Retail Sales For June Provide An Early Boost, But Bond Yields Mostly Calling The Shots

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The first week of earnings season wraps up with major indices closely tracking the bond market in Wall Street’s version of “follow the leader.” Earnings absolutely matter, but right now the Fed’s policies are maybe a bigger influence. In the short-term the Fed is still the girl everyone wants to dance with.

Lately, you can almost guess where stocks are going just by checking the 10-year Treasury yield, which often moves on perceptions of what the Fed might have up its sleeve. The yield bounced back from lows this morning to around 1.32%, and stock indices climbed a bit in pre-market trading. That was a switch from yesterday when yields fell and stocks followed suit. Still, yields are down about six basis points since Monday, and stocks are also facing a losing week.

It’s unclear how long this close tracking of yields might last, but maybe a big flood of earnings due next week could give stocks a chance to act more on fundamental corporate news instead of the back and forth in fixed income. Meanwhile, retail sales for June this morning basically blew Wall Street’s conservative estimates out of the water, and stock indices edged up in pre-market trading after the data.

Headline retail sales rose 0.6% compared with the consensus expectation for a 0.6% decline, and with automobiles stripped out, the report looked even stronger, up 1.3% vs. expectations for 0.3%. Those numbers are incredibly strong and show the difficulty analysts are having in this market. The estimates missed consumer strength by a long shot. However, it’s also possible this is a blip in the data that might get smoothed out with July’s numbers. We’ll have to wait and see.

Caution Flag Keeps Waving

Yesterday continued what feels like a “risk-off” pattern that began taking hold earlier in the week, but this time Tech got caught up in the selling, too. In fact, Tech was the second-worst performing sector of the day behind Energy, which continues to tank on ideas more crude could flow soon thanks to OPEC’s agreement.

We already saw investors embracing fixed income and “defensive” sectors starting Tuesday, and Thursday continued the trend. When your leading sectors are Utilities, Staples, Real Estate, the way they were yesterday, that really suggests the surging bond market’s message to stocks is getting read loudly and clearly.

This week’s decline in rates also isn’t necessarily happy news for Financial companies. That being said, the Financials fared pretty well yesterday, with some of them coming back after an early drop. It was an impressive performance and we’ll see if it can spill over into Friday.

Energy helped fuel the rally earlier this year, but it’s struggling under the weight of falling crude prices. Softness in crude isn’t guaranteed to last—and prices of $70 a barrel aren’t historically cheap—but crude’s inability to consistently hold $75 speaks a lot. Technically, the strength just seems to fade up there. Crude is up slightly this morning but still below $72 a barrel.

Losing Steam?

All of the FAANGs lost ground yesterday after a nice rally earlier in the week. Another key Tech name, chipmaker Nvidia (NVDA), got taken to the cleaners with a 4.4% decline despite a major analyst price target increase to $900. NVDA has been on an incredible roll most of the year.

This week’s unexpectedly strong June inflation readings might be sending some investors into “flight for safety” mode, though no investment is ever truly “safe.” Fed Chairman Jerome Powell sounded dovish in his congressional testimony Wednesday and Thursday, but even Powell admitted he hadn’t expected to see inflation move this much above the Fed’s 2% target.

Keeping things in perspective, consider that the S&P 500 Index (SPX) did power back late Thursday to close well off its lows. That’s often a sign of people “buying the dip,” as the saying goes. Dip-buying has been a feature all year, and with bond yields so low and the money supply so huge, it’s hard to argue that cash on the sidelines won’t keep being injected if stocks decline.

Two popular stocks that data show have been popular with TD Ameritrade clients are Apple (AAPL) and Microsoft (MSFT), and both of them have regularly benefited from this “dip buying” trend. Neither lost much ground yesterday, so if they start to rise today, consider whether it reflects a broader move where investors come back in after weakness. However, one day is never a trend.

Reopening stocks (the ones tied closely to the economy’s reopening like airlines and restaurants) are doing a bit better in pre-market trading today after getting hit hard yesterday.

In other corporate news today, vaccine stocks climbed after Moderna (MRNA) was added to the S&P 500. BioNTech (BNTX), which is Pfizer’s (PFE) vaccine partner, is also higher. MRNA rose 7% in pre-market trading.

Strap In: Big Earnings Week Ahead

Earnings action dies down a bit here before getting back to full speed next week. Netflix (NFLX), American Express (AXP), Johnson & Johnson (JNJ), United Airlines (UAL), AT&T (T), Verizon (VZ), American Airlines (AAL) and Coca-Cola (KO) are high-profile companies expected to open their books in the week ahead.

It could be interesting to hear from the airlines about how the global reopening is going. Delta (DAL) surprised with an earnings beat this week, but also expressed concerns about high fuel prices. While vaccine rollouts in the U.S. have helped open travel back up, other parts of the globe aren’t faring as well. And worries about the Delta variant of Covid don’t seem to be helping things.

Beyond the numbers that UAL and AAL report next week, the market may be looking for guidance from their executives about the state of global travel as a proxy for economic health. DAL said travel seems to be coming back faster than expected. Will other airlines see it the same way? Earnings are one way to possibly find out.Even with the Delta variant of Covid gaining steam, there’s no doubt that at least in the U.S, the crowds are back for sporting events.

For example, the baseball All-Star Game this week was packed. Big events like that could be good news for KO when it reports earnings. PepsiCo (PEP) already reported a nice quarter. We’ll see if KO can follow up, and whether its executives will say anything about rising producer prices nipping at the heels of consumer products companies.

Confidence Game: The 10-year Treasury yield sank below 1.3% for a while Thursday but popped back to that level by the end of the day. It’s now down sharply from highs earlier this week. Strength in fixed income—yields fall as Treasury prices climb—often suggests lack of confidence in economic growth.

Why are people apparently hesitant at this juncture? It could be as simple as a lack of catalysts with the market now at record highs. Yes, bank earnings were mostly strong, but Financial stocks were already one of the best sectors year-to-date, so good earnings might have become an excuse for some investors to take profit. Also, with earnings expectations so high in general, it takes a really big beat for a company to impress.

Covid Conundrum: Anyone watching the news lately probably sees numerous reports about how the Delta variant of Covid has taken off in the U.S. and case counts are up across almost every state. While the human toll of this virus surge is certainly nothing to dismiss, for the market it seems like a bit of an afterthought, at least so far. It could be because so many of the new cases are in less populated parts of the country, which can make it seem like a faraway issue for those of us in big cities. Or it could be because so many of us are vaccinated and feel like we have some protection.

But the other factor is numbers-related. When you hear reports on the news about Covid cases rising 50%, consider what that means. To use a baseball analogy, if a hitter raises his batting average from .050 to .100, he’s still not going to get into the lineup regularly because his average is just too low. Covid cases sank to incredibly light levels in June down near 11,000 a day, which means a 50% rise isn’t really too huge in terms of raw numbers and is less than 10% of the peaks from last winter. We’ll be keeping an eye on Covid, especially as overseas economies continue to be on lockdowns and variants could cause more problems even here. But at least for now, the market doesn’t seem too concerned.

Dull Roar: Most jobs that put you regularly on live television in front of millions of viewers require you to be entertaining. One exception to that rule is the position held by Fed Chairman Jerome Powell. It’s actually his job to be uninteresting, and he’s arguably very good at it. His testimony in front of the Senate Banking Committee on Thursday was another example, with the Fed chair staying collected even as senators from both sides of the aisle gave him their opinions on what the Fed should or shouldn’t do. The closely monitored 10-year Treasury yield stayed anchored near 1.33% as he spoke.

Even if Powell keeps up the dovishness, you can’t rule out Treasury yields perhaps starting to rise in coming months if inflation readings continue hot and investors start to lose faith in the Fed making the right call at the right time. Eventually people might start to demand higher premiums for taking on the risk of buying bonds. The Fed itself, however, could have something to say about that.

It’s been sopping up so much of the paper lately that market demand doesn’t give you the same kind of impact it might have once had. That’s an argument for bond prices continuing to show firmness and yields to stay under pressure, as we’ve seen the last few months. Powell, for his part, showed no signs of being in a hurry yesterday to lift any of the stimulus.

TD Ameritrade® commentary for educational purposes only. Member SIPC.

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I am Chief Market Strategist for TD Ameritrade and began my career as a Chicago Board Options Exchange market maker, trading primarily in the S&P 100 and S&P 500 pits. I’ve also worked for ING Bank, Blue Capital and was Managing Director of Option Trading for Van Der Moolen, USA. In 2006, I joined the thinkorswim Group, which was eventually acquired by TD Ameritrade. I am a 30-year trading veteran and a regular CNBC guest, as well as a member of the Board of Directors at NYSE ARCA and a member of the Arbitration Committee at the CBOE. My licenses include the 3, 4, 7, 24 and 66.

