this morning, on non-bank financial institutions, the conference covers quite a wide range. I think this is a particularly important element of it, but I would, wouldn't I? And I want to refer first to some remarks that Francesco Mazzuferro made in his opening remarks, his opening remarks. In particular, I want to thank him and the staff and Tomas and the staff of the two committees of the ESRB on which I serve as chairman of the advisory scientific committee and co-chairman of the joint expert group on non-bank financial institutions. I have benefited enormously from the work put in by a small staff, a dedicated staff and incredibly qualified and competent and imaginative staff, and I simply commend them all to you. The second point, the ESRB as a think tank, I would refer you to the working papers on the website, working papers series which has a number of papers of exceptional interest and quality, some of which come out of the financial data that Francesco referred to when he was speaking. And the insights you get out of those papers, and I have to say, the reports of the advisory scientific committee, I also commend to you there on the website, and for this session in particular, the non-bank financial institution risk monitor. We used to call it the shadow banking risk monitor, but that's not allowed these days, and it remains the same in quality and relevance and so forth, lots of interesting data but also analysis and illumination of the risks in the non-bank financial institution sector. The question about data, I think, and the issues about data are extremely important. Despite the very hard work of the staff and staff in other institutions, you will see when you look as you must at the non-bank financial institution risk monitor, you will see that there is still a segment called other financial institutions where although the flow of funds data show the money flowing, as it were, we cannot identify who these institutions are, what sectors they belong to. And the work conducted over the past few years has narrowed that category, but it is still important, and we need, in that sense, we still have, certainly still have shadow banking because the data are in the shadows. The role of national authorities to which Francesco referred, I see in every meeting and interchange that I have with the joint expert group on non-bank financial institutions. It's one academic, me, and a lot of regulators, and a lot of participants from the national competent authorities. And the work that they put in, and I think some of the output that we generate, testifies to the cooperation among those authorities in the context of the ESRB. That said, we know that there are difficulties in cooperation, and I put that at the institutional level, if you like, not at the individual level, but it's an important issue for us. And finally, again, coming out of the remarks of Francesco, the role of academics. The advisory scientific committee is, of course, a group of academics. It has exceptional importance in the constitution, the statute, as it were, of the ESRB. And this was, I think, very wise of the framers of the legislation to do this. And the advisory scientific committee, I think, has performed a substantial and important role in the deliberations of the ESRB general board. I would say that I hope that as we go on, that academics will get more and more access to the data that the amazing databases that have been generated by EMEAR, by AIFMD, by SFT. I understand the confidentiality issues perfectly well in using these granular data, but they are an incredible resource. And academics have participated, along with staff here and in other central banks, in using these data. You will find some of those working papers on the website that involve collaboration between academics and central bank staff. And I think we should have as much of this as we could possibly organize, given the constraints. I understand the constraints. Turning to our panel this morning, I will be very brief. They speak for themselves. They cover, I think, very interestingly, quite a wide range. And it shows how big this non-bank financial institution, sector, and how wide it is. The discussions we're having, we're going to have in a moment, one uses U.S. data, the second uses broader European data, the third uses U.K. data. And they cover the corporate loan market, money market funds, and demographics and the role of insurers in housing markets. All these issues are central in the non-bank financial institution sector. And there's a lot more. If I could round up 10 panelists on 10 different aspects of NBFIs. They wouldn't be as high quality, of course, as the three that we have. And I hasten then to introduce them. I think the running order will be, first, Nelce van Hollen from the Bank of England, where she's Senior Research Advisor. The second will be Maria Sunta Johnetti from the Stockholm School of Economics. And the third, Victoria Ivashina from Harvard Business School. And I give the floor to Nelce first, 15 minutes each in your initial presentations. Then we'll have some interchange among the panel, myself, I suppose. And then we'll have the floor involved. Nelce. Thank you, Richard, for the opportunity to present at this very interesting panel. So today I want to discuss the role of non-banks and how it relates to demographics. And I want to make clear that any views expressed here are those of myself and cannot be taken to represent those of the Bank of England or any of its policy committees. So the UK, as in many European countries, is an aging society. At the moment, already the number of retirees is surpassing the number of children. But it's projected to increase even further and surpass vastly the cohorts of older workers and younger workers. And these older cohorts have seen their wealth grow over time. So what this graph shows is in the pale line is the wealth in 2006 and the dark line is the wealth in 2016 of the different age groups. And the bars depict the change in this wealth between these two time periods. And what we see is that the wealth of the younger cohorts, everyone younger than 60 years old, has declined while the wealth of the older cohorts has increased. This is combined with a drop in disposable income of younger people. In the UK, we've seen a rise in student loans. And we've seen that the non-housing household consumption of those under 30 has been in decline since 2001, while that of the 65 years and older is actually rising. And this is combined with a sharp increase in housing prices. It's seen in many countries in Europe. It's especially affecting the London area and the key cities in the area around London. It's a lot less pronounced in the rest of England. And this is also combined with big changes that we see in home ownership since the 80s. So what this graph shows is the percentage of each age group that is a homeowner and what we can see in 1981, 1991, 2001 and 2013. And what we can see is that over time, the share of the probability of owning a house is going down for all those cohorts younger than 65. But if you look at the older cohorts, then you see that the share of home ownership is actually increasing. So what we've now have is a pretty strong correlation between age and the probability of owning a house. And so all the cohorts are now much more likely to own a house, while young cohorts have a very low probability of owning a house. And this strong correlation is something recent. We haven't seen this in the early 2000s. And this has led to a change in demand in the mortgage market. On the one hand, we see that the share of households buying with a mortgage is in long term decline. And at the other hand, we see that the share of households owning a property outright is increasing. And while this is not the only factor, but this is one of the reasons behind while we see that there are fewer mortgages originated now in the UK. So here we see both the number and the volume of mortgages. And you see it has not come back to the levels we saw prior to the crisis. If we then start going deeper, and that's where data comes in, because the UK has very good data on all the mortgages originated and who is originated these mortgages, we see that there is a growing role of non-banks. So the UK mortgage market is very concentrated. It's the big six banks are capturing currently still 70% of the market share. We see an increasing role of challenger banks, but we also see an increasing role of non-banks, the red line. And it's still a very low base. It's only 5% of the mortgages originated in 2018, but it's growing fast, especially since 2016. This, of course, are aggregate because if we start to look more carefully in which segments of the market these non-banks are active in, we see that it's almost entirely concentrated in low LTV mortgages, those with less than 50% LTV. And we see that there there is a very rapid increase in the role of non-banks, from 5% to close to 20% now of the market share. And we can also see that it's almost entirely concentrated in 55 plus households. So what's behind it? It suggests that there's something very specific going on in a mortgage market that attracts certain types of non-banks and these non-banks happen to be insurers. So it has to do with a particular type of product that has become more popular. It's the equity release mortgage, also the reverse mortgage or lifetime mortgage. And this specific mortgage is a loan that is paid to a borrower. It is secured against their home. And the loan is only repaid when the house is sold, upon death or upon entry into long-term care. In some instances it's possible to prepay voluntarily at earlier stage, but normally the house and the loan is repaid only upon death. And what this does, it allows wealth to be turned into cash. So this is a niche market, but the amount is growing for several reasons. One, there is a large proportion of retirees that are asset rich but cash poor. So they have a house but they don't have cash to use. And this gives them a way to release this cash. This cash can be used for consumption purposes, for care, but also in order to transfer money to their children in order to buy a house. Because another big problem or a big issue in the UK, but in most European countries, is that it's increasingly getting harder for young people to get on the property ladder. So these kind of mortgages provide parents with a possibility for intergenerational transfers outside of bequests. And why we think this market will be grow? Well, the over 50s untapped property wealth is growing fast and is projected to be double by 2036. So in the UK, this market is now growing with 70% each quarter since 2016, and in 2018, we had nearly 4 billion in equity release lending. However, the continued growth of this market and whether it's going to become an important market in other countries will depend on a lot of factors. One is the need, the demographic pressures we see in a society. How much can do retirees have to consume and for care cost? An important factor in this will be whether the future care costs will be funded socially by the government or whether individuals have to privately fund these care costs. It's also going to depend at country level preferences of consumers for intergenerational wealth transfers. On the other hand, what's going to matter is the feasibility. How big is home ownership in a country? It varies widely within European countries. How high is mortgage debt and what is the residence turnover rate? And then obviously, there need to be lenders that are willing to provide these products, and I'll get back to that in a second. And then, of course, the price. So the interest on these loans have been quite high in the UK but are falling recently quite fast, and that's partly because of competitive pressure with more insurers stepping into this market. So in order to understand what kind of lenders are attracted to these kind of mortgages, we need to understand the key loan characteristics. So these are loans with very long maturity, so 15 to 20 years. And these are fixed interest rates and they apply until maturity, so they're rolled up and they're only paid back at the moment that the house is sold. One of the guarantees behind these mortgages is a no negative equity guarantee, which means that if the loan is repaid, it's kept at the house value, so if there's a risk here for the insurer, if the house prices drop substantially. So the key risk, as I said, is the house price risk. The other one is the longevity risk. And then especially when it comes to banks, an interest rate risk because these are fixed rate mortgages. So because of these characteristics of these mortgages, there are barriers for banks to provide these equity release mortgages.  