Risk & Return with Professor Mark Gooley
Hello, everybody. Thank you for joining us today for Northeastern University’s webinar. Today’s webinar will feature Professor Mark Gooley, who will be presenting one of his lectures on risk and return. My name is Tay and I will be your moderator for today.
Joining us today, we also have one of our senior advisors, Khurshid Iqbal, who will answer any questions regarding the application process following the presentation. Of course, we have Professor Mark Gooley. Professor Gooley holds an MBA from Northwestern University’s Kellogg Graduate School, and is currently completing a PHD in Economics from Northeastern. It is an honor to have Professor Gooley join us today to have a discussion on risk and return.
Please feel free to ask questions by submitting your questions through the questions tab. Following the presentation, we will briefly go over the program overview, and then we’ll open up the floor for Q&A.
Hi, everyone. As Tay said, please feel free to type in any questions. It’s hard to judge your audience when you can’t see them, so I’ll do my best. Most of today’s talking about how we might measure risk. This is fairly typical finance stuff. I’m not sure how much of this you’ve had or not had. As you look on the screen, you can see there’s generally a trade-off between risk and return. On the lower left, there’s things like treasury bills and government bonds, and as you climb up and get towards stocks and smaller company stocks and hedge funds, you take on more risk if you move to the right, but you’re also on average should expect more return. That’s the theory.
If we go to the next slide, and I realize there’s a lag here, you’ll see that that theory’s been backed up by the evidence over the past 75, 80 years, et cetera. If it hasn’t switched, you can just refresh your screen and it should update for you. For instance, treasury bills have had an average return of 3.5%, and they haven’t had much risk as measured by standard deviation. The returns don’t vary all that much around that 3.5%. To get an extra 2% risk premium to take on the investments of long-term government bonds, you have to handle almost three times the risk. Corporate bonds return a little bit more, and surprisingly, have been a little less risky as measured by standard deviation and so on. So, those asset classes with more risk have historically earned higher returns.
Now, that risk premium over on the side shows you the extra return that investors have to expect in order to tolerate that. For instance, you would never invest in a small-company stock if you only thought you would earn 3.5 or 4%, the same as treasury bills. You need to expect on average to earn more in order to take that gamble. If you’re not gonna earn more, why take the gamble? Why not just take the safe bet?
If we go to the next slide, and again I’m gonna encourage you to refresh that screen. I’ll mention when I’m switching slides ’cause it seems definitely quicker for me when I do that. We can measure the risk premium by the difference from the T-bill so that that was the same 2% number exception we saw from the last slide.
Alright, so maybe if we can use standard deviation to measure the risk of asset classes on the next slide, we can use it to measure the risk for individual securities too. Here I picked three stocks, and I had done this report from January 2011 to December 2015. The first column is the average monthly returns. Over that five year period, Ford had returned .16% per month, which isn’t very good to be honest. That’s my math. 12 times 12 would be 1.4%, so this seems like it’s about 1.8% a year. Not a great story for Ford. Procter & Gamble’s about 7.7% per month. That’s the bottom one there. That’s about 8.4% a year, a little better.
If we go to the next slide, which just adds the red boxes on the bottom, we can see there’s two ways to look at risk here. One is again to use standard deviation, so even though Procter & Gamble averaged seven tenths a month, its returns vary widely. 4% up and down a month, that 3.96 is not atypical at all for Procter & Gamble, even though its average return wasn’t all that high. We can also look at the minimum the worst month and the maximum the best month during that time period. Two ways of measuring risk here. One the standard deviation, and the other just the range of returns. What’s the biggest and smallest return we can have for a particular month? By these measures, we’d say that Ford and GM are relatively similar. It looks like GM’s a little more risky, and that Procter & Gamble is the safest of the stocks.
So, on to the next one. The spoiler here is that standard deviation is going to turn out not to be the best measure of risk, and mostly because as an investor you’re unlikely to just hold one asset. It’d be very unlikely that you would only hold Ford stock, or only hold GM stock, or only hold one other stock. I would suggest that if you’re doing that through your company’s work place retirement plan, you should diversify a little bit, and we’ll talk about that. I’m not picking on your company. I’m just mentioning that holding one stock is, perhaps, not the most efficient thing you can do, no matter what that stock is.