Source: Retail Sales For June Provide An Early Boost, But Bond Yields Mostly Calling The Shots

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Critics:

Retail is the process of selling consumer goods or services to customers through multiple channels of distribution to earn a profit. Retailers satisfy demand identified through a supply chain. The term “retailer” is typically applied where a service provider fills the small orders of many individuals, who are end-users, rather than large orders of a small number of wholesale, corporate or government clientele. Shopping generally refers to the act of buying products.

Sometimes this is done to obtain final goods, including necessities such as food and clothing; sometimes it takes place as a recreational activity. Recreational shopping often involves window shopping and browsing: it does not always result in a purchase.

Most modern retailers typically make a variety of strategic level decisions including the type of store, the market to be served, the optimal product assortment, customer service, supporting services and the store’s overall market positioning. Once the strategic retail plan is in place, retailers devise the retail mix which includes product, price, place, promotion, personnel, and presentation.

In the digital age, an increasing number of retailers are seeking to reach broader markets by selling through multiple channels, including both bricks and mortar and online retailing. Digital technologies are also changing the way that consumers pay for goods and services. Retailing support services may also include the provision of credit, delivery services, advisory services, stylist services and a range of other supporting services.

Retail shops occur in a diverse range of types of and in many different contexts – from strip shopping centres in residential streets through to large, indoor shopping malls. Shopping streets may restrict traffic to pedestrians only. Sometimes a shopping street has a partial or full roof to create a more comfortable shopping environment – protecting customers from various types of weather conditions such as extreme temperatures, winds or precipitation. Forms of non-shop retailing include online retailing (a type of electronic-commerce used for business-to-consumer (B2C) transactions) and mail order

6 Math Foundations to Start Learning Machine Learning

As a Data Scientist, machine learning is our arsenal to do our job. I am pretty sure in this modern times, everyone who is employed as a Data Scientist would use machine learning to analyze their data to produce valuable patterns. Although, why we need to learn math for machine learning? There is some argument I could give, this includes:

  • Math helps you select the correct machine learning algorithm. Understanding math gives you insight into how the model works, including choosing the right model parameter and the validation strategies.
  • Estimating how confident we are with the model result by producing the right confidence interval and uncertainty measurements needs an understanding of math.
  • The right model would consider many aspects such as metrics, training time, model complexity, number of parameters, and number of features which need math to understand all of these aspects.
  • You could develop a customized model that fits your own problem by knowing the machine learning model’s math.

The main problem is what math subject you need to understand machine learning? Math is a vast field, after all. That is why in this article, I want to outline the math subject you need for machine learning and a few important point to starting learning those subjects.

Machine Learning Math

We could learn many topics from the math subject, but if we want to focus on the math used in machine learning, we need to specify it. In this case, I like to use the necessary math references explained in the Machine Learning Math book by M. P. Deisenroth, A. A. Faisal, and C. S. Ong, 2021.

In their book, there are math foundations that are important for Machine Learning. The math subject is:

Image created by Author

Six math subjects become the foundation for machine learning. Each subject is intertwined to develop our machine learning model and reach the “best” model for generalizing the dataset.

Let’s dive deeper for each subject to know what they are.

Linear Algebra

What is Linear Algebra? This is a branch of mathematic that concerns the study of the vectors and certain rules to manipulate the vector. When we are formalizing intuitive concepts, the common approach is to construct a set of objects (symbols) and a set of rules to manipulate these objects. This is what we knew as algebra.

If we talk about Linear Algebra in machine learning, it is defined as the part of mathematics that uses vector space and matrices to represent linear equations.

When talking about vectors, people might flashback to their high school study regarding the vector with direction, just like the image below.

Geometric Vector (Image by Author)

This is a vector, but not the kind of vector discussed in the Linear Algebra for Machine Learning. Instead, it would be this image below we would talk about.

Vector 4×1 Matrix (Image by Author)

What we had above is also a Vector, but another kind of vector. You might be familiar with matrix form (the image below). The vector is a matrix with only 1 column, which is known as a column vector. In other words, we can think of a matrix as a group of column vectors or row vectors. In summary, vectors are special objects that can be added together and multiplied by scalars to produce another object of the same kind. We could have various objects called vectors.

Matrix (Image by Author)

Linear algebra itself s a systematic representation of data that computers can understand, and all the operations in linear algebra are systematic rules. That is why in modern time machine learning, Linear algebra is an important study.

An example of how linear algebra is used is in the linear equation. Linear algebra is a tool used in the Linear Equation because so many problems could be presented systematically in a Linear way. The typical Linear equation is presented in the form below.

Linear Equation (Image by Author)

To solve the linear equation problem above, we use Linear Algebra to present the linear equation in a systematical representation. This way, we could use the matrix characterization to look for the most optimal solution.

Linear Equation in Matrix Representation (Image by Author)

To summary the Linear Algebra subject, there are three terms you might want to learn more as a starting point within this subject:

  • Vector
  • Matrix
  • Linear Equation

Analytic Geometry (Coordinate Geometry)

Analytic geometry is a study in which we learn the data (point) position using an ordered pair of coordinates. This study is concerned with defining and representing geometrical shapes numerically and extracting numerical information from the shapes numerical definitions and representations. We project the data into the plane in a simpler term, and we receive numerical information from there.

Cartesian Coordinate (Image by Author)

Above is an example of how we acquired information from the data point by projecting the dataset into the plane. How we acquire the information from this representation is the heart of Analytical Geometry. To help you start learning this subject, here are some important terms you might need.

  • Distance Function

A distance function is a function that provides numerical information for the distance between the elements of a set. If the distance is zero, then elements are equivalent. Else, they are different from each other.

An example of the distance function is Euclidean Distance which calculates the linear distance between two data points.

Euclidean Distance Equation (Image by Author)
  • Inner Product

The inner product is a concept that introduces intuitive geometrical concepts, such as the length of a vector and the angle or distance between two vectors. It is often denoted as ⟨x,y⟩ (or occasionally (x,y) or ⟨x|y⟩).

Matrix Decomposition

Matrix Decomposition is a study that concerning the way to reducing a matrix into its constituent parts. Matrix Decomposition aims to simplify more complex matrix operations on the decomposed matrix rather than on its original matrix.

A common analogy for matrix decomposition is like factoring numbers, such as factoring 8 into 2 x 4. This is why matrix decomposition is synonymical to matrix factorization. There are many ways to decompose a matrix, so there is a range of different matrix decomposition techniques. An example is the LU Decomposition in the image below.

LU Decomposition (Image by Author)

Vector Calculus

Calculus is a mathematical study that concern with continuous change, which mainly consists of functions and limits. Vector calculus itself is concerned with the differentiation and integration of the vector fields. Vector Calculus is often called multivariate calculus, although it has a slightly different study case. Multivariate calculus deals with calculus application functions of the multiple independent variables.

There are a few important terms I feel people need to know when starting learning the Vector Calculus, they are:

  • Derivative and Differentiation

The derivative is a function of real numbers that measure the change of the function value (output value) concerning a change in its argument (input value). Differentiation is the action of computing a derivative.

Derivative Equation (Image by Author)
  • Partial Derivative

The partial derivative is a derivative function where several variables are calculated within the derivative function with respect to one of those variables could be varied, and the other variable are held constant (as opposed to the total derivative, in which all variables are allowed to vary).

  • Gradient

The gradient is a word related to the derivative or the rate of change of a function; you might consider that gradient is a fancy word for derivative. The term gradient is typically used for functions with several inputs and a single output (scalar). The gradient has a direction to move from their current location, e.g., up, down, right, left.

Probability and Distribution

Probability is a study of uncertainty (loosely terms). The probability here can be thought of as a time where the event occurs or the degree of belief about an event’s occurrence. The probability distribution is a function that measures the probability of a particular outcome (or probability set of outcomes) that would occur associated with the random variable. The common probability distribution function is shown in the image below.

Normal Distribution Probability Function (Image by Author)

Probability theory and statistics are often associated with a similar thing, but they concern different aspects of uncertainty:

•In math, we define probability as a model of some process where random variables capture the underlying uncertainty, and we use the rules of probability to summarize what happens.

•In statistics, we try to figure out the underlying process observe of something that has happened and tries to explain the observations.

When we talk about machine learning, it is close to statistics because its goal is to construct a model that adequately represents the process that generated the data.

Optimization

In the learning objective, training a machine learning model is all about finding a good set of parameters. What we consider “good” is determined by the objective function or the probabilistic models. This is what optimization algorithms are for; given an objective function, we try to find the best value.

Commonly, objective functions in machine learning are trying to minimize the function. It means the best value is the minimum value. Intuitively, if we try to find the best value, it would like finding the valleys of the objective function where the gradients point us uphill. That is why we want to move downhill (opposite to the gradient) and hope to find the lowest (deepest) point. This is the concept of gradient descent.

