I believe this is a good article to read. It talks about how human beings have a tendency to mistake their (inherently limited) perception of a situation for objective reality.
Viewed in this light, I believe it will help us acknowledge that numbers alone cannot paint the entire picture for an investment decision.
For example, the impact of a decision made by us on our future decisions cannot be captured numerically, but is undoubtedly more powerful than that one decision we will make on the basis of a set of numbers.
Dr Malik publishes details analysis on top on analysis received from members who participate his workshops. This is his summary of 2800 listed companies.
Going back to the recency bias: the recent recovery of the markets and the ensuing optimistic expectations of the future, is that a bias? or as the bulls would argue is it the still recent memory of the carnage in Mar/ Aprâ20 (thatâs keeping many investors waiting on the side) a bias? Who knows? Isaac Newton had reportedly exclaimed when asked about the directions of stock markets âI can calculate the motions of the heavenly bodies but not the madness of the people in the markets."
Our advice? Itâs not wise to bet against the Fed but itâs also foolish to ignore the fundamentals (they always catch up). A calibrated and nuanced approach, keeping in mind your overall asset allocation and portfolio composition is needed, as they say, God is always in the details.
One of the best lecture series I have seen on Investing. Mr Rajeev Thakkar goes into gory details about how portfolio returns work. He starts by describing the historical perspective, the Beta/CAPM/Efficient Market hypothesis theories. Points out why they are incorrect. Then goes into what he calls Factor-models. This is far beyond the basics (Greenblattâs Magic Formula investing). The crux of his argument is that returns are driven by 6-7 factors:
Risk free Rate
Equity Risk Premium
Size (of the company)
Value
Momentum
Beta
Rajeev clearly differentiates between a factor which affects returns, versus the actual implementation of that factor in real life. As an example, different practitioners might find value in different ways. P/DCF, P/E, P/B are all reasonable ways in which practitioners find value. The analogy to factor-investing he gives is sports. For considering who to pick on a basketball team, height of player would certainly matter, but it would only be one factor which decides who gets picked. Rajeev also points out the pitfalls of data-mining. Having worked extensively in machine learning myself, I can attest to everything Rajeev claims. If you torture data long enough, it admits to almost anything. These factors should ideally be found in a top-down first-principles way, perhaps not bottom-up. He also points out that all factors do not work all the time. There are times in the market even the worst possible company gives 10-12% returns (beta is high). There are times when momentum outperforms value and vice-versa as well. Since this is a multi-hour lecture series, I am only posting a very high level summary. Rajeev deep dives into each factor individually and shares his thoughts. For example, on Value factor, he points out why the academia gets it wrong when they use P/B multiples and why cash flow or earnings based measures make more sense.
One of the best parts I found is his concept of Quality overlap. Regardless of whatever youâre doing, the returns would be highers if the company is high quality. In fact the reason small caps tend to under-perform is mostly due to the low abundance of quality. If one were to apply the quality overlay on top of the small size factor and the value factor, then outperformance is likely. This, in essence, also feels like what ValuePickr forum tries to do. Iâd highly encourage everyone to watch
A good article - combing time correction concept of the stock market with life. Not directly connected to stock market knowledge but gives a deep understanding of life.
Please feel free to flag it you find it irrelevant to this thread.
IBM has built a new chemistry lab called RoboRXN in the cloud. It combines AI models, a cloud computing platform, and robots to help scientists design and synthesize new molecules while working from home.
New drugs and materials traditionally require an average of 10 years and $10 million to discover and bring to market. Much of that time is taken up by the laborious repetition of experiments to synthesize new compounds and learn from trial and error. IBM hopes that a platform like RoboRXN could dramatically speed up that process by predicting the recipes for compounds and automating experiments.
The history of taxes in India
In India, the system of direct taxation as it is known today, has been in force in one form or another even from ancient times. There are references both in Manu Smriti and Arthasastra to a variety of tax measures. Manu, the ancient sage and law-giver stated that the king could levy taxes, according to Sastras. The wise sage advised that taxes should be related to the income and expenditure of the subject. He, however, cautioned the king against excessive taxation and stated that both extremes should be avoided namely either complete absence of taxes or exorbitant taxation. According to him, the king should arrange the collection of taxes in such a manner that the subjects did not feel the pinch of paying taxes.
A major portion of Arthasastra is devoted by Kautilya to financial matters including financial administration. According to famous statesman, the Mauryan system, so far as it applied to agriculture, was a sort of state landlordism and the collection of land revenue formed an important source of revenue to the State. The State not only collected a part of the agricultural produce which was normally one sixth but also levied water rates, octroi duties, tolls and customs duties. Taxes were also collected on forest produce as well as from mining of metals etc. Salt tax was an important source of revenue and it was collected at the place of its extraction.
China outplays US in TikTok (for now - story still to play out)
Chinaâs Commerce Ministry added new items to its list of export controls late Friday. Now, artificial intelligence interface technologies such as speech and text recognition, as well as methods to analyze data and make personalized content recommendations, are matters of national security. But with AI and its content recommendation engine among the key ingredients of the companyâs success, Beijing becomes the arbiter of TikTokâs fate. Not the U.S. administration. As much as critics â including U.S. senators and the secretary of state â express concern about the data TikTok collects, itâs really the algorithms that matter most to the company, and anyone who buys it. These are the magic formulae that tell the app which data points will predict future behavior, and keep you staring at the phone longer.
TikTokâs algos are gold. At least, thatâs what bidders seem to think. And it looks like Beijing agrees. Effectively, the Chinese government is saying, âYou wanna buy TikTok? Go ahead, but that doesnât mean youâll get your hands on the secret sauce.â
Use of dark patterns are rampant in online retail
Back in April, Amazon made an extraordinary decision. As the company struggled to fulfil a surge in orders related to the pandemic, it subtly tweaked its website to encourage consumers to buy less, not more. In addition to modifying shipping timelines and inventory, Amazon disabled a recommendation feature that displays items frequently bought together, like batteries to go with the toy already in your cart.
Dark patterns are digital design elements that manipulate users into making decisions they otherwise wouldnât, often to a corporationâs benefit.
The rise of the industrialised chicken
At the turn of the 20th century, chicken was almost always eaten in the spring. The priority for chicken raisers at the time was egg production, so after the eggs hatched, all the male birds would be fed up and then quickly harvested as âspring chickensâ â young, tender birds that were sold whole for roasting or broiling (hence the term âbroilersâ). Outside the spring rush, you might be buying a bigger, fatter fryer or an old hen for stewing.
During the second world war, however, red meat was rationed, and a national campaign encouraged the consumption of poultry and fish to save âmeatâ (beef, pork and lamb) for âthe army and our alliesâ. Eating chicken became more common, but the preference for young broilers, and white breast meat, persisted.
The modern chicken is fully industrialised. With more than 500 chicken breeds existing on Earth, it might surprise you to learn that every nugget, breast, and cup of chicken noodle soup youâve ever eaten likely came from one breed, a specialised cross between a Cornish and a white rock.
A 4 hour long video on various angles and perspectives with regards to coffee can structure and Marcellus philosophy. I think this would be very relevant for alot of people here who have or are following a coffee can approach. I agree thatâs itâs very long but itâs worth it. Iâve sent my Sunday in parts to watch this completely. This video is a two way interaction between the entire team of Marcellus and the investors in Marcellus.
