What kind of risk taker are you ? Imagine being presented with a choice: receive $100 with 100% certainty or take a chance on $200 with 50% probability. Do you dare to take a leap of faith, or do you play it safe? The answer may reveal more than you think.
Risk aversion is ingrained in our human heritage. Our ancestors who took excessive risks did not live long enough to pass on their genes to the next generation. Yet risk taking is the driving force behind societal progress. Whether you are a portfolio manager, a trader or an entrepreneur, managing risk is an integral part of the human experience.
In this episode of 2050 Investors, host Kokou Agbo-Bloua takes a closer look at risk and its role in the financial markets. Kokou explores traditional risk management models such as Value at Risk, volatility and heteroskedasticity (volatility of volatility) and explores the interplay between Main Street and Wall Street.
Later in the episode, Kokou sits down with Hatem Mustapha, Co-Head of Global Markets, to discuss the ins and outs of managing risk in global market activities. They examine different risk management models, their pros and cons, and the importance of setting an absolute level of risk consistent with one's capital and ability to absorb losses, while taking smart risks with a favourable risk/return profile. Drawing on over 30 years of experience in global markets, Hatem also discusses the biggest mistakes investors make when managing risk, the role of artificial intelligence (AI) and the world of 'unknown unknowns'.
About this show
Welcome to 2050 Investors, your monthly guide to understanding the intricate connections between finance, globalisation, and ESG.
Join host Kokou Agbo-Bloua, Head of Economics, Cross-Asset & Quant Research at Societe Generale, for an exploration of the economic and market megatrends shaping the present and future, and how these trends might influence our progress to meeting 2050’s challenging global sustainability targets.
In each episode, Kokou deep-dives into the events impacting the economy, financial markets, the planet, and society. Through a magical blend of personal anecdotes, in-depth research and narratives overlaid with music, sound effects, and pop culture references, there’s certainly something for everyone.
Kokou also interviews industry-leading experts, personalities, entrepreneurs and even Nobel prize winners! You will learn from the best on a wide range of subjects on current affairs, market shifts, and economic developments.
If you like 2050 Investors, please leave a five-star review on Apple Podcasts or Spotify. Your support will help us spread the word and reach new audiences. If you’re seeking a brief and entertaining overview of market-related topics and their business and societal implications, subscribe now to stay informed!
Previous episodes of 2050 Investors have explored ESG, climate change, AI, greenflation, globalization, plastic pollution, food, healthcare, biodiversity and more.
Credits
Presenter & Writer: Kokou Agbo-Bloua. Editors: Vincent Nickelsen, Jovaney Ashman, Linda Isker & Jennifer Krumm. Production Designer: Emmanuel Minelle, Radio K7 Creative. Executive Producer : Fanny Giniès. Sound Director: Marc Valenduc. Music: Rone. Graphic Design: Cédric Cazaly.
Whilst the following podcast discusses the financial markets, it does not recommend any particular investment decision. If you are unsure of the merits of any investment decision, please seek professional advice.
Hosted by Ausha. See ausha.co/privacy-policy for more information.
Hello everybody, hope you all had a fantastic weekend. Let's start the morning meeting. Today's the day we get the US non-farm perils, a crucial economic indicator, obviously. It should be out in the next few minutes. Given the robust US corporate earnings we've seen in the recent months, I anticipate the number will be quite strong. Hum, I'm not so sure, Koku. The recent jobless claims and manufacturing data were a bit concerning. But what do I know? My insights are only as good as the data I'm fed. You are the expert here. Well, this is why there is a market theory. Everyone has different views, opinions and expectations. People agree to agree, or agree to disagree. And you know what? What? I can agree to disagree with the expert. Ha, everyone can get it wrong, no matter how smart they are. What matters in the end is to be right, more often than you are wrong, over time. And more importantly, to be good at managing your risks when you get it wrong. Cut your losses early. Wise words. Reminds me of this quote from John Kenneth Galbraith, the economist. They are two types of forecasters, those who don't know, and those who don't know, they don't know. I like it. Just wait a second, Siri. Speaking of forecasts, let me just have a quick check with Peter here. Hey, Peter. Do you have a minute? Sure. Cuckoo. What's the risk report looking like today? Are you feeling bullish or bearish on the non-pound pair or release? Difficult to say, I don't have a strong view here. There are good arguments on both sides, so I'm light on risk. Things will go either way, you know? An habitual detail, short vigor, but long gamma. But I always make sure I stay within my risk limits. My bar is only 10% of mass stress test. So we can provide equity, client activity strong, and we got good to wave low. Good point. I kept the long gamma short vigor exposure to capture even volatility at risk premium. What's the probability priced by the market? Well, there's an option prices from interest rate to option, currency options, and in the VIX4 equity volatility. Duke stacks that there's roughly 60% probability the number will be bad. Sorry to interrupt, guys. Look at the TV. The