1. That Was Then This Is Now
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1. That Was Then This Is Now
I just had to watch and listen to your message at Covenant Church. I was blessed by it and have shared it. I am so sorry that you continue to be harassed, but it is clear that our dangerous God thought your work needed amplification. I pray that you continue to feel his pleasure and presence.
Thanks, Chris, for engaging. As I see it today, God has represented himself both cosmologically and anthropologically. I believe that our anthropology depends on the comsological symbols, heaven and earth (Isaiah 66:1; Neh. 9:6). They come first in Genesis 1:1. Man comes from the earth, and he represents the earth, most especially in its press toward Sabbath rest, and the inauguration of the new heavens and new earth, when the realm of God and angels will transform the realm of men.
Others think that Paul is addressing the cult of Artemis and correcting bad doctrinal teachings that taught that Eve was created before Adam and that Adam was the one who was deceived and women would be kept safe during childbearing if they prayed to Artemis.
For the nearly 20 years I have been Reformed, I have loved it that the Reformed community is full of intelligent women who care about theology. It used to bore me to pieces to be among women who could only talk about trivial topics, and never topics in which I had any knowledge or interest. That the confessionally Reformed community is full of women with whom I can communicate, and men who take my contribution seriously, thrills me. That my husband is overjoyed to discuss theology with an interested wife pleases me.
Consider a strategy that has seen its relative valuation multiples soar relative to the market and relative to its own historical valuation norms. Its past returns are artificially inflated by this revaluation, and its future returns may be seriously compromised if there is any mean reversion in its relative valuation levels.
Consider the leftmost red bar, which represents the quality factor in US large-cap stocks. For all of history from mid-19684 to March 2016, we calculate a blended relative-valuation ratio for the 30% of the US large-cap market with the highest gross profitability, relative to the 30% with the lowest gross profitability. The top and bottom of the leftmost red bar indicate the 10th and 90th percentiles, respectively, of the historical relative valuation for our portfolio of high-profitability stocks relative to our low-profitability portfolio versus the short portfolio. The bar (on a log scale) runs from 1.4 to 2.4. This means that, from 1968 until March of 2016, the market rarely pays less than a 40% premium or more than a 140% premium for high-quality stocks relative to low-quality stocks.
Each bar also has a white dash roughly in the middle of the bar, and a circle. These represent the prior median relative valuation for each factor, and the then-current relative valuation as of March 2016. For quality, by happenstance, the circle on the bar rests on the median line (actually slightly below it). In March 2016, high-quality US large-cap stocks were priced at an average premium of 84% relative to low-quality stocks; this was very near the long-term historical median premium of 90%.
Fast forward six-and-a-half years. We repeat our earlier relative-valuation comparison of March 2016, but for September 2022. We note that low beta has swung from very expensive to very cheap for US large-cap stocks, as has momentum. And over the last six-and-a-half years, quality beat performance expectations, in part because high-profitability stocks became more expensive relative to low-profitability stocks7, leaving quality as the most expensive US factor today. Value fell far short of expectations, in part because value stocks got much cheaper relative to growth stocks, leaving US large-cap value at the 15th percentile of historical relative cheapness for the value factor.
Value investing hit its nadir during the summer following the advent of Covid, almost exactly at the end of August 2020. No one knew how long lockdowns would last. Nor could we know how much of the economy was headed for bankruptcy (almost all of this narrative aimed directly at value stocks) or how long the wait would be for an effective vaccine. We, as well as others, observed at the time that by some measures growth had never been as frothy relative to the broad market, nor value as cheap, even at the peak of the 2000 dot-com bubble.
We have often said that the most pervasive and dangerous mistakes in investing are performance chasing and data mining. This is no less true in the quant community, where the conceit is that our models, our reliance on supposedly long-term historical data, and our objective and disciplined implementation of our strategies, will surely protect us from performance chasing. Quantitative methods do not protect us from these mistakes, if we choose our strategies and models in part based on past performance. The too-common practice of using backtests to improve our backtests is the most pernicious form of data mining.