Gradient Descent (Image by Author)

There are few terms as a starting point when learning optimization. They are:

  • Local Minima and Global Minima

The point at which a function best values takes the minimum value is called the global minima. However, when the goal is to minimize the function and solved it using optimization algorithms such as gradient descent, the function could have a minimum value at different points. Those several points which appear to be minima but are not the point where the function actually takes the minimum value are called local minima.

Local and Global Minima (Image by Author)
  • Unconstrained Optimization and Constrained Optimization

Unconstrained Optimization is an optimization function where we find a minimum of a function under the assumption that the parameters can take any possible value (no parameter limitation). Constrained Optimization simply limits the possible value by introducing a set of constraints.

Gradient descent is an Unconstrained optimization if there is no parameter limitation. If we set some limit, for example, x > 1, it is an unconstrained optimization.

Conclusion

Machine Learning is an everyday tool that Data scientists use to obtain the valuable pattern we need. Learning the math behind machine learning could provide you an edge in your work. There are many math subjects out there, but there are 6 subjects that matter the most when we are starting learning machine learning math, and that is:

  • Linear Algebra
  • Analytic Geometry
  • Matrix Decomposition
  • Vector Calculus
  • Probability and Distribution
  • Optimization

If you start learning math for machine learning, you could read my other article to avoid the study pitfall. I also provide the math material you might want to check out in that article.

 

By: Cornellius Yudha Wijaya

Source: 6 Math Foundations to Start Learning Machine Learning | by Cornellius Yudha Wijaya | Towards Data Science

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Critics:

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data“, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the “signal” or “feedback” available to the learning system:

  • Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
  • Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximize.

References

The Symptoms of The Delta Variant Appear To Differ From Traditional COVID Symptoms. Here’s What To Look Out For

We’ve been living in a COVID world for more than 18 months now. At the outset of the pandemic, government agencies and health authorities scrambled to inform people on how to identify symptoms of the virus.

But as the virus has evolved, it seems the most common symptoms have changed too.

Emerging data suggest people infected with the Delta variant — the variant behind most of Australia’s current cases and highly prevalent around the world — are experiencing symptoms different to those we commonly associated with COVID earlier in the pandemic.


Read more: What’s the Delta COVID variant found in Melbourne? Is it more infectious and does it spread more in kids? A virologist explains

Clear explanations about the pandemic from a network of research experts

We’re all different

Humans are dynamic. With our differences come different immune systems. This means the same virus can produce different signs and symptoms in different ways.

A sign is something that’s seen, such as a rash. A symptom is something that’s felt, like a sore throat.

The way a virus causes illness is dependent on two key factors:

  • viral factors include things like speed of replication, modes of transmission, and so on. Viral factors change as the virus evolves.
  • host factors are specific to the individual. Age, gender, medications, diet, exercise, health and stress can all affect host factors.

So when we talk about the signs and symptoms of a virus, we’re referring to what is most common. To ascertain this, we have to collect information from individual cases.

It’s important to note this data is not always easy to collect or analyse to ensure there’s no bias. For example, older people may have different symptoms to younger people, and collecting data from patients in a hospital may be different to patients at a GP clinic.

So what are the common signs and symptoms of the Delta variant?

Using a self-reporting system through a mobile app, data from the United Kingdom suggest the most common COVID symptoms may have changed from those we traditionally associated with the virus.

The reports don’t take into account which COVID variant participants are infected with. But given Delta is predominating in the UK at present, it’s a safe bet the symptoms we see here reflect the Delta variant.


The Conversation, CC BY-ND

While fever and cough have always been common COVID symptoms, and headache and sore throat have traditionally presented for some people, a runny nose was rarely reported in earlier data. Meanwhile, loss of smell, which was originally quite common, now ranks ninth.

There are a few reasons we could be seeing the symptoms evolving in this way. It may be because data were originally coming mainly from patients presenting to hospital who were therefore likely to be sicker. And given the higher rates of vaccination coverage in older age groups, younger people are now accounting for a greater proportion of COVID cases, and they tend to experience milder symptoms.

It could also be because of the evolution of the virus, and the different characteristics (viral factors) of the Delta variant. But why exactly symptoms could be changing remains uncertain.


Read more: Coronavirus: how long does it take to get sick? How infectious is it? Will you always have a fever? COVID-19 basics explained


While we still have more to learn about the Delta variant, this emerging data is important because it shows us that what we might think of as just a mild winter cold — a runny nose and a sore throat — could be a case of COVID-19.

This data highlight the power of public science. At the same time, we need to remember the results haven’t yet been fully analysed or stratified. That is, “host factors” such as age, gender, other illnesses, medications and so on haven’t been accounted for, as they would in a rigorous clinical trial.

And as is the case with all self-reported data, we have to acknowledge there may be some flaws in the results.

Does vaccination affect the symptoms?

Although new viral variants can compromise the effectiveness of vaccines, for Delta, the vaccines available in Australia (Pfizer and AstraZeneca) still appear to offer good protection against symptomatic COVID-19 after two doses.



Importantly, both vaccines have been shown to offer greater than 90% protection from severe disease requiring hospital treatment.

A recent “superspreader” event in New South Wales highlighted the importance of vaccination. Of 30 people who attended this birthday party, reports indicated none of the 24 people who became infected with the Delta variant had been vaccinated. The six vaccinated people at the party did not contract COVID-19.

In some cases infection may still possible after vaccination, but it’s highly likely the viral load will be lower and symptoms much milder than they would without vaccination.

We all have a role to play

Evidence indicating Delta is more infectious compared to the original SARS-CoV-2 and other variants of the virus is building.

It’s important to understand the environment is also changing. People have become more complacent with social distancing, seasons change, vaccination rates vary — all these factors affect the data.

But scientists are becoming more confident the Delta variant represents a more transmissible SARS-CoV-2 strain.


Read more: What’s the difference between mutations, variants and strains? A guide to COVID terminology


As we face another COVID battle in Australia we’re reminded the war against COVID is not over and we all have a role to play. Get tested if you have any symptoms, even if it’s “just a sniffle”. Get vaccinated as soon as you can and follow public health advice.

By: Research Leader in Virology and Infectious Disease, Griffith University

Source: The symptoms of the Delta variant appear to differ from traditional COVID symptoms. Here’s what to look out for

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Critics:

Deltacoronavirus (Delta-CoV) is one of the four genera (Alpha-, Beta-, Gamma-, and Delta-) of coronaviruses. It is in the subfamily Orthocoronavirinae of the family Coronaviridae. They are enveloped, positive-sense, single-stranded RNA viruses. Deltacoronaviruses infect mostly birds and some mammals.

genesis

While the alpha and beta genera are derived from the bat viral gene pool, the gamma and delta genera are derived from the avian and pig viral gene pools.

Recombination appears to be common among deltacoronaviruses.Recombination occurs frequently in the viral genome region that encodes the host receptor binding protein. Recombination between different viral lineages contributes to the emergence of new viruses capable of interspecies transmission and adaptation to new animal hosts.

References

  1. Lau SKP, Wong EYM, Tsang CC, Ahmed SS, Au-Yeung RKH, Yuen KY, Wernery U, Woo PCY. Discovery and Sequence Analysis of Four Deltacoronaviruses from Birds in the Middle East Reveal Interspecies Jumping with Recombination as a Potential Mechanism for Avian-to-Avian and Avian-to-Mammalian Transmission. J Virol. 2018 Jul 17;92(15):e00265-18. doi: 10.1128/JVI.00265-18. Print 2018 Aug 1. PMID: 29769348

External links

High Turnover? Here Are 3 Things CEOs Do That Sabotage Their Workplace Culture

She has one too many deadlines to deal with

Every CEO wants long-standing employees, but their ineffective leadership causes organizational stress that cripples the workplace culture. Quite often, we read articles or hear of CEOs abusing their power and tarnishing their company’s reputation.

This is due to them neglecting feedback from their team and making decisions based solely on their own judgement. Not only does this erode trust, but it sets a standard that employee and leadership voices are not welcome.

When employees are taken care of, they go above and beyond to drive the company forward. Conversely, when they don’t feel valued, appreciated or kept in the loop, employees quickly become disengaged. The cost of a disengaged employee impacts more than the bottom line.

It decreases productivity, creates negative client experiences and destroys the company culture, to name a few. According to a Gallup survey, the State of the American Workplace 2021, 80% of workers are not fully engaged or are actively disengaged at work.

While CEOs claim to embody a people-first and feedback-driven culture, they believe, due to their position, that they know better than everyone else. Todd Ramlin, manager of Cable Compare, said, “if a person is fortunate to have the opportunity to be a CEO, they need to ask themselves if they can live by the company values, expectations, rules and processes that are in place.” They can’t pick and choose which rules and processes to abide by, yet punish others when they do the same. Doing so cultivates a toxic workplace and demonstrates poor leadership.

Here are three things CEOs do that sabotage their workplace culture.

