Hello, everyone. Welcome to the retirement puzzle brought to you by the Monash Center for Financial Studies. I'm Umul Rudba, a researcher at MCFS and one of the co-hosts of the podcast, where we explore the current and critical trends in the pension sector and try to explain how the impact has all. In this episode, we're going to talk about the Self-Managed Superfunds, SMSFs in short, who are an important part of the Australian Superannuation System. As of last September, they had about $728 billion in asset undermanagement, which was about 25% of our total super assets. Despite their huge size, not much information is available about their performance, persistence, preferences, or prejudices. Researchers at MCFS have been working with a large set of SMSF investors, day-to-day trading data, covering eight years, which puts them in a unique position to fill in some of this knowledge gap. Today, I have here with me two of my estimate colleagues, Dr. Naah Pham and Dr. Bay Sui from MCFS, who will share their first-hand knowledge of the trading behavior of the SMSF investor community. Hello, Naah. Hi, Uma. Hi, Bay. Hi, Uma. Thanks for inviting us. Both Naah and Bay have PhDs in finance. Naah is also a CFH charter holder. Their areas of research include sustainable investment, corporate governance, retirement, and superannuation. So, now let me start with you. MCFS just finished a research on the performance of SMSFs. Could you please give our listeners some ideas about these funds that you studied? Hi, Uma. Thanks for the question. I have to say that the research on SMS apps with self-well-limited, our industry partner on the project, was the highlight among some of our recent projects. We have written two research groups and a white paper on the performance and investment strategies of SMSF investors. They are all on our website. The self-wealth database allows us to examine the daily trading behavior of more than 60,000 SMSF investors with their Australian financial portfolios. Our analysis started with over 27,000 portfolios with a total value of $12 billion in 2012. Our sample expanded to more than 43,000 portfolios as of May 2020, right after the match COVID panic of the market with a total value of over 25 billion dollars. So, what was the average size of these portfolios and how did they evolve over time? Well, on average, the value of the portfolios increased from around $470,000 in 2012 to about $730,000 in 2020. The median value was just half of the mean value, though. This means that the average values of the portfolios were driven by some very large SMS apps in the sample, the multi-million dollars of assets. Yes, I mean some portfolios were quite large. The largest one in the database held almost $390 million of Australian financial instruments in 2020. What are the different assets that these SMSFs invested in? Well, yeah, umo lemme address this question. The SMSFs can invest in wide range of assets such as stocks, including both in domestic and international markets, bounce options and some type of real assets. However, our study only examined the performance of SMSF investors of their domestic financial assets portfolio, including Australian equities, listed investment companies, ETFs, options, warrants and some fixed income assets traded on ASX. And so, among all those non-catch financial asset portfolios, I just mentioned and the equity was the biggest components, which constituted around 80%. So, we only look at part of an SMSF portfolio, but I think that's the most exciting part. Ah, yes. Could you tell us about the overall performance of these SMSF portfolios that you studied? Well, I have to say that it was not a very row-sea picture that we have seen. Overall, when we analyzed this whole SMSF investor community in the database, we found that on average SMSF investors did not beat the market. We created index to represent the performance of the SMSFs in the sample. To put it in dollar terms, a dollar invested in the S&B ASX200 from mid-2012 would give you almost $2.50 up until the pick before COVID blunch in March 2020. The performance of SMSFs was worse than that. A dollar invested in 2012 would become only less than $2.20 just before the pandemic. SMSFs also had lower volatility than the broader market index. During the years, we examined however, even when we accounted for the lower risk, the risk adjusted returns of SMSF index were still lower than the broad market. Basically, it just means that for most of these SMSFs, they would have been better off investing in an index fund that gives them the broad exposure to the Australian market than managing their own portfolios. We also compared the performance of the SMSFs index with a number of broad market-style based indexes. If you are interested in this comparison, we have the details in our research briefs on the website. However, there is one thing that I need to add that our data did not allow us to consider the benefits of franking credits that SMSF investors are normally after. Did you find any relationship between size and performance? Because the Productivity Commission found that the large portfolios more or less kept up with the app-regulated funds. It is a small one that performed poorly compared to the large SMSFs. Did you find any such pattern in your sample? Yes, we did look into that. We divided portfolios with at least 20 stocks into four groups based on their size. The largest group had assets of more than $1.65 million and the smallest group had less than $400,000. We find that large funds performed better than small funds during the first period of our analysis. In the subsequent period that includes a pandemic time, the performance of the groups were relatively similar. However, we think that the performance persistence of large funds over the whole study period was better than small funds. So your findings were more or less similar to the one that Productivity Commission found. Bay, let me ask you, while reading the report, what I found very interesting was that even though on average the SMSFs in the sample did not perform very well, not just explained, there were some among them who did quite well. Could you please tell us how you found those patterns? Sure, Umo. So most SMSFs in our study indeed felt to keep up with the market. However, we have seen persistence among the best SMSF performers in the community. It suggests that there was some level of wisdom within the crowd among the top performers. And in that spirit, we developed an approach to use historical performance indicators to identify the past all performers. Our expectation was that these portfolios could continue to do well in the future. We have also a back-tested various risk and performance indicators to pick up the top performers each year. And indeed, we saw that these top performers do perform well in the following year, which validated our conjecture. It is an interesting contradiction, right? Since there is a belief that past return is not an indicator of future returns. Well, yes, Umo. That is an