BCR Publishing
We are the leading provider of news, market intelligence, events and training for the global receivables finance industry.
Working with industry leading organisations, experts, governments and universities, BCR Publications delivers expertise in factoring, receivables and supply chain finance to a global audience.
BCR has long been a beacon of innovation and excellence in the realm of receivables finance, playing an instrumental role in shaping the industry’s international landscape. Through its comprehensive conferences, insightful publications, and thought leadership, BCR has facilitated crucial dialogues and connections among industry professionals, driving forward the development of receivables finance globally.
Follow BCR Publishing
Free passes
For corporate treasurer roles/functions!
Re-inventing treasury workflows: Smart Treasury
| 3-8-2017 | Nicolas Christiaen | Cashforce | Sponsored Content |
Example FX management workflow
Hedging your FX exposure risk made easy
A common problem is the lack of visibility on the existing (global/local) FX exposure risk.
In order to calculate the FX transaction risk, transactional data from the TMS & ERP systems need to be consolidated effectively. Typically, this happens to be a (very) painful exercise. With Cashforce, however, using our off-the-shelf connectors (for ERP & TMS) and our full drill-down capabilities, you have all FX exposures at your fingertips.
FX Exposure Management – Current positions & exposures
But there is more to it. Imagine that linked to your FX exposure, an automated proposal of the most relevant FX deal would be generated to properly hedge this risk. A grin from ear to ear you say?
FX Exposure Management – Suggested hedge
And what about forecasting FX exposures? It’s now all within reach!
Whether you choose to take on an intercompany loan, a plain vanilla FX forward or another more exotic derivative product, chosen deals could then be automatically passed on to your deal transaction platform, to effectively execute the deal without any hassle. After execution, deals will automatically flow back into the system. Consequently, a useful summary/overview will be generated to effectively manage all your financial instruments.
Workflow integrated cash forecast
Finally, integrated cash management
New financial instruments / deals will generate a set of related cash flows. Ideally, these are directly integrated in your cash flow forecasts. In Cashforce, this data is automatically integrated within the cash flow forecast module, and will be put into a dedicated cash flow category. Learn more one how to set up an effective cash forecast in this article or this webinar.
Cash flow forecast overview
The analysis possibilities are now limitless, thanks to the ability to drill down to the very transactional-level details. The real number crunchers strike gold here: the analysis features open doors to unlimited in-depth analysis and comparison of various scenarios (E.g. the simulated effects of various exchange rate movements).
Drill-down to the transaction level
Using our big data engine, the delivery of rich and highly flexible reporting is facilitated. It’s fair to say that the typical SQL server (which currently 95% of the TMS systems use) can’t hold a candle to this. Through an advanced ‘self-service’ interface, users can drill down completely into respective amortization tables, historical transactions and effortlessly create customized reports and dashboards. We’ll talk more about why we believe Big Data engines are crucial for any Treasury software in our next blog.
Integration with ERPs & payment platforms
Next to this, Cashforce will automatically generate the accounting entries (in the format of your ERP/accounting system) related to your deals. The appropriate payment files will be generated in a similar fashion.
So…
As might be clear after reading this article, we strongly believe that integrated data flows & a Big Data engine are the foundation of a new type of Treasury Management System that runs like clockwork and can serve effective treasury departments, but also renewed finance/controlling/FP&A departments.
You are curious to hear more about effective treasury management? We’ve recently recorded a webchat on how to set up an efficient cash flow forecast process.
Nicolas Christiaen
Managing Partner at Cashforce
Bitcoins or banks, who is taking care of the business?
| 2-8-2017 | Hans de Vries |
The recent Ransomware attacks, that had an enormous impact on numerous companies and governmental institutions at a global level, showed however a less favorable aspect of this new technology. Due to its lack of control on the specifics of account ownership, Bitcoin proved to be the ideal means to collect the ransom money the victims have to pay to free their systems. This piracy trend will in my view also seriously hamper the future development of these sort of bank independent transaction mechanisms. Even more threatening for the Bitcoin development are the recent crypto robbery cases in which millions of dollars’ worth balances were stolen from the accounts. These incidents show the vital role of the banks as TTP since most banks are obliged to deliver their services according to the rules and regulations of their national and super-national banks. As indicated before, this means that for opening accounts lots of formalities have to be endured (the KYC rules are in some countries stretched to the absolute max). At the same time., due to the international regulations the control on international transactions are very extensive and therefore at the same time very costly for the banks. Every violation of the international code book on transactions to banned countries can have severe financial consequences for the banks involved. An last but not least banks have to maintain an international network of correspondent banks to make sure that the international transactions reach their beneficiaries in a reasonable timeframe and at reasonable costs.
This whole system has of course been developed to gain maximum control on transaction flows locally and worldwide. However it also provides the trust needed to be able to deal with (inter) national trade flows crucial to our economic day to day operations. As long as there are no ways to secure your transactions and balances in a bitcoin like environment as most transaction banks are providing today, Bitcoins remain a very interesting technological experience but will in no way replace the role of banks as TTP shortly.
