1. A Record 91% of Fund Managers Expect Interest Rates to Go Lower
Marketwatch-As of this month, a record 91% of fund managers surveyed expect short-term interest rates will drop over the next 12 months, up from 87% in Dec. 2023. Those figures mark the highest levels of bullish sentiment on interest rates since BofA’s surveys first started two decades ago in 2001. By Louis Goss
2. U.S. Treasury Issuance is Set to Double in 2024 to $2 Trillion
Dave Lutz at Jones Trading Coming flood of US Treasury issuance unsettles some investors after blazing rally – While expectations for Fed easing may be driving bond prices now, some believe U.S. Treasury issuance, expected to nearly double to $2 trillion in 2024, could be a counterweight. Yields – which move inversely to bond prices – would have to rise from current levels to entice demand for the flood of new debt, they say. Such concerns helped drive Treasury prices to 16-year lows when they intensified in October. In a survey of investors by BofA Global Research, 23% said a bet on lower Treasury prices was their “highest conviction” trade for 2024, while 21% said the same for bets on higher Treasury prices.
3. 10-Year Treasury Yield Moved Back Above 4%
4. Earnings Reports Everyone Talking AI
From Jim Reid at Deutsche Bank
5. More Presidential Elections Seasonality Data.
Nasdaq Dorsey Wright
Based on average returns, the fourth year of a president’s term has historically been the second lowest for SPX and RUT. The best time for these domestic benchmarks has been the third year (which rang true in 2023).
During the fourth year, SPX and RUT have typically softened around the 60-trading day window (late March/early April) before reaccelerating into year-end around the 220th trading day (early November).
6. Since March 2022, U.S. developers have signed 57 supply agreements representing about 73 million metric tons of LNG annually
WSJ Russia’s invasion of Ukraine kicked U.S. exports into overdrive. Since March 2022, U.S. developers have signed 57 supply agreements representing about 73 million metric tons of LNG annually, according to S&P Global Commodity Insights—more than four times the number of contracts they signed between 2020 and 2021.
7. Uranium Spiking
8. Grayscale Bitcoin Trust (BTC) (GBTC) Sees Outflows of $579m
Emily Graffeo-(Bloomberg) — Investors have pulled over a half of a billion dollars from the Grayscale Bitcoin Trust during its first days of trading as an ETF.
The fund, which won US Securities and Exchange Commission approval to convert to an ETF from a trust last week, has seen outflows totaling about $579 million, according to data compiled by Bloomberg. It’s a stark difference from the other nine spot Bitcoin ETFs, which have pulled in a total of nearly $1.4 billion.
“Thanks to the ETF conversion this is the first time we’ve had clear sight into flows of GBTC,” said James Seyffart, an ETF analyst at Bloomberg Intelligence, who noted that investors may be profit-taking.
The flow data is a more complete look at how the ETF fared in the wake of SEC approval. While over $2.3 billion of GBTC shares changed hands its first day, the outflows now indicate that a portion of that volume was due to selling. “Grayscale has dominated the market for regulated Bitcoin investing for over a decade. Now that other issuers have come to market, we are naturally seeing some rotation into these new products,” said Zach Pandl, Grayscale’s managing director of research. “Total net inflows into Bitcoin investment products are what matters for prices, not substitution from one product to another.”
The outflows from Grayscale’s ETF aren’t entirely unexpected. Bloomberg Intelligence forecasted that the fund will drain over $1 billion over the coming weeks.
“Lots of this capital will find its way back into other Bitcoin exposures,” Seyffart said.
Some investors are fleeing to cheaper spot Bitcoin ETFs. With an expense ratio of 1.5%, GBTC is the most expensive US ETF that invests directly in Bitcoin. The second-most expensive fund, the VanEck Bitcoin Trust, charges 0.25%.
https://finance.yahoo.com/news/traders-flee-grayscale-bitcoin-etf-171627443.html
9. Empty Nesters Own Twice As Many Large Homes As Millennials With Kids-Redfin
by Dana Anderson and Sheharyar Bokhari
Empty-nest baby boomers own 28% of the nation’s large homes, while millennials with kids own just 14%.
Empty nesters take up a lot of large homes because affordability was better when they were young, and there’s no financial incentive to sell now: Most boomers own their homes free and clear, and most who have a mortgage have a low rate.
The landscape has transformed over the last decade: 10 years ago, young families were just as likely as empty nesters to own large homes.
Empty nesters take up at least 20% of large homes everywhere in the U.S.
