DR LAURA RUSU | Diversiview

It's all about the data and technology for Dr Laura Rusu, the Founder and CEO of LENSELL, a Fintech that aims to democratise access to financial and non-financial corporate performance information, helping people to make better decisions with the Diversiview app. Diversiview analyses historical data about a range of shares and ETFs to show their correlation with each other and level of risk.

She invested in some shares with her husband and on any platform you could see a high level of diversification across asset classes, but you could not see the actual diversification of the portfolio. She pondered with her mathematical and logical inclination, if she replaced those companies in those five industry stock groups with other companies from the same industry groups, whether she would end up with the same diversification.

Her gut feeling was that the answer was no. She wanted to research how it could be better calculated.

She also realized that her husband had a lower risk tolerance. He was more stressed about things so she wanted to be able to ascertain the portfolio volatility to match personal risk tolerance.

During the interview we mentioned a number of links for further information. They are listed below:

- Example of different diversification for the same industry groups: https://www.linkedin.com/pulse/high-level-portfolio-diversification-enough-lensell-group/

- Research article on Investor's Top 20 preferences in first quarter of 2022: https://www.sharesight.com/blog/whats-driving-investors-mind-or-emotions/

- Investment risk tolerance assessment: https://pfp.missouri.edu/research/investment-risk-tolerance-assessment/

- Learn more about the concepts mentioned: https://diversiview.online/faq and https://lensell.online/diversiview-learn.html

- Learn what's in a Diversiview analysis: https://blog.lensell.online/whats-in-a-diversiview-analysis

- Subscribe to the LENSELL newsletter for future articles and updates: https://lensell.online/newsletter-subscribe.html

- Connect with Laura: https://www.linkedin.com/in/laurairinarusu/

Diversiview is an AI-based digital investment planning and optimisation solution, used by active investors to build, review and refine personalised investment portfolios.

Diversiview is also used by financial advisors and planners to help their clients with tailored investment strategy. With its several unique features, Diversiview saves time and allows professionals to explore a multitude of options for their clients, fast.

Use the promo code SFB2022 - it will give you 25% off the price of ad-hoc analyses (unlimited) or monthly subscription (for 12 months)

TRANSCRIPT FOLLOWS AFTER THIS BRIEF MESSAGE

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EPISODE TRANSCRIPT

Phil (0s):
G'day and welcome back to Shares for Beginners. I'm Phil Muscatello. How can you know if your portfolio is properly diversified? What's correlation and how can this affect your investments? Joining me today to explain is Dr. Laura Rusu. Hello, Laura.

Laura (15s):
Hello Phil

Phil (16s):
Dr. Laura Rusu is the founder and CEO of LENDSELL: a FinTech that aims to democratise access to financial and non-financial corporate performance information, helping people to make better decisions with the Diversiview app. Before we get started, can we talk about your background? You're from Romania and when you were young, you weren't expected to have any kind of career whatsoever?

Laura (40s):
Yeah, so I wouldn't put it really like that.

Phil (43s):
Oh, how would you put it, sorry.

Laura (45s):
I had a bit of a career in Romania. I worked for seven years for the government department, the forestry department as an analyst programmer and then for a small software company before I immigrated to Australia.

Phil (59s):
But when you were growing up as part of your education, because you didn't even have a computer at home when you were learning.

Laura (1m 5s):
Well, yes, I didn't have a computer at home and it was really difficult to access one at university because it was, you know, lack of resources at the time and many students and few computers. So we had to book time, let's say two hours on Monday from 10 to 12 or something like that to go and do programming and do assignments and whatever we had to do for university. And to replace that lack of access, we probably had a lot of more theory learning, sort of, to make up for that. So growing up, I didn't think I'd get into computer science. I loved mathematics from, I was very young, from year five.