If we go to the next part of this slide, I use a way of building these just the next one. Instead of just investing in Ford or just investing in GM or just investing in P&G, I made up a portfolio here. Half my money in Ford; half my money in GM. What we can see as we click through, the return is just the weighted average, in this case 50-50, of the two stocks returns. So, Ford averaged .162% per month, and GM averaged .346% per month. I do a weighted average, 50% Ford, 50% GM, the portfolio’s return is .254, halfway between them. If I had it more in Ford, it would weight more towards Ford. If I had more in GM, it would weight more towards GM, and the return would be lower or higher respectively.
As you can see, the extreme returns are smaller than the single stocks. Alright. The minimums and maximums get pulled in. In fact, Ford’s worst month was down 15%. GM’s worst month was down 17%, but the portfolio’s worst month was down only 14%. By having some of your money in each, you pull in those extremes.
If we click through again, we’ll look at the next one where we look at standard deviation. If I just averaged the standard deviation, that circle on the right. If I just did it the same way I did returns, I would expect my portfolio, 50% Ford standard deviation, 7.356, and 50% GM standard deviation, 8.584, to get 7.97 standard deviation, but when I look left, I see it’s actually only 7.616. It’s not the arithmetic average. Something is going on here.
By putting these two stocks together, I get the average return, but I get something less than the average risk. So, I’ve managed to kind of shortcut that chart on the first page, where it says, “In order to get more return, I have to take more risk.” That’s true in the larger sense, but I can get rid of some of that risk by combining assets into portfolios, or groups of assets, in this case Ford and GM. In this case, my reduction in risk is about 4.4. 7.6 is 4.4% less than 7.97, or 7.97 is 4% more risk. I can’t remember which way I calculated it. I think I probably used 7.97 as the base. Alright, so we can reduce our risk, while still getting the average asset return.
Let’s try another one. Let’s go on to the next one here. We’ve created another portfolio, where we put 50% of our money in Ford still, but now we put 50% of our money in Procter & Gamble. Again, the average return, .428, is just a weighted average. .5 times .162 plus .5 times .694 is .428, so that’s where we get that average return number. Now we can see an even bigger difference in the extremes.
Let’s look at the circled numbers on minimum and maximum. The worst month is now less than even the very safe, or relatively safe, Procter & Gamble, -7.1 versus -7.862, even though Ford is half the portfolio. Ford ranged from -15 to 20, and the best month happens to be … This is lucky. It’s 11.447, which is pretty close to Procter & Gamble’s as well. We pulled the returns in to roughly the extremes of Procter & Gamble, while having Ford in the mix. Now, Ford’s performance over this point in time is not very good, but the point here is to show how even putting a risky stock with a less risky stock, we might even be lower risk than the both together.
If we look over here on the average standard deviation … I think I have that circled on the next slide. Let’s see. Yeah, I do, so refresh your screen here and you should have a few red boxes over the bottom and we see that if I just did an average of standard deviations, 50% times 7.356 and 50% times 3.955, I’d get 5.65. That’s the average standard deviation, but the portfolio standard deviation, when I create this portfolio and measure the returns of the stock is only 4.556. That’s almost a 20% reduction from diversification. So, we’ve managed to get rid of some of the standard deviation just by holding these stocks together, or if I can just get rid of the standard deviation, have some of that variation get canceled out by something new to the portfolio, then it doesn’t seem like it’s the best measure here.
Let’s take a look and see what the pattern is here. On our next slide, you should see a correlation matrix. Here, the measure of the market is the S&P 500, and we have the correlations between Ford and the S&P, GM and the S&P about .6, Proctor & Gamble and the S&P about .45. Then, we have the correlations between the stocks themselves. GM and Ford are relatively highly correlated, that .825 number. That means they move together, roughly, to be a little casual in my language about 82% of the time. That’s not surprising. The things that affect Ford’s prospects, to a large extent affect GM’s prospects. So, if gasoline prices change a lot, it’ll probably have the same effect on Ford and GM, whereas, it might have a different effect on Procter & Gamble.
If you look over on how Procter & Gamble is related to these, it’s a little less related to the S&P 500, but it’s not related much at all to Ford and GM. I wish that that circle had been around the Ford, Procter & Gamble intersection. It happens to be the same number, which is why I think it threw me off, but I’d love that circle on the bottom line to be on the Ford and Procter & Gamble intersection, and not the Procter & Gamble and GM intersection. You can see they’re much less related. The less related securities are, the more benefit you’re gonna get from diversification when you put them together. So, we would expect that adding Toyota to a portfolio of Ford or GM would have not as much effect as adding Toyota, for instance, to a Procter & Gamble portfolio. We’d expect these correlation coefficients to look similar.
