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Coronavirus Concerns See Equity Markets Suffer As Investors Head for Safe Havens
27-02-2020 | treasuryXL | XE |
Coronavirus took the headlines again with the World Health Organisation warning that the world should prepare for a pandemic. The WHO stated it was too early to call the outbreak a pandemic but countries should be “in a phase of preparedness”. As with before, the Dollar and Swiss Franc are benefiting from their safe-haven status, with CHF hitting a 4-year high against the EUR. Both strengthened against most major currencies, as risk-averse investors fear that if the virus becomes a pandemic it will have a significant negative impact on global growth.
GBP/USD fluctuated around 1.29 for the day, despite US Equities collapsing yesterday, as investors adopt a risk off approach. Long-term bond rates fell sharply as worries about a recession increased. The 10-year treasury note is not far from its 2016 record low of 1.32%. In turn, Gold has continued to climb higher, hitting fresh multi-year highs and edging ever closer to the $1,700 mark. Despite the fear amongst wall street, the US Dollar has shrugged off the negativity as it moved higher thanks to its safe-haven status.
EUR/USD moved towards 52-week lows yesterday, falling to 1.0804 yesterday. Coronavirus related events in Italy had affected the EUR, with around 50,000 people under lockdown and the death toll rising to four. The pair however recovered towards the end of the day, hitting one-week highs of 1.0850 as the S&P 500 closed in on the low of 2020.
GBP/EUR appears to be relatively stable as investors assess the impact of coronavirus to be potentially greater within Europe than it is in the UK. Also, news that the UK budget – which is due to be announced on March 11th – could be bigger than expected should help the Pound remain relatively well supported against the Euro and other major currencies. In the meantime, the market keeps one eye on the build up to the UK-EU trade talks, due to commence next week. Some potentially positive news for the Pound yesterday was reports that the EU’s latest draft mandate indicates the EU will not be pushing for ‘dynamic alignment’. Dynamic alignment is essentially a requirement for the UK to adhere to a certain set of laws and standards, set by the EU, in order to have a free-trade agreement. Today, the EU leaders will be holding a general affairs council, with the Brexit Strategy on the agenda. This could provide greater detail around the EU’s position ahead of next weeks’ talks, any demands that may emerge from the agenda could have implications for the Pound.
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4 top Questions and Answers about Cash Management
| 25-2-2020 | by Kendra Keydeniers |
Cash Management is one of the three primary disciplines of Corporate Treasury. (Risk Management and Corporate Finance are the other two.) Cash management is often described as monetary logistics management. This analogy works quite well. It is the discipline of Treasury that is devoted to the management of planned expenditures, so it is highly focused on operational efficiency and process optimisation. It is about optimising the flow of money coming in from customers, some money going into savings, and other money going out to pay the bills. Since this is such a vital process to any organization, it is not hard to understand how cash management can make or break a company.
Here are 4 top questions and answers about Cash Management
1. Why should a company have more than one bank account?
Historically, large companies have used multiple bank accounts to gain insight into the activities of their local subsidiaries. In an extreme example, a retail chain might have held a different bank account for each local shop, so that a large retailer could easily hold over 500 accounts! Today, such unnecessary complexity is considered unprofessional. Insight into local activities can be achieved through proper bookkeeping, and extra bank accounts cost extra money. Modern bank accounts also tend to feature more extensive payment capabilities than in the past. A single European bank account, for example, will allow you to send payments within most European countries. Nevertheless, the operations of global businesses still often require multiple bank accounts. In addition, many companies like to hold accounts with banks that are widely recognized by customers in the local market.
2. Why is cash management separate from bookkeeping?
In small companies, cash management need not be separate from bookkeeping. The two activities may be done within the same department. In larger companies, though, these activities are specialised. This is because the skill set required for bookkeeping is different from that of cash management. Furthermore, in larger companies it is important to segregate duties for purposes of operational control: the one sending the invoice should not be the one who processes the related payment. Lastly, it is important to mention that in larger companies a distinction is made between cash flows, on the one hand, and income and expenses, on the other. In the financial accounts of a large corporation, revenue is booked when a sale is made. However, it might take some time before this revenue actually reaches the company in the form of cash. Until it does, booked revenue is generally irrelevant to the cash manager. The same is true for expenses. An expense may be booked, but from a Treasury perspective, until an expenditure is disbursed, it is still considered cash on hand.