Laura (1m 45s):
My father was an engineer, he's now retired, he loves mathematics as well. And I had a very good mathematics teacher and, sort of, both of them instilled this love for mathematics and for logic and for finding, sort of, solutions to problems. And being at the time a communist country and not having a lot of other, sort of, entertainment for kids, all of us probably, or most of us focused a lot of just dedication learning, reading, doing a lot of homework from school, at home. So doing that sort of repeatedly, I got this love for exact sciences.

Phil (2m 27s):
So you're bringing mathematics and finance together, but I'm assuming in a communist country, there wasn't a lot of finance to talk about at the time.

Laura (2m 34s):
At that time, no. Not at that time. No. So the finance part came really few years ago here while I was working in now in IBM research and I was involved in some project around financial services in AI for financial services from fraud detection up to that ratings and things like that. All sorts of aspects where you can apply data mining and later AI and so on.

Phil (2m 60s):
So you had no interest in finance prior to that project?

Laura (3m 4s):
Before IBM, I worked in Computershare technology services and I also worked in, it was a project about a portfolio management solution; trust Architect it was called at the time. So they are dealing with, it was an application for investment managers who had trust products.

Phil (3m 21s):
So what was the first inkling that happened to you where you started thinking about how you can apply your mathematical knowledge with finance?

Laura (3m 29s):
So, as I said, there worked in all these projects, so it sort of combined the work aspect with my sort of personal findings. At work I realized that there is a lot of data and there is a lot of very good technology which you can apply it to get all sorts of insights. Even if it wasn't a total different project or totally different financial area than portfolio management, but still the perspective was the same. You have a lot of data, you can analyse it, get insights which you can then use to improve some solution. On my personal side, I was an investor myself. I figured out that I did not have enough information on the existing platforms.

Laura (4m 10s):
For example, I invested in some shares at some point with my husband and on any platform you could see, you know, share price graphs, you can see a high level diversification on asset classes, but you cannot see more than that. So if you look at share price, you can maybe figure out a bit of volatility by yourself, but there was no way to see the actual diversification of the portfolio. So if I replace, for example, I had five at some point, 5 industry groups. And I thought maybe with my mathematical or logical sort of inclination, I said, "Does this means that if I replaced those companies in those five industry stock groups with other companies from the same industry groups, is it the same diversification or not?"

Laura (4m 53s):
And my gut feeling was that is not. And that kind of later become a point of, sort of, research for me in how you can calculate that better. Same as I said, you could look at share prices. And I figured out, you know, like the time we invested in Afterpay, it was kind of going up and down quite often, you could figure out which one is more volatile than other, but you couldn't figure out how volatile is the entire portfolio. And also another angle is each person is different. I realized that my husband maybe has a lower risk tolerance than me. So he was more stressed about things than I was. And probably that comes back to how volatile is your total portfolio and how that matches your personal risk tolerance or expectation.

Laura (5m 40s):
So each person is different. Each portfolio is different. We cannot apply blanket approaches. Like for example, I don't agree with a 60:40 approach because it is a blanket approach, which doesn't apply to all portfolios. And we have a data, as I said, we have a technology and we can calculate all these indicators and ratios and all these things, which can tell us a bit more than just, you know, finger in the air, kind of, an overview.

Phil (6m 5s):
That's quite interesting that you were talking about having a diversified portfolio in your own portfolio. And you'd have, I think you said five different companies across five different sectors,

Laura (6m 17s):
At that time

Phil (6m 18s):
Because a lot of people think that if you diversify across sectors, that's going to automatically give you diversification, but you can have more or less diversification depending on which companies you pick in those sectors. If you got an example of that, how that works,

Laura (6m 32s):
I can maybe provide you with a link which you can include for the audience later, don't know by heart. Not because they're real numbers, but looking at different portfolios, I notice that there is a difference. So even if it's not a big difference between companies from different industry groups, there is still some correlation. So while maybe it could be zero five correlation or zero six or zero seven, you may think it does not matter so much, but when you calculate overall portfolio, depending on how much weight you have in each, how much percentage in each stock, it may or may not make a difference overall. So yeah, I can prepare an example and maybe share it with your, for audience later.

