Donald van Deventer

Very insightful! "Granular Treasury Demand with Arbitrageurs" by Kristy A.E. Jansen, Wenhao Li, and Lukas Schmid "We construct a novel dataset of sector-level U.S. Treasury holdings, covering the majority of the market. Using this dataset, we estimate maturity-specific demand functions and elasticities of different investors and the Fed, and integrate them into a dynamic equilibrium model of the Treasury market with risk-averse arbitrageurs. Quantifying the model reveals that (1) strong arbitrage leads to an elastic Treasury market and a steeply downward-sloping term structure of market elasticity; (2) monetary tightening raises term premia due to arbitrageurs interacting with investors exhibiting high cross-elasticities; (3) QE has limited impact unless the Fed credibly commits to sustained balance sheet expansion." https://lnkd.in/e3RUkFBu

5

12 Charts to Watch in 2024 [Q2 Update] https://lnkd.in/gGANnk84 Key charts and issues to keep track of in H2 and beyond...

4

#Mathematics (IF 2.4, JCR Q1; ISSN: 2227-7390) #FinancialMathematicsSection ✏️ Editor's Choice Article: Facing a Risk: To Insure or Not to Insure—An Analysis with the Constant Relative Risk Aversion Utility Function 👉 Link to Study: https://buff.ly/4c5ki3v ✏️ Keywords: decision analysis; risk analysis; CRRA utility function; deductible 🎉 Congratulations on the success of this article! @MDPIOpenAccess @ComSciMath_Mdpi

2

Aggressive tariff rises under a possible second Trump presidency would likely result in a reduction in US and global output in comparison to an illustrative baseline scenario with no further increases. We have estimated the impact on the world economy of a renewed trade war by conducting a series of macroeconomic simulation scenarios using the Oxford Economics Global Economic Model. In scenarios where the US lifts tariffs aggressively, the US would see short-term hits to the level of GDP of 0.4%-0.8%. If the US’s trading partners retaliate with tariff rises of their own, the modelled impact would be larger – up to 1.1% – and more persistent. For the US’s trading partners, the hypothetical impact is greater in scenarios with retaliation, with China, and particularly Canada and Mexico seeing the largest hits to GDP (averaging 1.8% in the worst-case scenario). The latter results reflect large-scale trade linkages with the US relative to Canadian and Mexican GDP. For a free-to-air summary from colleagues Brian Coulton and Alex Muscatelli, see https://lnkd.in/e3MqrgA2. For the full scenario analysis, subscribers can visit https://lnkd.in/eiNG-mHY #fitchoncredit #tradewars #tariffpolicy #macro #globaltrade

28

Very excited to share a revised version of our paper 🎃 "Zombie lending to U.S. firms" 🎃, in which my coauthors Giovanni Favara, Ander Pérez-Orive and I show that since the Global Financial Crisis, there is no evidence of zombie lending by U.S. banks, but 📢 nonbanks have had more lenient lending standards to zombie firms than banks.  In addition, despite some evidence of credit migration from banks to nonbanks, 📢 zombie firms file for bankruptcy at higher rates than comparable financially-distressed firms, suggesting that nonbanks' zombie lending does not help these firms survive.   📔 Our paper is now part of the Federal Reserve Bank of Atlanta WP series and can be downloaded on SSRN: https://lnkd.in/ew2xQvM9 Your comments are welcome! #zombiefirms #zombielending #banking #bankcapital #bankruptcy

73

4 Comments

AI_Rules_Part 2 by Eric Cohen This column continues an ongoing series of postings to develop helpful guidance for financial professionals related to artificial intelligence. It is developing a list of guidelines and advice, with the hope that we can collaboratively make some of them more organized and permanent. If you missed the introduction to the series, with the first five suggestions, focused on things to know before you start providing input to, or read and share the output from, a generative artificial intelligence (GenAI) tool, you will want to read that post, which began with five areas: https://lnkd.in/gcGrENeh

