John Normand’s Post

🎙 Podcast Interview with The Wall Street Skinny 🎙 I had the great pleasure of speaking with Jennifer Saarbach and Kristen Kelly on their podcast series The Wall Street Skinny, which they launched in 2023 to bring financial education to a new generation. Jenn and Kristen keep the tone light but the content substantive, and have attracted a broad following across various social media channels. We started with the standard questions about careers path, then moved on to the structure of the pension fund industry; differences between buy-side and sell-side research; current views on markets; AI; and what academic philosophy has to do with practical Finance. Thank you to them both for the invitation, and to our mutual friend Ashish Goyal for introducing us. Here's a link to the video (https://lnkd.in/dgWtZQ4s). Below are markers for the topics we discussed: ▶ 2min: career path from sell-side to buy-side  ▶ 6min: the necessity (or not) of advanced degrees in Finance ▶ 11min: the uniqueness of Australia’s pension system, differences between buy-side and sell-side research  ▶ 16min: pensions — defined benefit, defined contribution, pay-as-you-go, and hybrid systems ▶ 20min: pension fund/endowment structure, internal vs external asset management, public vs private market allocations ▶ 24min: current market views  ▶ 29min: AI and job security  ▶ 33min: Japan, the yen, and carry trades  ▶ 39min: how asset owners generate outperformance  ▶ 45min: financial crises and public policy  ▶ 47min: Philosophy applied to Finance ▶ 52min: Philosophy applied to Philanthropy  ▶ 55min Closing thought: the importance of continuous learning

AustralianSuper has expanded its intern program in New York and London, where the Fund has been building out its investment capabilities rapidly over the past several years. See the link below for details on how to apply.

The CFA Institute's new certification in Private Markets and Alternative Investments covers the fundamentals of Private Equity, Private Credit, Infrastructure, Real Estate, Hedge Funds and Commodities. It provides useful background for those who have more experience with public markets than private ones, or for those who know more about some private markets than others. It's self-paced, and worth the time. CFA Institute CFA UK

I’m thrilled to be joining such an outstanding organization. Details to follow in a few weeks. AustralianSuper

🔘 A resource guide on #AI and AI #ethics, for #Finance professionals short on time 🔘 For me, #AI applied to #Finance is fascinating for the same reasons that #ESG is: both are #megatrends that disrupt the industry, while embedding #normative (ethical) issues within #empirical ones. The more thoughtful approaches on AI integration will draw on multiple perspectives rather than just the sciences. 📚 I wrote the attached article as a curated guide to on-line #courses, #videos, #monogrpahs and #books for those in Finance who feel like they don’t know enough about #AI, #AIethics or both, and who are also time-constrained. 👩🎓 Most resources are #free, which is a bonus for any #students in the audience who want to sample material as they mull their degree and career choices. #Hyperlinks are provided throughout, for easier sourcing. This guide is for the #crossover audience, not for those who already are — or want to be — domain experts. If you want to be a domain expert, better to earn a degree(s) than to do on-line courses or read a few books. My target audience includes: 1️⃣ #Analysts, #Economists, #PortfolioManagers and #CIOs who haven't studied #DataScience or read much #Philosophy; and 2️⃣ #Data Scientists, #QuantAnalysts and #QuantPMs who haven’t studied AI ethics. 🔔 Spoiler alert: only one of resources in this article focuses on #existentialrisk from AI. That’s because I think there are dozens of more immediate concerns worth addressing, before the machines realise that we humans are too primitive to be worth working for. I hope others enjoy exploring these topics as much as I have. As always, comments appreciated in the thread. #AI #ArtificialIntelligence #AIethics #Finance #Philosophy Coursera Codecademy CFA Institute Google DeepMind The London School of Economics and Political Science (LSE) Stanford Institute for Human-Centered Artificial Intelligence (HAI) Future of Humanity Institute (Oxford University)

