Inspire – issue 12 by Loughborough University

INSPIRE LOUGHBOROUGH UNIVERSITY SCHOOL OF BUSINESS AND ECONOMICS BI-ANNUAL MAGAZINE ISSUE 12

SPECIAL ISSUE:

DECISION SCIENCES: FROM DATA TO DECISIONS

How facilitated simulation modelling is helping the health sector PG20 Forecasting under uncertainty PG26 Does too much information lead to mental health issues? PG28

Editor: Ondine Barry Assistant Editor: Sym Samria

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WELCOME Welcome to the twelfth edition of Inspire, the magazine of the School of Business and Economics at Loughborough University. This issue focuses on Decision Sciences, an area of research strength in the School. With the ever-growing volume and variety of data available, it is increasingly important that organisations maximise the use of that data.

Decision Sciences is concerned with how data is transformed into information and knowledge, and ultimately, managerial decisions. The School’s Centre for Information Management and newly formed Centre for Productivity and Performance both speak to this need, along with researchers in operational research and across the School’s rich breadth of expertise, as demonstrated in this issue. On a different note, as Issue 11 went to press, the result of the EU Referendum vote had just been divulged. With more time to digest the potential impact of Brexit on the School, we are looking at three possible areas of impact: Students: Early warnings about an immediate loss of international student applications have not been borne out, partially helped by the fall in the value of the Pound. Will the UK remain an attractive place to study for students from the remaining EU countries? At present, they are subject to UK fee levels, but that may change. With uncertainties about freedom of movement, EU-based students may be put off applying to the UK and could (but may not) have travel restrictions placed on them. For students outside of EU countries, will the UK be a less-attractive place to study, given that it may no longer be an entry point for the EU? On the other hand, as a colleague pointed out: the possibility of increased control on immigration could lead to a loosening of restrictions for post-study work visas from non-EU countries, which would be a plus for certain students. Staff: UK universities are heavily reliant on staff from EU countries. According to the Chartered Association of Business Schools, 13 per cent of academic business school

staff are EU nationals. For SBE to thrive, it is important that we can continue to retain and attract the best staff. While in the longer term, a significant change in our ability to employ EU staff seems highly unlikely, the shorterterm uncertainty may make working in the UK less attractive. Research Funding: At present, EU policy still allows bids for EU funding from UK universities. But uncertainty about the future and how this will be organised may lead to fewer UK-led bids being funded. Other non-EU countries (e.g., Switzerland) have access to EU research funding, but we do not know what the UK post-Brexit model will look like. If a proportion of the money that has flowed to the EU is diverted into UK research councils, we may end up with a similar – or indeed, better – level of funding access. Of course, Brexit itself is an example of political decision making, which has implications for societal, economic and managerial decision making across the UK and further afield. To be continued… I hope you enjoy the range of articles and features in this issue of Inspire.

Sincerely yours,

Stewart Robinson Dean, School of Business and Economics, Loughborough University

TRANSFORMING PRACTICE, INSPIRING WINNERS 03

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NEWS

Sir Dermot Turing gives insightful talk on Alan Turing, Bletchley Park and the origins of big data SBE hosts the UK Automotive 30% Club Annual Conference The Centre for Automotive Management recently hosted the UK Automotive 30% Club where 70 school girls visited Loughborough’s engineering faculty and met automotive industry leaders in a career speed-networking session at the School of Business and Economics. The purpose of the 30% Club is to work towards the aim of ﬁlling 30 per cent of key leadership positions in their member automotive industry organisations by 2030 – through a “30 by 30” strategy. Sir Dermot Turing

Sir Dermot Turing spent his career in the legal profession, most recently as a partner of Clifford Chance, and is now focusing on the history of cryptology and is active as a trustee of Bletchley Park. Dermot Turing is also the nephew of Alan Turing, oft-called the inventor of modern-day computing, and author of the latest biography on Turing (‘Prof’: Alan Turing Decoded, published in 2015 by The History Press). Dermot recently visited the School to discuss his uncle’s work at Bletchley Park, the origins of the machinery approach to code-breaking and how this developed at Bletchley. During the course of World War II, the Government Code and Cypher School transformed decryption from the work of cottage artisans to an industrial-scale intelligence factory. Alan Turing’s lesserknown role in the Machine Coordination and Development section casts light on one of the other secrets of the War – just what was Alan Turing doing after his work on Enigma? The event was hosted by the Centre for Information Management and had a brilliant turn out. Many thanks to Dermot for a very insightful talk! You can read our subsequent interview with Dermot on the SBE Blog blog.lboro.ac.uk/sbe 04

Some of the SBE’s current students were also given the opportunity to meet with automotive manufacturers and retailers who are currently in the procedure of recruiting graduates and ask any questions they had on the application process.