1

Special Article | In this paper, we make a case for routinely computing the CoHD alongside the CPI-F. We show that, though correlated with the consumer price index for food, they do not always move in the same direction and that the CPI-F is a poor predictor of CoHD. Thus, attempts to draw inferences on the health and nutritional implications of food inflation using the CPI-F can lead to erroneous conclusions. We propose that the CoHD be used as a barometer for barriers faced in attaining healthy diverse diets across time as well as space and as an input into policy decisions, such as costing social protection programmes. https://lnkd.in/e7tZxwve

1

Special Article | In this paper, we make a case for routinely computing the CoHD alongside the CPI-F. We show that, though correlated with the consumer price index for food, they do not always move in the same direction and that the CPI-F is a poor predictor of CoHD. Thus, attempts to draw inferences on the health and nutritional implications of food inflation using the CPI-F can lead to erroneous conclusions. We propose that the CoHD be used as a barometer for barriers faced in attaining healthy diverse diets across time as well as space and as an input into policy decisions, such as costing social protection programmes. https://lnkd.in/e7tZxwve

3

Agency cost of debt as incentive for executive inside debt: Evidence from empirical study The substantial holdings of inside debt cause both academia and industry to wonder about the reasons. Jensen and Meckling (1976) suggest that debt-like compensation could lower the agency cost of debt. Nevertheless, empirical evidence is still lacking for whether the management really chooses to use inside debt as mitigation of agency cost of debt. In this study, recently published in our journal Corporate Ownership and Control, the author from the USA uses the value of non-tax-deductible deferred compensation from the ExecuComp database as the measure of inside debt, leverage as the proxy for agency cost of debt, and examines the causal effect of changes in leverage on inside debt. Using phased increases in corporate income taxes in US states between 2006 and 2016 using difference-in-differences regression, the author identifies the causal effect of tax-motivated corporate leverage on the balance of deferred compensation. Firms increase their deferred compensation balance by $85,000 with a 1% increase in leverage. This finding implies changes in the agency cost of debt as a potential reason for variations in inside debt values and provides evidence for Jensen and Meckling’s (1976) theory. #corporategovernance #debt #agencycosts #boardofdirectors #finance Find the full-text paper below https://lnkd.in/drX84AVs

14

New corporate bond-level machine learning panel data with 341 predictors live on Open Source Bond Asset Pricing, OSBAP (https://lnkd.in/g_qDXCU4). Predictors are cross-sectionally ranked and re-scaled to the -1:1 interval. Merge this data with corporate bond return data on OSBAP and start forming factors with PyBondLab (https://lnkd.in/gaZeBU8d) or get some compute power and start data mining 😉. Example code for generating some factors from the 341 predictors can be found here: https://lnkd.in/guqs8CAa

115

3 Comments

U.S. Commodity Futures Trading Commission: CFTC Technology Advisory Committee Advances #Report and #Recommendations to the CFTC on #Responsible #AI Artificial Intelligence in #Financial #Markets The Committee made five recommendations to the Commission as to how the CFTC should approach this AI evolution in order to safeguard financial markets. The Committee urges the CFTC to leverage its role as a market regulator to support the current efforts on AI coming from the White House and Congress. The Committee’s Recommendations 📍The CFTC should host a public roundtable discussion and CFTC staff should directly engage in outreach with CFTC-registered entities to seek guidance and gain additional insights into the business functions and types of AI technologies most prevalent within the sector. 📍The CFTC should consider the definition and adoption of an AI Risk Management Framework (RMF) for the sector, in accordance with the guidelines and governance aspects of the National Institute of Standards and Technology’s (NIST), to assess the efficiency of AI models and potential consumer harms as they apply to regulated entities, including but not limited to governance issues. 📍The Committee is not recommending that the CFTC impose additional enumerated AI-related risks to existing risk management requirements (at least initially) and instead to develop appropriate firm-level governance standards over AI systems. This would accord with the NIST Framework. 📍The CFTC should create an inventory of existing regulations related to AI in the sector and use it to develop a gap analysis of the potential risks associated with AI systems to determine compliance relative to further opportunities for dialogue on their relevancy, and potential clarifying staff guidance or potential rulemaking. 📍The Committee recognizes that existing regulations require registrants to manage risks—regulations that likely already reach many AI-associated risks. In other areas, regulations may need to be clarified through staff guidance or amended through rulemaking. 📍The CFTC should work toward engaging staff as both observers and potential participants in ongoing domestic and international dialogues around AI, and where possible, establish budget supplements to build the internal capacity of agency professionals around necessary technical expertise to support the agency’s endeavors in emerging and evolving technologies. Link: https://lnkd.in/ecHrxNyy