Phil (7m 11s):
Yeah. That'd be great. Always love to have something to point listeners to. So let's go back to some of the basics here. What's your definition of risk?

Laura (7m 21s):
My definition of risk. So, risk is very, again, if you go consult academically risk is very difficult to define.

Phil (7m 29s):
It's risky to define risk.

Laura (7m 31s):
Yes, it's difficult. So it means something else. If you give someone a number is not enough. So it's sort of, it has to be on a scale or it has to be so competitive. As I said, if your own risk tolerance, probably the risk is how much you can, you can lose if we put it very simple. And why risk is sort of matched with volatility in terms of stocks and portfolio is because volatility is how much we're share price goes up and down. So of course, when it goes up, everyone is happy, but when it goes down, it means you can lose. And how much you can lose. It's very sick of losing some money, more money or all your money. So how big is that volatility will show how risky investment can be.

Laura (8m 14s):
And this is why, you know, cash or term deposits or hours of sort of almost zero volatility. If we sort of ignored inflation, but other stocks or crypto now, or some of our new on your stocks, or, you know, investments may be very volatile just because there is a lot of more, let's say interest. And also people react much faster to different news about the particular investment. It's kind of a good example is Elon Musk tweets about something young people go and buy or sell very quickly. So that means it's very volatile. So if you invested in something like that, you could lose or you could gain a lot.

Laura (8m 57s):
So I am not my self against taking risks. It's more I'm for taking informed risk. If you know what you're getting into, it's up to you to get into whatever you want, cryptocurrencies or volatile investments or less volatile investments is just matching what you think you get with what you want.

Phil (9m 18s):
It's also very difficult to ascertain your own level of risk tolerance unless you live through volatility, isn't it?

Laura (9m 26s):
True, true. Yeah, it's very difficult, but there is quite good research. And again, I can share a link from a university in the US and two professors who did the very kind of extensive research on that. And they came up with a sort of questionnaire, 20 questions on different, you know, investment angles. And they give you a sort of indication on five star scale. Let's say where you are in terms of risk tolerance. And it's quite extensive research is not just done by someone, you know, who had some knowledge it's done on thousands of people and by professors in, in finance. So I would, I would suggest no people have a look at that and try to see where on the risk tolerance level,

Phil (10m 9s):
There's a traditional understanding of diversification and you're coming up with something a lot more nuanced. What's the difference between that traditional view of diversification and what you are now looking to offer?

Laura (10m 25s):
So I think traditional view, as you said, these two, just thing that just splitting between asset classes or inside the asset class, like inside the security between industry groups is it's enough to, to diversify. But as we see, even nowadays, we've always happening in the world. Companies are very interconnected. So you cannot say that everyone in mining will do one way and everyone in energy we'll do a different way and they're not connected. So the correlations are not sort of between companies in the same industry group there they exist between different industry groups. And you don't know, unless you sort of again, go to mathematics and calculate that correlation based on where history, how they move or not together.

Laura (11m 14s):
So I am saying that we're current approaches is good, but it's not enough is not sort of saying it's not good. That's the starting point. You start first, look at how you can split into maybe international diversification, or like I said, different asset classes or within, well, one asset class, let's say different securities types or industry groups or bonds or whatever else or cryptocurrencies as well property, what else? Cash? You know, so that's a first step, but that when you go into kind of deeper and you have maybe a preference for more security is listed securities, you need to know how correlated they are is not just a bucket of securities and vets enough or a bucket of crypto and enough cryptocurrencies also have some correlations between themselves or between themselves and by securities ETFs.

