For now, See. Me generates revenue by taking a 5 percent transaction fee on tips and donations made from user to user, but eventually the company will get into postcard creation and distribution. After that, See. Wearables, FTW! A small number of shares will be reserved for non-investor types who are active on the See.
Me platform, and will be offered in the form of a grant. Me is using the Alphaworks site, the betaworks site, and its own website to promote the initiative. For community-based products, like marketplaces, games, and social networks, this is the first time crowdfunding has been a viable way to raise capital. Me founder William Etundi Jr.
Though the JOBS act has opened up lanes for this type of financing, the law still requires that only accredited investors can purchase equity. Technically, however, it's a point on a line, specifically the point where the line crosses the y axis. And that line is the linear regression line between the portfolio's returns minus the risk-free rate on the y axis and the benchmark's returns minus the risk-free rate on the x axis.
In other words, you make a graph out of a lot of dots. Each dot has the benchmark's return minus the risk-free rate as its x value and your return minus the risk-free rate as its y value. You then find the straight line that best fits all the dots, and that's your linear regression line. Where it crosses the y axis is your alpha, and the slope of the line is your beta.
When beta is high above one , the portfolio's returns vary a great deal proportionally to those of the benchmark. When beta is low below one , either the portfolio's returns and the benchmark's are relatively uncorrelated or the variation in the portfolio's returns is significantly less than that of the market's. When beta is negative, the portfolio's returns move opposite to those of the benchmark.
As for alpha, to put it plainly, it's the expected return of the portfolio when the benchmark's return equals the risk-free rate. The most obvious performance measure is simple return: the amount in the portfolio at the end of a certain time period divided by the amount at the beginning, minus one.
However, this measure doesn't take into account what the rest of the market is doing, and it's entirely dependent on the beginning and ending dates. It's a more or less meaningless measure. A more meaningful measure would be less dependent on beginning and ending dates and would take into account the movements of the market. There are various such measures besides alpha. Average excess return, median excess return, and the information ratio are three of them.
All of them rely on simple subtraction : one subtracts the monthly or weekly market returns from the portfolio returns to obtain the excess returns. You then take either the average, the median, or the average divided by the deviation from the average for these respective measures.
Subtraction, however, is less sophisticated and less accurate a tool than regression. Please note that from now on, for the purposes of keeping this article relatively simple, I will not be subtracting the risk-free rate from monthly returns. Using only excess returns would mean either using the orange line to describe the data or moving it wholesale up or down, keeping its slope the same.
You wouldn't be able to tilt that line at all. In other words, the line doesn't fit the data very well, even if you move it down a few percentages. That's why alpha and beta, for many money managers, are the ideal numbers for characterizing portfolio returns. Those two numbers capture the relationship of the portfolio to the benchmark better than any others I've come across. Shortly after alpha and beta were introduced to the investment world in the s, it was discovered that alpha and beta were inversely correlated, no matter what asset class you looked at.
The inverse correlation of alpha and beta is inherent in the mathematical relationship of the two variables as long as the market's returns are more likely to be positive than negative. It's clear from this that if you have two strategies with the same return x and y are the same , and if the adjusted market return x is greater than zero, the strategy with the higher beta will have the lower alpha; it's also clear that most investable asset classes have market returns which are greater than zero more frequently than they are less than zero.
In fact, I have proven mathematically that alpha and beta are indeed inversely correlated when market returns are usually greater than zero. But this idea did not occur to the scholars who came up with the ideas of applying linear regression to finance, and instead they have spent close to 50 years trying to come up with behavioral reasons for the inverse correlation. Portfolio returns tend to correlate with alpha far better than they correlate with beta. Now even though alpha and beta tend to be inversely correlated and portfolio returns tend to correlate better with alpha than with beta, this does not mean in a mathematical sense that portfolio returns and beta are necessarily negatively correlated.
That depends on the degrees of correlation. But evidence shows that high-beta securities tend to underperform low-beta securities within the same asset class. Beta was rightly proposed as a proxy for risk; indeed, the higher the beta, the more market-related risk the investor is taking on. But many people in the s believed that the higher the risk, the greater the return, which presented them with a paradox.
William Sharpe, one of the scholars who came up with the ideas of alpha and beta, put his faith in high-beta securities because of this belief. The idea that one can get better returns by avoiding risk was not taken seriously back then.
At any rate, one of the nice things about alpha is that it takes returns and adjusts them for market-related volatility. It is just as much of a risk-adjusted return measure as the Sharpe or information ratios. Theil's method was, in Sen's words, "very simple" - he simply took the median of all the slopes determined by all pairs of points. Beta is ordinarily computed by performing least-squares regression. Here's a relatively simple example which I'll use to illustrate the two methods.
In this chart, there are 15 blue points, and the blue line is the conventional linear regression. The x- axis corresponds to the benchmark; the y- axis corresponds to my monthly returns. So the highest point shows that during one month the benchmark lost 2. This blue line gets as close to the 15 points as possible if you measure closeness by the square of the vertical distance between the line and each point and add them all together.
This regression method is thus called "ordinary least squares," or OLS for short, and it's been in use for over years. Beta is the slope of the line in this case 0. The orange line is the alternative regression line calculated by Theil and Sen's method. Notice that there are six points above the OLS line and nine points below it. But the TS line Theil and Sen line leaves seven points above it and seven points below it, and it intersects one point.
TS regression lines, which are calculated by using medians, always leave just as many points above and below them. Neither line fits the data very well, but to my eyes, at least the orange line seems like a better fit. To get this line, you go through two steps. First, you calculate beta by taking all possible slopes between pairs of points and getting the median.
With 15 points, that means calculating different slopes. The median of all of them turns out to be 0. Second, you calculate alpha, or the y -intercept, by taking the y values, subtracting beta times the x values, and taking the median of all of those.
Here, the TS alpha is only 0. Among the few who have examined the subject, the scientific consensus appears to be that no matter what the data is, TS regression is superior to OLS regression.
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The pilot company working alongside Alphaworks is a new type of social network named See. It is an online database of sorts through which visual artists can share their work. In addition to sharing, the artists have the option of selling their work through the platform to the other users.
Me earns its revenue by charging a small fee for any purchases, tips and donations made towards the artists or their work. However, they intend to create a new model for collecting revenue by creating and selling items such as postcards and t-shirts. Me, in a shockingly brief period of time. However, like all new companies, they are expected to face some challenges.
David Lee of SV Angel says that it will be a learning experience, particularly when it comes down to identifying what type of startup specific users will be interested in investing in. Please feel free to contact us for information on any subject we cover. We also love to hear your feedback and comments on what we write about. Disclosure: iExpats. It is specially designed to provide its users with general information.
We may from time to time publish content on this site that has been created by affiliated or unaffiliated contributors. February 20, Where Do British Expats Live? Top 10 Crypto Whales. But instead of gobbling up all those shares on their own, a chunk of the round will be left open. Me , the pilot venture deal for Alphaworks, as an example.
Me has an active user base of designers, photographers, filmmakers and creators who can share their work and earn money from it on See. For now, See. Me generates revenue by taking a 5 percent transaction fee on tips and donations made from user to user, but eventually the company will get into postcard creation and distribution. After that, See. Wearables, FTW! A small number of shares will be reserved for non-investor types who are active on the See.
Me platform, and will be offered in the form of a grant. Me is using the Alphaworks site, the betaworks site, and its own website to promote the initiative.