⏺ A useful comparison of America’s increasingly-restrictive anti - #ESG laws ⏺ Harvard Law School Forum on Corporate Governance, which produces lots of useful content for investors focussed on #regulatory risk, has just published a short note comparing anti - #ESG laws across states like #Florida, #Arkansas, #Montana, #Idaho and #Utah. It was prompted by Florida’s passage this month of a highly restrictive bill that prohibits state #pensionfunds from basing investment decisions and proxy votes on anything other than #pecuniary factors. The bill defines #pecuniaryfactors as those expected to have a material effect on the #risk and #returns over an appropriate investment horizon. This definition excludes “the consideration of the furtherance of any social, political, or ideological interests.” The bill’s chief proponent has been Governor Ron #DeSantis, who is also seeking the 2024 #Republican nomination for #President. To me, the US state-level debate around ESG highlights an unfortunate muddle about a serious subject. Rather than argue pro-con, I'd instead approach the issues like this: 1️⃣ It’s sometimes said that investing is #values-free, so non-tangibles should be ignored. I disagree. Investing is very clearly about values — it’s about maximising #returns to ensure an individual's financial #wellbeing, which contributes to overall well-being. There is nothing wrong with valuing financial security, in order to pursue whatever else gives shape to one’s life. 2️⃣ Valuing one's well-being often requires considering newer and broader factors. Some of those might have seemed non-pecuniary earlier, but they become material as our empirical methods improve. Our #ethical evolution, which leads #regulatory change, also creates tangibles from the previously intangible. 3️⃣ My preference has been to avoid rigid E, S & G categories, and think more broadly about whatever factors are material for asset prices over a reasonable horizon. For example, #megatrends like technological #disruption (#AI-driven) do not sit easily within the standard pillars, but this factor is arguably as impactful across sectors as #climatechange. So rather than think by category, investors should incorporate myriad themes into an all-things-considered view. 4️⃣ Florida’s prohibition against considering #social interests may strike many as callous. I think that judgement depends on a few conditions. In countries with large, private sectors, there has always been a #divisionoflabor between what companies and governments take the lead on. Separate spheres respects principles of #efficiency, #competency and #legitimacy. I'm all for companies letting governments lead on social policy, as long as they pay fair #taxes (i.e. don’t engage in legal tax avoidance), promote fair #labor markets, and don’t engage in subversive #lobbying. Companies that violate these principles can’t claim that the division of labor generates a better outcome in aggregate. #sustainableinvesting

⏺ A word on all the previously-reliable #recession indicators that are failing in 2023 ⏺ Given so many full-throated forecasts for both #recession and #bearmarkets, I've started amassing the scrapheap of indicators that historically have signalled a recession but which are now failing tactical investors. 1️⃣ #LeadingEconomicIndex has never plunged so sharply without the economy already being in recession: https://lnkd.in/e4b2x5Zh 2️⃣ #LendingStandards have never been so tight (+45 on SLOOS) without the economy already being in recession: https://lnkd.in/efnt6gNB 3️⃣  #ConsumerConfidence has never been so low for so long (sub-70 on Michigan) without the economy already being in recession: https://lnkd.in/eG5Fq_BU I invite LinkedIn readers to add to the heap. I've tended to be more pluralistic when thinking about when recessions occur and how to invest around them, even though I've been a committed #recessionista since last summer. (June 2022 is when the Fed first started flagging a path to restrictive policy.) But to me, the causal path and the strategy run like this: ✅ #Fed tightens to #restrictive, so real cash rates above potential growth. Job done. ✅ #Corporateprofits recession begins, and runs for a year before layoffs begins, in turn triggering a #GDPrecession with more market drawdown than occurred during Fed hikes. A profits recession began in Q4 2022 but stalled in Q1 2023 (i.e. profits growth near zero). Corporate resilience delays a GDP recession and market capitulation. ✅ While the Fed tightens, some markets will fully price recession early, for idiosyncratic reasons. US housing (#MBS, #Homebuilders equities) and #UK High Grade Credit were 2022's examples; some Banks are 2023 examples. These are proper value plays. ✅ Other markets trade a range or edge higher on a Fed #pause, which I call the #LastHurrah. These moves last 6 - 12mos. ✅ #Strategic investors (1 - 5 yrs) should ignore the Last Hurrah and stick with defensive assets like #Cash, #Bonds, #Gold and some #HGCredit. ✅ #Tactical investors (1 - 3mos) should own #Equities, #Credit and #EM where (a) #activitydata are improving during the Fed pause; (2) #datasurprises are positive; and (3) #valuation are not very rich. Many of these conditions are being violated now, which is why I think the 2023 Last Hurrah ends this summer. See this April post for details: https://lnkd.in/eTE6yw7b 📈 Finally, mind the graphs in the PDF, showing US #joblessclaims (level and change) in the year before and after a recession starts. Yes, claims have jumped sharply from their lows in a manner suggestive that a recession has already begun. But the level of claims is nowhere near the recession threshold. The #economic process of recession belongs in 2024. The #market process of repricing for recession begins in Q3, because Last Hurrah supports are now fading. #recession #LastHurrah #assetallocation

