Every since Pat Quinn(my Da) made me a ‘wee’ Bodhran, I have ditched my more expensive purchases in preference for these more leaner meaner versions. Personally, I prefer a slightly smaller bodhran, (12” x 6”) and these are perfect as they pack the same ‘boom’ that a larger drum provide at a fraction of the space. He made a few more and started selling them and the Pat Quinn Bodhran series was born.
There are a few of the first generation ones left (see slide show below) retailing at £175, and there will be a new and improved second generation coming soon. If you are curious about how they sound pop down to the Duke of York any Sunday and the venerable Noel Maguire will be rattling away (if he gets bored of his mandalo-singing exploits).
Mahogany Veneered Bodhran
Mahogany Veneered Bodhran
Walnut Veneered Bodhran
Walnut Veneered Bodhran
Walnut Veneered Bodhran
Walnut Veneered Bodhran
Three 5 point tuneable 300mm Bodhrans
The basic frame is laminated from aircraft quality Birch plywood, clad with exotic hardwood veneers. Pat manufactures the tuners himself, designed for ease of access and adjustment. The skins used are best- quality goat skins used for Lambeg drums. 5-point tuning systems.
Phone:- UK— 028 79632342 or MOB 0287851189103
ROI— 0044 2879632342 or 048 79632342 MOB. 00447851189103
The banking industry in Northern Ireland (NI) mirrors the rest of the UK in its concentrated nature, with ten high street banks (Bank of Ireland, Danske Bank, First Trust Bank, Ulster Bank (RBS group), Barclays, Lloyds Banking Group, HSBC, Santander UK, Clydesdale and Nationwide Building Society) and a handful of banking businesses owned by retail groups (Co-operative bank, Sainsbury’s Bank, Tesco Bank and Post Office Money) who largely operating in the unsecured short term loans market1. Bank balance sheets in NI are facing some significant challenges as a result of the recent political instability. The EU exit and the assembly’s collapse (and subsequent polarised snap election results) due the mismanagement of the ‘ash for cash’ green energy scheme (RHI) 2 places real pressures on both business and consumer lending. The RHI scandal is likely to put pressure on a depleted small to medium enterprise (SME) lending portfolio adding further financial woes to the farming industry 3, while the economic fallout of an EU exit puts pressure on debt-fuelled consumer spending.
One way to understand these pressures better is to assess the liquidity risk of NI banks. Specifically, figure 1 presents an aggregate look at the funding liquidity ratio (borrowings a a proportion of deposits4) for individuals and a selection of SME business categories over the period 2013 Q3 to 2016 Q35.
Over this period, lending to individuals averaged around 35% of the total loan book. Figure 2 presents the share of total SME lending by business category. The SME business categories investigated in figure 1 capture over 75% of SME loan book share and can be considered the biggest liquidity risk management. Finally, for comparison, figure 1 also includes a sector average.
The calculated ratio approximates a bank’s liquidity position, with too high a value suggesting that a bank will not have enough funds available to absorb unforeseen funding shocks, while too low a value suggesting profitability issues due to a bank not earning as much as it could from its liquid assets. The data is sourced from the British Banking Association and captures over 95% of the banking business in Northern Ireland.
Some interesting points emerge. The large difference between quarterly SME (orange, light green, and dark green dots) and consumer lending (purple dots) ratios illustrates the stark differences in bank lending practices. SME business lending is secured against a combination of collateral, government backed income and EU subsidies. For example, EU and government backed income is purported to account for some 87% of farming income in NI 6. The SME sector has seen a reduction in the average liquidity ratio over the period, from 138% in 2013 Q3 to 82% in 2016 Q3, which is largely due to a depletion of the loan book size. This depletion has been driven by a deleveraging process in the real estate and construction industries7 due to large non-performing loan write offs and the sale of debt portfolios to investment companies such as New York investment fund Cerberus.