There's the obvious expertise that is needed in this kind of mortgages to assess longevity risks, which is not the key expertise that banks have. But banks can and do act as underwriter or originator of these mortgages because they can use their brand and customer basis. And this is a way to generate fee income, especially when marches on normal mortgages are going down. It is a very attractive product for insurers, because in this low interest rate environment they are searching for yield. It gives them an option to diversify their exposure. It's a very good match for their long dated fixed rate liabilities, their annuities, so they can avoid maturity transformation and interest rate risk. They have ample expertise in assessing longevity risk and their cash flows vary with more longevity. So as with any product, you can think about the benefits and risks that these kind of products can bring to the financial system and to the real economy as a whole. So what are the benefits of these equity release mortgages provided by insurance? Well, it's a diversification of funding sources for the real economy. And it is a way of unlocking cash at this moment in time that can increase current consumption. It can help fund deposits for first-time buyers as a way to help the younger cohorts on the property ladder. With of course the caveat that this only benefits children with wealthy parents, so it can have distributional consequences. When we look and think about it from the risk point of view, there are the standard solvency and conduct risk. The solvency risk, the risk that inherent in any product that a lender provides in this case is the non-negative equity guarantee that exposed an insurer possibly to a reduction in house prices. There is the risk of sector concentrations and insurers, while they have a lot of experience in assessing longevity risk, they have very little experience in assessing risk in the mortgage market. The mitigants that currently are in place is solvency to regulation and capital requirements. The conduct risk on the other hand is also a risk that we need to be aware of. The uncertainty and enforceability of some of the product terms, especially because these products are so new, and the potential of regest cost also associated with bringing new products to the market. With the mitigants, the pillar 2A capital requirements and FCA engagement. And then there are the risk of financial stability. This is still a very niche market. So currently there are very small risk for any financial stability concerns. However, if this market is growing and if these products become more important and if insurers in general become more active in the mortgage market as we see, for example, in the Netherlands, then it can lead to an increased interconnectedness with both banks and insurers exposed to the housing market. So while for banks, the key issue will be with a house price decline, the potential of borrower default and the reduction in the value of collateral. For the insurers, it will be this no negative equity guarantee that can create risks for the insurers. So to conclude, as we know, the economy is always changing and the financial system is constantly evolving. And demographic changes are one of the drivers behind these changes at the moment. There are clear benefits of a well-functioning diversified financial sector and in some cases, for example, these equity release mortgages, non-banks might actually be better suited to provide these particular financial products. However, it's very important that we monitor these changes and especially when they bring some interconnectedness within the financial system. Because over time, it might need that we need to focus that our focus of regulation may need to shift. Thank you. Thank you very much. That theme of interconnectedness, I think, is extremely important in this whole discussion of the role of non-bank financial institutions. They're interconnectedness with banks. I'll come back to that perhaps a bit later onwards to Victoria. Thank you, Richard, for having me here. So I will talk about a recent reform of the US Money Market Fund and the topic might have turned out to be extremely timely. Just last week, after 10 years, they fed that to inject liquidity in the repo market. Now, my research doesn't directly talk to this topic, but provides a mechanism for why this market might have become particularly volatile. So what happened to US Money Market Funds? So US Money Market Funds invest on their assets. So their asset size consists of short-term liquid securities, that is commercial papers, short-term obligations, and including repos. They were typically used by US investors as a slightly more lucrative alternative to deposits, meaning that, of course, they are not covered by deposit insurance, but most of the US Money Market Funds were trading at constant NIV, meaning that notwithstanding they were investing in marketable security, typically, the capital, the nominal value of the capital, was guaranteed unless the fund was breaking the buck, as happened during the global financial crisis. In that situation, the US government guaranteed all the liabilities of Money Market Funds. But of course, it became clear that this structure was extremely prone to run. And this has started the sweeping regulatory effort. I will focus on one particular regulation change that was announced at the end of 2014 and was implemented in the October 2016. So what happens in order to decrease the extent to which Money Market Funds are prone to run? It was decided that the liability of Money Market Fund that invests in non-government securities and that are marketed to institutional investors would become floating. So Money Market Fund in October 2016 started to trading with a floating net asset value. So these should, according to our models, should have made the funds less subject to runs. And overall, since they invest in relatively low risk security, we would think that nothing might have happened. Nevertheless, we think that this might be a good setting to think whether the structure of the liabilities might have changed the way investors perceive Money Market Funds and the strategies, the investment strategies of Money Market Fund. And the reason is simple. When an institutional investor has to book on its balance sheet, the Money Market Funds, now that they have a floating NIV, has to update the value of the fund every day. So these assets might have been reclassified from completely safe as cash to slightly risky. So what happened? Well, these might have been a minor change, but actually a lot happened to the assets of the Money Market Fund industry. So the dark solid line is basically the drop of the total NIV asset under management of Money Market Funds in the months leading to the implementation of the Money Market Funds reforms. And this is all driven by institutional Money Market Funds that were affected by this aspect of the reform. The effects for the retailer classes are much weaker. So basically, in these contests, we ask how the clienteals and the behavior of institutional investors in Money Market Funds changed and how the investment strategies of the Money Market Funds have changed. In principle, if an intermediary wants to provide an asset that is almost as safe as before, could just start investing in even a safer security. The value of this security will not change much over time. And this should guarantee a safe investment to institutional investors. But there might be commitment problems that prevent institutional investors from doing so. So all my presentation is based on a research paper. We have a lot of empirical evidence and tests in the research paper. I will try here to summarize and give the insights that I think are more interesting for the current situation. So what we observe is that in the months after the announcement of the reform and before the implementation, there is a huge recomposition in the Money Market Fund industry. And in particular, the safe Money Market Fund exited the industry. One way to see graphically how investors started to perceive differently the liabilities of Money Market Funds is through this graph. So this graph is showing you that in the period before the announcement of the reform, basically the flows into Money Market Funds and the flows into Money Market Funds just investing in government securities were highly correlated. So this tells in a rough way that Prime Money Market Fund were considered a substitute for government securities. This changes after the announcement of the reform. The period after the announcement of the reform is when all the redemption from the Money Market Fund industry occur. And what you see is that institutional investors redeem from Prime Money Market Fund and start investing in government securities. So the liability of Money Market Funds are no longer money-like. Now they are no longer a substitute for government securities. And then there is the new equilibrium after the Money Market Fund industry stabilizes and finds a new equilibrium. And what you see is that the correlation becomes basically zero. So these assets are no longer substituted as before. So this suggests that the behavior of institutional investors investing in Money Market Fund has changed and that the clientele might have changed. A more rigorous test that one can run in financial intermediation is to see how the performance of the fund is related to the change in net asset management net asset under management of the fund. That is a very important relation. Why? Well, the fund managers has an income that depends directly on the net asset under management. So we'll try to maximize the net asset under management. What we see is that in this industry, and especially for Money Market Funds marketed to institutional investors, the relation between previous performance and net asset under management has become usually higher. Meaning that institutional investors into Money Market Fund seem to search for a yield to a much larger extent. Now, searching for yield in the Money Market Fund industry is different from mutual funds. One may argue that an active mutual fund can select between different ranges of stock and may have skills to produce abnormal performance. In the Money Market Fund industry, these are very short-term securities that almost anybody can agree upon on the value. So having higher spread, higher performance means taking more risk. So basically, the changes in the clientele and the change in the behavior of Money Market Funds may have given Money Market Fund stronger incentives to take risk. Is that true? Is this what we observe? Well, this is what this figure shows you. So we are lucky in exploring the implementation of the reform because remember, there are some Money Market Funds that have a subtly the same strategy, a subtly the same business model, but are marketed to retail investors. And those structural liabilities did not change. So basically, what I am plotting here is how the investment in different type of securities with different risk changed for institutional Money Market Funds in comparison to retail Money Market Funds. So in this way, I am trying to hold the constant of what is the supply of security out there that, of course, affects investment strategy. So what do you see? You have some lines that increase dramatically after the implementation