⏺️ New Brookings article on #AI #productivity boom, but better read alongside a 2021 BLS study ⏺️ For those investing — or just watching developments — in #artificialintelligence, I recommend reading the note below published by The Brookings Institution last week. Unlike sector-level studies (some by same author Erik Brynjolfsson of Stanford University), this note focusses on aggregate, economy-wide effects. It's an easy read, like an Economics 101 text on how to measure #productivity; its constituent parts (total factor productivity, capital intensity, labor force composition); and its indispensability for raising living standards. The authors don’t estimate AI’s impact on the US economy precisely, but instead provide arguments to support estimates by Goldman Sachs economists that this new tech boom could add 7% to the US economy over the next decade. I suppose that estimate is plausible, given what the last #tech boom (1997-2005) associated with computer and internet adoption did to worker productivity. US productivity growth had been running at 1.4% annually in the 1980s to mid-1990s, then nearly doubled from 1990s to mid-2000s to 3.2%. The difference sounds small, until one considers the US economy's size ($22trn) and long-term compounding effects. Indeed, the subsequent collapse in worker productivity post-2005 cost the economy an estimated $10trn cumulatively, according to the Bureau of Labor Statistics. There are #fiscal consequences to an economy that's smaller than it could have been, like lower tax receipts, higher #deficits and higher #indebtedness. For a copy of that 2021 BLS study “The US Productivity Slowdown”, click this link: https://lnkd.in/eKSfHVfS. It's worth a full read. It surveys academic studies of America’s profound productivity slump over the past 15 years, by sector and region. Amongst the factors constraining productivity: 1️⃣ market power and industry #concentration; 2️⃣ restraints on #competition; 3️⃣ drag from #GFC; and 4️⃣ diminishing returns to innovation relative to late-19th/early 20th centuries. 📈 When constraints are detailed dispassionately in this way, it’s easier to see both the promise of and constraints on the AI boom. This technology is revolutionary, but if developed by incumbents that stifle competition, then benefits may accrue as concentrated, wider #profitmargins rather than as economy-wide productivity gains. So it would be right to bet on a revival of #BigTech / #Growth / #US outperformance over the long term, but it might be overly optimistic to bet on a step-up in US trend #realGDPgrowth, a return to #budget surpluses, or structural #USD appreciation. One great aspect of this #megatheme is that it will play out over years rather than over months or quarters. So while there’s time, do read the Brookings paper alongside the BLS one, so that we all understand how complicated the underlying productivity issue is. #ai #productivity #BigTech #GrowthEquities #USD