TedX Loughborough Dr David Roberts and Dr Cheryl Travers were both invited recently to give talks for the Tedx Talks series, both of which should be available online soon. Dr Travers, a Senior Lecturer in Organisational Behaviour and Human Resource Management, gave a talk entitled: ‘If you like it, then you should put a goal on it’. Cheryl has worked with a wide range of managers, business leaders and students across industry sectors and levels, developing what she calls ‘Reflective Goal Setting’. Dr Roberts, a Senior Lecturer in International Relations, spoke about the benefit of visual presentations: ‘A visual feast of the mind’. David’s research on imagery in teaching methods can be read in more detail on page 08 of this issue. His main research focus is on peace-building in post-conflict areas, and he teaches undergraduate introductions to International Relations and Third World Politics.

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New Professor of Decision Sciences hired Detlof von Winterfeldt has just been named the SBE’s Visiting Professor of Decision Sciences where he will help lead the School in worldrenowned research in decision sciences. Professor von Winterfeldt is also a Professor of Systems Engineering at the University of Southern California, and the Director of the National Centre for Risk and Economic Analysis of Terrorism Events (CREATE), the first university-based centre of excellence funded by the United States Department of Homeland Security. Detlof served as CREATE’s co-founding Director from 2003 to 2008, after which he assumed the post of Director for the International Institute for Applied Systems

Analysis (IIASA) in Laxenburg, Austria and also of Centennial Professor of Management Science at the London School of Economics and Political Science. The School welcomed Detlof last autumn as the guest speaker for the “Terrorism: The Risks, Public Responses, and Economic Consequences” event wherein he spoke to a packed audience about the various risks posed to society by modernday terrorism. Dr Gilberto Montibeller, Professor of Management Sciences at the SBE, said of the new appointment:

in studying the behavioural aspects of decision and risk analysis modelling. “His international reputation, experience and mentoring would bring extensive benefits to the School, given his expertise on behavioural decision sciences and risk and decision analysis.” We warmly welcome Professor von Winterfeldt to the University. Below: David Saal, Detlof von Winterfeldt and Gilberto Montibeller

“Professor von Winterfeldt’s research interests are in the foundation and practice of decision and risk analysis applied to the areas of technology development, environmental risks, natural hazards and terrorism, and he is a pioneer and a worldwide leading scholar

Earn to Learn scheme seeks to combine middle-office work with student jobs Ian Herbert

A long-standing and well-respected research study by Ian Herbert on shared service centres and outsourcing argues that in the face of a number of threats to professional careers, it is time for a new partnership between employers, universities and Government to help students earn while they learn and to get started on the career ladder. He envisages that in a structured Earnto-Learn scheme, students could work in business processing hubs on, or close to, UK university campuses. Part-time work in administrative roles across finance, IT operations, procurement and human resources during term time and holidays could build useful work experience and complement academic studies. Students could then graduate work-ready and with substantially less debt. By harnessing a

relatively untapped and flexible workforce, organisations would have a strong incentive to keep their workforce UK-based. With the new apprenticeship levy starting in 2017, companies will have an extra incentive for exploring the use of graduate apprenticeship schemes, in which the majority of the university study fee would be paid by the Government rather than by students. An event on December 8th saw businesses coming on campus to discuss the logistics of the scheme. Watch this space for further information on Earn to Learn – and if your organisation is interested in this scheme, we invite you to contact Ian Herbert on i.p.herbert@lboro.ac.uk 05