In 2016 Q3, liquidity risk in construction has now fallen below 100% while the real estate business remain well above the sector average at 181%. Agricultural business, while capturing a smaller portion of the SME lending stock (8% average for period), has remained a high liquid risk, well above the average liquidity ratio in every quarter.
This high liquidity risk position of agri-SME lending can be partly explained by the heavily subsidised nature of the industry. That said, a financial crisis in farming is an issue that has been raised a number of years ago when the big four banks were called to Stormont to give evidence8. A key concern for this SME category is the ‘ash for cash’ government backed scheme that has likely been used as lending collateral. In 2013, assurances by the finance department to the banks that the 12% rates of return on these scheme will be ‘grandfathered’ providing certainty regardless of future reviews, likely means that deleveraging in this sectors has been less of a concern to the banks over this period.9. With these guarantees now likely to be under review in a newly formed Assembly the high liquidity risk of agri-SME lending may be ossified by increased default risk if promised guarantees are now in doubt.
From a liquidity perspective, lending to individuals is increasingly well covered by more deposits, with the ratio falling from 73% in 2013 Q3 to 64% in 2016 Q3. That said, this may raise some profitability concerns as individual lending makes up a 35% of the bank lending in NI. GB and NI consumers have been largely unaffected by Brexit-induced economic slowdown or inflation pressure as UK consumer credit grew more than 11% in the year to December, an increase not seen since 2005 according to the Bank of England figures.
Banks in Northern Ireland, as in the rest of the UK, should be weary of such debt-fueled consumption as unsecured personal debt has historically made up 90% of the household loan losses in the UK. Mark Carney has cautioned on this type of debt-driven demand becoming more sensitive to income and employment levels. Another key concern is that UK households are saving at record low levels with saving rates as a proportion of disposable income dropping to 5% (20 year low). While employment levels are at their highest since records began in Northern Ireland, 70% according to the Department of the Economy, the real wage rate in NI has been falling steadily since 200910.
In summary, political uncertainty has some clear liquidity risk management challenges for NI bank balance sheets. While a large deleveraging process in construction and real estate has significantly lowered liquidity risk in SME lending this is likely to squeeze profitability in these areas. More importantly, lending to the farming industry will require careful risk recalibration as RHI scheme guarantees may prove illusory after a review. Perhaps more encouraging is the well covered individual lending portfolio which is 35% of the total loan book. This low liquidity risk may also be a challenge for bank profitability as they struggle to on-lending some of these highly liquid deposits assets.
Northern Ireland also has a well established credit union sector which, while more developed than its UK counterparts, operate mainly in the short term unsecured lending market ↩
In 2013, the largest four banks were called to the assembly to provide information and opinion on a perceived financial crisis in NI farming. For details of these briefings see (2013) Financial Crisis in Farming Northern Ireland Assembly Briefings with Banks 2013. Danske Bank , Bank of Ireland, Ulster Bank, First Trust Bank. ↩
Borrowings are defined as total loans and overdrafts while deposits includes all current account balances ↩
This is the complete period for which data is available ↩
One of the many good things about working as a Finance lecturer at Queen’s Management School is the opportunity to research overseas. As a senior research associate of the International Research Centre for Cooperative Finance (IRCCF) at HEC Montreal, I have the pleasure of visiting this eclectic bilingual city each year. Over the academic year I have two visits as part of my sabbatical leave. The visits are centred around three exciting projects:
This research hopes to provide evidence that enlightens the following research puzzles:
What business model features have engendered resilience in the Canadian financial system ?
Do credit union mergers enshrine membership benefits?
How does compliance with core supervisory standards set by the Basel Banking Supervision Committee affect systemic risk in developed economies?
Projects 1 and 2 are Canada focused while project 3 takes a global approach. Each of these projects pose very different quantitative challenges which I relish as an card-carrying empiricist.
In project 1 we are using a data clustering approach to identify distinct business models based on an institution’s funding and activities. This model-free approach reveals the BBM diversity in the Canadian sector over the period 2010-2015. Following the seminal work on BBM global monitors by my co-author Professor Rym Ayadi (Director of the IRCCF) in Ayadi et al (2011 2014, 2015 and 2016)1 this exercise will illustrate the unique architecture of the Canadian financial services industry and shed light on factors that promote resilience to globally systemic banking problems.