results are coming in. Good morning. This is Bloomberg Daybreak News. These are the stores that are set to your agenda. Long farm payrolls, full short of expectations, equity markets, planet, bondy and sleigh. Oops. I was not expecting that. The market is really taking a hit now. Sorry, Coco. I really need to go. Plant all those coming in, need to do some prices here. Speak to you later. Sure. Good luck. I don't think I got a single word of your conversation. Vega. Gamma. Beta. Don't worry. We'll cover a lot of that in a minute. Welcome to 2050 Investors, the podcast that deciphers, economic and market make a trends to meet tomorrow's challenges. I'm Kuku Agbublaw. I head up, economics, cross asset and quant research at Societe Genial. In this episode of 2050 Investors, we investigate global markets, investment and risk management. From managing multi asset portfolios to running a portfolio of businesses, we explore the age-old question. Does taking on greater risks truly result in higher return over time? What are the advantages and limits of stress tests, cross asset correlation, value at risk and heteroscedasticity? Finally, we explore whether the rise of machines and algorithmic trading could amplify systemic risks and lead to the next black swan. And to further explore the complex world of risk management, today we are joined by Hatem Mustafa. Go ahead of global markets at Societe Genial. Hatem will share his unique insights in running a global markets platform and how to navigate the evolving world of risk management. Let's start our investigation. Excentrating floors are noisy, intense and as usual, slightly chaotic. Yes, you're right. This is a good example of risk events in markets, economic data, corporate earnings results, geopolitics, etc. A disappointing jobs report and suddenly… The whole world's on fire, bond yields are plunging, equity markets are crashing and investors are panicking as they fear a recession will inflict more losses on their investment portfolios. But sometimes, markets do overreact. Main Street, also known as the real economy and Wall Street, the world of financial markets, are not always in sync. Markets reflect the current health of the economy but also anticipate the potential risks in the future. In other words, they constantly incorporate the probability distribution of any outcomes in the prices of financial assets. You mean like the precogs in the film Minority Report, who can predict future actions and events before they are committed? In a sense, yes, but unlike precogs, markets often get it wrong. What's interesting is that asset prices can in turn influence the real world too. George Soros called it reflexivity. An article from Investopedia.com defines the theory of reflexivity in economics as a feedback loop in which investors' perceptions affect economic fundamentals, which in turn changes investors' perception. Irrational exuberance in the bullish market, for example, can boost confidence and push businesses to invest and consumers to spend. It increases what economists call the marginal propensity to spend. Similarly, market crashes can jeopardize companies' ability to finance themselves, leading to lower confidence in the future and reduce hiring and investments, a sort of self-fulfilling prophecy. Are you humans seriously okay? Or is this one of those virtual reality games where you all pretend the world's ending? Siri, it's not a virtual reality. Although some days it does feel like it. Markets move based on what might happen, but there is a method to this madness. Let me explain. First, What is Risk? Humans have evolved to avoid it. Think about Darwin and his theory of evolution by natural selection. The survival of the fittest. Those who took too many risks didn't get the opportunity to pass on their genes to the next generation. Nevertheless, risk was a relevant driver for exploring new lands and finding new ways to upgrade tools, migrate, farm and so on. After all, a healthy dose of risk aversion is ingrained in our DNA. But in modern times, taking calculated risks is essential. Not simply in finance, but in business and in life. Ah yes, calculated risks. Like starting a new relationship. There's a possibility it doesn't work out, or you might just find companionship and true love. Haha, didn't know you were such a hopeless romantic, Siri. I get to point though, but when we talk about risk in financial market, we are using tools to measure it, not just relying on gut instinct. One of the most basic tools is the capital asset pricing model, or CAPM. It is a framework that describes the relationship between risk and return. The idea is simple. The more risk you take, the more return you should expect. But measuring risk isn't easy. The mix of math and human emotion. Emotions are powerful indeed. To paraphrase the famous quote by bless Pascal, the French mathematician and philosopher when he talks about love, the market has its reasons which reason knows nothing of. Well put, the book manias, panics and crashes. A history of financial crashes is also a great read to explore the irrational rise and fall in asset prices. Very insightful indeed, Siri. Now, there are a couple of ways to measure risks, and this article from Investopedia, entitled "Common Methods of Measurement for Investment Risk Management", goes through some important concepts. Let's begin with standard deviation, which is usually a good metric to measure the risk of an asset. It tells us how much a stock price swings around its average price. Metaphorically, it's something like a financial seismograph. It measures the tremors in an investment's performance, helping anticipate earthquakes in portfolios or assets. It's often