of the reforms. Those lines that increase are the spread, the net spread, that directly capture how risky is the portfolio of these funds. We look also at the portfolio weights. So what you see is that, for instance, the holding risk of institutional Money Market Funds increases with respect to the holding risk of retail Money Market Funds. What does that mean? It means that when they choose between bank obligation and the repo, these institutional Money Market Funds, in order to try to maximize asset under management, are overweighting in their portfolio the bank obligation, not the repo, not the very safe security in which we have observed liquidity shortage. And what do you see on the two lines that are decreasing? Well, that is the liquid share in the portfolio of these funds. That is the proportion of securities that will mature within a week. And, of course, there is a measure of low risk taking. And the proportion of a safe asset. Prime Money Market Funds, even if their main business is investing in corporate bank short-term security, can also hold government security. That's their proportion of safe asset. And what you see is that it decreases. So there is a clear tendency to increase risk in the portfolio of these funds. So then the question is, what happens to the US issuers that were relying to Money Market Funds? And we do these analysis at different level. So first of all, in the US, one of the different regulation force Money Market Funds to report monthly all their holdings in different securities. So we can look at who is funding US issuers. And which US issuers are getting relatively more short-term financing from Money Market Funds? So what do you see here is that, well, US issuers that have a relatively higher probability of default get relatively more funding after the implementation of the reform. Funding for US issuers that are relatively safer, that have a relatively lower probability of default, has decreased after the implementation of the reform. Now, of course, this is funding from US Money Market Funds. This is a very integrated business model. I don't know whether these issuers are getting funding from someone else. And the Fed doesn't know either. If I look at a different source and at the commercial paper that has been issued by US company, I see that there is a temporary decrease in the period that is leading to the implementation of the reform, especially for the clients of institutional investors that were relatively safer. But this seems to be a temporary effect. And let me conclude with this figure. So one conclusion when we've wrote the paper was, well, good news is that since markets are integrated, perhaps the reform made the US Money Market Funds less and less subject to runs. But some other fund providers, perhaps even European Money Market Funds, are investing in US issuers. And this is consistent or was consistent with some evidence on the yield on short-term securities issued by US companies and banks that increased temporarily in the period leading to the 2016 reform and then dropped. However, after last week, I looked again at this figure and it looks like that the yield of these securities has become more volatile. So what we don't know, and I think that we don't have a European data to access, to ask this question, is who has been investing before in US commercial paper, short-term bank obligation, and so on, and what determines these volatilities. So let me conclude what my presentation aimed to do was just to describe a reform of US Money Market Funds and the effect that a slight change in the Money Market Fund structure of liabilities may have on investment strategies and access to fund for corporations and banks. Thank you. Thank you. I liked it. I liked it, especially when you said, I don't know. And the Fed doesn't know either. But I think that's your initial remark that this all may be relevant to these extraordinary developments in the repo market in the US over the past few weeks. These spikes in rates, I have seen at least half a dozen explanations for this. If you can find half a dozen explanations for something, you know that nobody really understands it. And it may well be related to what's been going on in the Money Market Fund sector. The interconnection is a game. So onwards to Victoria. Victoria Ivashina. All right, so we're going to shift gears and talk about corporate loan market. And that is non-financial corporate loan market. So one fact, and there are many ways to illustrate this fact, there are many different graphs that point to the same. But the takeaway is that the corporate credit, and this is a figure that corresponds to US, after the 2008 had grown dramatically. And again, this is a US figure. And you can see that as compared to the previous credit cycle, we are talking about figures that are twice as large. Now, within this conversation about gross and corporate sector in the US, the focus had been actually on two segments. One of them is private debt. And actually, I will not be talking that much about it. Size-wise, this is not the largest market. Growth-wise, that's the largest market. Private debt is growing very fast. But if we aggregate the magnitude, this is something that is, well, we don't have concrete numbers about private debt. There are several industry numbers that are out there. There is no consensus as to what do we get to call private debt. And by the way, it's private non-bank debt. Because at least in academia, we traditionally call bank debt as private debt. So some of the numbers suggest that there is about 300 billion as of the moment of private non-bank debt originated, and another 300 billion that is sitting in what is called dry powder. In other words, capital that is committed for purposes or investment in private debt that is not yet deployed. But this is a number that most frequently quoted. So you can see that in magnitude, this is not yet that striking. And furthermore, if you start thinking about what exactly is private debt, it very quickly gets chopped into smaller segments because some pockets of the private debt are not comparable with others. Some are a little bit more dangerous than others. So I'm going to leave it at that and instead focus on the leverage loan market, which has been another focus of attention. Both of the segments are primarily funded by institutional investors, non-bank institutional investors. So private debt that's entirely on a non-bank side. Leveraged loans are originated by banks, but they are funded by institutional investors, by non-banks. So I'm showing you two figures, and this is issuance of the leveraged loans in the US and in Europe. Now, what is a leveraged loan? It's actually rather straightforward, even though the precise definition doesn't exist. But you can think for yourselves that leverage loan is basically a non-investment grade loan. Now, technically, it doesn't have to be rated. It just has to have a risk profile, in other words, the spread or interest rate of non-investment grade loan. So in the US, you can see that these numbers had been very consistently large and higher than the peak leading to 2007. In Europe, these numbers are actually much smaller. But in relative terms, it's a market that had been developing as well. Now, it will be relevant for Europe from the US perspective as well, through several channels. One of them is the fact that some of the larger originators of the leveraged loans in the US are actually European banks. These institutions like Deutsche Bank or Credit is playing quite an important role. Another element to notice here is the fact that actually, despite the fact that much of the action is concentrated in the US, if you look at the leverage ratio in the Europe, it's actually higher than the one in the US. So the current numbers of leverage, and in this market, this is not like a mortgage market, where we measure it loan-to-value. In this market, this is cash flow lending, cash flow-based lending. So this is measured with respect to your EBITDA earnings before interest taxes and depreciation and amortization. So you can see that in US, it's about 5.4 with the V4.1, which was in 2009, as compared to 4.1. And in Europe, the spike is very similar, and it's actually slightly higher than the US. So European banks play an important role in the US for origination of these loans. And in local market here in Europe, you have to be watching for the leverage. It indicates something despite the fact that the magnitudes of the activities are not so large. So focusing on this leveraged loan market to think through whether this represents a threat to financial fragility, there are two core questions that you have to ask. One is, is there risk on the second one who is holding it? Because just the fact that those markets are growing is not a concern per se, right? We should expect something in an environment with low interest rates. There would be more risk taking, and so we would expect that the markets, all type of high yield markets, would be growing. The question is, is any of the risks in this market mispriced? And if so, who is holding it? And particularly the questions that we are concerned are the institutions that hold them are runnable, as they levered, as they short term funded, as they generally in healthy positions that they would be able to withstand a negative shock. All right, let's start with the first one. And here, this is a multi-dimensional problem, but I will go after one particular point that is very important. And so the general idea here is that there's been generally erosion of contractual terms, but it wouldn't go beyond that. For example, leveraged loans are senior secure debt. So bank debt is senior secure debt. This is a particular type of bank originated debt. It's senior secure. But the reality is that the senior secured layer of debt had been growing. So in that sense, there's been erosion in that what used to be historically known as senior secured, actually not perfectly matching in today's profile of senior secured. But putting this point aside, a more important point is this contractual weakness. But again, contractual weakness comes in many flavors as well. So here's a particular argument for people to say that this is not an issue to be concerned about. Once we hear securitization, of course this reminds us about the securitization of the mortgages leading to the financial crisis. And the typical arguments that you hear to pushing back that this is not the same says well, then it was securitization of mortgages and it's individuals and they have income and there are real estate prices. This is all too complex. Whereas now we are lending to these companies that are very large. They are basically, but most of them are public companies. So the transparency is much, much larger. Well, that is true. But the problem is that you are not taking a claim on the company entirely. You are taking a very specific layer of very specific claim. So the claim of the loan comes with a contract attached and you write as a senior secure claim on this company and determined by this very complex document with hundreds of pages and many features. Now several issues being raised about what to look for. Starting with the simplest one, such as the famous covenant lightness, which means that how the financial covenants are enforced. That is something we can objectively measure and track over time and not surprisingly, we've been paying a lot of attention to that. But there is a more subtle elements to look for. And one specific point is that let's look at the following. So beyond financial covenants and covenants lightness by the way is entirely about financial covenants. But beyond financial covenants, a loan contract has several other buckets of covenants that protect features like liens, which is basically means your collateral indebtedness. That relates to financial covenants. Payments and several other. Not all of them are measured in financial ratios. And not surprisingly that senior secure debt among the most prominent covenants would be liens, which is the