Just to recap … Going to the next slide, you can see a more diverse portfolio is gonna provide a greater reduction in risk relative to the individual items in the portfolio. If you were trying to diversify your portfolio, a phrase that’s fairly common, you want things that move in unlike directions. You want a variety of investments. You want stocks. You want bonds. You want stocks of different types. Those will get you greater benefits from diversification, and you should still get, on average, the average return for asset classes such as small-stocks, or large-stocks, or T-bonds.
If we continue along … This slide might be a little small. You should see a bunch of numbers down the left and a graph on the right. This is old research. This is not new finance theory stuff, but if you don’t know it, it’s important to know. A guy named Statman, Meir Statman, randomly combined stocks in a portfolio.
Let’s start with this first one. He took a random stock and found out that its standard deviation was about 50% per year, that 49.236 number. Then, what he did, was he randomly added another stock to it. He said, “Oh, that two stock portfolio, on average, only has a standard deviation of 37%.” So, these are random stocks. They each have, on average, a standard deviation of 49% probably per year, right? We were looking at per month standard deviations before, but if you put two of ’em together, your standard deviation’s only 37. If you put four random ones together, it’s only 29. You can see that the standard deviation of the portfolio declines relatively rapidly. That blue line on the right graphs that 49, 37, 29 here on the left.
We can see is that line goes down to a point, and then it levels out. It levels out. Adding more stocks does not reduce the risk of the portfolio. I think the easiest way to think about that is, I’m not finding something different to add. I’m not finding something that I don’t already have in there once I’m at 500 stocks, or 600 stocks, or 1000 stocks, or whatever this is. About 80% of the benefits going from 49 in the infinity, which is the standard deviation of the market here, 19.158.
You can get about 80% there on average with 10 random stocks. Now look, you could get unlucky. Your 10 random stocks could be Ford, GM, Toyota, Nissan, BMW, et cetera, in which case your blue line isn’t gonna go down nearly as fast, but that’d be pretty unlucky. On average, you’d get Ford and Exxon and Apple and some other things that are slightly less related, and your line would look like the blue line. You can get about 95% of the benefit here. You don’t have to hold a portfolio of a thousand or an infinite amount of stocks, you can get about 95% of the benefits with about 45 stocks.
The rule of thumb is 30-50 things in the portfolio will get you relatively well diversified. Again, that assumes that they’re not Ford, GM, Chrysler, Toyota, et cetera.
Let’s go on to the next one. The next one just adds this color shading on the graph. The green part there is what’s called the non-diversifiable or systematic risk. This is the part of the risk you cannot get rid of through diversification. If I held every single stock that’s available, I would still have some movement in that portfolio. I wake up tomorrow, and I look at the value of my portfolio, and it’s gonna be different from today. When I wake up the next day, it’s gonna be different than that. There’s gonna be some variation in those returns, and we can measure that by standard deviation.
I can’t get rid of that, but if I just held one stock … If I were way over here on the left of this chart where it says one, I’m not only gonna have that variation, but I’m gonna have the variation that just affects Ford, or just affects Procter & Gamble, or just affects GM. That extra amount, that yellow, is the diversifiable, or the non-systematic risk. Well, that part I don’t have to take. I don’t have to take that risk because I can diversify it away. I can add more stocks to my portfolio, and as I move right here, the blue line goes down and the percentage, the height of the yellow area, gets smaller and smaller until it pretty much goes away, and I’m left with just the part of the risk I can’t diversify away. Alright? It doesn’t seem to make much sense to measure risk as standard deviation because it’s pretty easy for me to get rid of a good chunk of that standard deviation just by holding it as part of a portfolio.
Is this making sense? I haven’t seen any questions or, “Holy cow! What’s this guy talking about?” But feel free to do that if you like.
Since standard deviation isn’t the best measure, I can just basically get rid of it by holding it with something else. We need another measure, and that measure we’re gonna call beta. This is the measure of the non-diversifiable risk that a particular security contributes to a portfolio. This is what professionals call the relevant risk. It’s the part I have to care about, or I should care about.
Let’s see how we might go about getting beta. We got to the next slide, and you should see two relatively small scatter plots. The one in the upper left … and it’s not important to read the exact numbers here … has four monthly returns on the y-axis and a monthly market returns as measured by the S&P 500 on the x-axis. You can see that the dots generally go from lower left to upper right. Lower left means the market had a downturn, and Ford had a downturn. When the market goes down, Ford often goes down too. The dots on the upper right have the market being up. It’s to the right of the line at the y-axis, and Ford is above the x-axis. So the market went up, and Ford’s stock also went up.