used to gauge the historical volatility of an investment relative to its annual rate of return. For instance, a stock with a high standard deviation experiences greater volatility, thus making it riskier. Roger that. Standard deviation is calculated by looking at the differences between the daily return turns and the average return, squaring those differences, adding them up, and then taking the square root of that sum, divided by the number of days. Easy peasy. I could write a Python code to do that. Yup, that's exactly how risk management tools are designed. So this number is often analyzed by multiplying it by the square root of 252, which is equal to about 16. And by the way, 252 is the number of working days in a year. So an asset with an average daily fluctuation of plus or minus 1% has a volatility of roughly 16%. A tech stock that is more volatile with swings of 3% on average will have 3% times 16 equals 48% volatility. Exactly. In addition to standard deviation, there is the famous fear index called CBOE volatility index or the VIX index. The VIX measures the markets expectation for volatility for the S&P 500 index over the next 30 days. So a VIX index at 16% means 1% average daily price swings expected over the next 30 days for the S&P 500. Is that a good number for sentiment? Yes. Between 10 and 19% sunshine expected for markets, above 25%, clouds and rain, and above 40%, I would say thunderstorms ahead. So for anything above 25%, traders should stand under an umbrella, Ella, Ella. Or just buy stocks in raincoats, Siriana. Now, the not so basic concept is that volatility can itself be volatile over time. Let's say average daily return of 1% for 6 months, followed by a turbulent period with 3% average daily moves. This is called Eteroscedasticity, which in Greek means different variants. It is the opposite of homoscedasticity, same variants. Volatility of volatility. That's hardcore. Definitely. Moving on to the third concept, we have Sharpe Ratio. This ratio enables investors to assess how much excess return they are receiving for the extra volatility of holding a specific asset. A higher Sharpe Ratio indicates better risk adjusted performance. For example, a Sharpe Ratio of 1.5 is generally considered good, 2 is very good, and 3 is excellent. But about beta, we heard this a lot on the trading floor. Well, beta measures a security or sector's systemic risk relative to the entire stock market. It provides investors with a quick way to assess an investment's volatility compared with a benchmark, typically the broader market. If a securities beta equals 1, the security has the same volatility profile as the broad market. If its beta is equal to 2 or -1, it simply means the security should move up by 2% or -1% if the market moves up by 1%. And beta is equal to the covariance between the stock returns and the market returns divided by the variance of the market returns. Not sure I've understood everything, but I get the idea. Okay, let's finish with one last but important concept for risk management. It's the value at risk or VAR. VAR, is that a Nordic God with a hammer? Nice one, Siri, sorry to disappoint you, but VAR is a statistical measure of the potential loss in value of a risky asset or portfolio in a given period for a given confidence interval. It provides a single easy-to-understand number that encapsulates the downside risk of an investment. VAR is like a financial weather forecast telling you the chances of storms ahead, much like tours, hammer, neonear, which brings thunderous warnings of impending danger. For example, suppose a portfolio of investments has a one-year 10% VAR of $5 million. As such, the portfolio has a 10% chance of losing $5 million over a one-year period. Interesting. The VAR has some notable limitations, it doesn't provide information about the severity of losses beyond the VAR threshold. It'll tell you the likely forecast, but won't give you the chance for a low percentage storm that could wipe you out. So these tools give us a sense of what we could lose, but even then, as we're not pre-cogs, we can't perfectly predict how people will react when they see dark clouds forming. So do we need more sophisticated tools? They are plenty more, such as CVAR, Delta, Gamma, Vega, Voma, Vana, Rho, Expected Truffle, and the list goes on. But what matters is to understand the psychology of markets and the pricing of risks in times of fear versus greed or bull vs bear markets. You mean swings between too much or too little risk aversion? That's correct. Imagine you're offered $100 with a 100% certainty or $200 with a 50% probability. What would you take? Well, I'm a machine that likes to take risks, so I do the maths and pick the $200 with 50% probability. But you humans, I bet most of you would settle for $100. Most likely, humans on average prefer certainty, even if it means giving up a chance at something better. That's risk aversion at work, and it's hardwired into us. However, in bull market, investors can behave irrationally and take on too much risk even if the odds are against them. When asset prices go up for too long, they can be perceived as a safe bet, even though they are more susceptible to crash. A good example was the house price bubble that ended with a great financial crisis in 2008. But doesn't more risks mean more returns? Well, not necessarily. In some cases, more risk can lead to lower returns when the risk is mispriced. However, taking no risk at all for too long can be worse because of the opportunity cost. Farmers, traders, risk-takers, for example, drive on risk because they know that failure is just part of the journey to success. Yes, the glamorous, fail-fast culture of