Discover and read the best of Twitter Threads about #sentiment

Most recents (24)

1/ Bear markets are notorious for brutal fakeouts, which is why it's critical to identify targets and invalidations.

Here are key levels to watch in both directions.📉📈

#BTC analysis 🧵
#NFA

More from Material Indicators here... mi1.pw/mitwmay
2/In bear markets everyone is looking for a relief rally to exit or add short. Those are good strategies if you can tell the difference between a fakeout and a breakout. #FOMO + failure to identify invalidation levels are why so many people get rekt. #BullTrap #ShortSqueeze
3/ Identifying targets is easy. Determining breakout or fakeout is more challenging, and while there are no sure things, there are some things we can look at to help mitigate risk of getting trapped. To get some perspective, let's start with an ultra wide macro view of #BTC
Read 17 tweets
1/ The recent survey of global fund managers by Merrill Lynch is quite interesting from a contrarian's perspective.

Over 300 money managers with circa trillion in assets under management very polled on the various portfolio, finance & economy questions.

#sentiment update 👇
2/ Cash balance happens to be the highest since September 11 crash in 2001 (@ 6.1%). 😲

To say they are quite worried would be an understatement.

Yes, it is a small sample size (the law of small numbers leads to errors) but when others are fearful, we should be greedy.
3/ Do you remember that classic line from Back To The Future:

"Marty McFly, are you chicken?" 😂

It looks like global managers have chickened out from taking risks, as their exposure to risk sinks to the lowest since the Global Financial Crisis.

The sentiment is in the gutter.
Read 20 tweets
Can computational methods assess the #sentiment of complex texts? In an article with my fabulous colleagues from the @WZB_Berlin, @unipotsdam @LMU_Muenchen @JungeAkademie, we answer this question by applying #dictionary and #scaling methods on a sample of #literature reviews🧵
The article is also a practical #guide to help researchers select an appropriate #method and degree of preprocessing for their own data. Our #corpus consisted of 6.041 summaries of reviews of contemporary German #literature acquired from @perlentaucher00 (2/9)
The linguistic #complexity of #literature reviews differ from other texts with regard to their #language-- ambiguity, irony, metaphors, etc.-- are comparatively difficult to capture with #computational approaches. So how did our different methods fare? (3/9)
Read 9 tweets
1. A thread on three #PBOC 2021 Q2 Macro sentiment surveys from Bankers (Sample 3200), Industrial Companies (5000) and Urban Bank Customers (20,000). Surveys data are considered less informative and need to be heavily discounted, but could be complimentary to other data source.
2. Though data came from two diff. surveys, bankers and entrepreneurs have similar view:

Entrepreneur(Bankers) view on Macroeconomy 23.9%(25.5%) as “Cold”,72.6%(71.8%) “Normal”,3.5(2.7%)% “Hot”. Normal got 50 and HOT got 100. Indexes recovered to pre-Pandemic, Q3 lower than Q2
3. Product Selling Price (Raw Material Purchasing) Sentiment, 24.4%(47.1%) expect “Increase”,67.6% (49.6%) "Same",8.0%(3.2%) "Decrease”. Expect China PPI/CPI finally converge somewhat as companies raise prices on consumers. Some Q3 pull back from Q2.
Read 14 tweets
In August we saw #inflation growth moderate further, for the second consecutive month, at least relative to the impressive rate of growth in #prices witnessed around mid-year.
Core #CPI (excluding volatile food and energy components) came in at 0.10% month-over-month and 3.98% year-over-year, which was considerably less than the consensus forecast and was driven higher by #shelter components.
Meanwhile, headline #CPI data printed at a solid 0.27% month-over-month and came in at 5.20% year-over-year.
Read 11 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (69.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (57.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python Image
I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python Image
I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.4%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (53.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (61.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (61.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (62.8%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (67.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (59.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (63.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (62.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python Image
I analyzed the sentiment on the last 600 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (64.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 600 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.7%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
Where are all the bears?

#Sentiment very much feels like 2011, 2015, and 2018.
The time to start paying attention to risk management is when the day stock indices continue making new record highs,

while fewer & fewer index components do NOT confirm those new highs.

Last we saw this, in February 2020, we sold a lot of our holdings.

$SPY $ACWI
The lowest quality corporate credit is signaling risky times ahead, as spreads tighten towards 2007 levels.

While no indicator is perfect, historically very tight spreads between CCC junk bonds & Treasuries have indicated euphoric sentiment and overbought public securities.
Read 4 tweets
Whichever way you personally lean, the most consequential narrative that will determine the framework for major investment decisions is #inflation vs #deflation.

ICYMI: @JeffSnider_AIP presented his case on MacroVoices

Whether our future challenge is deflation vs inflation will ultimately determine the value of not only traditional assets but also new ones.

#Bitcoin & #cryptocurrencies in general have had an evolving narrative to explain away the madness - most current one is "digital gold".
The overwhelming consensus is one of an inflation ravaged future economy.

It's just so obvious, isn't it?

And so effortless to make the argument that it would be asinine to suggest otherwise... and yet, recall Granville on the obvious in financial markets.

h/t @NuitSeraCalme Image
Read 30 tweets
Quick look at a few #sentiment charts:

Investor Intelligence newsletter sentiment metric continues to recover with bulls 60.8% (+6.4%), bears 16.7% (-.8%) and the correction camp at 22.5% (-5.6%)

chart via @WillieDelwiche
Retail investor #sentiment much more optimistic with those expecting the stock market to continue to rally at the highest levels since January 3rd 2018:

chart via @WillieDelwiche
The 4 week MA of bearish AAII respondents is close to a record low as well:

chart via @hmeisler
Read 11 tweets
While #sentiment for gold is most definitely depressed and close to historic extremes...

chart via @MacroCharts Image
The Bullish Percent index for the gold miners/equity sector is not as extreme: Image
Here's another breadth metric, the percent above 200EMA. It fell to almost 10% on Mar 8th and since then it has bounced strongly: Image
Read 5 tweets
New important thread.

Explored: Rockefeller communique on #contacttracing, Verified (UN) guide on #vaccine #communications, insights via #Vaccine Confidence Project, & a Behavioral Insights report. Today> special report from Oct 15 2020 meeting, #WHO, Technical Advisory Group. Image
"On 15 Oct 2020, the WHO Technical Advisory Group (TAG) on Behavioural Insights & Sciences for Health held a special meeting w/ the WHO Department of Immunization, Vaccines & Biologicals to discuss behavioural considerations in relation to COVID-19 #vaccine #acceptance & uptake." Image
The report was developed by members of the WHO Technical Advisory Group on Behav. Insights & Sciences for Health, chaired by Cass Sunstein, largely credited w/ developing/popularizing notion of "nudges" as policy tools.

Technical Advisory Group members:

who.int/our-work/scien… Image
Read 36 tweets

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