We submit that an academic seeking tenure has no better path to that goal than to identify a new factor that has generated great past performance. But where is the incentive to willingly test if the strategy owes its past performance to upward revaluation, unless that test is required in order for the research to be published? That test could force our academic to discard months of diligent work. Why would an asset manager choose to run such a test on their best-performing backtest, unless clients and consultants demand it? That test could spoil the marketing potential for a hot new product.
5. The 15 factors trading rich were US large low beta, US large momentum, US large value, US small quality, US small low beta, US small momentum, US small value, Developed quality, Developed low beta, Developed momentum, Developed size, EM quality, EM low beta, EM momentum, and EM size. We analyze here 19 factors rather than 20 (i.e., five strategies in four regional markets) because there is no size factor for US small; this factor measures small versus large.
6. The nine factors that negatively impacted investor performance over the six-and-a-half-year period from March 2016 to September 2022 were US large low beta, US large momentum, US large size, US large value, US small low beta, US small value, Developed low beta, Developed size, and EM low beta.
8. The eleven factors that are trading in their cheapest quintile are an average of about 1.31 sigma cheap. Those that are trading rich are an average of about 0.43 sigma rich. Given that the annual subsequent performance seems to be 1.1% per one-sigma difference in the starting relative valuation, this 1.74 sigma difference should point to 957 basis points of relative performance (1.1% x 1.74 sigma x five years), albeit with considerable uncertainty. For example, the far stronger linkage between starting z-score and alpha since August of 2020 suggest that this may be a substantial underestimate.
10. We are grateful that a respected thought leader chose to provide a detailed review of one of our papers. Many observations were highly valuable in improving our understanding of the nature of revaluation alpha and identifying better ways to turn the findings into incremental performance.
Not long afterwards, it was proposed, on the basis of pharmacological, biochemical and receptor-binding data, that the P1 purinoceptor could be subdivided into A1 and A2 adenosine receptors [5, 6]. A major advantage held by those in the P1 purinoceptor field was the early and progressive development of agonists and particularly antagonists that acted selectivity between different adenosine receptor subtypes. This ensured that our knowledge and understanding of the number of P1 purinoceptor subtypes and their pharmacological properties were much more advanced than our understanding of P2 purinoceptors.
The first major breakthrough regarding inhibition of P2 purinoceptors by a commercially available compound was made by Lubo Kasakov, who was undertaking a sabbatical from the Bulgarian Academy of Sciences with Geoff. Lubo had a long-standing interest in the role of ATP and P2 purinoceptors in the atropine-resistant, neurogenic contractions of the urinary bladder. He was characterising the contractile actions of the agonist α,β-methyleneATP, which was much more potent than ATP, in strips of guinea pig urinary bladder, and saw that the contractions were transient. Furthermore, reproducible contractions could only be evoked if an extended interval was left after the washout of α,β-methyleneATP before it was reapplied. Consequently, Lubo hypothesised that this desensitising action of α,β-methyleneATP could be used to study the contribution of P2 purinoceptors to parasympathetic neurotransmission in the urinary bladder. His subsequent experiments demonstrated clearly that repeated administration of α,β-methyleneATP depressed the atropine-resistant component of neurogenic contractions [13]. Contractions evoked by exogenous ATP, but not acetylcholine or histamine, were also inhibited by α,β-methyleneATP pretreatment, showing that the inhibition was selective for P2 purinoceptors. This was the first clear demonstration that ATP and acetylcholine are cotransmitters from parasympathetic nerves.
As was often the case with Geoff, the subject of my PhD was not rigidly defined. Instead, he made several suggestions for potential experiments and told me to read the literature and identify gaps in our knowledge and understanding. I was expected to then find and develop something interesting about P1 and P2 purinoceptors. Initially, I performed some experiments using the guinea pig taenia coli, the tissue that had contributed so much to me being at UCL, but they came to nothing and I moved on to portal vein, which, at that time, in the rabbit was the best example of purinergic inhibitory neurotransmission outside of the gastrointestinal tract [19, 20]. These experiments produced two papers on the modulatory actions of purines on sympathetic neurotransmission [21, 22], but they did not meet the challenge of being interesting. 041b061a72