Hans de Vries
Treasury/Cash Management Consultant
More articles of this author:
Will the European banks strike back?
The Euro from a treasury perspective
New norms in banking: More than 30 new areas emerging. Pick your fights!
Moving Averages – how to calculate them
| 1-8-2017 | Lionel Pavey |
In the second article in this series we will be looking at different types of moving averages. Moving averages are used to determine the current trend of a price. They filter out the extremes within a range of data and present a smoother picture. They are almost exclusively calculated using the arithmetic mean. Some studies have been done using the median, though no advantages have been discovered. The following 3 methods are the most common approaches. In all following examples we shall assume an average calculated over a continuous series of 10 data points.
Simple Moving Average (SMA)
We take 10 consecutive values and calculate the simple arithmetic mean. When we calculate the next value we drop the oldest value in the series and add the newest value. We are constantly using the most current data in our calculation. Every data point receives the same weighting i.e. 10 per cent of the complete series. Whilst being very easy to calculate criticism is levelled at the fact that all data points receive the same weighting. This can distort the average when the market is volatile – more recent data is closer to the true market price.
Weighted Moving Average (WMA)
Here the 10 data points are assigned different weights, usually based on a simple mathematical progression. The 10th data point (most recent data) would be multiplied by 10; the 9th data point (second most recent data) would be multiplied by 9; etc. The product of these calculations would then be divided by 55 to produce a weighted average. This weighted average applies more importance on the most recent prices and, therefore, more closely match the current price.
Exponential Moving Average (EMA)
This is another form of a weighted average, but the weighting factors decrease exponentially. As such, whilst the older data points decrease exponentially in value, they never stop. Therefore, this average encompasses considerably more data than the previous 2 examples whilst still being an average calculated with only 10 data points.
The results
EMA is more responsive than SMA. An EMA graph will accelerate faster, turn quicker and fall faster than a SMA graph. This is due to the weighting given to the most recent data. However, these are all lagging indicators – they will always be behind the price. Furthermore, if a market is trapped in a very small trading range the averages will not be as smooth as the actual data. One of the main goals of using averages is to see if prices break out of a range and start a new trend.
Moving averages can be used simply to see what the current trend is. They can be further used by applying different 2 moving averages (one for 10 days and another for 50 days) to ascertain the change in momentum by 2 different time lines. But they all lag the market data.
Most of the time prices will tend to concentrate in a small area, with occasional larger movements up or down establishing the next area of consolidation. Is there an alternative way to design moving averages that take this into consideration?
Adaptive Moving Average (AMA)
Instead of just weighting the data, AMA also look at the price volatility. When prices are in a small range AMA will notice this lack of volatility and provide a trendline that is almost flat. As prices break out of the range AMA will move quickly up or down, depending on the change in prices. The advantages of AMA are that, visually, when prices are reasonably flat (little volatility) a clear flat line is shown so that even if the actual market price is lower than the AMA, it is clear that it is still within a range. As AMA is more sensitive to volatility, it can contain more data about the current trend. An initial breakout from a tight range will result in a very steep line for AMA. The trend can continue, but AMA will clearly show earlier than other averages when the trend is weakening. The only basic problem with AMA is the calculation – it is far more complex to calculate and is not so intuitive when you come to explain it to someone who does not know it.
As stated earlier, all moving averages suffer from lag – they are behind the actual price curve. Our last example is an average that attempts to remove this lag, whilst being more reactive to the current price.
Hull Moving Average (HMA)
Initially, a WMA is taken for 10 data points. Then a WMA is taken for half this period (5 data points) and is calculated with the 5 newest data points. The difference between these 2 is then combined with the WMA for the shorter period to arrive at a new average – the HMA.
The HMA is faster, smoother and eliminates most of the lag that is present in the other moving averages. In fact, it most closely resembles the actual market data.
All these averages are used to attempt to show what the trend is in the actual price, whilst filtering out the noise from all the prices, and presenting the data in a smooth form. Yet again, as previously mentioned, a change in the underlying fundamentals of the price will always have more impact on the price than any form of technical analysis.
However, if we concede that for a large majority of time prices are just trending, a moving average can be used to try and predict when the prices have moved out of their range and are on a new path with fresh momentum until that slows down and the following range is established.
When charting data we need to appreciate the amount of data we will be producing. Even if we just use the price at the start of the day, or the end of the day, we will accumulate at least 255 data points every year. If prices are in a small range, then more data is added to the chart series to provide a more dynamic picture. But this can make the visual data more cluttered once we include the actual data and 1 or 2 moving averages. Would it not be better if we could eliminate time and just look at price?
Read also my first article in this series where I tell you more about several types of forecasts.
In the last article in this series I will look at 2 common methods of showing price data devoid of timelines.
Lionel Pavey
Cash Management and Treasury Specialist