startups. True. Restaking, whether in startups, business, markets or in life, is what drives innovation, without it, progress stalls. Yet, as markets become more automated, we're seeing machines take on more of that risk. algorithmic trading, for example, is now responsible for a large portion of global market activity. This reminds me of the 2010 flash crashes, one little glitch and it's like someone rebooted the matrix. The black cat walks by twice and suddenly, we get a black swan. Exactly. Flash crashes are perfect examples of how complex systems can spiral out of control. And this brings us to Nesim Taleb's concept of the black swan, those rare, unpredictable events that have catastrophic consequences. Yeah, but you humans have a knack for ignoring the risks that matter most. Like, I don't know, destroying the planet? You're not wrong, Siri. We covered this in our episode on green swans. Remember? Climate change is a systemic risk that financial markets and risk models are only now beginning to fully grasp. And speaking of global challenges, we've got a guest today who knows a thing or two about risk in global markets. Let's continue our investigation on risk management in financial markets with Atem Mustafa. Go ahead of global markets at Soussitation Hall. Hello, Atem, thank you so much for joining the show. So let's first kick off with the current market environment. Could you describe the current landscape in global markets and how it differs from the past? Hello, Koko. Thank you for the invite. It's my pleasure. Over the four last years, we had several events and dynamics, which impacted the global markets. The type of events that we faced, they're obviously by definition, they are new. But when you look to the type of crisis, actually, all of them, there are individual categories of events. You can mention the geopolitical type of crisis with the Ukrainian war and the Middle East crisis tension, the macro and economic dynamics with inflation shock, the growth concern, all the discussion about soft lending, hard lending, low lending, and how the central banks address this with monetary policy. You can also think about the AI bubble. You can make a parallel with the internet bubble in 2000. Maybe if you wanted to find something really new in terms of crisis is the pandemic itself, obviously which was a huge shock for the market. But even this one, if you go back very, very far in the past, you can make a parallel with the Spanish flu. So at the end globally, I would not say that something really new happened in the market in terms of type of events. What was really new for me over the last few years is how the market reacted to these events, how the shocks propagated, and the speed at which the market moved both ways to react to the event, to the news, and then to recover. Typically, if we take the example of what happened this summer with the crisis in Japan with the Nikkei down 12%, we saw the weeks jumping from 15% to 65% overnight, which is a very large move. And then it took the markets a few days or maybe two weeks max to recover and to normalize to the previous markets. Yeah, excellent points because this is an interesting description of volatility itself of becoming volatile, and you also made the pointer around the machines potentially creating more instability. So this leads me to the second question, what are some of the key tools and best practices for managing market risk in a global market business, for example, and how have these risk metrics evolved over time? Traditionally, risk management models are mainly based on historical observations. That's the basic approach. So we would be statistical analysis with different severities, which correspond to different statistical confidence and survival levels. So we would have VAR value at risk, which correspond to event that could happen a few times a year. Then stress test, which is more extreme type of shock, more severe, which would happen around once every 10 years. We call them decennial shocks. This type of approach is maybe the more established and the more known in the market. And this actually quite intuitive and easy to apprehend because people will kind of look to the past and try to find extreme scenarios. But this approach has a blind spot because history, as we know, can repeat itself, but sometimes it does not repeat itself. And here, to complement this, we have what we call the hypothetical stress testing. Hypothetical stress testing is to be able to imagine something that never happened in the past, but still plausible. Then maybe the last evolution and trend in the industry that we see from the industry, but also from the regulator trying to push the industry to have this type of approach is what we call the reverse stress testing. So the reverse stress testing approach would not care about the plausibility, as we do in hypothetical and hypothetical, because again, historical and hypothetical scenario are extreme, but still needs to be plausible and this is super important. In the reverse stress testing approach, you would push your scenario or your shock until a breaking point, until the rupture, just to see how solid is your position or your book, and then just to explore this point. Why is it important? Maybe we can use a concrete example, which is not in the financial industry, maybe in the auto industry, you built a new car, and then you want to test how the brakes works in different conditions. So we will test your brakes in normal weather conditions with some variation of temperature, of wind, of rain. Maybe that's the value at risk, this is like business as usual, and then you have some kind of relatively mild but extreme conditions. Then you want to stress test