measured by beta and you get more return by increasing risk. Risk-adjusted return is measured by the Sharpe ratio and the efficient frontier shows how much return you get for each level of risk. For simplicity, we will assume that choosing beta is the only investment choice you make. You are buying a house tomorrow all cash, you should set aside that much in liquid assets today. (Return = who cares, Beta = 0) Your kids go to college in 5 years, so you invest funds now with a 5 year investment horizon to produce, with a reasonable level of certainty, the needed cash then. (Beta = low) You wish to leave money in your estate. Invest for the highest return with a horizon of your lifetime. (Return = maximum, Beta = who cares) In other words, you set risk based on how important your expenses are now or later. And your portfolio is a weighted average. On paper, let's say you have sold yourself into indentured servitude. In return you have received a paid-up-front annuity which pays dividends and increases annually. For someone in their twenties: This adds up to a present value of $1 million. When young, the value of lifetime remaining wages is high. It is also low risk, you will probably find a job eventually in any market condition. If your portfolio is significantly smaller than $1 million this means that the low risk of future wages pulls down your beta, and therefore: Youth invest aggressively with available funds because they compensate large, low-risk future earnings to meet their desired risk appetite." Question is, what is this number 0.01140924 13.69/12=0.01140924 In addition, how does one come out with the EIR as 13.69% pa? When calculating payments, PV = 9800, N=36 (months), PMT=333.47, results in a rate of 1.140924% per period, and rate of 13.69%/yr. No idea how they claim 7.5% In Excel, type =RATE(36,333.47,-9800,0,0) And you will get 1.141% as the result.  36 = #payments, 333.47 = payment per period, -9800 is the principal (negative, remember this) And the zeros are to say the payments are month end, second zero is the guess.  Edit - I saw the loan is from a Singapore bank. It appears they have different rules on the rates they quote. As quid's answer showed the math, here's the bank's offer page -  The EIR is the rate that we, not just US, but most board members, are used to. I thought I'd offer an example using a 30 year mortgage.   Yo can see above, a 6% fixed rate somehow morphs into a 3.86% AR. No offense to the Singapore bankers, but I see little value in this number. What surprises me most, is that I've not seen this before.  What's baffling is when I change a 15yr term the AP drops to less than half. It's still a 6% loan and there's nothing about it that's 2 percent-ish, in my opinion.  Now we know. Understanding the BS equation is not needed. What is needed is an understanding of the bell curve.   You seem to understand volatility. 68% of the time an event will fall inside one standard deviation. 16% of the time it will be higher, 16%, lower.  Now, if my $100 stock has a STD of $10, there's a 16% chance it will trade above $110. But if the STD is $5, the chance is 2.3% per the chart below. The higher volatility makes the option more valuable as there's a highr chance of it being 'in the money.' My answer is an over simplification, per your request. So basically they are trying to see two things.  One is whether prices are correlated to each other for long periods of time as a preliminary study suggested (which would go against efficient markets hypothesis, since you could use that info to game the market) or if that result is illusory and the long term returns are close to a standard normal distribution which would follow the effiecient markets hypo.  The second thing I don't follow as well, but they're trying to solve the first thing so that they can then look at why, when they look at returns at different time scales, (1minute, one hour, one week), the model which had been proposed for these returns is not supported by the data  (the first thing). They say that the old model (Levy) says that the variability should not be the same at the different time scales, but the data suggests that it is.  So they then propose a modification of the old Levy model, and say that it would also explain the strange first result they looked at. (that prices are correlated for longer periods).  That probably doesn't make any sense, but you might have more luck by posting in /r/statistics.