Project 2 uses a proprietary data set from Deposit Insurance Corporation of Ontario (DICO) to investigate 20 years of consolidation in this region’s credit unions. The analysis will use a flexible model which captures the uniquely cooperative objective of membership benefit maximisation. The project will empirically expose the cooperative traits of mergers/amalgamations in credit unions and hopes to reveal the nature of the membership value of such activity.
Finally, project 3 is a global exercise which uses a number of quantitative measures to capture systemic risk of a financial institution and identify to what extend regulatory compliance can mitigate this risk. This is a follow on piece of work from Ayadi et al (2016) 2. We have used a large slice of data science to compile a unique sample representing global banking and its regulatory infrastructure. Our key variable measures the compliance of a financial system with the principles of regulatory best practice proposed by the Basel Committee for Banking supervision.
In short I have my work cut out! Watch this space for some interesting preliminary results from these projects.
Ayadi, R., Arbak, E. & Pieter De Groen, W., 2011. Business Models in European Banking: A Pre-and Post-Crisis Screening. Centre for European Policy Studies.
Ayadi, R. & DeGreon, W.P., 2014. Banking Business Models Monitor 2014 Europe. Centre for European Policy Studies.
Ayadi, R., Arbak, M. & GreonWP, D., 2015. Regulations of European Banks and Business Models: Towards a new paradigm.Centre for European Policy Studies.
Ayadi, R. & De Groen, W.P., 2016. Bank Business Model Monitor for Europe 2015. International Research Center for Cooperative Finance. ↩
Ayadi, R., Naceur, S. B., Casu, B. & Quinn, B. 2016, Does Basel Compliance Matter for Bank Performance?, Journal of Financial Stability. 23, p. 15-32 ↩
FTSE 100 Index options are a excellent tool for investors to benefit from/protect against large scale UK equity market moves. Specifically, investors can use puts to protect against a decline in stocks after the OUT vote, and calls to reap the benefits of a rally after a REMAIN vote.
I investigate the extend of these two investment plays by assessing the implied volatility skew of the FTSE 100 Index. Specifically, I assess the difference between 5% out of the money puts and calls for the at one month expiry. Below I have graphed this spread for the past 2 years.
We can see that the spread between bearish and bullish bets in the options market is at its highest in 2 years. After a market fall, bearish purchases of puts are frequently met by the buying of calls on speculation that the negative sentiment is overdone. We do not see this here. The options markets are suggesting that investors are protecting themselves against a potential drop but few investors are trying to benefit from the potential for a rally.
So perhaps a rally in the FTSE 100 stocks will prove illusory after the referendum.
This short post is inspired by some friends asking whether they should cash in their euros stash ahead of the Brexit result today. As a finance academic and a former currency market participant I think the best answer here is to ask
“what do the currency markets think ?”
More specifically, what is the expected impact of Brexit on the chunnelrate (the EUR/GBP currency pair) according to the FX option markets. We can use expectation theory to provide us with an estimate of the impact based on the probability of Brexit and the expected % change in the chunnel rate for either scenario. Mathematically:
Bloomberg has some excellent estimates to fill in the blanks on the right-hand side of the above equation. Specifically, they have used the EUR/GBP and USD/GBP FX options markets to produced both an implied probability of Brexit and its % impact on EUR/GBP rate. Table 1 shows these estimates averaged for the last 30 days1. I have also included the implied probabilities of Brexit from a number of other sources as a comparison.
Source of Probabilities
Expected Move in EUR/GBP | Brexit
Expected Move in EUR/GBP | Remain
Expect Impact of Brexit
BBG FX Options
All numbers are in percentages. I have made the assumption that there is a symmetrical impact between for the EUR/GBP rate. Columns 2-5 are 30 day averages.