Embraces Data, Dodges Emotions

The workplace is made up of a diverse group of experiences and perspectives. CEOs who lack the emotional intelligence to understand another person’s viewpoint or situation will find themselves losing their most valuable people. Sabine Saadeh, financial trading and asset management expert, said, “companies that are only data driven and don’t care about the well-being of their employees will not sustain in today’s global economy.”

Businessolver’s 2021 State Of Workplace Empathy report, revealed that “68% of CEOs fear that they’ll be less respected if they show empathy in the workplace.” CEOs who fail to lead with empathy will find themselves with a revolving door of leadership team members and employees. I once had a CEO tell me that he didn’t want emotions present in his business because it created a distraction from the data. His motto was, “if it’s not data, it’s worthless”.

As such, he disregarded feedback of employee dissatisfaction and burnout. Yet, he couldn’t understand why the average tenure of his employees very rarely surpassed one year. Willie Greer, founder of The Product Analyst, asserted, “data is trash if you’re replacing workers because you care more about data than your people.”

Micromanages Their Leadership Team

One of the ways a CEO sabotages a company’s culture is by micromanaging their leadership team. Consequently, this leads to leadership having to micromanage their own team to satisfy the CEOs unrealistic expectations. When leadership feels disempowered to make decisions, they either pursue another opportunity or check out due to not being motivated to achieve company goals.

As such, the executives who were hired to bring change aren’t able to live up to their full potential. Moreover, they’re unable to make the impact they desired due to the CEOs lack of trust in them. Employees undoubtedly feel the stress of their leadership team as it reverberates across the company.

Arun Grewal, founder and Editor-in-chief at Coffee Breaking Pr0, said, most CEOs are specialists in one area or another, which can make them very particular. However, if they want to drive their company forward they need to trust in the experts they hired rather than trying to make all of the company’s decisions.

At one point during my career, I reported to a CEO who never allowed me to fully take over my department. Although he praised me for my HR expertise during the interview, once hired, I quickly realized he still wanted full control over my department. Despite not having HR experience, he disregarded everything I brought to the table to help his company.

I soon began questioning my own abilities. No matter how hard I tried to shield my team from the stress I endured, the CEO would reach out to them directly to micromanage their every move. This left our entire department feeling drained, demoralized and demotivated. Sara Bernier, founder of Born for Pets, said, “CEOs who meddle in the smallest of tasks chip away at the fundamentals of their own company because everything has to run through them”. She added, “this eliminates the employee’s ownership of their own work because all tasks are micromanaged by the CEO.

Neglects Valuable Employee Feedback

Instead of seeking feedback from their leadership team or employees, CEOs avoid it altogether. Eropa Stein, founder and CEO of Hyre, said, “making mistakes and getting negative feedback from your team is a normal part of leading a company, no matter how long you’ve been in business.”

She went on, “as a leader, it’s important to put your ego aside and listen to feedback that will help your business grow. If everyone agrees with you all the time, you’re creating a cult mentality that’ll be detrimental to your business’ success in the long run.” This results in a toxic and unproductive workplace culture.

What’s worse than avoiding constructive feedback is receiving it and disregarding it entirely. Neglecting valuable feedback constructs a company culture where no individual feels safe voicing their concerns. Rather than silence those who give negative feedback, CEOs should embrace them. These are the individuals who are bringing issues forward to turn them into strengths in an effort to create a stronger company.

Follow me on Twitter or LinkedIn. Check out my website.

I’m a Leadership Coach & Workplace Culture Consultant at Heidi Lynne Consulting helping individuals and organizations gain the confidence to become better leaders for themselves and their teams. As a consultant, I deliver and implement strategies to develop current talent and create impactful and engaging employee experiences. Companies hire me to to speak, coach, consult and train their teams and organizations of all sizes. I’ve gained a breadth of knowledge working internationally in Europe, America and Asia. I use my global expertise to provide virtual and in-person consulting and leadership coaching to the students at Babson College, Ivy League students and my global network. I’m a black belt in Six Sigma, former Society of Human Resources (SHRM) President and domestic violence mentor. Learn more at http://www.heidilynneco.com or get in touch at Heidi@heidilynneco.com.

Source: High Turnover? Here Are 3 Things CEOs Do That Sabotage Their Workplace Culture

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Critics:

Organizational culture refers to culture in any type of organization including that of schools, universities, not-for-profit groups, government agencies, or business entities. In business, terms such as corporate culture and company culture are often used to refer to a similar concept.

The term corporate culture became widely known in the business world in the late 1980s and early 1990s. Corporate culture was already used by managers, sociologists, and organizational theorists by the beginning of the 80s. The related idea of organizational climate emerged in the 1960s and 70s, and the terms are now somewhat overlapping,as climate is one aspect of culture that focuses primarily on the behaviors encouraged by the organization

If organizational culture is seen as something that characterizes an organization, it can be manipulated and altered depending on leadership and members. Culture as root metaphor sees the organization as its culture, created through communication and symbols, or competing metaphors. Culture is basic, with personal experience producing a variety of perspectives.

Most of the criticism comes from the writers in critical management studies who for example express skepticism about the functionalist and unitarist views about culture that are put forward by mainstream management writers. They stress the ways in which these cultural assumptions can stifle dissent towards management and reproduce propaganda and ideology. They suggest that organizations do not encompass a single culture, and cultural engineering may not reflect the interests of all stakeholders within an organization.

References

  • Schein, E. H. (1990). Organizational culture. American Psychologist, 45, 109–119. doi:10.1037/0003-066X.45.2.109
  • Compare: Hatch, Mary Jo; Cunliffe, Ann L. (2013) [1997]. “A history of organizational culture in organization theory”. Organization Theory: Modern, Symbolic and Postmodern Perspectives (2 ed.). Oxford: Oxford University Press. p. 161. ISBN 9780199640379. OCLC 809554483. Retrieved 7 June 2020. With the publication of his book The Changing Culture of a Factory in 1952, British sociologist Elliott Jaques became the first organization theorist to describe an organizational culture.
  • Jaques, Elliott (1951). The changing culture of a factory. Tavistock Institute of Human Relations. [London]: Tavistock Publications. p. 251. ISBN 978-0415264426. OCLC 300631.
  • Compare: Kummerow, Elizabeth (12 September 2013). Organisational culture : concept, context, and measurement. Kirby, Neil.; Ying, Lee Xin. New Jersey. p. 13. ISBN 9789812837837. OCLC 868980134. Jacques [sic], a Canadian psychoanalyst and organisational psychologist, made a major contribution […] with his detailed study of Glacier Metals, a medium-sized British manufacturing company.
  • Ravasi, D.; Schultz, M. (2006). “Responding to organizational identity threats: Exploring the role of organizational culture”. Academy of Management Journal. 49 (3): 433–458. CiteSeerX 10.1.1.472.2754. doi:10.5465/amj.2006.21794663.
  • Schein, Edgar H. (2004). Organizational culture and leadership (3rd ed.). San Francisco: Jossey-Bass. pp. 26–33. ISBN 0787968455. OCLC 54407721.
  • Schrodt, P (2002). “The relationship between organizational identification and organizational culture: Employee perceptions of culture and identification in a retail sales organization”. Communication Studies. 53 (2): 189–202. doi:10.1080/10510970209388584. S2CID 143645350.
  • Schein, Edgar (1992). Organizational Culture and Leadership: A Dynamic View. San Francisco, CA: Jossey-Bass. pp. 9.
  • Deal T. E. and Kennedy, A. A. (1982, 2000) Corporate Cultures: The Rites and Rituals of Corporate Life, Harmondsworth, Penguin Books, 1982; reissue Perseus Books, 2000
  • Kotter, J. P.; Heskett, James L. (1992). Corporate Culture and Performance. New York: The Free Press. ISBN 978-0-02-918467-7.
  • Selart, Marcus; Schei, Vidar (2011): “Organizational Culture”. In: Mark A. Runco and Steven R. Pritzker (eds.): Encyclopedia of Creativity, 2nd edition, vol. 2. San Diego: Academic Press, pp. 193–196.
  • Compare: Flamholtz, Eric G.; Randle, Yvonne (2011). Corporate Culture: The Ultimate Strategic Asset. Stanford Business Books. Stanford, California: Stanford University Press. p. 6. ISBN 9780804777544. Retrieved 2018-10-25. […] in a very real sense, corporate culture can be thought of as a company’s ‘personality’.
  • Compare: Flamholtz, Eric; Randle, Yvonne (2014). “13: Implications of organizational Life Cycles for Corporate Culture and Climate”. In Schneider, Benjamin; Barbera, Karen M. (eds.). The Oxford Handbook of Organizational Climate and Culture. Oxford Library of psychology. Oxford: Oxford University Press. p. 247. ISBN 9780199860715. Retrieved 2018-10-25. The essence of corporate culture, then, is the values, beliefs, and norms or behavioral practices that emerge in an organization. In this sense, organizational culture is the personality of the organization.
  • Compare: Flamholtz, Eric; Randle, Yvonne (2014). “13: Implications of organizational Life Cycles for Corporate Culture and Climate”. In Schneider, Benjamin; Barbera, Karen M. (eds.). The Oxford Handbook of Organizational Climate and Culture. Oxford Library of psychology. Oxford: Oxford University Press. p. 247. ISBN 9780199860715. Retrieved 2018-10-25. The essence of corporate culture, then, is the values, beliefs, and norms or behavioral practices that emerge in an organization.
  • Jaques, Elliott (1998). Requisite organization : a total system for effective managerial organization and managerial leadership for the 21st century (Rev. 2nd ed.). Arlington, VA: Cason Hall. ISBN 978-1886436039. OCLC 36162684.
  • Jaques, Elliott (2017). “Leadership and Organizational Values”. Requisite Organization: A Total System for Effective Managerial Organization and Managerial Leadership for the 21st Century (2 ed.). Routledge. ISBN 9781351551311. Retrieved 7 June 2020.
  • “Culture is everything,” said Lou Gerstner, the CEO who pulled IBM from near ruin in the 1990s.”, Culture Clash: When Corporate Culture Fights Strategy, It Can Cost You Archived 2011-11-10 at the Wayback Machine, knowmgmt, Arizona State University, March 30, 2011
  • Unlike many expressions that emerge in business jargon, the term spread to newspapers and magazines. Few usage experts object to the term. Over 80 percent of usage experts accept the sentence The new management style is a reversal of GE’s traditional corporate culture, in which virtually everything the company does is measured in some form and filed away somewhere.”, The American Heritage® Dictionary of the English Language, Fourth Edition copyright ©2000 by Houghton Mifflin Company. Updated in 2009. Published by Houghton Mifflin Company.
  • One of the first to point to the importance of culture for organizational analysis and the intersection of culture theory and organization theory is Linda Smircich in her article Concepts of Culture and Organizational Analysis in 1983. See Smircich, Linda (1983). “Concepts of Culture and Organizational Analysis”. Administrative Science Quarterly. 28 (3): 339–358. doi:10.2307/2392246. hdl:10983/26094. JSTOR 2392246.
  • “The term “Corporate Culture” is fast losing the academic ring it once had among U.S. manager. Sociologists and anthropologists popularized the word “culture” in its technical sense, which describes overall behavior patterns in groups. But corporate managers, untrained in sociology jargon, found it difficult to use the term unselfconsciously.” in Phillip Farish, Career Talk: Corporate Culture, Hispanic Engineer, issue 1, year 1, 1982
  • Halpin, A. W., & Croft, D. B. (1963). The organizational climate of schools. Chicago: Midwest Administration Center of the University of Chicago.
  • Fred C. Lunenburg, Allan C. Ornstein, Educational Administration: Concepts and Practices, Cengage Learning, 2011, pp. 67
  • “What Is Organizational Climate?”. paulspector.com. Retrieved 2021-05-01.