3. Do cash managers also do foreign exchange (FX) management?
In smaller companies, cash managers also manage foreign exchange, but FX management is a separate field of expertise. It might be the role of a cash manager to set up bank accounts in various currencies. By doing this, the cash manager lowers operational costs by preventing repetitive transactions between the same currency pairs, which can generate unnecessary fees; but, this is not FX Management. (The FX Manager researches market developments that have an impact on Treasury operations, and often plays a more analytical or strategic role.)
4. For cash managers, what was the impact of the introduction of the international bank account number (IBAN)?
When the IBAN was introduced a few years ago, this new standard for bank account numbers made international electronic payments easier. In theory, it allows companies to work with fewer bank accounts, making cash management easier and cheaper. However, some companies argue that doing local business requires having a local bank, so they maintain multiple international bank accounts, despite the convenience of the IBAN.
Read more about Cash Management and check out these treasury topics as well:
What is Treasury?
What is Corporate Finance?
What is Risk Management?
What is Working Capital Management?
Community & Partner Manager at treasuryXL
Do Androids dream of Electric Spreadsheets? A Beginner’s Guide to AI in Treasury – Part I
| 24-02-2020 | treasuryXL | Cashforce |
Do Androids Dream of Electric Sheep by Philip K. Dick, adapted to the movie Blade Runner in 1984 (yes, it’s that old), ponders the question whether technology can replace humanity in every aspect of life. Whether advanced technology could attain a comprehensive cognitive interpretation of dreaming is a philosophical conundrum that I’ll leave for the brightest among us. However, while this doomsday scenario in Hollywood movies in which robots rise against their human creators is far from happening, the reality is that a computer has already surpassed the level of strategic thinking of a human being. This rise of artificial intelligence carries the potential to disrupt any industry, including treasury, but often leaves you wondering if the hype lives up to reality.
[Spoiler alert] In the dystopian world of Blade Runner, the protagonist called Deckard, a bounty hunter or “blade runner” hunts outlawed androids or “replicants” while feeling no remorse due to them being machines. An interpretation endorsed by the iconic unicorn dream sequence hints that his human memories might have been artificially implanted, implying he might be an android himself. Is this the course artificial intelligence will eventually take us to?
Man vs. Machine – A Boardgame Evolution
In 1997 IBM’s computer Deep Blue beat Gary Kasparov, the world champion at that time, in a game of chess. Deep Blue was able to analyze thousands of high-level chess games that were stacked into its system. When proposed with a move, it would choose the best outcome out of different scenarios. By basic number-crunching it was picking out the move that would lead to the best position on the board. This milestone was heralded as a boon for technology and viewed as almost exclusively disruptive for many industries.
Go, an abstract strategy board game invented in China, has simpler rules than chess, but many more moves at each point in the game. Just to give you an idea, the size of possible outcomes is larger than the number of atoms in the universe. Looking too far ahead in the game, or considering all possible moves and counter-moves is therefore nearly impossible. In 2016, distinguished Go player Lee Sedol was put up for the task to beat the next high-tech invention, named AlphaGo. Created by the sharp minds at Google’s DeepMind, its intelligence is based on its ability to learn from millions of Go positions and moves from previous games. Once again, machine triumphed over its human equivalent when it came to strategic thinking.
AlphaZero, released in 2017, is a version of the same program that takes it a step further. It can play chess, Go and other games and is only given the rules of the game, nothing more. By playing millions of games against itself without any previous knowledge of plays, tactics or strategy, it was able to master these games on its own. So how much time went by from the moment they launched AlphaZero to the moment where it achieved a superhuman level of playing Go? Less than 24 hours. Even more baffling is, while humans have been playing it for the past 2500 years, it came up with brand-new strategies that have never been seen before. While it is ‘only’ about fun and games, this sheds a new light on technological concepts that seemed at first like far-out fiction.