From the last column in Table 1 in all scenarios the expected impact is negative for my mate’s stash of Euro. In conclusion, cash in the stash before they are devalued!
UPDATE POST BREXIT:
Clearly my symmetrical assumption on Brexit impact was misguided and if I had considered my other post, this lack of symmetry would be obvious. If i had assumed a no change assumption for a remain vote the calculations would have looked much different:
Source of Probabilities
Probability Brexit (%)
Probability Remain (%)
Expected Move in EUR/GBP | Brexit
Expected Move in EUR/GBP | Remain
Expect Impact of Brexit
BBG FX Options
Figure 1 and Figure 2 are time series graphs of underlying indices that these averages are based on:
Figure 1: BRXBEUR Index (Bloomberg Expected Move in EUR/GBP in the Event of BREXIT)
Figure 2: BRXBLEAV Index (Bloomberg Probability of BREXIT from the FX Options Markets)
A credit union is one of the purest forms of cooperative banking, striving to balance both economic and social goals for the benefit of its membership.
Credit unions are a prevalent part of society and have long been seen as a stable and risk-averse form of banking. In Canada, credit unions compete directly for market share with shareholder-owned banks, and dominate in some regions. Overall this heterogeneous banking system has been perceived to be relatively stable, especially since the recent financial crisis.
This research project aims to provide key insights into the dynamic contribution financial cooperatives make to the overall stability of a banking system. Focusing on the Canadian banking system, the analysis aims to assess key credit union characteristics that influence stability in a banking sector in periods of crisis and calm. The projects’ empirical design has four paradigms; business model heterogeneity; structural performance; survival, and viability.
This research project is part of my work as a Senior research associate of the International Research Centre for Cooperative Finance in HEC Montreal.
Regulatory reform in Irish credit unions is a hot topic. A recent Irish times article 1 highlights the juxtaposition of opinions of the various stakeholders in Ireland.
In this research project we investigate the existence of a tiered structure in Irish credit unions. We let the data speak and provide some empirical evidence to help inform this current policy debate.
So far our findings conclude that a multi-tier system in terms of business model complexity is present. This findings is based on a novel approach which endogenously identified an optimal tiered business model strategy based on key financial viability characteristics of Irish credit unions.
One of my latest pieces of research investigates business model complexity within the Irish credit union movement using a novel technique which allows identification of a multi-layered system based on financial viability characteristics.
This study examines the business model complexity of Irish credit unions using a latent class approach to measure structural performance over the period 2002 to 2013. The latent class approach allows the endogenous identification of a multi-tiered business model based on credit union specific characteristics. The analysis finds a three tier business model to be appropriate with the multi-tier model dependent on three financial viability characteristics. This finding is consistent with the deliberations of the Irish Commission on Credit Unions (2012) which identified complexity and diversity in the business models of Irish credit unions and recommended that such complexity and diversity could not be accommodated within a one size fits all framework. The analysis also highlights all tiers are subject to decreasing returns to scale at the sample mean. This may suggest that credit unions would benefit from a reduction in scale or perhaps that there is an imbalance in the present change process. Finally, relative performance differences are identified for each tier in terms of technical efficiency. This suggests that there is an opportunity for credit unions to improve their performance by using within tier best practice or alternatively by switching to another tier.
This study is part of the Not-for-profit and Public sector Research (CNPR) centre’s working paper series. For a copy of the full paper please visit the financial institutions section of:
With the curtain closed on the 2014/15 Premier League season, the oligarch funded Chelsea reign supreme, looking over the shoulders at new money Manchester City. This season also sees another Russian owner, Maxime Demin, financially facilitate Bournemouth’s emergence from the Championship wings to trip the boards on the main Premiership stage next season. This emergence can also be attributed to Eddie Howth, Bournemouth’ young critically acclaimed manager, who has provided the vital strategic input for the playing success of the team. Bournemouth’s path to the top has be frought with financial instability, including entering into insolvency proceedings in 2008. But will Bournemouth’s rise be fleeting and the struggle to stay up result in an overspend well beyond the income that they generate ?