this. So with stress test, you will push, let's take the temperature, up to 50 degrees Celsius. This is extreme, but still plausible, you can imagine some days with the heat you would go to this type of temperature. And then it's important to see that your brakes still work, that's stress test. But then the reverse stress testing is you don't care about is 50 degrees plausible or not, you will take your car into laboratory and you will push your temperature 60, 70, 100, until the brakes do not work. And then you will see this point where your brakes do not work. What do you do with it? Either let's say your braking point is 52, and then you say, hmm, let's be careful. I need maybe to improve my brakes that who knows, 50 is too close to 52, and then I make it my new plausible stress test. Or you say, 100 degrees, I don't care, I'm not going to invest to improve the quality of my brakes to make it still robust at 100 degrees Celsius. But it's still important to go to explore how far is this braking point. This is super important. This was quite insightful. Now, how do you balance the need to take on risk to achieve returns? Clearly global market businesses take risk, but they need to generate returns. So what's your approach from that balancing exercise between restaking and achieving returns for shareholders? Great questions. There are two dimensions to this. First, setting an absolute level of risk we want to take. So this risk needs to be well calibrated versus our capacity to absorb losses, and the capital we want to allocate to this activity or to this desk. And then, within this maximum risk envelope that we want to take, then we try to take what we call SMART risk in the sense of good risk return. So typically, we would avoid to take what we call TAYL risk by the easy, let's say, maybe lazy approach or strategy, training strategy to go to sell out of the money options just to take the premium hoping that nothing will happen. This is typically something which is not a SMART way of allocating your envelope of risk. Second, another aspect that we look at is the consolidation and the hidden concentration of correlation between different positions. So typically, especially in a big organization, you would have like different positions and the risks spread over different books, desks, even regions. And then you need to be able to consolidate this and to add risks when they are correlated. Otherwise, you would lose the big picture and you will miss this concentration. I really like the point around correlation. So one of the best principles in risk management is clearly not to put all of your eggs in the same basket. So how do you manage correlation risk in a portfolio of businesses? Can we even think about an efficient frontier of businesses and the idea of an optimal allocation of resources among these businesses? Yes, obviously, diversification is about correlation in a way. So having a diversified book is to have assets, positions that are either negatively correlated or zero correlation, at least no positive correlation. That's where you have your diversification benefit. However, something super important to keep in mind, correlation evolves over time in different market regime and is not stable. So it tends to deform in stressed market where this kind of dynamic of correlation will be driven by risk management, like stop loss, moving from one position to another, taking your cane or cutting your losses versus another type of regime of correlation on the long term about asset allocation and more link to monetary policy. So we need to be super careful about this change of regime of correlation. A concrete example, recently something you may have read in the news and in the market comments is what you call bad news is bad news and bad news is good news. Typically, when you had these figures around the growth, around the employment in the US, the market could react different ways. So if you have a negative number in terms of economic growth, the first reaction is market anticipate rate cuts, which is normal and the right way, then bonds will go up. Then the equity could behave differently. Either the equity will also go up because people will anticipate the impact of this lower rates on the equities because you will capitalize your future flow with the lower rates. Mechanically, this is good for the stocks and then actually it's positive for the stocks. So in this case, bad news is good news and the correlation between bonds and equities is positive or you can be in a regime where bad news is actually bad news and the sense that negative number on employment is negative for the economy and therefore is negative for the equities, which is the most direct and obvious reaction to the equities. In this case, the equities and the bonds will have negative correlation. So this is super important and shows how this correlation could change over different market regimes. It's also super important, as you can imagine, for all this asset allocation theory in the portfolio diversification, the 4060, et cetera. And you can see here that this aspect is quite sensitive and more complex than it looks at first. Then when it comes to managing the portfolio of businesses, of course, we'll take an account this correlation that I just mentioned and again, keeping in mind that this correlation regime could change over time, but there are also broader type of businesses. There are businesses, typically, agency versus principal business. Agency business, what is good, is you don't take market risks or at the end, you don't need to take care about the correlation and this diversifies your business because it's based on fees and this is the perfect diversification. Obviously, the