protection of the collateral and the level of indebtedness that you can take. So this is based on a very large sample of the loan contracts and this comes from my research. And what we're looking at here is incidents in the recent period. So we are looking post financial crisis of these features. As you would expect for senior secure debt, most of them will have protection of the collateral and most of them will have some restriction on debt. Now that comes however with small fund, which is what became known in the industry as baskets on car routes, but leaving the lingo aside, the point is that these things can be done with a small fund. For example, we can even redefine the definition of a bidder itself. It's no longer an accounting element, but something that we will specify in the contract. So to put it simply, when you start looking at the small fund and you go through these categories, we said almost everything had the protection on the collateral because it's a senior secure debt. But once we zoom in, actually only 13% of those contracts don't have the small fund that corresponds to the collateral. Almost all of them have restriction on indebtedness, but when we look at them, only 7% of those contracts have strong provisions around what it actually means. So there is a lot of devil in the detail in how this contractual features are defined, and this goes beyond simply counting financial covenants or beyond simply counting the covenants in general. And so this goes back to the fact, well, is this a transparent asset class? Well, you would need to be able to due diligence all of that, right? And so in a typical collateralized loan obligation, which is a securitized vehicle funding, essential for funding of leveraged loan, you will have about 100 of these loans. So now you have to do the diligence on the 100 of these loans with each one of the contract presenting this range of contractual features and very subtle language that you have to dig through. So this is a danger of this market and how to measure it and how to price it. Now as to who holding it, this question becomes a little bit on the one hand, it's the information that we have is reassuring, but on the other hand, this is a very important question on which we are playing a guessing game. So once again, the question here is not so, we know who is originally investing in a leveraged loan. So if you look at the leveraged loan market, we can say, okay, well, very small fraction goes to banks, then there are mutual funds and big chunk of it goes to collateralized loan obligations. So when we think about who is holding it, we're actually talking about tranches of collateralized loan obligations and not just, it's not enough to say that 60% at the origination of the leveraged loans goes in the portfolio of collateralized loan obligations. So who's holding tranches of collateralized loan obligations? This is why the question gets fuzzy. So on the positive side, because with the grip of their regulatory grip and supervision grip that we have post 2008, we can say it's not banks. That's good because banks are definitely very important institutions and all of that. But, and we can count it actually. We can say 90 billion, that's what banks are responsible on the spread and there are a couple of banks that should be watched because they have big exposure. But let's take that out. That leaves us with how a trillion of the tranches, who is holding them? And this ultimately becomes a guessing game. I can also tell you that there is in the US the way the conversation takes place, is that we quickly say, okay, it's not banks. Pension insurance may be, a lot of foreigners are holding it. So a question for you, are you those foreigners who are holding them? So, but, and it wouldn't be surprising by the way. So here is the European Insurance and Occupational Pension Authority, YOPPA, which is also in Frankfurt. And you can see through the since 2009, they've been saying insurers and pensions are very sensitive to reaching for yield in the low rate environment. So the key ingredients, the key motive is there to start buying this type of assets. I'm gonna shift gears a little bit and show you a little bit of evidence on a different side of pension funds around the world reaching for yield. So what I'm showing you here is a following. I'm looking at the alternatives asset classes. So private debt would be part of it, but private equity would be primarily. So real estate infrastructure, anything that is not public basically. I'm taking their allocation to alternatives. In 2017, part of their portfolio, just looking at their portfolio, looking at the number and dividing it, but what it was in 2008. And basically, if you double it, the number would be two. If you increase it by 10 times, like close to what Italy had done, then the number will be 10. The dark blue indicates two countries that, and we have very good data for that. This again comes from my own research. The dark blue indicates two countries that done it more than twice. More than double their allocation to alternatives. The group in the middle is one and a half. And you can see, and I put arrows for you that there are quite a few European countries that are popping out there. But more broadly you can see that this is a large global phenomenon of pushing pensions into the alternative space, which is an indication of the reaching for you. This is just shifting slightly the measurement. So this was value weighted, so the largest pension fund would show up here. This is, but an interesting part is that's actually it's equal opportunity. Small and large pension funds are doing it, which again kind of consistent with the shot. This is reaching for you. This is not structural. This is not about large pension funds. This is just pension funds under pressure. And again, I'm showing you a few countries and you can see France, Italy, Spain, Ireland, and Germany showing up as the countries that more than double their allocation to alternatives. And so this is for me would be some of the suspects to look for whether they're investing in leverage loan market as well. This is it. Yes, I won't say the usual suspects. Not the usual suspects in a way. But the, I can assure you that IOPPA is very conscious of these trends and the risks involved. Question is what to do about them. And I want to turn in fact now to some interchange among the panelists and then discussion from the floor. And I hope we can have some focus on the regulatory implications for the implications for macro-approved regulations. Some of you will have been here. Most of you I expect. Yesterday when there was a little exchange between myself and John Cunliffe and I didn't have the opportunity to reply, it wouldn't have been appropriate. But I would simply note that in the first panel this morning, Louise, the chair, Klaus, Knott and other panelists stressed that macro-proof so far does focus on banks. And we need much more focus on macro-proof beyond banking. Louise specifically mentioned asset management and there is currently a very constructive and deep I think debate to which the asset managers themselves have contributed quite a lot about what might be appropriate, what might be inappropriate in thinking about macro-proof regulation of asset managers. But of course asset managers are only one part of the space. On the other hand, in Europe, the position is rather different from the US because in Europe, the majority of the large asset managers are owned by the banks. And that's another aspect of this interconnection story. This is not the case in the US, but 14 of the 25 largest asset managers in Europe are owned by European banks. And that I think is just one example of the potential risks involved. So I now want to turn to the panel and ask whether each of them has any comments on what the others have spoken about and then we'll move to the audience. Niltia? Yeah, so Iwashina, on the leverage loan part, where do you see the biggest risk versus in the US versus Europe coming up? I mean, if we just talk about magnitude, it seems to be concentrated in US by far. But as I pointed out, there are several channels through which this can go back into Europe, right? Starting with the fact that the European banks are involved in origination, starting with the fact that there is something going on in the European markets that despite the magnitude allows for such high leverage. So there is restaking despite the fact that the market is not so good. And then the third point that I was making is the fact that you have to, it seems like this engagement, the funding for the leverage loan market in the US and in Europe is international. And we have not a very good grasp of where it is because it's coming through vehicles. It doesn't come directly. And so the magnitude sign US, but there are several connections, there is one little additional point in that in Europe, there is also, I mean, while it's a region, it's a big region, there are individual countries. And so the market also thinks about it as individual pockets. And I'm wondering, so for purposes of financial constraint, if something happens to the market, it seems like in US, it's an aggregate shock. Whereas in Europe, these countries can be reacting, individual markets, they react separately. And so that could magnify the reaction in terms of the financial constraints if there is a negative shock. Anything else you want to raise now, sir? No, sir? Anything else you want to raise at this point? No, not at this point. All right, so Nitta. So I was wondering regarding an LTE presentation, if we should consider this growing asset class for UK family as risk or just as financial innovation. I would think that the loan to value ratio is not as high as for new mortgages and so on. Should we really think that the risk will be there? I think the risk is on a very different aspect. So it's not so much on the borrower side. It's the risk is more at the insurer level and it comes at the end of the mortgage. So it's a very new product, which it requires a different way of thinking of risks in the housing market, the risk to a decline in housing market and how that then transfers to the real economy. Because the risk here is that non-negative equity guarantee that insurers face, that if house prices decline, then you will see that they face a loss. And that then comes on top of what's happening in the banking sector when borrowers default. And you can then get this whole interconnection between a housing price decline that first was only affecting the banks, but now and the borrowers, but now indirectly also via the insurers. I agree, but I would think that this crucially depend on the loan to value ratio. I am not, I don't know the details about this asset class, but I would think that he is lower than for new mortgages that are granted to, and that would be a mitigating factor. So that's- Yeah, that's true, but it will depend on, so if somebody is going to become very, very old, there is an accumulation of the interest rate. And the maximum that's the, so the borrowers at this point in time then accumulates his interest. And the whole idea is that everything will be repaid up on sale of the house. The bullet, yeah. So if the price then drops, then the insurer becomes exposed because of that non-negative equity guarantee. And the past few years have seen accumulation of risks in the insurance sector, for sure. That's definitely, so that's- Concerns about those risks. Victoria. I mean, first of all, I found it incredibly interesting and to hear both of the presentations. I perhaps have some clarifying questions that maybe I'll ask later, but actually I was curious whether Maria assumed a view of her research as a favorite interpretation of the repossituation in the US. So I think also if I look again at the figure on the gills of these securities, I would think that there have been other provider of funding entered, but perhaps they have more volatile demand for funding. And we don't know who they are. There is some super, super indirect evidence that they could