267

4 Comments

For this week’s discussion, the team explored the paper “Financial Statement Analysis with Large Language Models”. This paper introduces the use of LLM in analyzing financial statements, evaluating the performance of LLM in predicting the directionality of future earnings and the discussion is led by Chen Wei Png. By utilizing financial statements and chain-of-thought prompts, the paper manages to achieve a similar performance in predicting a company’s future financials as compared to a first-year analyst purely based on financial statement data. This paves a way for companies to utilize LLM models to assist them in complementing their current approach in conducting fundamental analysis. Key Insights: 1. Effective financial statement analysis: LLM has shown to have much higher f1-score  in evaluating financial statements and predicting future EPS as compared to current solutions such as artificial neural networks and logistic regression models, especially with the combination of chain-of-thought prompts.  2. LLM results for analysts: Despite the higher predictive power of LLM models, the evaluation of binary prediction may not prove useful, especially when investors are more concerned about the extent of change in EPS of a company. However, analysts and investors are still able to use LLM to complement their analysis, as LLM can provide an alternative viewpoint that analysts and investors may have left out when conducting fundamental analysis. This paper opens the possibility of the use of financial statement analysis as a complementary tool to help analysts reduce bias towards companies. With further development in the LLM space in terms of mathematical analysis, LLMs may be able to do more than binary EPS prediction in the future. 💡 What are your thoughts on the paper? How effective do you think current LLM solutions are effective in analyzing financial statements? You can read more about this paper here: https://lnkd.in/gm5JcZnR #TimeSeries #MachineLearning #AI #FinancialAnalytics #LLM #ChatGPT

18

Decent paper from the BIS describing how standard optimization for long-horizon portfolios commonly overstates returns or understates risk by relying on square root of time volatility adjustment. I've never seen an ALM that properly addresses this and this gets worse as you add more private asset classes. "It is a widespread practice to use daily or monthly data to design portfolios with investment horizons equal or greater than a year. The computation of the annualized mean return is carried out via traditional interest rate compounding – an assumption free procedure –, while scaling volatility is commonly fulfilled by relying on the serial independence of returns’ assumption, which results in the celebrated square-root-of-time rule. While it is a well-recognized fact that the serial independence assumption for asset returns is unrealistic at best, the convenience and robustness of the computation of the annual volatility for portfolio optimization based on the square-root-of-time rule remains largely uncontested. As expected, the greater the departure from the serial independence assumption, the larger the error resulting from this volatility scaling procedure. Based on a global set of risk factors, we compare a standard mean-variance portfolio optimization (eg square-root-of-time rule reliant) with an enhanced mean-variance method for avoiding the serial independence assumption. Differences between the resulting efficient frontiers are remarkable, and seem to increase as the investment horizon widens" https://lnkd.in/e3Ruq_4Q

22

5 Comments

Inflation came in as expected with only small variations to the components of our bottom-up near-cast. Used correctly, the Monthly CPI is proving to be a very useful source of component data for the Quarterly CPI confirming that while the cost of living rebates may be dominating the headline numbers, we are seeing a more broad disinflationary trend unfolding and we are even more confident that core inflation will be back within the RBA's target band in the first half of 2025. #inflation #CPI #RBA

17