Laura (12m 6s):
Again, many people go for a ETFs because it's a good way of diversifying your portfolio and that's true. Theoretically at the ETF level, the owners, people who created the ETF, they look to create a diversified product. But between ETFs, where often there is overlap and how much the overlap is, would impact on how correlated they are. And actually, I, I, we published yesterday research article, with Sharesight it's on where blog. Again, I can provide a link where we look at the top 20 investor preferences in the first quarter of 2022. I think eight of those 20 were ETFs.

Laura (12m 47s):
And there is a plot fair about the correlations between those ETFs. And it's very interesting to see that there's a very high, you know, there is a high correlation between like zero eight or zero nine, very close to one, which means they go together between some ETFs, which you, you don't think they go together again. I don't know, example by heart now, but again, I'll provide the link and people can have a look at the diagram. So what I'm saying again is it's good to go to ETF for diversification, but you need to look again at the correlation between ETFs or between ETF sense, separate individual stocks. So you get whatever diversification you want, but deep diversification you want.

Laura (13m 28s):
And it is not just sort of halfway diversification.

Phil (13m 33s):
You mentioned before the 60:40 portfolio. And that's the traditional idea that you have 60% in stocks and 40% in bonds. And we've seen recently how poorly bond and bond ETFs and any kind of bond ETFs have been performing. Is that something that you can see that there has been some sort of correlation in the past, or it hasn't been correlated or is this something new we're seeing?

Laura (13m 58s):
What I think again, so yeah, bonds have a difficult time now and some securities will have a difficult time again because of everything, what happens.

Phil (14m 8s):
Yep. And traditionally you think, you know, why have a 60:40 portfolio is that when stocks are performing badly that the bonds will be holding up, but that doesn't always work.

Laura (14m 18s):
Yeah. My idea again was to have a diversification to go up versus going down and keep sort of balanced various sort of optimal portfolio, which will give you the best return for that sort of a level of risk. And what I think is, again, probably it's a very close to the truth, but it cannot be the same approach that 60% bucket of stocks, if it's a very volatile, low return, stocks cannot be the same. If it's a bucket of 60% less volatile high-performing stocks. So at, I'm saying you need to look into that 60 bucket into that 40 bucket and into how they go together is not about correlation between securities and bonds that we know that one goes up, one goes down, it's actually about those individual, securities, or bonds or bond ETFs or whatever we are inside those two buckets.

Laura (15m 16s):
So we can argue forever. If it's 60:40 is better when 70:30, unless you do the mathematics and see exactly where each should be to get the optimal. And what we did actually did do is to take the modern portfolio theory from Harry Markowitz, as we know he got the Nobel Prize, so he knows what he's talking about. He says that there is a, you know, an efficient frontier and the efficient portfolios or positions where you want to be. One of them is optimal one, which gives you a best return for the lowest risk, at the same time, and is just the fact that you cannot calculate that by hand, that are millions of positions, millions of possibilities of combining everything in your portfolio.

Laura (16m 0s):
And it's possible whether that's 60:40, maybe 58:42 or 32:68, whatever combination, again, needs to go to better mathematics and corporate towards that particular composition, but sort of weights associated with each security or each bond or each ETF or whatever you have to get that optimal portfolio. It is a mathematics behind, we know the equations, we have a data. So he came up with a technology tool to do that, rather than just assuming a general approach like 60:40.

Phil (16m 35s):
That's all about the data with you. Isn't it.

Laura (16m 40s):
And technology.

Phil (16m 41s):
So for correlation, is correlation risk or correlation. The metric that you use is a scale from zero to one. Is that how it works?

Laura (16m 51s):
So correlation, correlations are from minus one to one. One means that to those two stocks or securities or investments could be anything really not just listed securities. They go together up or down, but reacting to some market events. So if something happens and new crisis or a new legislation or something, they will record the same. Exactly the same as one. Or and they react totally opposite. Not one goes up and goes down it's minus one. And zero means very independent. There is nothing, you know, between them in no correlation at all. So what we see from calculating, so we calculated correlations for all, for all ASX listed securities for repast based on our past 10 year history.