include Euro area money market funds. And of course, there were other things happening in the Euro area in last week, the introduction of the tiering that might give a different incentive to invest for banks and so on. And everything is interconnected, but it's very hard to pin down because there are no data, basically. It's like for the CLO. We don't know who is holding that, so. I have to say this is very concerning. Here we have a phenomenon that, the repo market is central to the financial system now. And it's clear that there are aspects of its plumbing that we simply do not understand. And if you don't understand it, there's not much you can do about it, except the kind of ad hoc interventions that the Fed has made so far. But this is not good. So I think we now move to the audience and I'd be very pleased to hear your questions. I've got one there and then we'll go to Javier. Yes. Hi, it's a question for Professor Veschina. Could you identify yourself please? Yeah, Mario Padula, Italian supervisor on pension funds. Thank you. And it's actually first a clarification on the last graph you have shown, which shows that Italy's quite high in the alternatives. I think this is due actually to the definition of alternatives because that includes also real estate, right? Correct. Yeah. Because real estate is very much present in some pension funds for professionals. This is an historical trend. I mean, it's a public, these are public data, these are available from our website. And you are free and welcome to download it because while for private debt and private equity actually the share of asset and then management invested in private equity and private debt is very much small. Even said that, my question is the following. I mean, the experiences that we have from the US for private equity are kind of mixed in terms of success, but also there are also sort of disaster stories. The retirement system, the Alabama retirement system, the California public employee retirement system, the Kentucky retirement system. These are all stories where pension things didn't go well for pension funds investing in private equity. And of course, private debt is a slightly different animal but my question is if there is anything we should be doing as a supervisors and regulator to prevent the kind of risk that materialized in the private equity, in pension fund investing in private equity to materialize also for, because this idea, this magic portion, private equity is clearly not the magic portion for our returns. I think the same applies to private debt. And as a supervisors and regulators, we have to do a lot of work to prevent that this magic portion turns into a poison, basically. There are a lot of risk for side returns come with high risk. Then if there is anything we should be doing in terms of the governance of public pensions and to make incentives to be aligned between pension funds managers and investment managers, what kind of things we could do in this direction. So, you were absolutely right that the figure included real estate but I want to be clear that it included private real estate, not investment in public vehicle. In that sense it was investments through alternatives, non-traditional, illiquid, illiquid vehicles. On the private equity, first of all, I teach this subject to our students so I'm ultimately a believer in an asset class but it is a very complex asset class and there are some, and the problems go beyond the times that I possibly have to discuss but you are spot on in that this is a subject that needs to be approached very carefully and the problems are not only on the private equity side, there are a lot of problems also on the limited partner side, on the pension funds and it's them who oftentimes push for imperfect performance measures. It's them who oftentimes are entering an exiting asset class in some very inconsistent way. It's them who decide pursue asset class directly or through co-investment without putting much. So, but again, there is a lot to watch on the private equity side as well. I apologize for being self-promoting but actually it's something that's been in the recent book that I published with George Lerner where we discuss the issues on the GP side and on the LP side and it kind of goes through the way we would envision what needs to be addressed and governance being one of the points that appears there. You needn't apologize for self-promotion. It's one of the deformation professional of my profession, of our profession, academics. We just do it all the time and it's on the whole healthy. Javier Suarez. Thank you very much for the presentations. They were absolutely great and very informative. I'm gonna take on a little bit on Richard's regulatory sort of perspective but I think Richard, you wanted to talk about the regulation of known banks and my question is gonna be rather on the implications of bank regulation for the phenomena that we observe among the known banks which is more related to the interconnections. Also emphasizing several presentations. So in the first presentation, to which extent the regulatory treatment of reverse mortgages in bank regulation might also add to the factors that you mentioned in explaining that this segment of the mortgage market is mainly occupied by known banks. In connection with Maria Sunta, the evolution of the money market funds industry in the US was in part driven also by the lack of attractiveness of deposits. So one can interpret that that industry was born as an instance of regulatory arbitrage to some extent, I mean quote unquote arbitrage because on the other hand there were no explicit warranties on the money market funds. So I just wanted, I found your results fascinating on how the removal of something that looks like making money market funds money like eventually leads to the reactions that one would expect. For Victoria presentation. So again, how much is the development of these collateralized loan obligations phenomenon by product of bank regulations? So what we might see is that firms that used to lend from banks given the regulatory treatment of loans in banks books given the re-emphasis on bank regulation could have been moved elsewhere. And so actually I will take this as an opportunity for a more general remark and check how you feel about it. All this narrative on the search for yield that sounds a little bit worrying. In the end I see as a market mechanism at work. So you put regulatory pressure on some products in some parts of the financial system and the issuers of that type of risk instead of just not issuing place their issuances elsewhere, right? And through basically changing prices, increasing yields, they manage someone else to buy those risks. So in some sense we need this holistic approach to judge whether the reallocation of risk resulting from repricing in presence of regulation and thus making the outcome more de-seeable and less de-seeable. So to me it's absolutely not clear whether the disappearance of some risky segments of lending previously taken by banks going to the CLO market and hauled by other agents is actually good or bad for the system. So very interesting questions. Let's take them in turn if we may, don't you? Yeah, so I think the regulation plays a big role in this market. So for banks it's because of regulatory reasons. It's not very attractive to, but partly regulatory reasons, partly because of what their balance sheet looks. It's not an interesting market to move into, but for insurances because of the way their balance sheet looks, but also because of regulation in Solvency II which is a matching adjustment. They have a clear benefit, regulatory benefit, to actually engage in this kind of lending. And I think that's also what we need to take into account when we think about interconnectedness and the macroprudential policies that we wanna think about because it really depends on the country framework and the regulation on other sides of the financial system that might affect in which part of a particular activity you will see that non-banks become more active. You can see clearly in terms of mortgages in the UK versus the Netherlands. In the UK they're active in this equity release market which has very low LTVs and the risk lies more in non-negative equity. But in the Netherlands they tend to act a little bit more like banks. They are in similar types of mortgages which higher LTVs would bring different types of risks. So when you think about it from a macroprudential context and how we need to think about regulation, I think it's very important that we also look not so much on the activity itself but where in the particular activities these non-banks are stepping in. This is one aspect of a point that Marie Sunder raised I think is of very general relevance and we ought to be thinking about analytically is how the structure of liabilities affects the structure of assets. And I'm not a finance economist, I'm just a macro economist. But I think that's a fascinating issue. Marie Sunder, there was a question directed to you. So first of all, on a broad level I completely agree that we should think of different institutional investors and how to regulate them. But there are always spillovers and typically if we prevent someone from doing something or it gets costly within a financial intermediary it soon moves in some other type of institution which makes the problem of huge complexity. Regarding the specific question that Javier raised, I mean I completely agree that regulation here as a huge component. So a fraction of money market funds are basically affiliated with the financial institution and banking groups. And of course these funds that provide short term loans but they don't have capital requirements even after the introduction of regulation that they have some liquidity requirement but they is not comparable to the capital requirement of a bank. So the banks had incentive to push investors in demand for liquidity to invest in money market funds and the feature of the constant NAB was also from an accounting point of view a very easy way to achieve this. So regulation from one sector matters for sure a lot. Victoria. So collateralized loan obligations that's securitization of corporate loans actually one market where regulation didn't play first order role. Regulation did play first order role in jumpstarting private debt where bulk of a private debt is actually sitting in what is used to be middle market where the banks withdrew. The banks were never important holder of high yield debt and leverage loans are high yield debt. So in that sense things that are pushing that market are primarily the fact that this has always been outside of regulation to large degree the risk retention didn't play such an important role for the evolution of this market. And it's primarily the low interest rate environments that fools that market. Now which brings us to the second point that you say well it's only and which I acknowledged during my presentation it's only natural to see the high yield market balloon when the interest are low. So what is the evidence of the actual mispricing of risk rate? And there are a couple of ways of approaching those questions. Some of it is I'm working recently which directly thinks about pricing of CLO tranches but let me push the example a little bit further the ones that I was giving where I basically was appealing trying to make an illustration for you of do you believe in that the risk is correctly priced. So imagine that your pension fund buying triple A tranche of a CLO. A CLO holds a hundred loans roughly. You would need to understand you would need to have a sophistication of being able to understand across the six major categories of the covenants the type what that little font means for purposes of your recovery rate in default. Are you gonna still recover the historical 75 cents or is it gonna be closer to 40 right? So that's the stake in the question. So now you not only need to have sophistication to dig through one contract you need to do it a hundred times not even a hundred of times