Chinese Developer Woes Are Weighing on Asia’s Junk Bond Market

https://images.wsj.net/im-189934?width=620&size=1.5

Financial strains among Chinese property developers are hurting the Asian high-yield debt market, where the companies account for a large chunk of bond sales.

That’s widening a gulf with the region’s investment-grade securities, which have been doing well amid continued stimulus support.

Yields for Asia’s speculative-grade dollar bonds rose 41 basis points in the second quarter, according to a Bloomberg Barclays index, versus a 5 basis-point decline for investment-grade debt. They’ve increased for six straight weeks, the longest stretch since 2018, driven by a roughly 150 basis-point increase for Chinese notes.

China’s government has been pursuing a campaign to cut leverage and toughen up its corporate sector. Uncertainty surrounding big Chinese borrowers including China Evergrande Group, the largest issuer of dollar junk bonds in Asia, and investment-grade firm China Huarong Asset Management Co. have also weighed on the broader Asian market for riskier credit.

“Diverging borrowing costs have been mainly driven by waning investor sentiment in the high-yield primary markets, particularly relating to the China real estate sector,” said Conan Tam, head of Asia Pacific debt capital markets at Bank of America. “This is expected to continue until we see a significant sentiment shift here.”

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Such a shift would be unlikely to come without a turnaround in views toward the Chinese property industry, which has been leading a record pace in onshore bond defaults this year.

But there have been some more positive signs recently. Evergrande told Bloomberg News that as of June 30 it met one of the “three red lines” imposed to curb debt growth for many sector heavyweights. “By year-end, the reduction in leverage will help bring down borrowing costs” for the industry, said Francis Woo, head of fixed income syndicate Asia ex-Japan at Credit Agricole CIB.

Spreads have been widening for Asian dollar bonds this year while they’ve been narrowing in the U.S. for both high-yield and investment grade amid that country’s economic rebound, said Anne Zhang, co-head of asset class strategy, FICC in Asia at JPMorgan Private Bank. She expects Asia’s underperformance to persist this quarter, led by Chinese credits as investors remain cautious about policies there.

“However, as the relative yield differential between Asia and the U.S. becomes more pronounced there will be demand for yield that could help narrow the gap,” said Zhang.

Asia

A handful of issuers mandated on Monday for potential dollar bond deals including Hongkong Land Co., China Modern Dairy Holdings Ltd. and India’s REC Ltd., though there were no debt offerings scheduled to price with U.S. markets closed for the July 4 Independence Day holiday.

  • Spreads on Asian investment-grade dollar bonds were little changed to 1 basis point wider, according to credit traders. Yield premiums on the notes widened by almost 2 basis points last week, in their first weekly increase in six, according to a Bloomberg Barclays index
  • Among speculative-grade issuers, dollar bonds of China Evergrande Group lagged a 0.25 cent gain in the broader China high-yield market on Monday. The developer’s 12% note due in October 2023 sank 1.8 cents on the dollar to 74.6 cents, set for its lowest price since April last year

U.S.

The U.S. high-grade corporate bond market turned quiet at the end of last week before the holiday, but with spreads on the notes at their tightest in more than a decade companies have a growing incentive to issue debt over the rest of the summer rather than waiting until later this year.

  • The U.S. investment-grade loan market has surged back from pandemic disruptions, with volumes jumping 75% in the second quarter from a year earlier to $420.8 billion, according to preliminary Bloomberg league table data
  • For deal updates, click here for the New Issue Monitor

Europe

Sales of ethical bonds in Europe have surged past 250 billion euros ($296 billion) this year, smashing previous full-year records. The booming market for environmental, social and governance debt attracted issuers including the European Union, Repsol SA and Kellogg Co. in the first half of 2021.

  • The European Union has sent an RfP to raise further funding via a sale to be executed in the coming weeks, it said in an e-mailed statement
  • German property company Vivion Investments Sarl raised 340 million euros in a privately placed transaction in a bid to boost its real estate portfolio, according to people familiar with the matter

By:

Source: Chinese Developer Woes Are Weighing on Asia’s Junk Bond Market – Bloomberg

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Critics:

The Chinese property bubble was a real estate bubble in residential and/or commercial real estate in China. The phenomenon has seen average housing prices in the country triple from 2005 to 2009, possibly driven by both government policies and Chinese cultural attitudes.

Tianjin High price-to-income and price-to-rent ratios for property and the high number of unoccupied residential and commercial units have been held up as evidence of a bubble. Critics of the bubble theory point to China’s relatively conservative mortgage lending standards and trends of increasing urbanization and rising incomes as proof that property prices can remain supported.

The growth of the housing bubble ended in late 2011 when housing prices began to fall, following policies responding to complaints that members of the middle-class were unable to afford homes in large cities. The deflation of the property bubble is seen as one of the primary causes for China’s declining economic growth in 2012.

2011 estimates by property analysts state that there are some 64 million empty properties and apartments in China and that housing development in China is massively oversupplied and overvalued, and is a bubble waiting to burst with serious consequences in the future. The BBC cites Ordos in Inner Mongolia as the largest ghost town in China, full of empty shopping malls and apartment complexes. A large, and largely uninhabited, urban real estate development has been constructed 25 km from Dongsheng District in the Kangbashi New Area. Intended to house a million people, it remains largely uninhabited.

Intended to have 300,000 residents by 2010, government figures stated it had 28,000. In Beijing residential rent prices rose 32% between 2001 and 2003; the overall inflation rate in China was 16% over the same period (Huang, 2003). To avoid sinking into the economic downturn, in 2008, the Chinese government immediately altered China’s monetary policy from a conservative stance to a progressive attitude by means of suddenly increasing the money supply and largely relaxing credit conditions.

Under such circumstances, the main concern is whether this expansionary monetary policy has acted to simulate the property bubble (Chiang, 2016). Land supply has a significant impact on house price fluctuations while demand factors such as user costs, income and residential mortgage loan have greater influences.