Laura (17m 38s):
And probably most of them fall in our sort of positive correlation, but lower, positive correlation. So there is some correlation, they move somehow they are dependent or not depending on each other, but moving in some way together. With some, you know, very few or some of them going up to one or 0 8, 0 9 over zero nine, meaning that they are strongly correlated. So if we invest in one of them, they all go up together. So you can gain twice, let's say, but the, if they go down, we will go down together as well. This is not just going up. It's going down as well. So theory again from modern portfolio theory from Harry Markowitz, he said that the actual diversification, what you need to look in your portfolio is to find the securities or investments that are not correlated so zero, which is very difficult to find zero correlation, but there is, or negatively correlate, like you said, have a 60:40 securities versus bonds.

Laura (18m 40s):
Why there is one, one goes up, one goes down. So again, you need to calculate all this correlation and look which have good returns. Of course, this is what everyone wants, what the returns, but at the same time, zero or negative correlation. So whenever something happens, some of your portfolio go up some down, but overall you are not in a good position.

Phil (19m 2s):
And the data that you use is from a historical data.

Laura (19m 10s):
Yes, historical data.

Phil (19m 10s):
Okay. So tell us a bit more about Diversiview, how it works and what a user coming to the website will find.

Laura (19m 19s):
So we, we tried to make it as easy as possible to use. When a user will come to Diversiview, which is diversiview.online, you have an option first to try one analysis for free. So just see how it works. And when, if you want to continue, or there is a small fee.

Phil (19m 38s):
So you can just analyse your own portfolio just as a one-off.

Laura (19m 42s):
Yes. If it's a small one, so it's up to five securities, it's it's free. So you would just go and plug in your securities. If you have more than five, and it's a pay analysis, you can upload from CSV as well. If you have 20 or more. So we don't do manual entry, you can also specify weights when your portfolio, if you don't specify weights, it will assume equal weights because it has to be something

Phil (20m 7s):
Has to have some assumption. Yeah. Yeah.

Laura (20m 11s):
But you can specify weights and it will go and calculate lots of things. First, it will calculate and give you a sort of analysis page. First, it will calculate portfolio level indicators like expected portfolio return, which is a weighted average of individual investments. So securities returns, portfolio volatility, the risk, which I mentioned I couldn't find. So I had to calculate it. It gives us where that is very complicated to calculate by hand on an Excel. You can do it for 2, 3, 5, if you're very, you know, patient for more. But if you have more than 10 securities, it will take forever to calculate.

Laura (20m 51s):
Bandwidth to give you portfolio better, which shows how volatile is your portfolio compared to the entire market. And when we say entire market, there are different ways of assuming sort of entire market for, for better. We assume it is All Ords index. In some other platforms, people are some ASX 200 index, but I think All Ords is more suitable. In my opinion. Then it gives you a portfolio alpha, which it's an indication is everyone sort of seeking alpha approach. But for the alpha tells you how much more than the return of the entire market you could get. It's expectation. Again, is not a promise, but it's expectation of return.

Laura (21m 33s):
And also sharp ratio, which is based on risk and expected return. And again, many investors will know that people expect or want to have a portfolio of sharp ratio greater than one, which means you have at least, you know, some positive return comparative of market risk return for the level of risk you take. So it comes always back to risk your risk tolerance or level of risk you want to take for the return you expect. So these are five main indicators. When you get a very interesting image diagram, you're invited to, to try one and see, I call it a, we call it portfolio universe.

Laura (22m 14s):
And actually it gives you a subset because there are millions, but the subset of potential positions for your securities and probably people don't know, but you, you can combine for example, a three securities in over 4,800 ways, you know, different percentages, but more vivid for short. And so as your portfolio grows, there are many, many possibilities you can combine them. So we calculate that again with technology, we calculate that we show where is the efficient frontiers. So those which are most efficient for the level of risk. And we show where your portfolio is, your combination of weights if you supplied some.