interesting and important twist, I would say. The top performers in our study exhibited a higher level of persistence in their performance compared to the crowd. Can you explain to us in some detail how you selected this top performers, which are labeled as winners from your sample? And how well did the perform compare to the others? Well, overall, the resulting strategy is based on two underlying principles. The first is that a reliable winner portfolio must be in the top they sell compared to its peers and the market in all risk-adjusted indicators. And the second one is that it must not be in the top they sell of risk indicators compared to its peers. We repeated the selection of winners every financial year based on the past year's performance. And then when we tracked the performance of the selected winner portfolios over time, the resulting SMSF winners, you did around 9.3% annualized return compared to the 6.4% of the S&P ASX 200 total return index. At the same time, the SMSF winners we selected had lower standard deviation compared to the ASX 200 Umo. That is impressive. Did they have a more diversified portfolio compared to the community? Well, the number of different stocks held collectively by winner portfolios range from around 400 up to 800 per day during eight year time frame with examined. ASX 200 stocks accounted for around 70% of the winner holdings. Although individual SMSF winners typically held a variety of stocks, their holdings collectively were highly concentrated on certain stocks. During our study, we see that the average dollar amount invested in the 20 most popular stocks held by the SMSF winners accounted for around 50% of the dollar amount of total holdings. Did you find any preference for particular sectors among your winners? Oh, yes. Our average compared to the weight structure of the ASX 200 index, the SMSF winner portfolios top 20 holdings were overweighted in healthcare, industrial and information technology, and awaited mainly in the financial sector, Umo. I'm very tempted to know that what are some of the popular stocks among your winners? Yes, sure. Let me give you some examples. On the top three stocks, the hold across the sample period were CSL limited, Commonwealth Bank and WestPak. For the CSL limited, the daily average weight in the SMSF winner portfolios was around 8.9%, which is 5.5% higher than the weight in the ASX 200 index. In your white paper, you talked not only about these winners, but also about the portfolios that did not perform very well, which you classified as the losers or the worst performers. Did you find any difference in the investment strategies of the winners and those of the worst performers? Yes, we have seen that there was a lot of overlap between what the best and what the performers held over the whole sample period. However, there was still some differences in their timing and the weight of the stocks in their portfolio. So we believe that the winners all performed the market because they are overweighted as a red stock at a red time. Well, the worst performers end up perform because they are overweighted in wrong stocks and at the wrong time. So when we developed an investment portfolio that selects the top overweighted stocks in the winners portfolio and filtered out the stocks, most overweighted by the worst performers, that portfolio significantly all performed the market. Let me ask you a very critical question regarding this. So in all your calculations, did you consider the transaction cost of rebalancing the portfolios? Yes, almost thanks for asking this. We indeed included transaction costs in the analysis. And after including the transaction cost, the risk adjusted return performance of the winner portfolios stays strong. Only if the cost increased to an unrealistic level of 250 to 270 basis points, would we see the all performance and enrolled it? This suggests that the strategy produced the superior's performance compared to the market net of the transaction cost. So what we have learned so far from you, Na and Bay, it can be safely stated that there is indeed wisdom within the crowd of these SMSF investors. Now I'd like to know what happened during the COVID-19 period. How did this self-managed Superfunds community behave and perform? Did you observe any panic selling during the market trough in March 2020? Any observation on how these SMSFs change their investment port priorities and asset allocation strategies during the first phase of the pandemic? Well, that is a very interesting question, Umu. The analysis of the investment strategies of the self-managed Superfunds during the onset of the COVID pandemic was very interesting. We did not see any evidence of panic behavior. What we have seen was a swift and huge decline in value of many portfolios, but then a very fast recovery for most of them. What was the extent of this decline in value and how much of it was recovered? Well, let me be very specific within the first three weeks of March on average, SMSF portfolios reduced by 30%. The total decline in dollar terms of the whole community was more than $7 billion. There were SMSFs that lost millions of dollars. The biggest drop of value that we saw was more than $100 million. Who can sit on this money and watch it go by millions of dollars every day? It was a very tough time. However, within three weeks after that until mid-Apo 2020, most funds had recovered around 90% of their pretty much value. The reason that this SMSFs could come out of the market panic drop without hurting themselves much was because there was no panic selling as seen in some pockets of the broader market. But there was no panic sell. Did the buy during those weeks of panic? Interesting. There was actually a net buy in the community of almost $50 million in January 2020. And the value of the net buy of the community doubled in February and tripled in March to more than $140 million. Well, I think perhaps buying the dips has helped these SMSFs quickly recover from the loss. They were in a good position when the market bounced back after March 2020. These are some very fascinating findings. Thanks, Na and Bay, for your time and sharing your research insights of this less explored investor community. From today's discussion, we learned that there are some very smart investors within the SMSF community. More importantly, the results reveal that to optimize a crowdsourced investment strategy, one should examine the strategies of both the best and the worst performing portfolios. One significant potential real world application of these research, particularly for the share trading platforms with ready access to large investor databases, is that they could use these findings, for example, to help guide the refinement and development of exchange traded share funds and other crowdsourced investment products. Thank you for listening to the retirement puzzle from Monash Center for Financial Studies. If you have enjoyed it, please tell your colleagues and friends about us. 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