References

Why Your Workforce Needs Data Literacy

Organizations that rely on data analysis to make decisions have a significant competitive advantage in overcoming challenges and planning for the future. And yet data access and the skills required to understand the data are, in many organizations, restricted to business intelligence teams and IT specialists.

As enterprises tap into the full potential of their data, leaders must work toward empowering employees to use data in their jobs and to increase performance—individually and as part of a team. This puts data at the heart of decision making across departments and roles and doesn’t restrict innovation to just one function. This strategic choice can foster a data culture—transcending individuals and teams while fundamentally changing an organization’s operations, mindset and identity around data.

Organizations can also instill a data culture by promoting data literacy—because in order for employees to participate in a data culture, they first need to speak the language of data. More than technical proficiency with software, data literacy encompasses the critical thinking skills required to interpret data and communicate its significance to others.

Many employees either don’t feel comfortable using data or aren’t completely prepared to use it. To best close this skills gap and encourage everyone to contribute to a data culture, organizations need executives who use and champion data, training and community programs that accommodate many learning needs and styles, benchmarks for measuring progress and support systems that encourage continuous personal development and growth.

Here’s how organizations can improve their data literacy:

1. LEAD

Employees take direction from leaders who signal their commitment to data literacy, from sharing data insights at meetings to participating in training alongside staff. “It becomes very inspiring when you can show your organization the data and insights that you found and what you did with that information,” said Jennifer Day, vice president of customer strategy and programs at Tableau.

“It takes that leadership at the top to make a commitment to data-driven decision making in order to really instill that across the entire organization.” To develop critical thinking around data, executives might ask questions about how data supported decisions, or they may demonstrate how they used data in their strategic actions. And publicizing success stories and use cases through internal communications draws focus to how different departments use data.

Self-Service Learning

This approach is “for the people who just need to solve a problem—get in and get out,” said Ravi Mistry, one of about three dozen Tableau Zen Masters, professionals selected by Tableau who are masters of the Tableau end-to-end analytics platform and now teach others how to use it.

Reference guides for digital processes and tutorials for specific tasks enable people to bridge minor gaps in knowledge, minimizing frustration and the need to interrupt someone else’s work to ask for help. In addition, forums moderated by data specialists can become indispensable roundups of solutions. Keeping it all on a single learning platform, or perhaps your company’s intranet, makes it easy for employees to look up what they need.

3.Measure

Success Indicators

Performance metrics are critical indicators of how well a data literacy initiative is working. Identify which metrics need to improve as data use increases and assess progress at regular intervals to know where to tweak your training program. Having the right learning targets will improve data literacy in areas that boost business performance.

And quantifying the business value generated by data literacy programs can encourage buy-in from executives. Ultimately, collecting metrics, use cases and testimonials can help the organization show a strong correlation between higher data literacy and better business outcomes.

4.Support

Knowledge Curators

Enlisting data specialists like analysts to showcase the benefits of using data helps make data more accessible to novices. Mistry, the Tableau Zen Master, referred to analysts who function in this capacity as “knowledge curators” guiding their peers on how to successfully use data in their roles. “The objective is to make sure everyone has a base level of analysis that they can do,” he said.

This is a shift from traditional business intelligence models in which analysts and IT professionals collect and analyze data for the entire company. Internal data experts can also offer office hours to help employees complete specific projects, troubleshoot problems and brainstorm different ways to look at data.

What’s most effective depends on the company and its workforce: The right data literacy program will implement training, software tools and digital processes that motivate employees to continuously learn and refine their skills, while encouraging data-driven thinking as a core practice.

For more information on how you can improve data literacy throughout your organization, read these resources from Tableau:

The Data Culture Playbook: Start Becoming A Data-Driven Organization

Forrester Consulting Study: Bridging The Great Data Literacy Gap

Data Literacy For All: A Free Self-Guided Course Covering Foundational Concepts

By: Natasha Stokes

Source: Why Your Workforce Needs Data Literacy

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Critics:

As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.

Data literacy is distinguished from statistical literacy since it involves understanding what data mean, including the ability to read graphs and charts as well as draw conclusions from data. Statistical literacy, on the other hand, refers to the “ability to read and interpret summary statistics in everyday media” such as graphs, tables, statements, surveys, and studies.

As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills. A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed.

Resources created by librarians include MIT‘s Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh’s Data Library and the University of Minnesota libraries’ Data Management Course for Structural Engineers.

See also

Zuckerberg Grows $5.1 Billion Richer After Judge Throws Out FTC’s Antitrust Case Against Facebook

Facebook CEO Mark Zuckerberg Testifies At House Hearing

A federal judge has given Mark Zuckerberg and Facebook investors a trillion or so reasons to smile.

Judge James E. Boasberg on Monday tossed aside several antitrust cases brought by the FTC and state authorities, turning away the most concerted campaign yet to police the social network. The decision sent Facebook’s stock sharply higher, allowing the company to cross the $1 trillion threshold in market value for the first time. These rising shares were a boon for common shareholder and billionaire alike: The 4.1% in stock price during after-hours trading added $5.1 billion to Zuckerberg’s fortune.

Boasberg’s decision—and the stock movement—underscore the complexities about Facebook’s future. The social network has developed a wide swath of critics from both sides of the political spectrum and received a major black eye for its handling of user data.

Led by New York Attorney General Letitia James, some of the company’s opponents had hoped antitrust legal action might deliver what they’ve long craved: A blow to Facebook to reduce its ballooning scale and importance and deliver a measure of regulation. The antitrust case revolved around Facebook’s 2012 acquisition of Instagram and its WhatsApp purchase two years later.

But bringing Facebook to heel won’t be as easy as its detractors might’ve hoped. Boasberg dismissed the states’ case over timeliness, saying it too much time had elapased since those acquisitions. Meanwhile, Boasberg tossed out the FTC’s argument and argued in a 53-page opinion that regulators hadn’t produced enough facts to support their argument. The FTC could still tak

The other thing is: Despite a steady drum beat of negative news for much of the past four years, Facebook’s stock has been a winner. Its shares have doubled since March 2018, when the full ramifications of the Cambridge Analytic become public, igniting this new era in Facebook’s history. Over that period, the S&P 500 went up less than 60%—while Zuckerberg, his position at the company bolstered by the company’s increasing share prices, has watched his fortune go from less than $60 billion to nearly $100 billion.

Follow me on Twitter. Send me a secure tip.

I’m a senior editor at Forbes, where I cover social media, creators and internet culture. In the past, I’ve edited across Forbes magazine and Forbes.com.

Source: Zuckerberg Grows $5.1 Billion Richer After Judge Throws Out FTC’s Antitrust Case Against Facebook

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Critics:

An antitrust suit against Facebook by the FTC and several states had the wind taken out of its sails today by a federal judge, who ruled that the plaintiffs don’t provide enough evidence that the company exerts monopoly control over social media. The court was more receptive, however, to revisiting the acquisitions of Instagram and WhatsApp, and the case was left open for regulators to take another shot at it.

The court decision was in response to a Facebook motion to dismiss the suit. Judge James Boesberg of the D.C. circuit explained that the provided evidence of monopoly and antitrust violations was “too speculative and conclusory to go forward.” In a more ordinary industry, it might have sufficed, he admits, but “this case involves no ordinary or intuitive market.”

It was incumbent on the plaintiffs to back up their allegation of Facebook controlling 60 percent of the market with clear and voluminous data and a convincing delineation of what exactly that market comprises — and it failed to do so, wrote Boesberg. Therefore he dismissed the complaints in accordance with Facebook’s legal argument.

The company wrote in a statement that it is “pleased that today’s decisions recognize the defects in the government complaints.”

On the other hand, Boesberg is sensible that lack of evidence in the record does not mean that the evidence does not exist. So he his giving the FTC and states 30 days to amend their filing, after which the complaints will be reevaluated.

The 3 Biggest Mistakes the Board Can Make Around Cyber Security

The role of the Board in relation to cyber security is a topic we have visited several times since 2015, first in the wake of the TalkTalk data breach in the UK, then in 2019 following the WannaCry and NotPeyta outbreaks and data breaches at BA, Marriott and Equifax amongst others. This is also a topic we have been researching with techUK, and that collaboration resulted in the start of their Cyber People series and the production of the “CISO at the C-Suite” report at the end of 2020.

Overall, although the topic of cyber security is now definitely on the board’s agenda in most organisations, it is rarely a fixed item. More often than not, it makes appearances at the request of the Audit & Risk Committee or after a question from a non-executive director, or – worse – in response to a security incident or a near-miss.

All this hides a pattern of recurrent cultural and governance attitudes which could be hindering cyber security more than enabling it. There are 3 big mistakes the Board needs to avoid to promote cyber security and prevent breaches.