I'm mistaking more than that because that's only one little investment in your entire portfolio. You happen to hold 70 of the CLO investments. So it's pretty clear that the triple A tranche is not capable and doesn't have ability to due diligence at all rate. So now what is left? They're relying on two things. One is rating agencies and let me just shortcut and say that the process of rating accounting for what problems that we just described is not perfect. The second line sent you for the equity tranche and you tell me who holds the equity tranche. If you can't, we agree that there is a problem. Thank you. This begins to sound rather reminiscent of events more than a decade ago. I'm not sure we wanna go there but I've got two more questions. So keep them and we haven't got that much time. So be concise. Elu Fontaden first. Yeah, thank you. Elu Fontaden University of Mannheim previously advisory board of the ESRV. I was fascinated by this brief exchange before between Ned and Maria Sunta about what to make of the new mortgages and new mortgage loans in the UK. And Javier picked up on some of this but let me maybe take sort of a different tack from Javier's remark. So Javier says of course things are changing so new players do new things. That's how financial markets work and that's good. Javier seemed to say, yeah, we have put so much regulation on banks. Obviously these things migrate to non-banks. At least that's one way of putting up this straw man for this question. I would think maybe it's the other way around. Maybe fundamentals are changing and that is what your exchange seemed to say. If there is now a new risk in the economy, longevity risk and related issues, then you want new products to deal with that in the economy. We seem to see one response to this by these new ERMs. Maria Sunta spontaneously asked, yeah, but LTVs are not that high so where's the problem? I think that's very telling about how we have to deal with this because LTVs in this case are not the right risk measure because we have a new macroeconomic risk and we want to understand this differently namely by measures in particular of demographics and so on. So when we discuss shadow banking, as Richard said in his introduction, I think we should be very careful to first to understand what types of risk are we actually talking about? Is it just a good old mortgage problem of 2007? And we just want to in the shadow banking monitor, classify everything as dangerous which looks like that or do we rather want to look at the types of risks that are being traded by these OFIs, other financial institutions in our wonderful little metric? And then we have to look at the metrics with which we evaluate this type of new instrument. And so that may be very useful to have this and why shouldn't insurance deal with that rather than banks or maybe in Victoria's case is much more problematic if the US population doesn't want to hold residual high yield corporate risk shifting it to foreigners, maybe a bad idea. So as long as we understand what these risks are and who is holding them, then we can have a much more useful description of what the shadow banking market is doing. And I think that's when we are getting there, we are understanding more, but we need to understand what types of risk we are talking about and what type of institutions deal with what type of risk. And banks can maybe hold some types of risk, fair enough, and others may hold other types of risk but we shouldn't pretend that by regulating the banks we have controlled risk and then the rest that spills over is bad. Just before turning to the panelist, one point and that is longevity risk is really difficult because the demographers have consistently failed, consistently failed in getting life expectation estimates right. You just go back for decades, tell you this and then, and it's just, you'd think that this was not so difficult. Turns out to be very difficult, sorry. Go ahead. I mean, I fully agree with you and that's why I think this is such a nice example of the positive role non-banks can play, the importance of understanding where exactly in the market they entering and why and what kind of risks they bring. Because in this case, this is clearly, so in the UK, so mortgage markets differ across countries, but in the UK, mortgages tend to be very short-term floating rates and these are long-term loans with fixed rates. So the traditional banks in the UK, for them it's not a particularly interesting product. It provides, gives a lot of risks and they can hedge against it but they are better, their tools are better to enter in the normal, the standard mortgage market. For insurers, these are, they have the long-term liabilities. They are used to, they want fixed rates and they're used to dealing with the key risk, the longevity risk in these kind of products. So you could see here that having a broader diversified financial sector can really help in order to provide more funding to a changing need of the society because this is an issue that is part of many, many countries and becomes more important. There are a lot of people that have, that are acid-rich but cash-poor so if the financial system can provide products that can help to release that cash when people need it then it can be very beneficial for society. However, it does, if it starts to grow we need to understand and we need to monitor and need to understand better the risk because we think traditionally in the risks lie in high-LTVs. So if we then just purely look at it from the perspective of high-LTVs yes, we think there is no risk because these are all low-LTVs but the risk lies somewhere else and we need to understand that and also the concentration of insurers in housing market or exposure to the housing market. It requires to think, yeah, to include that when we monitor the risks in the financial system. One more question here and you've got a microphone. Jace Kosciński, National Bank of Poland. I have two questions as a matter of fact. One to Maria Sota. You have focused on this MMFs that serve institutional investors. But what about the other ones? So the serving retail investors. Have their business model changed because of the change of this model of MMFs that serve these institutional investors? And to Victoria, you have analyzed the pension funds reaction. I mean investment in this alternative vehicles. But have you done analysis also in context of the difference between the defined benefit and the fund contributions pension funds? Is there a difference that can explain the difference in context of the countries? Thank you. Okay, the panelists can see how much time is left. So Taylor, your interventions, your responses appropriately. Go ahead. Reform was sweeping and had many aspects. So as a part of the implementation of this reform, both in retail and institutional money market funds, there is the possibility of suspending redemptions in case there is a drop in liquidity. So the reform affected also retail money market funds. But what we can see from the data, this happened to a much lower extent. This redenomination of the net asset value of the institutional investors seemed to have the first order effect. And all the changes that I showed you are much more dramatic for institutional money market funds. We see something to a much lower extent also for retail money market funds. So regarding the defined benefit, defined contribution, I emphasize the fact that it's a global phenomenon. I emphasize the fact that it's small on large. And actually, thank you for this question because in our sample, we do not find the difference between defined benefit and defined contribution. Now, while it's a little bit more obvious why the defined benefit would do that, right? Because of the pressure. But actually, if you think about it, the reality is that the private pension funds are also facing quite a bit of pressure rate. It's in a low interest rate environment. This is a typical high reaching for yield setting where, because they're essentially runnable because their clients can switch their accounts. And so they also face the pressure of delivering that little extra yield. So different channel, but both are subject to the pressure for to reach for yield. And in our sample, we do not find the difference between them. So I have two minutes, I think, for final remarks. They obviously are not adequate for a summing up and the range covered, as I told you at the beginning, so wide that the summing up really wouldn't be appropriate. But first, a bit of self-promotion.  There's no guilt, right? Why not? And that is, again, have a look at the non-bank financial institutions risk monitor, fourth edition, which just came out a couple of months ago, because it does contain a lot of both data and analysis relevant to the topics that were covered here. And second, I want to thank the panelists who have been, I think, first of all, no, I want to thank the audience because as an academic, I'm used to looking out at large groups and trying to figure out whether they're really focusing on what is being said, what I'm saying, of course, in that context, what I'm saying, or whether they're, you know, regard other things to do or think about. And I'm quite confident that the great, great, great majority of you were focusing very closely on what our panelists said. And that's because they said a lot of very interesting things. And I think we're all very grateful to them for that and we should give them a proper round of applause.


Hello, everyone. I hope you all enjoy the conference so far. How do you like it? Nice. I hope that I live up to the expectations for this talk, but let's get started. So I'm gonna talk about quantifying self like with all different materials like variables, different apps and everything like the older data stream that you can get and what the problems are with out-of-the-box apps and what you can do about it. A little bit about me. So as already mentioned, I kind of settled an MLE role, but in reality you are never and just in a machine learning engineer you like everything your MLOPs, your data scientists, your machine learning engineer, and I am engineer by day and crazy data scientists by night. I absolutely love playing around with new technologies, with new gadgets if I can get my hands on and yeah, it's a little bit perk of the work. I can get some pretty cool things. So if you want to connect, I'm on Twitter, Mastadon, Discord. You can also find me in the Discord on Nearpyton. Just ping me if you have any questions afterwards. So let's dive in. What about quantifying self? There are different types of this. So I wanted to give you my motivation why is it interesting for me and why I think it's important for everybody, then we'll dive into different areas of tracking, what it is, how it is done, what biases it creates and what biases it fights. I'll give you some examples, more concrete and tips how to minimize not so good parts about that. Then I'll give you overview of sample analysis of your data that you, I wouldn't say supposed to run, but something an idea to play around with where you can get more insights. And in the end we will have time for Q&A. I will try not to be too slow, so do you have enough time to get something to eat and be in front of others? But let's see how it goes. So data collection. Beforehand we start with this. There is a market of mobile apps and wearables. There are two markets. One is health apps and one is lifestyle apps. Health apps are medical grade, strongly regulated and rather big market, but not it's also very expensive and not for everyone. So health issues, we're not going to touch in this talk. We're going to talk about lifestyle apps. Fitness tracking, meditation, sleeping, everything that regulations kind of say, yeah, you should be careful with the data, you should not use untrusted algorithms and so there are some specific guidelines from medicine, but generally speaking this is just someone came up with an idea for a sort of form of apps. Then I'm going to talk about consumer grade devices. I'm not going to talk about algorithms and devices used in medical field because again you won't be able to get your hands on this and they are actually very hard to use outside of the hospital context. We're also going to concentrate on structure data collection, but there are other ways like taking pictures, freestyle journal that you can use in your everyday life. In this talk we're going to go from active or manual tracking to automated tracking or hybrid and hybrid in between.  