Clearly, there’s some other places in the lower right, the market went up, but Ford’s stock went down. As we said before, Ford didn’t have such a great period during that five-year period, but to a larger extent there’s a fairly clear pattern here that Ford’s prospects, or Ford’s returns, are influenced by the market. The same holds true for Procter & Gamble, but the affect is more muted. The slope of those dots, and when we draw a line through ’em, the slope of that line is less steep. Procter & Gamble reacts less to changes in the market than Ford does.
Let’s go to the next slide, where we add those lines in there. What I did on the next slide is add the regression line in here. Don’t squint to read the formulas on those ’cause we’ll have ’em in bigger type on the next slide. You can see that the line that goes through the Procter & Gamble box on the lower right is a much less steep line than the line that goes through the Ford dots … the line that best fits through the Ford dots … in the upper left.
The slope of that line … It’s gonna end up being, I think, 1.3 for Ford and .5 or .48 or something for Procter & Gamble … is beta. That’s what you call beta, and it’s the average change, relative to the market of Ford stock, 1.3 times, or Procter & Gamble, roughly .48 times, to a 1% change in the market or a certain percent change in the market. Your bigger betas here, your bigger numbers, say that this stock is more volatile and more risky. On average, when the market moves 1% either up or down, Ford moves 1.3% up or down. On average when the market moves up or down 1%, Procter & Gamble only moves up or down about half a percent. It’s a less risky, a less reactionary security.
Let’s move on to the next slide. Like I said, we have it in slightly bigger format here, and again you can refresh. I’ve got the audience view up here, and I’m done refreshing it too. So, if you refresh it or come up pretty much as soon as I turn it, and I’ve been trying to remind you. Here what we’ve done is we’ve just … I just used Excel here to draw this line. The predicted monthly excess return … Being a little fancy here, it’s just Ford’s return above and beyond the risk-free rate for that month … equals some intercept alpha plus beta times the excess return for the market. The amount the market went up or down in excess of risk-free rate here. That’s what those graphs were.
We’ve drawn the line through, and here we get that Ford’s excess return … the amount of essentially of risk premium I get for Ford … is -1.16 per month plus 1.3 times however well the market did. That’s what I get out of this regression output below. As I said on the last slide, this means, on average, Ford is 1.3 times as risky as the average stock. We’ll get back to alpha in just a bit.
If we do the same thing on the next slide with Procter & Gamble, we can see that Procter & Gamble, same form of the equation, the excess return for Procter & Gamble is .21% per month plus .48 times the market excess return. So, the beta for Procter & Gamble over this period of time is about .48 or .5. It’s about half as risky as the market. The nice thing about beta is you can measure the beta of the portfolio just by averaging the betas of the stocks in the portfolio.
Unlike standard deviation where that averaging technique didn’t work, for beta it does. If you have a portfolio with a beta of 1.3, and you add a stock like P&G into it, which has a beta of .5, it will pull that down in proportion to the percentage of Procter & Gamble in your portfolio. As you add more low-beta stocks, the portfolio average will go down towards those. If you add more high-beta stocks, it will go up towards those. As it says on the bottom of the slide here, adding Procter & Gamble to a portfolio would contribute less to that portfolio’s risk than Ford.
Let’s go back to alpha here. Alpha was that first number, that intercept number. Alpha is often interpreted as excess returns by management. If we measure the success of a portfolio, we’re not only saying, “Hey, I made 10% last year, or I made 15% last year, or I made 5% last year.” We’re also saying, “How did that compare to the market?” If you made 10% on your stock portfolio last year, and the market was up 5%, you probably did pretty well, but if you made 10% on your portfolio last year, and the market was up 30%, you didn’t do so well. Right?
The 10% isn’t so relevant. It’s 10% relative to some benchmark, and the benchmark is something of similar risk. If your portfolio had a beta, on average, of about one, then you’d expect it to do about as well as the general stock market. If you didn’t do as well as the regular stock market, you’re under-performing for the risk.
Let’s look at that in the context of a particular stock. Here’s Ford. These are the excerpts from the regression output that you saw before. Ford’s intercept was this -1.16. That’s bad, right? This says, on average, each month it starts in the hole by 1.1%. If we look over at the t-stat, general rule of thumb is t-values above two in absolute value are significant. This one’s 1.5, but we’ve got 60 months here, so looking at our p-value, we’re about 86% sure that that coefficient is different than zero, and it’s different on the bad side, right? We don’t want a negative return there. This would be a pretty good indication that Ford’s management was not giving a good return to its shareholders relative to the risk.