1- Downgrading it

“We have bigger fishes to fry…”

Of course, each organisation is different and the COVID crisis is affecting each differently – from those nearing collapse, to those which are booming. But pretending that the protection of the business from cyber threats is not a relevant board topic now borders on negligence and is certainly a matter of poor governance which non-executive directors have a duty to pick up.

Cyber attacks are in the news every week and have been the direct cause of millions in direct losses and hundreds of millions in lost revenues in many large organisations across almost all industry sectors.

Data privacy regulators have suffered setbacks in 2020: They have been forced to adjust down some of their fines (BA, Marriott), and we have also seen a first successful challenge in Austria leading to a multi-million fine being overturned (EUR 18M for Austrian Post). Nevertheless, fines are now reaching the millions or tens of millions regularly; still very far from the 4% of global turnover allowed under the GDPR, but the upwards trend is clear as DLA Piper highlighted in their 2021 GDPR survey, and those number should register on the radar of most boards.

Finally, the COVID crisis has made most businesses heavily dependent on digital services, the stability of which is built on sound cyber security practices, in-house and across the supply chain.

Cyber security has become as pillar of the “new normal” and even more than before, should be a regular board agenda, clearly visible in the portfolio of one member who should have part of their remuneration linked to it (should remuneration practices allow). As stated above, this is fast becoming a plain matter of good governance.

2- Seeing it as an IT problem

“IT is dealing with this…”

This is a dangerous stance at a number of levels.

First, cyber security has never been a purely technological matter. The protection of the business from cyber threats has always required concerted action at people, process and technology level across the organisation.

Reducing it to a tech matter downgrades the subject, and as a result the calibre of talent it attracts. In large organisations – which are intrinsically territorial and political – it has led for decades to an endemic failure to address cross-silo issues, for example around identity or vendor risk management – in spite of the millions spent on those matters with tech vendors and consultants.

So it should not be left to the CIO to deal with, unless their profile is sufficiently elevated within the organisation.

In the past, we have advocated alternative organisational models to address the challenges of the digital transformation and the necessary reinforcement of practices around data privacy in the wake of the GDPR. They remain current, and of course are not meant to replace “three-lines-of-defence” type of models.

But here again, caution should prevail. It is easy – in particular in large firms – to over-engineer the three lines of defence and to build monstrous and inefficient control models. The three lines of defence can only work on trust, and must bring visible value to each part of the control organisation to avoid creating a culture of suspicion and regulatory window-dressing.

3- Throwing money at it

“How much do we need to spend to get this fixed?”

The protection of the business from cyber threats is something you need to grow, not something you can buy – in spite of what countless tech vendors and consultants would like you to believe.

As a matter of fact, most of the breached organisations of the past few years (BA, Marriott, Equifax, Travelex etc… the list is long…) would have spent collectively tens or hundreds of millions on cyber security products over the last decades…

Where cyber security maturity is low and profound transformation is required, simply throwing money at the problem is rarely the answer.

Of course, investments will be required, but the real silver bullets are to be found in corporate culture and governance, and in the true embedding of business protection values in the corporate purpose: Something which needs to start at the top of the organisation through visible and credible board ownership of those issues, and cascade down through middle management, relayed by incentives and remuneration schemes.

This is more challenging than doing ad-hoc pen tests but it is the only way to lasting long-term success.

By: JC Gaillard

Source: The 3 Biggest Mistakes the Board Can Make Around Cyber Security – Business 2 Community

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Critics:

A data breach is the intentional or unintentional release of secure or private/confidential information to an untrusted environment. Other terms for this phenomenon include unintentional information disclosure, data leak, information leakage and also data spill. Incidents range from concerted attacks by black hats, or individuals who hack for some kind of personal gain, associated with organized crime, political activist or national governments to careless disposal of used computer equipment or data storage media and unhackable source.

Definition: “A data breach is a security violation in which sensitive, protected or confidential data is copied, transmitted, viewed, stolen or used by an individual unauthorized to do so.”Data breaches may involve financial information such as credit card & debit card details, bank details, personal health information (PHI), Personally identifiable information (PII), trade secrets of corporations or intellectual property. Most data breaches involve overexposed and vulnerable unstructured data – files, documents, and sensitive information.

Data breaches can be quite costly to organizations with direct costs (remediation, investigation, etc) and indirect costs (reputational damages, providing cyber security to victims of compromised data, etc.)

According to the nonprofit consumer organization Privacy Rights Clearinghouse, a total of 227,052,199 individual records containing sensitive personal information were involved in security breaches in the United States between January 2005 and May 2008, excluding incidents where sensitive data was apparently not actually exposed.

Many jurisdictions have passed data breach notification laws, which requires a company that has been subject to a data breach to inform customers and takes other steps to remediate possible injuries.

A data breach may include incidents such as theft or loss of digital media such as computer tapes, hard drives, or laptop computers containing such media upon which such information is stored unencrypted, posting such information on the world wide web or on a computer otherwise accessible from the Internet without proper information security precautions, transfer of such information to a system which is not completely open but is not appropriately or formally accredited for security at the approved level, such as unencrypted e-mail, or transfer of such information to the information systems of a possibly hostile agency, such as a competing corporation or a foreign nation, where it may be exposed to more intensive decryption techniques.

ISO/IEC 27040 defines a data breach as: compromise of security that leads to the accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to protected data transmitted, stored or otherwise processed.

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How Entrepreneurs Are Capitalizing on Digital Transformation in the Age of the ‘New Normal’

How Entrepreneurs Are Capitalising on Digital Transformation in the Age of the 'New Normal'

The Covid-19 pandemic has carried a significant impact on the rate in which businesses are embracing digital transformation. The health crisis has created an almost overnight need for traditional brick and mortar shopping experiences to regenerate into something altogether more adaptive and remote. While some businesses are finding this transition toward emerging technology a little tricky, it’s proving to be a significant opportunity for entrepreneurs in the age of the “new normal.”

Astoundingly, data suggests that digital transformation has been accelerated by as much as seven years due to the pandemic, with Asia/Pacific businesses driving forward up to a decade in the future when it comes to digital offerings.

With entrepreneurs and new startup founders finding themselves in a strong position to embrace modern digital practices ahead of more traditional companies, we’re likely to see a rise in innovation among post-pandemic businesses. With this in mind, let’s take a deeper look into the ways in which digital transformation are benefiting businesses in the age of the new normal:

Fast, data-driven decisions.

Any digital transformation strategy needs to be driven by data. The emergence of big data as a key analytical tool may make all the difference in ensuring that startups take the right steps at the right time to ensure that they thrive without losing valuable resources chasing the wrong target audience, or promoting an underperforming product.

Enterprises today have the ability to tap into far greater volumes of data than ever before, thanks largely to both big data and Internet of Things technology. With the right set of analytical tools, this data can be transformed into essential insights that can leverage faster, more efficient and accurate decisions. Essentially, the deeper analytical tools are embedded in business operations, the greater the levels of integration and effect that may have.

By incorporating more AI-based technology into business models, it’s possible to gain access to huge volumes of big data that can drive key decisions. The pandemic has helped innovations in terms of data and analytics become more visible in the world of business, and many entrepreneurs are turning to advanced AI capabilities in order to modernise their existing applications while sifting through data at a faster and more efficient rate.

Leveraging multi-channel experiences.

Digital transformation is empowering customers to get what they want, when they want, and however they want it. Today, more than half of all consumers expect to receive a customer service response within 60 minutes. They also want equally swift response times on weekends as they’ve come to expect on weekdays. This emphasis on perpetual engagement has meant that businesses that aren’t switched on 24/7/365 are putting themselves at a disadvantage to rivals that may have more efficient operations in place.

The pandemic has led to business happening in real-time – even more so than in brick and mortar stores. Although customers in high street stores know they’re getting a face to face experience, this doesn’t mean that business representatives can offer a similar personalised and immediately knowledgeable service than that of a chatbot or a live chat operative with a sea of information at their disposal.

Modern consumers are never tied to a single channel. They visit stores, websites, leave feedback through mobile apps and ask questions for support teams on social networking sites. By combining these interactions, it’s possible to create full digital profiles for customers whenever they interact with your business – helping entrepreneurs to provide significantly more immersive experiences.

Fundraising via blockchain technology.

Blockchain technology is one of the most exciting emerging technologies today. Its applications are far-reaching in terms of leveraging new payment methods and brokering agreements via smart contracts, and while the use cases for these blockchain applications will certainly grow over the coming years, today the technology is already being widely utilised by entrepreneurs as a form of raising capital through Initial Token Offerings (ITOs), also known as Initial Coin Offerings (ICOs).

As an alternative to the use of traditional banks, venture capital firms, angel investors or crowdfunders, ITO tokens can be made available for exchanges where they can trade freely. These tokens are comparable to equity in a company, or a share of revenue for token holders.