⏺ What would happen to markets if #Congress raised the #debtceiling next month ⏺ It’s obvious in some indictors that the current US #debtceiling melodrama has become more intense than in 2011 & 2013. As examples, US #CDS spreads have risen to record levels; yields on #Tbills maturing around the X-date (presumed date when US could default) have spiked by about 50bp over non X-date paper; and Google around topics like "debt ceiling” and “US default” have exploded. To me this recurring melodrama, which results from the US being the only major economy whose legislative process delinks #spending from #borrowing authority, is more serious than in 2011 or 2013. It’s more worrying for one reason: the #Republican majority is slimmer now than during clashes with #Obama, so negotiations are more hostage to extreme, holdout legislators. My base case is like most investors': a temporary extension (a few months), such that the debt ceiling remains a #blackswan (or maybe a red elephant) event, whenever Republicans control Congress under a Democratic President. Sadly, this issue has become the #fiscal equivalent of hot-button #social issues that divide the parties. I hold this view for the same, complacent reason as most. We all assume limits to stubbornness and stupidity, even if the time required to discover those boundaries is tough to predict. If Congress lifts ceiling within the next month, which markets benefit? #CDS spreads and x-date #Tbill yields will collapse to pre-melodrama levels, but those are not assets that global — or even individual — investors traffic in. Unfortunately for value hunters, there isn’t much dislocation outside of these niches. If the world's largest economy and issuer of the dominant reserve currency were at serious risk of default, we should expect decent #riskpremia across liquid assets too. For example, #Equities & #Credit should trade cheap to current cyclical conditions. #USD should trade cheap to policy drivers, like rate differentials. #Gold should trade expensive to macro anchors, like real interest rates. But, only one of these three indicates sovereign stress (see PDF). ❎ #USEquities trade about 10% expensive to the current level of the ISM (bottom chart). ❎ #USD (DXY Index) also screens about 10% to strong versus rate differentials to the rest of the world (top right chart). Only Gold looks significantly misaligned (top left chart). ✅ #Gold near $2000/oz usually requires real 10Y yields from TIPS to be negative, whereas current real rates ate about +1.2%. 📈 So what to do in major markets if Congress/Biden broker a fragile peace? Take short-term profits on overbought Gold, and return to analysis like figuring out whether the next #recession should play out like previous ones across and within Equities, Credit, FX and Commodities. I doubt the historical playbook can be recycled so easily. #debtceiling #Congress #Biden #Democrats #Republicans #Equities #Bonds #Gold #USD #politicalrisk

⏺️ New study on how #GenerativeAI / #ChatGPT impacts #Equities at the single-name level ⏺️ It’s sometimes argued that #BigTech has outperformed the rest of the S&P this year, due to #AI hysteria. That’s an unfortunate statement, because it’s so blinkered to the myriad influences on Equities this year, and to the granularity of #artificialintelligence’s impact on stocks. The Tech sector has been leading all others on cost/job cuts this year, which means they’ll be better positioned to reclaim #margins after the coming #recession, when #revenues improve. Much of BigTech has #Quality characteristics, which are important for style/factor investors. Finally, there are entire equity regions (like EMU Equities) that have delivered 15% YTD returns (not far from #Tech & #CommunicationServices sectors’ +20%), even though this region is renowned for its chronic lack of corporate innovation. So any account of why certain markets have performed as well as they have this year needs to look beyond simple answers like the deafening AI buzz. For a serious study on what a #generativeAI app like ChatGPT means for Equities at the single stock level, see the paper below published by the #NBER today, entitled “Generative AI and Firm Values”. It’s by #UCLA and #USC business school professors Andrea Eisfeldt, Gregor Schubert, and Miao Ben Zhang. The paper answers a simple, two-part question: 1️⃣ Which companies should realise the greatest input cost savings/productivity gains from integrating models like ChatGPT into their operations; and 2️⃣ How much would an investor have gained by being long companies with high exposure to AI benefits and short those with low exposure to those benefits. Importantly, the authors are not attempting to project revenue gains from ChatGPT — they focus only on #efficiency gains from reducing #labour costs. Below are a few takeaways: ✅ Their methodology ranks the US firms with highest and lowest exposure to ChatGPT (Table II, page 44). ✅ They also identify the #occupations that are most and least likely to be replaced by #LargeLanguageModels (Table C3, page 62). ✅ They estimate a 0.4% daily return differential on long/short portfolios in the weeks immediately after ChatGPT’s release, which is non-trivial for a high-frequency model. 📈 To me, the implication of this paper is that the analysis of AI’s impact on share prices needs to be much for granular by company, and must encompass both revenue gains (for AI #producers) and cost savings (for AI #consumers). Simply focussing on a handful of trillion dollar companies doesn’t do justice to the issue. Talking about #LLMs impact only on Tech is like talking about #climatechange’s impact only on #Energy companies. https://lnkd.in/ekTPaczV #artificialintelligence #largelanguagemodels #ChatGPT #NBER