Procter & Gamble, on the other hand, they have a positive alpha here, .2% per month, two-tenths of a percent. Given our five-year sample, our 60-month sample, we’re not very confident that that’s different than zero. That’s probably just noise. In fact, we’re only about 33, 34% sure that it’s different than zero. You can also see that by our confidence in our growth there.
The main point of this was to talk about how to measure risk. For asset classes, you can look at standard deviation of the returns … how volatile they are, but for individual securities, it’s not such a good concept because you can get rid of that risk through diversification by combining even random assets as we saw through Statman’s work.
A better measure of that risk is beta, which is simply the slope of the regression line put through the relationship between market movements and stock movements. Then, the intercept of that regression line gives us some indication of whether the stock is over-performed or under-performed relative to the market. Here, Procter & Gamble looks like it’s done a fair return for its risk. It gets 8.4%, which is the .7 times 12 months. It’s about right for a beta of about .5. Whereas, Ford has some, not conclusive evidence, but relatively indicative evidence, 86% sure, that it under-performed relative to its risk.
That’s the end of my comments for today. I’m happy to answer questions. Otherwise, I’ll pass it back over to Tay. Once I pass it back over to him, if you still have questions or comments we can … Tay and I can probably play nice together and handle ’em. Thank you.
Thank you, Professor. Before we go to the Q&A session, I just wanted to mention some of the features of the program. As Professor mentioned, please, we invite you to use this time to provide us with your questions so we can answer them accordingly. As you can see the program is based on real-world practices, and much of what you learn in this program can be applied to your current career or any future career aspirations. The program is very flexible and can cater it to your life and schedule. I am now going to hand it over to Khurshid, who will provide you with more insight into the program.
Thank you, Tay.
Hello, everybody, once again. I hope you are enjoying the webinar today. Now, speaking about the Masters of Science in Finance program itself, the program is highly specialist program. It is designed for full-time working professionals wanting to move up the career ladder into senior level positions within finance or business setting. You have 10 courses, or 30 credits to complete in total.
You’ll be taking one five-week class at a time. You can complete the program in as little as 16 months; however, it’s a highly flexible program and can be stretched to five years depending on your needs and requirements and accommodates the needs of working professionals. It can be completed 100% online, with no residency requirements. You also have the option of steering your learning curve geared either towards investment track or corporate finance track.
Corporate finance is for individuals who want to try … really focuses on the sourcing and allocation of capital in an organization to achieve overall objectives of maximizing the shareholder value. It will cover areas such as business planning and analysis, treasury management, the paycheck planning, capital budgeting, financial risk-management, mergers and acquisition, private equity, venture capital, and investment banking.
Whereas, investment banking, on the other hand, is for professionals that want a track that focuses mainly on the analysis of financial instruments and portfolios of securities. It will cover topics such as, investment management and securities, equity and fixed-income securities, options and features, performance analysis, financial risk-management, and private wealth management.
We have had three upper graduates in the past come back to us giving us a feedback on what they felt that they got out of the program. Marcy Reyes, she sounds very excited and proudly expresses about her growth and promotion that she has had upon completing the program. Scott Desmond, he spoke highly about the flexibility of the program and the online formats, and Brian Pellegrini, he mainly spoke about the time commitment and also touched notes on his career progression and advancements.
As you can see, here’s our contact information, Khurshid and myself, Tay Doan. We work on the online Master of Science in Finance program, so we would be more than happy to assist you with any further questions you may have in the future or if you’d like to apply into the program. Now, we’ll open up the floor for any questions and answers. We have had a few questions come in. We’ll try to get to as many questions as we can.
We do have a question here for Professor. Professor Gooley, we have a question in regards to, how many courses do you actually teach, and which courses are those specifically?
I, personally, teach a course called Financial Institutions and Markets. It’s one of the three core courses. I’m not an expert on the curriculum, but as far as I understand it, you start with any one of those three starting core courses. You have to take all three of those, but you can take them in one, two, three; three, two one; two, three, one; et cetera.
Financial Institutions and Markets has both a statistical component, which this is some of that stuff, and it also has some stuff about how markets function, how interest rates are determined, that type of thing. So, I have one of those. Since it’s a core class, it runs, I think, three times a year, but that’s the only one in the online program. I teach a variety of courses in the live program at Northeastern.