Interested investors can buy into the offering and receive tokens that are created on a blockchain from the company. The tokens could have some practical use within the company where they can be spent on goods or services, or they could purely represent an equity share in a startup or project.

There are currently numerous companies that use blockchain technology to simply and secure its operations. From large corporations like HSBC’s Digital Vault, which is blockchain-based custody platform that allows clients to access details of their private assets to small education startups like ODEM, which aim to democratize education.

Another company that’s pioneering blockchain technology within the world of business is OpenExO, which has developed its own community-driven utility token EXOS, to help build a new transformation economy that helps companies to accelerate, democratise and internationalise their innovation.

Salim Ismail, OpenExO founder, is the former Yahoo technology innovator who developed the industry of Exponential Organizations. He has become a household name in the entrepreneur and innovation landscape, and now he launches the blockchain ecosystem that includes Fortune 500 companies, cities and even countries.

Reaping widespread rewards.

Although digital transformation could begin with a focus on just one facet of a startup, its benefits can be far reaching for employees, consumers and stakeholders alike. It could limit the mundane tasks required of workers, offer greater levels of personalisation for consumers and free up new skills to be developed in other areas of a business.

This, in turn, helps to build more engaged and invested teams that know the value of fresh ideas and perspectives. Although the natural adaptability of entrepreneurs makes the adoption of digital transformation an easier one to make than for established business owners, the benefits can be significant for both new and old endeavours.

The pandemic has accelerated the potential of emerging technologies by over seven years in some cases, the adoption of these new approaches and tools can be an imperative step in ensuring that your business navigates the age of the new normal with the greatest of efficiency.

Dmytro Spilka

By: Dmytro Spilka / Entrepreneur Leadership Network VIP – CEO and Founder of Solvid and Pridicto

Source: How Entrepreneurs Are Capitalising on Digital Transformation in the Age of the ‘New Normal’

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Critics:

Digital Transformation (DT or DX) or Digitalization is the adoption of digital technology to transform services or businesses, through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technology. Digital solutions may enable – in addition to efficiency via automation – new types of innovation and creativity, rather than simply enhancing and supporting traditional methods.

One aspect of digital transformation is the concept of ‘going paperless‘ or reaching a ‘digital business maturity’affecting both individual businesses and whole segments of society, such as government,mass communications,art,health care, and science.

Digital transformation is not proceeding at the same pace everywhere. According to the McKinsey Global Institute‘s 2016 Industry Digitization Index,Europe is currently operating at 12% of its digital potential, while the United States is operating at 18%. Within Europe, Germany operates at 10% of its digital potential, while the United Kingdom is almost on par with the United States at 17%.

One example of digital transformation is the use of cloud computing. This reduces reliance on user-owned hardware and increases reliance on subscription-based cloud services. Some of these digital solutions enhance capabilities of traditional software products (e.g. Microsoft Office compared to Office 365) while others are entirely cloud based (e.g. Google Docs).

As the companies providing the services are guaranteed of regular (usually monthly) recurring revenue from subscriptions, they are able to finance ongoing development with reduced risk (historically most software companies derived the majority of their revenue from users upgrading, and had to invest upfront in developing sufficient new features and benefits to encourage users to upgrade), and delivering more frequent updates often using forms of agile software development internally.This subscription model also reduces software piracy, which is a major benefit to the vendor.

Digitalization (of industries and organizations)

Unlike digitization, digitalization is the ‘organizational process’ or ‘business process’ of the technologically-induced change within industries, organizations, markets and branches. Digitalization of manufacturing industries has enabled new production processes and much of the phenomena today known as the Internet of Things, Industrial Internet, Industry 4.0, machine to machine communication, artificial intelligence and machine vision.

Digitalization of business and organizations has induced new business models (such as freemium), new eGovernment services, electronic payment, office automation and paperless office processes, using technologies such as smart phones, web applications, cloud services, electronic identification, blockchain, smart contracts and cryptocurrencies, and also business intelligence using Big Data. Digitalization of education has induced e-learning and Mooc courses.

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Crypto Price Mayhem: Data Reveals Bitcoin Is Braced For A ‘Short Squeeze’

bitcoin, bitcoin price, crypto, image

Bitcoin traders and investors are still reeling from a steep sell-off that’s wiped around $1 trillion from the combined cryptocurrency market.

The bitcoin price has crashed from almost $65,000 per bitcoin to under $40,000 despite a flood of positive bitcoin news in recent weeks—including Twitter TWTR +0.2% chief executive Jack Dorsey teasing a bitcoin payments plan.

Now, analysis of bitcoin trading data has suggested the bitcoin price could be hit by a so-called “short squeeze”—when the price of an asset increases rapidly due to an excess of bets against it.

“Given bitcoin’s past market performance, when traders use excessive leverage to short the market during a horizontal price adjustment, there will often be a short squeeze phenomenon,” Flex Yang, the chief executive of Hong Kong-based crypto lender and asset manager Babel Finance, wrote in analysis seen by this reporter and pointing to market data that shows recent capital inflows are “from short-sellers and that leverage has greatly increased.”

Since the bitcoin and crypto market crashed in mid-April, the volume of bitcoin perpetual holdings on the crypto exchange Binance have increased by 110%, with the ratio of long to short traders reaching a new low of 0.89—pushing funding rates into the negative.

According to Yang, the reasons behind such excessive shorts include “many people are anticipating a bear market; bitcoin “holders are building hedges,” or “those who bought at high prices are locked in.”

Historical bitcoin price data between February and April 2018 and then again from June to late July 2020, suggests an increase in short-selling is often followed by a bitcoin price surge.

“In November 2020, there was a temporary sharp increase in the number of short-selling positions at a high price,” wrote Yang. “Afterwards, the price of bitcoin continued to rise, continuing its bull market position. No matter if the market outlook is trending downwards after rebounding or if bitcoin maintains its bull market status, short traders have always suffered the consequence of being squeezed out and liquidated.”

The early 2021 bitcoin price bull run was brought to a sharp halt in April when fears over a crypto crackdown in China and mounting concerns over bitcoin’s soaring energy demands sparked panic among investors.

Tesla TSLA +1.1% billionaire Elon Musk sent shockwaves through the bitcoin market when he announced Tesla would suspend its use of bitcoin for payments until the bitcoin network increased its use of renewable energy.

The bitcoin price has failed to recover its lost ground despite continued reports that Wall Street banking giants are increasingly offering bitcoin investment and trading services and the Central America country El Salvador revealed plans to adopt bitcoin as legal tender alongside the U.S. dollar.

Follow me on Twitter.

I am a journalist with significant experience covering technology, finance, economics, and business around the world. As the founding editor of Verdict.co.uk I reported on how technology is changing business, political trends, and the latest culture and lifestyle. I have covered the rise of bitcoin and cryptocurrency since 2012 and have charted its emergence as a niche technology into the greatest threat to the established financial system the world has ever seen and the most important new technology since the internet itself. I have worked and written for CityAM, the Financial Times, and the New Statesman, amongst others. Follow me on Twitter @billybambrough or email me on billyATbillybambrough.com. Disclosure: I occasionally hold some small amount of bitcoin and other cryptocurrencies.

Source: Crypto Price Mayhem: Data Reveals Bitcoin Is Braced For A ‘Short Squeeze’

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Critics:

Predictions of a collapse of a speculative bubble in cryptocurrencies have been made by numerous experts in economics and financial markets. Bitcoin and other cryptocurrencies have been identified as speculative bubbles by several laureates of the Nobel Memorial Prize in Economic Sciences, central bankers, and investors.

From January to February 2018, the price of Bitcoin fell 65 percent. By September 2018, the MVIS CryptoCompare Digital Assets 10 Index had lost 80 percent of its value, making the decline of the cryptocurrency market, in percentage terms, greater than the bursting of the Dot-com bubble in 2002.

In November 2018, the total market capitalization for Bitcoin fell below $100 billion for the first time since October 2017, and the price of Bitcoin fell below $4,000, representing an 80 percent decline from its peak the previous January. Bitcoin reached a low of around $3,100 in December 2018.From 8 March to 12 March 2020, the price of Bitcoin fell by 30 percent from $8,901 to $6,206.By October 2020, Bitcoin was worth approximately $13,200.

Bitcoin has been characterized as a speculative bubble by eight winners of the Nobel Memorial Prize in Economic Sciences: Paul Krugman, Robert J. Shiller, Joseph Stiglitz, Richard Thaler, James Heckman, Thomas Sargent, Angus Deaton, and Oliver Hart; and by central bank officials including Alan Greenspan, Agustín Carstens, Vítor Constâncio, and Nout Wellink.

The investors Warren Buffett and George Soros have respectively characterized it as a “mirage”and a “bubble”; while the business executives Jack Ma and Jamie Dimon have called it a “bubble” and a “fraud”, respectively. J.P. Morgan Chase CEO Jamie Dimon said later he regrets calling Bitcoin a fraud.

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