Millennials with kids take up less than 18% of large homes no matter where they live. They own the biggest share in the Midwest and the smallest share in coastal California.
https://finance.yahoo.com/news/traders-flee-grayscale-bitcoin-etf-171627443.html
10. Working With Automatic Thoughts
Thought patterns and chronic illness-Psychology Today– Katie Willard Virant MSW, JD, LCSW
KEY POINTS
- Our brain uses automatic thinking to streamline responses to stimuli.
- Sometimes automatic thinking can be maladaptive, requiring an override.
- We can correct for cognitive biases by working with our mental camera.
The predictive text function on my iPhone lately has been making an error. It reads the word “so” and assumes that I want to write “Sophie.” I’ve corrected it many times, but it sees “so” and stubbornly assumes that I am writing to or about my friend Sophie.
Our brain offers its own “predictive text” function when it makes assumptions based on our past experiences. Sometimes, this works very well. When we encounter a hot stove burner, for example, we don’t have to write a pros and cons list about whether we should place our hand there. Our brain quickly computes that a hot stove burner is dangerous and should not be touched. Thank you, automatic thinking!
However, just as with our iPhones, there are times when our brain’s attempts to shortcut do not serve us well. This post explores how certain types of automatic thinking can increase distress surrounding chronic illness.
Attentional Bias
When we exhibit attentional bias, we pay selective attention to specific information, failing to place that information in a broader context (Savioni & Triberti, 2020). Many people living with chronic illness experience a hyper-vigilance around symptoms. We are very attuned to our bodies, noticing every ache and pain. This makes sense, as our brain believes — and rightly so — that we need this information to keep ourselves safe. Attentional bias comes into play when our brain is so focused on identifying symptoms that it ignores or barely registers health.
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Take a moment to focus on a part of your body that is uncomfortable. What’s it like to zoom the camera of your mind’s eye on only that sensation? If your right hip hurts, for example, focus only on the pain you are experiencing in that area. Now zoom the camera out to include your whole body. Does your knee hurt? Your foot? What about the other side of your body? You’re still acknowledging that your right hip has pain, but your brain is now placing the pain in the larger context of your whole body. Pain is part of your experience when you zoom the camera out, not the whole of your experience. When you correct for attentional bias, you receive a different picture of what is happening.
Interpretation Bias
Interpretation bias involves what we do with the information our brain has noticed (Savioni & Triberti, 2020). In chronic illness, there can be a tendency to interpret signals from the body as illness-related. There also can be a tendency to catastrophize.
As with attentional bias, this makes perfect sense. The brain knows that chronic illness symptoms often mean danger. Unfortunately, for many of us who live with chronic illness, the warning it provides sounds less like, “Just flagging these sensations for you. Do you think they are illness-related?” and more like, “RED ALERT! RED ALERT! THINGS ARE BAD AND THEY’RE ONLY GOING TO GET WORSE!”
Just as we did in addressing attentional bias, let’s pull the camera back. Observe the panic from a place outside of your big feelings. Speak gently to the panicked part of yourself, saying, “Boy, you’re really afraid. And it’s understandable. But you don’t have enough information to justify this high level of panic. Can you take a few breaths so we can evaluate what’s happening from a calmer place?” Treat yourself with respect and compassion. Once you’re able to calm yourself down, evaluate the symptoms you’re experiencing with a clearer head. Congratulations — you’re learning how to correct for interpretation bias.
Recall Bias
Recall bias involves focusing on particularly painful moments in our past experiences (Savioni & Triberti, 2020). We may remember vividly the harrowing moments in our illness journey, without also remembering the times when our health was relatively stable. Especially when we experience a bodily sensation that causes concern, our minds immediately may flash to images of our darkest times.
By now, you know the drill: We’re going to do some camera work with our mind’s eye. Instead of staying with the image of your scariest moment, we’re going to play the film forward. Let’s imagine that experiencing symptoms causes you to remember yourself lying in a hospital bed. Time didn’t stop when you experienced that moment, so you are going to call to mind images of you rehabilitating and coming home from the hospital. Unfreeze the camera and look at the entire memory rather than only its worst parts.
Why Correcting Automatic Thoughts Matters
Correcting automatic thoughts about illness grounds us in a more balanced reality. Keeping our stress levels in check is mentally and physically healthy, benefitting our quality of life. Putting in the work to identify and correct automatic thoughts helps us to rewire our brains, updating problematic thought patterns to more adaptive ones.
https://www.psychologytoday.com/us/blog/chronically-me/202401/working-with-automatic-thoughts