Artificial intelligence systems can dazzle us with their game-playing skills and lately it seems like every week there is a baffling breakthrough in the field with mind blowing results. It is almost unthinkable that the finance sector would be untouched by the rise of AI, any sector for that matter. Nonetheless, with the present hype around it, many of the used concepts and terminology seem to be used carelessly, which makes it hollowed and deprived of any meaning. You have probably heard of the terms “machine learning” and “deep learning”, sometimes used interchangeably with artificial intelligence. As a result, the difference between these concepts becomes very unclear. To understand this distinction and why AI will disrupt current technologies, we have to understand where it comes from.
Let there be l(A)ight – A brief History
Simply put, AI involves machines that can perform tasks that are similar to human tasks. A very broad definition which can go from simple solutions such as automated bank tellers to powerful and complex applications such as androids, which inspired the movie Blade Runner.
Surprisingly, the script on AI arises from a time when James Dean was rocking the screen and Elvis was celebrating his first “Blue Christmas”. While the statistical framework is based on the writings of French Mathematician Legendre from 1805, most AI models are based on technology from the 50’s.
1950, the world-famous Turing test is created by Alan Turing (who will soon be commemorated on the new £50 note). The test reflects on the question whether artificial intelligence is able to appear indistinguishable from a human in terms of thought and behaviour.
1951, the first artificial neural network was created by a team of computer scientists: SNARC (Stochastic Neural Analog Reinforcement Computer). They attempted to replicate the network of nerve cells in a brain. It imitated the behaviour of a rat searching for food in a maze. This was largely an academic enterprise.
SNARC computer
In the same way, 1952 rouses the birth of the first computer learning program or machine learning by Arthur Samuel. The program played checkers and improved at the game the more it played. Machine learning, a subset of AI, is defined as the ability to learn without being explicitly programmed what to “think”. It enables computers to learn from their own mistakes and modify to an altering environment. Machine learning also includes other technologies such as deep learning and artificial neural networks. Nowadays this technology can, among other things, use data and statistical analyses to predict possible future scenarios such as for Cash flow forecasting.
The Dartmouth Summer Research Project was a 1956 summer workshop and widely considered to be the starting point of artificial intelligence as a scientific field. With this uprising of technology there came a lot of excitement for the potential of automation in finance and treasury. It was believed to help accountants and bankers speed up their work. But if wishes were horses, beggars would ride. And in this case, androids would be riding along with them. Unfortunately, a reduced interest in the field and many failed projects leave artificial intelligence stranded in what is called the ‘AI winter’.
Luckily humans are not one trick ponies, so our story doesn’t end here. After a period of economic & technological proliferation in the 1980’s, Expert Systems found their way into the world of finance. These are computer systems that are capable of decision-making on the level of a human expert having been designed to solve complex problems. But when push came to shove, the technology wasn’t mature enough and didn’t meet client’s demands.
In 1991 the World Wide Web was opened to the public. It’s the start of the data revolution. In 2005, it reached 1 billion people and today more than half of the world’s population is connected to the internet.
Coming back to our first example, it was in this period (1997) that Deep Blue challenged the capacity of the human brain and proved it could think more strategically than a human being.
Today AI is demanding so much computing power that companies are increasingly turning to the cloud to rent hardware through Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings. That’s why around 2006, players such as Amazon Web Services (AWS) opened up its cloud environment to broaden the capacity of AI even further.
In the same year, Geoffrey Hinton coined the term “deep learning”, helping the progress of operating AI applications in the real world. This brought the world one step closer to bridging the fuzzy gap between humans and androids.
2015, AlphaGo is introduced to the world. Two years later in 2017, its successor AlphaZero sees the light of day.
2019, the first picture of the black hole M87 the constellation Virgo is rendered through artificial intelligence opening the door to new knowledge in the universe. The path of AI took us a giant leap forward, but we’re far from the finishing line. Roughly 90% of the universe exists of dark matter or dark energy that leaves us in confusion. Accordingly, a similar percentage of untapped dark data, the fundamental building block to understand a company’s future, isn’t being used.
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