How you're doing today from zero to ten? What was the highlight of the day and you write the free text and down the line to 24-7 variables that do not require any input. Hard rate monitors like watches, rings, something else like bands, you can imagine. Of course, hybrids. Hybrids would be also wearables, but you need to create a specific context like I'm starting a training now. Those gadgets are capable of identifying where you start a training, but funnily enough it doesn't track as a training, so weird fact. Also let's talk about biases. A bias is a logical fallacy in the end. It can stem from data collection process where there is a specific flaw in the process or flaw in the hypothesis about the customer. It can stem from data analysis. Again, different hypotheses, different algorithms that are not really usable for the data you collected or some wrong steps in cleaning of data. Different things can appear and data representation will skew your understanding about what you're doing wrong and actually will influence your further judgment and thus influencing the data collection step.  So let's dive in into active tracking. Within active tracking we have structured data or apps and unstructured apps. Let's briefly touch on unstructured. This would be basically therapy. It's your free source, it's journaling, it's mood journals, but where you write with your own hand, you can draw pictures and everything else, but the problem is this is so hard to standardize and to put for multiple different non-homogeneous customer groups into one app. So it doesn't make sense. Go to therapy. Always good. Structured on the other side is huge market. I think it was estimated, so lifestyle apps like mood, different chatbots and stuff, they're estimated around 10 to 20 billion in 2000 pre-pandemic time and it was growing crazy rates. So, and it's not even saturated now. So we're talking about apps that ask you once in a day, how is your mood? And you give a number from 0 to 10, from 0 to 5, or some specific things like, did you experience pain or some upset stomach if you're tracking these specific things? So in a nutshell, this apps or this group of apps is tracking a very specific question about very narrow part of your life. But there are some really problematic things about it. For instance, again, they operate and they're built on homogeneous group of people and this is never true, right? It's never the case. So they can have wrong scope.  If you're not productive, you are unhappy. It's situational thing, but still, for you personally, it would be a different scope. Then, wrong scale. There are quite a few apps that ask you to provide a number from 0 to 5, which is actually considered not enough because you are losing a lot of in-between data. For instance, am I meh feeling today or am I meh meh feeling today? Am I like, absolutely amazing, it's a 5, or is my 5 actually a 7? And between 7 and 10, there is a huge gap. So you are losing a lot of data, a lot of graduation, like gradual scaling, and in the end, you will flatten out in your estimates as well because like meh, everything is meh, everything is meh good, but I don't have a specific number for that. So time frequency is also a pretty cool thing because again, super example for mood, the app, most of the apps ask you either randomly during the date once or in the evening, how are you doing today? And I had a rollercoaster of emotions through the day. I was down the floor, I thought I'm going to die, or I was above sky, above the clouds from happiness. What do I put in? Average, highest, lowest. In this moment where you actually think about it, it's not worth anything. It's already biased. It's already just trash. And the representation, of course, is also pretty funny. Some apps show you the previous results before asking the questions, which conditions you to follow the trend or to follow your expectations about yourself again throughout the number. So the biases. You can go on to Wikipedia at least and do this like a small exercise for yourself, go through the list and say like, am I actually into these bias? Am I resilient to this one? Is this about me? Do I tend to do this? It's like a really good game that can occupy you for hours. But there are some biases that these apps are trying to minimize, like anchoring. For instance, a lot of people think that it's because of one particular thing. I'm not going to drink alcohol. Everything's going to be fine. I'm going to do sports. Everything's going to be fine. Not the case. People are complicated. Life is complicated. But at the same time, the next really funny bias is everything is connected. Like, there is a really cool example of correlation is not causation. For instance, the more people buy ice cream, the more pirate ships are out there in the sea.  This is not the case. But this is a temporal correlation that summer, right? Everybody likes eating ice cream in the summer. And in the summer pirates are more active because better weather, less storms and what's not doesn't have to do anything with each other. But this is the case. So these apps, by isolating different contexts, reduce these biases. And you can read this on and so forth. And again, for every single person, the set of biases is going to be different. But these apps are causing cognitive distortions. Like, everything is connected, right?  And they do this by having this baseline assumption about all the people, which again, not going to be true or might not be true for you specifically. But the algorithms trained on your app are not customized in most cases.  Or for instance, I feel great, but I didn't sleep much. And I'm like, oh, no, I actually supposed to feel sad. I probably going to crash today. And only because of this conditioning, I will feel worse, which makes no sense. Brains is crazy thing. Right. And things like hindsight or searching for information, those are minimized and this is a good thing. But I guess you have used something from here and this is crazy in my eyes. It's a complete overload. You can count calories, you can count your social interactions, you can count anything to do everything. And at some point you are oversaturated and this is also a problem of UI UX as well as the design of data collection flow. So there are a couple of solutions that I came up with. You can also decide for yourself whether they work. And in general, it's keep it simple. Try to concentrate on one thing. Ideally read up on some research papers or some general, I wouldn't advise blog posts that they because they are oversimplifying or sometimes otherwise do represent the issue to complex. So read it up if you have specific concern and try to see a search for an app that does exactly that. They're definitely one at least one. Then get data out of it and run your own analysis. I will show you in the end how it can be done. Automated tracking. So definitely there is a hypothesis that how you feel will reflect in your physical parameters. Like if I'm stressed right now, I'm at 127 BPM. I'm freaking out. And my watch does tell me that I have no alerts because they're at 150, but we'll get there. So yeah, variable and apps or active tracking is paired with an app and with a hardware. The hardware driven, it has better tracking because it's not depending on your I feel bad or something else. It can tell you basically something happened here, something happened here, something happened, nothing happened, you get. And you can assume or you can pair your mental state in this moment with the data and then get some results out of it. Manual input possible. You can adjust the numbers or you can again pair it with something that this tracking device was not supposed to or not exactly supposed to, but is not measuring. It represents graphs, numbers and real time feedback. But yeah, they since there is an app, it suffers from the very same problems as the manual apps. And of course, on top of that, you have some problems with the sensors. They are not perfect. You have problems with the context or situation where in, for instance, if I'm going for a swim. My garment is not considered to be the swimmers garment. So I probably get have to get another watch as always. But the problem is the connection to my skin is has an additional layer of water and thus reducing the accuracy of the signal. There are also some very specific differences, dark skin, lighter skin or even something else. So this data loss, battery lifetime is a problem still, especially in a very small devices. And of course, undisclosed algorithms. The most of the apps and producers of this wearables are writing papers and posting blog posts, but they never, as far as I know, telling you what the formula is and whether it makes sense for you as a specific group. Saying that there are very different sets of sensors and different forms of tracking devices. The most used sensors are optical sensors. This is like shining light through your skin and analyzes the returning light. How much energy did it lose? And based on that, according to different algorithms, you can estimate heart rate, heart rate variation, as well as your oxygen saturation. Don't ask me exactly about the algorithms or let's check it out together afterwards. Then bioimpedance sensor is trying to identify the resistance of your skin. It's not for heart rate, although I saw some claims that it can be used like this. I don't know how. But again, I'm not the one who is developing that. So this is more used in scales to identify your composition, like how much fat to have, how much percentage of the bone structure and stuff like that. But you can theoretically use it also for stress levels estimation. Then accelerometer, you know that. Temperature sensor, you know that because the general assumption is the higher your stress goes, the higher the temperature is. You can also use this type of process to estimate the things. Gyroscope to properly identify the movement, for instance, if you have a need for workout tracking or something else. GPS, not everywhere, of course, but watches are mainly equipped with GPS. And this is also pretty cool because you can more reliably estimate the area you've covered for your hike, for your walk and does more accurately. The algorithms are using additional sensor data to improve the algorithm prediction or estimation. Then I put those two down after many other sensors. ECG and EEG stand for electrocardiogram that sends an electro signal through one point in your body and reads it out in the other. It's kind of close to bioimpedance from very high level approach, but it's not. It's a medical-grade approach. You've probably seen this in hospitals if you've ever been there. They put a small sensor somewhere and it reads out your heart rate and all the variations. And EEG is an electroencephalogram where you put a cap with multiple different electrodes on your scalp or somewhere else and they're reading out your brain waves. Super complex topic, super interesting topic. Not going to talk about it because I just don't trust myself to be good enough in this. So these things are special based and there are consumer-grade bands for sleeping, for meditation and for focus. But in my experience I have no association with the companies, but the most known one is Muse for sleeping and on focus. And so these are more for developers yet. So like neurosity, emotive and stuff, they're still positioning themselves as researchers, consumer-grade gadgets. They're super expensive and they're rather big. So the problem is and also they are utilizing so-called dry electrodes which have a problem of just going somewhere on your head and you cannot guarantee the proper readings. So these two are prone to huge errors just from the reading collection. Otherwise watches, bands, rings, even some implants, not talking about implants because they're mostly medical-grade. So now you know where they are.  