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NEWS

EMOTIVE again proves correct, predicting a Trump victory based on emotions in Tweets School of Business and Economics academics correctly predicted Trump as the 2016 US President-Elect by capturing people’s feelings towards the election on social media using their unique EMOTIVE computer program. Using intricate software and analysing up to 3,000 tweets a second, the EMOTIVE system pulls from each tweet a direct expression of one of eight basic emotions: happiness, anger, disgust, fear, surprise, shame, sadness and confusion. In the past, this system has been used to monitor reactions of the public to major events, for example the recent Paris terrorist attacks. And last year EMOTIVE correctly predicted the outcome of the UK General Election when the majority of polls got it wrong. Professor Tom Jackson, Associate Dean [Research] at the SBE and head of the EMOTIVE team, said this in regards to the research: “Twitter is a very concise platform through which users express publicly how they feel about a particular event, be that a criminal act, an election or even a change in the weather. “We have already shown what a fantastic tool EMOTIVE is in capturing through Twitter the public’s mood. The system gives us, in real time, a snapshot of how people are really feeling, and from this, when looking at an election, we can make a prediction of how these feelings will be reflected in voting. “It also enables us to see how people’s emotions towards each candidate change, minute by minute. Our data was showing that it was a very close-fought campaign, which the traditional polls did not show.” Dr Martin Sykora, a Lecturer in Information Management and an integral member of the EMOTIVE team, added: “The system we created takes the eight emotions and gives them a rich linguistic context, so that we can chart the strength of emotions expressed in ordinary language and also in slang. We viewed how reactions grew and diminished over time towards Trump and Clinton leading to Trump’s victory.” Please visit the Emotive website to find out more: emotive.systems 06

The School wins Chief Executive’s Award for Transformational Leadership Programme A bespoke programme developed by the Executive Education team at the SBE in partnership with Lane4 and Anglian Water has won the 2016 Chief Executive’s Award at the annual Anglian Water Supplier Awards event which took place in December. Vicki Unwin, the SBE’s Business Development Manager, commented: “Winning the Chief Executive’s Award is a real confirmation of the collaboration and creativity both the Executive Education team at Loughborough and Lane4 have put into this unique programme. We are extremely proud to support Anglian Water in transforming their leadership.”

Above: Phil Brown, Vicki Unwin, Adrian Moorehouse and Steve Gynes

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SBE rated exceptionally well in 2016 National Student Survey Published in the summer, the 2016 National Student Survey results ranked extremely well with all of our courses achieving higher than 90 per cent satisfaction ratings! The survey is voted for by nearly half a million students and has been an annual occurrence since 2005. Primarily aimed at final year undergraduates, the NSS gathers students’ feedback of their course and experiences at university. Our standout rankings include:

The NSS is a prominent source of public information regarding higher education and allows students a platform to collectively voice their opinions and then help to shape the future of their course and their university. Dean of the School Professor Stewart Robinson, said: “The latest NSS results demonstrate our continuing commitment to providing an excellent education and the best levels of student support. Once again, we are pleased to have gained strong results across all of our degree programmes.”

• 1st for satisfaction with teaching in Information Management • 94% satisfaction with Economics • Top 5 for overall satisfaction with teaching in Finance

90%

• Top 10 in overall satisfaction for Management Studies

ALL OF OUR COURSES RATED HIGHER THAN 90 PER CENT SATISFACTION

NSS 2016

2016

Summer Graduation Reception This July, the SBE once again celebrated with its new graduates at our bi-annual post-graduation ceremony on campus. Graduates and their families came together with staff members across all programmes to celebrate their achievements, with particular students and staff members receiving awards for outstanding results or work. On December 19th we will be holding our Winter Graduation Reception at the SBE from 2-4pm. If you are graduating this winter, we look forward to seeing you there! For those of you graduating this summer, we look forward to celebrating with you!

Accounting and Finance courses consistently in the UK Top 10 With the latest rankings now out, the SBE is delighted to announce that Accounting and Finance at Loughborough University has never been ranked outside of the Top 10 in the Complete University Guide in the 10 years that the Guide has been running. In this year’s results, Accounting and Finance placed 5th in the Guide, with the School of Business and Economics scoring particularly well for Student Satisfaction (4.22) and Graduate Prospects (with 90%; in which category the course came joint second in the UK overall). Loughborough is also one of the top 15 universities most targeted by leading recruiters, according to the 2016 Graduate Market Review (High Fliers Research).

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By David Roberts

LIFE AFTER

DEATH BY… Have you ever wondered if there’s an alternative to PowerPoint and found yourself either afloat in fancy templates or seasick with Prezi? As a teacher, I had allowed myself to be lulled into delivering text-filled presentations, but I was worried my students weren’t engaged and active in the learning process.

DEATH BY POWERPOINT It seems I had unwittingly mastered Death by PowerPoint. Mostly it occurs when we fill slides with text and bullet points, as the software itself unconsciously encourages us to do, resulting in PowerPoint becoming ‘shovelware’ – the digital means to project all our pre-digital material at our students. But at the time alternatives like Prezi were thin on the ground and were still blanketing audiences with snow drifts of text. I wanted something paradigmatically different.