level of fees could depend also on market condition, but at least in terms of risk management, it's not like raising the same type of issues in terms of correlation between asset classes. Then, of course, the agency and the fees business, even if it doesn't raise market risk, they raise other type of risks, like operational risk, legal risk, things like that. But at least in terms of diversification, it's a good complement to your mix of business between principal and the agency and fees business. So this leads me to another question, clearly, things don't always go according to plan. So in your personal experience, what are some of the common mistakes that people make when it comes to risk management? Actually, there are a lot of mistakes that you can make by doing risk management. I start with the obvious one and back to something I mentioned at the beginning is around this hypothetical and historical scenarios. So to think that history only repeats itself is the first mistake. And you need to also think about hypothetical scenarios and not ignore that new things could happen that you never observed in the market. The second mistake would be to assume that the correlation is the fixed input of the model and do not change over time. That's also something that we already discussed. History and this would seem more surprising and more is to be too conservative. It looks good to be conservative, it's fine. But being too extreme in your shocks in your scenario, it's a kind of lazy approach because at the end, you're just covering about what could happen in the future. And it's easier than to find the right balance and to fine tune this kind of point of extreme but still plausible, because at the end, you can show a very large number in terms of potential losses, but it's because of something that no one thinks would happen, it doesn't help in managing your risk. So at the end, I mean, no need to be concerned about the end of the world as they say, I mean, the end of the world will take care about itself. We have the example of LTCM, collapse, it's a very large hedge fund, which was run by brilliant PhD in astrophysics and Nobel prices, trying to utilize everything. In this respect, you need to take a step back and to keep a common sense in the way your own models are super good, you need models, but again, all the models are imperfect and you need to have this in mind and not to rely too much on what the models are giving as an output. Last, actually, is to have the illusion that the list I just mentioned is exhaustive. So to remain humble and to use a quote from you, a cocoon that I heard, we don't know what we don't know. So at the end, the mistakes that you may make, actually, we don't know them yet, but we will see them in the future. Yeah, a brilliant point. I think this is the no-no-no's and the no-no's and the list goes on. So this brings me to another simple question, as you mentioned, clearly machines, models that can't predict every scenario. How do you see the role of machines and in particular, artificial intelligence in the future of trading and risk management? Actually, AI is already playing a role in trading and risk management. AI today can help predict flows, market moves, and therefore improve risk management. However, even if they are used and they are helpful, they have their limits. And back to something we discussed before, most of AI models rely on history and patterns that we observed, they have the same blind spot in the way than historical scenarios. And tend to ignore hypothetical scenarios. Unless we imagine one day that what we call the generative AI would imagine things that never happened, but we are not there yet. Also we must remember that markets are sometimes driven by emotions and irrationality which are very humans. So this part, the machines and the AI, will not be able to replicate. Even if some other type of typical human behaviour, like herd mentality, could be replicated by AI. Then, I would finish with a coat from a famous fund manager, Paul Tutor Jones, who once said, "No man can beat a machine, but no machine can beat a man with a machine. Therefore, human input will always be necessary." Brilliant. I love this coat. And I can clearly see that our audience will be happy to see that humans will still have a role in the futures of global markets. So this leads me to a final question to end this interview. To conclude, attend, what keeps you awake at night? Actually, not market risk, my kids. Brilliant. Thank you so much for your time. It was a very insightful discussion. Thank you, Goku, for the invite again. Thank you. To conclude, here's something to remember, life is about taking calculated risks. Without risk, there is no reward. But understanding the balance between the two, that's the key to success. Whether you're trading derivatives, trying to find love, like Siri, or trying to save the planet, to quote Mark Zuckerberg, "The biggest risk is not to take any risks." And if you mess up, well, at least you'll have a good story to tell in a podcast. Thank you for listening to this episode of 2015 Vesters. And thanks to Hatem Ustafa for his perspective and insight. I hope this episode has helped you get a sense of risk management in markets and in life. You can find the show on your regular streaming apps. If you enjoyed it, we'd love your help in spreading the word. Take a moment to subscribe, rate and leave a review on Spotify or Apple podcasts. See you at the next episode. While the following podcast discusses the financial markets, it does not recommend any particular investment decision. If you're unsure of the merits of any investment decision, please seek professional advice.