But there are like really implants, implants that go really deep and can only be extracted with surgery. And for instance, glucose readings where you just get a blood of like a couple drops of your blood, they are considered intrusive. They're not implants, implants, but yeah. But let's see, I really am skeptical whether they will go into consumer-grade. The future is gray. So, but also different apps and different algorithms and different types of sensors or even how they are created produce very different data. For instance, this is the same day, day or rather night, around the same score, but you can see the deep sleep is like two times different. One is Garmin, watch one is Oro Ring. Who is correct? I don't know. I would like to think that Oro is correct because otherwise I'm in trouble. So, and what do you do about that? You get another gadget that uses different algorithm, different sensor to get the third value to average it out and see who is right. And then you have a problem that this is also might be flawed. That's devil's circle. But just be aware, if you have multiple types of the same data collection, be very careful with that. So biases minimized and created by this one, sorry, about the same. They definitely cause less biases just because your input is not needed and you can't really all the time try to fake your stress. Like, yeah, you can start breathing really fast all the time, but you're probably not going to last for too long. And why would you do that? But biases minimized are of course everything that I showed in the previous slide for manual tracking. And it's in my experience again, you can experience something else. It works better because you are taking out the perception during the data collection bias out. Again, just if you have any wearables, go for the bias list and check what you think about it. Solutions for these things, search for an app or ask for a feature from your provider that applies filters for your data. Don't react to absolute numbers. Try to see it in progression. Absolute numbers will have a meaning if you have an absolute outlier. Then keep an eye on it. And then you can rely on the data provided by your app within small horizon. Because normally anomalies in health data, and this is already health data although we are talking about lifestyle app, is not occurring, it's accumulating over a long period of time, but there are specific triggers. And you have better chance of catching them by looking at up to two weeks' worth of data. Export data. Most of the apps are pretty nice although the structure is awful. They're still allowing you to export data or get it through API. Try to analyze it and in a way of comparing similar periods like first quarter of previous year with first quarter of this year. Or go for a full year trend, split it into different behavioral patterns changes. For instance, I know that I tend to sleep longer in winter, thanks not a lot of sun, and less in summer and I feel great. So you would see this difference as well. But don't correlate directly, not enough sleep bed, too much sleep bed. It will depend on so many contexts. You can calibrate your incoming data and the graphs with other gadgets, but again, this is like a spiral. And it costs so much. Analysis. So, a couple of these analysis. I tend to see my health data as a time series. And the first approach that I took is I wanted to break it down into trend cycles and noise. You can do a lot of with cycles. Along with this, you will know how actually you are progressing trend. And also, if you're a cyclic, like quite a lot of things in life are cyclic. Sun goes down, goes up. You tend to be hungry. You tend to be full. You tend to take and so on and so forth. And hormones are definitely regulating all these processes. And it's not a long stretch to think that there are different size cycles in your life. Mood, energy, stuff like that. For women, it's even more than that. So just if you are aware of those, it's already a pretty cool knowledge. And keep an eye on anomalies. Anomalies are not to throw away data. It's own data. It's own time series. And also check out, check up with your doctor regularly. Don't rely on the trackers. Okay. Let me go to my small thing. Can you see it well? Can you read it well? Okay. So just saying that I went, I'm not a statistician by trade. So I've learned this thing again and again. I do have some mathematical training, but not to adapt. So if you spot some not optimal algorithms, let's say, do let me know please. I would love to learn, but don't judge me too harshly. So let's get first my, this is my data. I'm fine with sharing it. You can check it out on the GitHub profile. I'm fine with this. You have my confirmation for GDPR. Just go ahead. Right. Let's check out what war gives me. And this is the BPM. And these four things are the arbitrary result that their algorithm comes up with for simplification of the results representation we don't need. But as you can see, it measures BPM every five minutes when I'm awake. And this is just awake data. So I need to do some data preparation, grouping, blah, blah, blah. Let's keep that. And I've for simplicity for this talk, I went with minimum maximum and average for the day on the BPM reading. And if you plot it, and also I am comparing the one month's January specifically from 2022. And 23. Just can tell you that it was very different in my memory. In one, I was rather stressed. And you can see it's the case. For instance, in this case, the red line is the later period and the green line is the older period. In the older period, I had the about the same direction, but lower numbers. And this January was rough. And this is a knowledge for you because you can compare your context and adjust the context based on the data, not on data or your behavior. The next one would be sleep. It's quite a lot. That's way, way, way more data than this, but I just wanted to see deep sleep, light sleep, REM sleep and total sleep duration. Like the general things that people are watching out for. Same thing. Prepare, prepare. And let's compare how many days, three months worse from January to March of 22 and 23. There are different, I'm going to show you two different algorithms. And this is what I meant. Whenever you work with time series, be careful because there are different modeling approaches and you can get kind of very different results. So you will be the only one who can judge that it makes sense or makes no sense. This is the original with red line would be the 2022 and the blue line 2023. Yeah, we're talking about deep sleep or total sleep. I am way more consistent in the first quarter of this year, as you can see. But at the same time, yeah, it was not so fixed. This is fine. But for some reason, and this is an interesting one. I have a declining, I also have a declining, slightly declining trend, but long, not that bad as it was in the 2022. Which again, I can identify specific stressors in my life and due to this data and try to avoid them, which is a cool thing. And these cycles are kind of weird. But yeah, you can also use something like Fourier analysis or autocorrelation analysis to get the period and even see if you have the cycle. Because again, would be nice to know that every 14 days I have bad night and you don't have to. And if you know that you can kind of give yourself a leeway and say like, I'm not a bad person. I'm doing what I can. My body works this way. Relax. And then you just by actually allowing yourself to make a mistake or have a bad day, bad night, you will get rid of the stress and stress is a huge problematic factor. And maybe you can improve this 14th day or night. So another one would be deep sleep. I guess this is slightly different algorithm that I wanted to show you that shows different trend upward going. So although I was sleeping less, I was sleeping more in the deep sleep phase, which is kind of good, I guess, according to the latest studies. Very cyclic behavior, like I tend to sleep good, good, good, bad, good, good, good, bad. And I don't know right now whether it has to do something with I sleep very badly on Sunday because the Monday I have the first meeting at nine. Maybe who knows. But you can definitely check that out for yourself with your calendar as an additional data source. Great. And more for the light sleep, same type of analysis. And it's similar to the total sleep. Everything goes down, which is not that great. The cycles are not that stable on the light sleep, REM sleep. It's really funny one. So the I was getting more light sleep in the beginning of the 2022. And it's definitely declining in the later year. But I know why if you want to know, just ask me personally. But also really interesting how the cycles are behaving. So the later trend is going haywire. I really need to do something about it. Okay, but what about mood? So this was data from the structure data. And now let's talk about a structure data mood thing. The app that I'm using is asking me once a day at nine o'clock. How my day was from zero to five. And I can put some tags, but I didn't realize them. Same approach, same model, same 30 days from June 21 and June 22. Super different periods. And as you can see, the early trend was horrible. I was rather fastly approaching my negative mood. Had some stress in life. Yeah. And right now I'm pretty happy. I also absolutely love Europe height. And I hope you also do. And this gives me a boost. Cycles going haywire. But it might be also the problem of the algorithm. So again, be careful about that. I wanted to show you this one cycle computation, but I've asked Charger PT to write it and it didn't work. So, I mean, it did give me a number, but it made no sense. So let's keep that one. All right. So in, oh my God, where are all my things? And after this analysis, what can you do? Look out for outliers. I rely on Short Horizon from the app whenever you see an outlier. Because they are pretty good still in what they're doing. Compare similar timeframes, compute trend, and trend and other components on your own, according to the algorithm that works best for you because there are seriously different things. Use multiple apps because the context matters. Within an app, they are optimized to answer a specific question. And not basically delete the borders. And don't sweat it. Just time to time, like once in the six months or so, just stop. Don't track. Give yourself life. And my personal insights, I found out what my meditation time is. If I don't meditate in the morning, I don't. Training also, if I don't train in the morning, I don't sleep. I also identified some bed sleep things that were very surprising for me again. Ask personally. And also, I figured out what helped me to go into the flow. I guess everybody wants to be super productive and have this feeling more often. You can use this data to identify your triggers and what helps you out with that. Again, ask me after the talk. And knowing your cycles, trends, where you are going, you can better organize yourself. You can, for instance, say I'm not starting this new habit now. I actually will start it. Wait for it. In the upward trend from your worst point in the mood. Because then you have enough motivation from just beginning and you have it. And then you go into I feel great period for long enough to keep the habit. Don't start at your highest point.  I still think about it and it kills my mood. Just be aware of that. And again, it will decrease your stress and many, many more things that you can learn for yourself. So let's turn into cyborgs with more variables and check out the resources. These are mainly research papers that I found surprisingly interesting and insightful as well. And thank you for coming for my talk. Thank you for your time.