Set against this backdrop is the continuing imposition of the Financial Fair Play (henceforth FFP) regulation by UEFA, whose controversial break-even rule is in reality an attempt to impose a measure of financial ‘fairness’ or efficiency on European Football clubs 1. Although recently it has emerged that there may be a softening of these rules, to date there have been 23 European clubs which have entered into settlements with UEFA as a result of the break even rule 2.
Myself and my colleague, Ronan Gallagher, are taking a professional interest in this financial regulation. Academia (Sloane 1971; Késenne 1996; Garcia-del-Barrio & Stefan Szymanski 2009) has long been aware that the business of football is best explained as maximising wins while just maintaining financial solvency although their financial (mis)management regularly makes headline news. To this end we calculate one accounting measure of financial efficiency which is used to assess how effectively an institution can turn its spending into income. Figure 1 displays the average wage to turnover ratio (%) along with 95% confidence intervals 3 for the Premiership and Championship clubs over the 2002/03-2013/14 period. This one-dimensional measure of a club’s productivity has some major weakness4, but its exposition below illustrates some interesting features prevalent in the English game 5.
Firstly financial inefficiency, as measured by higher wage to turnover ratios, has grown over the 2002/03-2012/13 period. This rising inefficiency is set against a backdrop of significant aggregate income growth of UEFA footballs teams of 45% 6 while European economies as a whole languished in a stagnant 1% growth period (Morrow ,2014). Secondly, the 2013/14 season has seen a dramatic drop in financial efficiency especially in the Premiership. This can largely be explained by two factors: 1) 2013/14 season was the first time club’s were subject to the Premier League’s Short Term Cost Control measure and also the first time that some club’s where subject to UEFA’s FFP break-even requirement; 2) 2013/2014 season seen a 29% year-on-year increase in total league revenue driven mostly (78% of the total increase) by the the first year of a new broadcasting rights package.7 . Finally, there is a clear financial efficiency gap between the average Premiership and Championship club, with the latter having unsustainably high wage bills in the last few seasons (wage to turnover ratios of over 100%). Furthermore, given the 95% confidence bands, this difference has become statistically significant from the 2007/08 season onward.8
In a recent paper, Goddard (2014) argues that this gap is likely due to the opening of competition through the promotion and relegation system. He argues this system has a detrimental effect on profitability, owing to the pervasive tendency to overspend in an effort to achieve promotion or avoid relegation. He points to the two tier system in English football as a point in case, where promoted teams fail to survive in the Premier league for more than one season, while regulated teams commonly experience financial duress upon arrival in the Football league. Table 2 provides some evidence to suggest this is true.
Table 2 Financial efficiency implications for promotion and regulation for seasons 2002/03 to 2013/14.
Mean Wage to Turnover Ratio (%)
Lower 95% Confidence Interval
Upper 95% Confidence Interval
Promotion Push Year
Avoid Regulation Year
These results confirm that the club’s promotional push involves an unsustainable overspend, illustrated by an average wage to turnover ratio of 112.5% in the promotion push year. Furthermore the immediate revenue boost to these newly promoted clubs is evidenced by the dramatic and statistically significant drop in financial efficiency one year after their promotional campaign. In contrast, relegated clubs experience a deterioration in financial efficiency the year they move into the Championship, indicated by an increase in the average wage to turnover ratio to 81.9% in that year.
While this preliminary analysis has some strong statistical health warnings we can attempt to provide some context to these findings. The Premiership clubs are more efficient than Championship clubs because the TV money and associated sponsorship monies is a financial game changer. Furthermore, the dramatic improvement in financial efficiency in 2013/14 is likely to be sustained with the dampening effect of cost control regulations extending beyond season 2015/16. Therefore, clubs will bust a gut to get there and stay there. Getting there takes a significant increase in financial input per unit output (i.e. a rise in your inefficiency in the year in which you go on the promotion push). It’s a “go big or go home” scenario and many clubs fail to turn their spend into adequate points to ensure premiership survival. Those that do “reach escape velocity” and stay in the premier league (e.g. the Stokes and Swanseas of the footballing world) ultimately achieve better long run efficiency but it’s high stake poker with more causalities than millionaires.