Phil (22m 55s):
Have you got any interesting stories about what some users have discovered about their portfolios?

Laura (23m 2s):
Yes. Yeah. I mean, we had some feedback as well from a few people who realize that their portfolio was right in the middle of a stack and not as efficient that they thought. It is. So again, applying a 60:40 or another very sort of rigid approach might not take you to the right position you want, so need to know what it is from a risk and return perspective. And then the next step is once you figure out that you need to think what you want to do. Again, we don't give advice, but using technology to help you to get to a, to a point you one. So before doing that optimisation, we have a deep diversification diagram and a diversification rating.

Laura (23m 46s):
As I said, just high level split across asset classes is not enough. So we show you a diagram with all your securities and correlations between them. And there are some filters on the left where you can maybe feel that out small positives, let's say and keep a, let's say high, positive correlation. So you want to see what could be, you know, problem in your portfolio and want to reduce that correlation, because I didn't mention those correlations. Whereas individual correlations between securities are critical to corporate portfolio volatility. You cannot calculate portfolio volatility, we've always did sort of individual correlations. So anyone who says that they know what portfolio risk or volatility, but they only have a high-level split between asset classes is lying because it's not, it's not possible.

Laura (24m 35s):
It's part of a formality in financial mathematics. So we show you a deep diversification view for you to see if you have a zero or negative, you know, correlation. So you're happy. Or if you have very strong correlation, which case, again, it's up to you. If you want to keep them or not, you may have is strong correlations and your portfolio may have a very good sharp ratio or a good return or risk return position. So you want to stay as it is, or you may figure out that you might need to make some changes is up to yours as a user. And the next section is optimisation part where we have four features.

Laura (25m 16s):
One is to analyse if your preferred returns. So in, in doing all the historical analysis, we calculate an expected return for each security.

Phil (25m 26s):
Over a period of time?

Laura (25m 28s):
We consider three years plus three years, because I think it's long enough to cover, like in our case, recent case with COVID and everything that happens. And it's short enough to not be many changes, you know, historical changes in the company, which may have changed how that works in operational. So three years, I think it's good enough. So we calculate that expected return for each security, and therefore we can calculate that expected return for the portfolio. But some people may not agree with that. Then they say, well, based on what I know or what I read vis security will make gain, and they have to, you know, X percent next year.

Laura (26m 8s):
So when you have this option on our diagram to calculate your preferred returns, so you can keep some of those which are calculated and you can change what you want to change. So you calculate all again, without paying again so all these features are included and it will give you again the diagram and when new position or resident position, based on what you, your preferred returns are. Another feature is for people who may be with might be more conservative or they want just to take the lowest possible risk with that portfolio. We have a feature called calculate minimum risk portfolio or minimum violence portfolio, which give you a that weights combination, which gives that minimum risk portfolio.

Phil (26m 51s):
This is a suggested portfolio.

Laura (26m 53s):
It's not suggestion is, theoretical is again, theoretical mathematical is one which has the lowest risk on the X axis. So the left most on that diagram. And to make apparent is, is that diagram is not our innovation, again comes from Harry Markowitz. He figured out he figured it out that if you plot on a two dimensional space, all of a potential risk return combination, you get the sort of shape in the diagram. And while leftmost is the lowest risk, and our feature is to calculate the optimal portfolio we discussed about 60:40. Again, you may not have bonds. You may have a securities or securities and crypto or crypto and cash and property or whatever combination there is mathematical, again, a combination which will give you the highest possible return for the lowest possible risk.

Laura (27m 45s):
And it's really an optimization problem behind you have these two functions and you maximize wanna minimize or so. And yeah, that's the point. And so we calculate a bet and the fourth option, which is a feature, which is probably again very interesting for many people who know about efficient frontier is to be able for, again, for your set of securities and by weights, which you provided or calculated using the previous features, you can sort of drive across the efficient frontier and pick one point. So I say, well, won't say from, is X percent risk, 10% risk to 30% risk.