Excellent! Thank you.
We have another question. Do you need a finance background before coming into the MSF program. [crosstalk 00:36:03]
I don’t do admissions. Sure. I don’t do admissions. I’ll certainly … I assume this is for me, but if not, other people can add to it. I don’t think you need one. I think that you will have to work harder if you don’t have one, than if you do because the core course are … They’re five weeks. They’re pretty fast, right?
You can learn the stuff, but it’s gonna be tougher to learn it than review it, or tougher to learn it than to add to it. If you’re coming from an accounting background or a math background or an econ background, I think it’ll be more close to what you already know. If you aren’t, I think it’s gonna ’cause more work, but I don’t believe it’s required. Tay, do you know that?
I’m sorry. Could you repeat that?
Or does anyone else in the-
You broke up.
Sorry. Do you know if it’s formally required to have a finance background? I don’t think it is. I know it would make it easier, but I don’t think it is.
I can definitely answer that, Professor. In order for you to qualify to apply into the program, you are required to have at least five years of full-time work experience demonstrating a financial skillset. Not necessarily that you need to have a finance background, but you need to have demonstrated budgetary roles. Also, if you can demonstrate number-crunching skillset.
Thank you Khurshid and Professor. We do have a follow-up question for that. It seems like most of it had been answered but just for you, Professor, what would you say are some of the tips for success for your particular courses? Anything you can recommend for future students?
Other than [crosstalk 00:37:51] a little harder.
… to success? Well, the way the program works as you mentioned is it’s five weeks, and then I believe it’s a week off, and then it’s five weeks, and then it’s a week off for the different courses. You’re taking one course at a time, but you’re taking it, essentially, at triple speed, right?
A normal college semester is 14, 15 weeks, so if you fall behind in a five-week program, it’s a challenge to catch up. I know there’s a preview part of the course early on, and I think using that to kind of get ahead in week one so you can stay ahead is key because let’s say that I say, “Ah, I’m not gonna do any work this week.” Well, now the next week I have to do two weeks of work, and I might have missed the deadline, but two weeks of work is the same as five or six weeks of a “normal” college class. It’s just hard to do that, especially since most people are, you know, working their regular job as they do this. It’s staying on top of it more than anything.
I know, in my class, I’m trying to give a full … I do the same class live. I try to cover the same stuff in the online program as I cover live, but since we’re going quicker, albeit with only one class at a time, there’s more reading each week, right? The assignments come quicker … that type of thing.
Thank you, Professor. We have a question here for Khurshid. It’s actually a two-part question. Is the GMAT required and what is the application process?
GMAT is not a mandatory requirement for the program if you do have five years of full-time demonstrated experience. GMAT would be waived. If you somehow are under that mark, a GMAT would be required. Depending on the number of years of experience one may have under their belt, we can determine what score could apply with that individual, so we can take it on a one-on-one basis.
As far as the application requirements are concerned, we do require you to bring in official transcripts of both undergraduate or any coursework that you may have completed. We need most current and updated resume. Two professional recommendation letters would be required, along with a statement of intent essay.
Thank you, Tay.
Thank you, Khurshid. Another question here for you. The degree you receive, does it state online?
No, the coursework that you’ll be doing while in the program, the curriculum, is going to be identical to what we offer on the campus. The faculty that you’ll be interacting with will be the same as well. The degrees and transcripts that you’ll be earning at the end of the day will be identical, and it will not state that it was earned online.
Thank you, Khurshid. We have another question here.
Okay. Another question we have. Maybe this will be for Khurshid. What career assistance is there for students enrolling on this degree program?
Yeah. That’s a great question. We do have career services that will be available to our students. Once you do complete 50% of the program, you will be given full access to our career services. You will be having an advisor working very closely with you and determining what your needs are. They’ll determine what your needs and goals are, and accordingly they will meet with you. Most importantly, these services will be available to you life-long. It is a lifelong service available.
Thank you, Khurshid. We are coming up to the end of the webinar. For those who have questions that we weren’t able to get to, we will reach out to you directly. If you come up with any questions after this, please feel free to contact Khurshid or myself via the contact information on your screen, and we will reach back to you accordingly. Thank you today to Professor Gooley. You have provided some excellent information, some great insight. Hopefully, you’ll see some of the participants today in your classes in the future. Have a great day everybody!
That’d be great.