So Bournemouth’s (and indeed all the promoted teams) fight to survive (or perhaps thrive) in next season’s Premiership will provide for some interesting challenges for their future financial stability/efficiency.
Peeters, T., & Szymanski, S. 2014. Financial fair play in European football. Economic Policy, 29(78), 343–390. doi:10.1111/1468-0327.12031
Garcia-del-Barrio, Pedro, and Stefan Szymanski. 2009. “Goal! Profit Maximization Versus Win Maximization in Soccer.” Review of Industrial Organization 34 (1). Springer US: 45–68.
Goddard, John. 2014. “The Promotion and Relegation System.” In Handbook on the Economics of Professional Football, edited by John Goddard and Peter Sloane, 23–40. Edward Elgar Publishing.
Késenne, S. (1996). League management in professional team sports with win maximizing clubs. European Journal for Sport Management, 2(2), 14–22.
Morrow, Stephen, and Morrow Stephen. 2014. “Football Finances.” In Handbook on the Economics of Professional Football, edited by John Goddard and Peter Sloane, 80–99. Edward Elgar Publishing.
Sloane, Peter J. 1971. “THE ECONOMICS OF PROFESSIONAL FOOTBALL: THE FOOTBALL CLUB AS A UTILITY MAXIMISER.” Scottish Journal of Political Economy 18 (2): 121–46.
See Syzmanski & Peeters (2014) paper for an excellent empirical exposition of this ↩
Daniel Geey’s has a nice exposition of these settlements as well as a country breakdown ↩
based on a t distribution to account for small sample bias ↩
a) It assumes constant returns to scale. b) It doesn’t capture the non-profit nature of football clubs; for decades the economic study of football teams has long believed that they are run on a non-profit basis, a more appropriate model being win maximisation contingent on some budget constraint. c) It suffers from the Fox’s paradox problem; a team could be partially efficiency in a number of areas but overall still performing poorly ↩
A notably, if obvious, feature that is illustrated by this simple ratio is highly labour intensive nature of the football business. This is a feature of all sporting sectors ↩
According to Deloitte Annual Football Review data for the 2006-2010 period ↩
Generally when comparing to parameter estimates, such as sample means, it is always true that if their confidence intervals do not overlap then the statistics are significantly different. This simplistic statistical finding comes with some important caveats. We assume that the mean are from independent sample and are approximately distributed normally. The t distributions does help with some small sample bias but not with the independence assumption. ↩
I have just finished reading The hour between dog and wolf by John Coates on the biology of financial risk taking. As a former derivatives trading Dr John Coates has first hand experience in the neurological patterns that traders experience during financial booms and busts and how this influences P&L account volatility.
The book is based on his years of academic research after returning to Cambridge University from industry to study a PhD in neuroscience and finance. He has since published considerable evidence refuting the neo-classical economic ideal that we choose our course of behaviour after thinking things through.
He argues that the aristotlean idea of the body and mind interconnectedness is a more coherent explanation for modern financial risk taking than traditional neck up economic rationalism.
The board tenant of the book is that the mind and body of traders works as a dynamic feedback loop, with testosterone being the molecule of irrational exuberance and a reason that ralllies change into bubbles.
One of Coates’ clear conclusion is that more female traders are needed to moderate the large fluctuations in financial markets. This resonates with some antecdote evidence I have from teaching my Trading Principles course on the MSc in Computational Finance and Trading. This course is based around real world trading simulations, and students are assessed on their trading book perfomance.
This year a third of the students where female. This cohort performed much better in their trading book cumulative NAV (measured over all 16 simulations). Also in the Law of One Price simuations the girls achieved much higher broker interest rebates, a clear indication that they are sucessfully offseting their risks from arbitrage trading.
Next year I hope to design a more scientific method to statistically confirm this antecdotal finding.