Laura (28m 25s):
I can have all these positions and this I'm happy with this position here, which gives me, let's say 25% risk and 40% return. Just example. And you can go, you know, again, click and it will give you a that combination, which gives you that particular position. And as I mentioned earlier, nobody can have it by hand. And this is why we had to come up with technology to do all this calculation behind the millions of calculations. Really.

Phil (28m 49s):
I love the sound of the efficient frontier. If you've got a link to something that will explain that for listeners,

Laura (28m 56s):
Yes. Actually on V or onward, diagramming on website as well. But over diagram or things that I mentioned, portfolio risk, beta, alpha, everything where has like an icon already people can click and get more information about what it means and how it's calculated.

Phil (29m 12s):
Okay. I love the audio of the efficient frontier in investing.

Laura (29m 17s):
Yeah. Well, again, Harry Markowitz came up with it. Yeah. And again, we don't recommend any intervals. It's a way of knowing what options you have for your portfolio and just go where you want, if you are happy to stay in your position, or if you want to take a lower risk, higher risk up to you, it's to help you calculate.

Phil (29m 38s):
I guess it's the information that you'd like to have to inform your decisions going forward based on data, rather than just some sort of idea you might have in your head or what a financial advisors told you. Because most of the time, they're just going to put you into a bucket of a traditional asset allocation that maybe doesn't have the correlation that you're looking after.

Laura (29m 59s):
Yeah, that's unfortunate. But on the other side, there are financial advisors and we have users who are financial advisors and use it to help our clients. Again, we don't try to replace anyone. They're doing the thinking and they probably, you know, use the tool as a way to visualizing what they explained to the, using a show them why that could be a particularly good, you know, composition for their circumstances

Phil (30m 23s):
And provide the data by way of backup.

Laura (30m 26s):
Yes. But backup or reinvestigate options. Again, it could be that way as well.

Phil (30m 30s):
So where can listeners find more about Diversiview?

Laura (30m 34s):
So diversiview.online is our website. We also have a blog on our LENSELL website where we publish regularly different, you know, articles about, we have a YouTube channel where we try to explain some concepts and how do things. And as I mentioned earlier, we, we wrote a research article recently looking at the top 20 Sharesitght, kind of preferable user investor's preferences. We wrote an article sometime ago, something similar. We try to kind of regularly look at some of this data, which is public out where, on where websites, or by, by collaborating with this sort of partners of ours.

Laura (31m 14s):
But trying to, to do a bit of research and show people what's really behind, but what they see. And the reason for that was really, I saw that in December that all recommendations on media about, you know, next year was top 10 best stocks to invest or top 15 something.

Phil (31m 34s):
Oh, they always do that. Don't they?

Laura (31m 37s):
Yes. So I found maybe, I don't know, 15, 20 websites from, you know, prestigious. I wouldn't give names, but what I tried to do in that article is to look at two of them, two of those recommendations from various return perspective, not just return, like being the best, but what risks you take. And it was really interesting to see that sometime they were recommending stocks, which you are very volatile for a very long term. So how can you make such a recommendation? You know, if you are, you know, so again, coming back to users, they should maybe pay attention what varied in what media, and actually do their own homework. But coming back to the Diversiview and where we can find more articles and subscribing to our newsletter, they'll receive, you know, all these materials and articles and insights to help them make better decisions because that's our aim.

Laura (32m 29s):
It started with my own interest or to make better decisions or better informed decision with data. And now we can help more people.

Phil (32m 36s):
So we'll put all those links in the episode notes and the blog posts. Listeners can find that. So Dr. Laura Rusu, thank you very much for joining me today.

Laura (32m 44s):
Thank you very much, Phil, it was a very interesting discussion and I hope, you know, if people have more questions they can maybe forward to you and happy to answer any time.

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