THE SCIENCE PART Professor Richard Mayer leads the field in Multimedia Learning (MML), which posits that ‘people learn more deeply from words and pictures than from words alone’ because humans are dual processors of information. We have two channels in our brains - audiotextual and visual. The first deals with sound and words. The second deals with visual information. Each has limits to the amount it can process ‘in the moment’.

I noticed that Greenpeace, Amnesty and Oxfam campaign adverts were grabbing, and holding, my attention. Their campaign posters told long, complex stories and were presented through imagery, with just a little text. A light bulb came on for me, and I started to research the validity of this method.

Orthodox lecturing privileges the former and undervalues the latter. It’s akin to a four-cylinder car engine running on only two cylinders. It’s running at greatly reduced capacity, when the potential is there to vastly improve performance.

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MML instead introduces imagery to balance the flow, so audio-textual capacity isn’t overloaded and visual capacity isn’t marginalised. Previously underused visual capacity comes online, whilst previously overused audio-textual capacity gets to cool down. Instead of squeezing everything we want people to understand through one pipeline, we open up another and spread the load more evenly. It isn’t just an opportunity to improve. Mayer’s maths is telling us that loading slides with text isn’t just dull: it’s counterpedagogic. He’s suggesting the way most of us use PowerPoint is harmful for learning, engagement, understanding and recall. That’s hard to hear. It implies that the way many academics teach is out of sync with how our students learn. Some may want to dismiss such an aspersion out of sight because it implies change (or may wrongly assume the approach means doing away with text, when in fact we can relocate it to ‘Notes View’). Happily, change is easy. It’s just a simple inversion of our long-held ways. Reduce text, increase images. It doesn’t have to be every slide, or even half. Even if we just split our text over more slides and don’t even bother with images, we are improving matters. But using images takes us to another level. We live in the most visual of all human eras. Globalisation and digitisation mean there’s more visual content out there now than ever before. There are literally billions of images available – Google’s Creative Commons 2.0 searches allow us to filter for copyright. For those interested, it requires some time investment, but we do that anyway when we prepare lectures. And, once done, unless there are huge changes in your field, they can be used over time. The images on the next page are some I use when teaching. All of them tell a story. They communicate meaning, taking advantage of visual processing capacity that’s normally wasted or under-exploited. You can use regular illustrative images too. As long as they are apposite to the subject and are used in conjunction with reduced on-screen text, they will relieve cognitive overloading and complement learning by exploiting visual processing. 10

I tested this method to see if MML does what it says on the tin over three years and with more than 100 student participants from nine disciplines. The data is compelling: increases of 50 to 80 per cent in levels of engagement and active learning; and a 30 per cent rise in the number of externally condoned ‘firsts’ in each year the method was used. The Higher Education Academy (HEA) was interested enough to add the method to their blog. TEDx also liked the ‘big idea’; the talk is out this month. The research is published in 2017 in the Journal of Active Learning in Higher Education and the Journal of Further and Higher Education.

— “Teaching and learning using orthodox, textcentric delivery is out of sync with how people learn and understand.” —

I’m expanding the research approach to use non-invasive frequency Near Infrared (fNIR) methods, joining with PhD research here in SBE to assess prefrontal cortex activity during exposure to images and text – evidence of engagement and activity. There’s a consultancy, and an international Community of Practice too, just starting. Teaching and learning using orthodox, text-centric delivery is out of sync with how people learn and understand, and out of sync with the world beyond the academy, from which our students come. The academy should consider MML scholarship because it makes such important claims that we cannot afford to ignore. Rendering teaching and learning relevant to the wider social context in which we operate, and considering more fully and seriously the emerging MML scholarship that connects that wider social context to our students’ needs, is central to remaining relevant and valuable to the people who pay us to come here. Using images is not a silver bullet; nothing is. They are not an ‘easy way out’, or us being ‘soft on our students’. We learn through our eyes from the moment we can see, long before we learn to read. And before there was language, there was cave art. We don’t stop using our eyes to process the world around us until our eyes, or we, give out completely. I believe MML is both an antidote to Death by PowerPoint and a valid challenge to the textual hegemony that persists in academia, despite our existence in an ever-more visual era. We can’t be behind this curve.

— “We learn through our eyes from the moment we can see, long before we learn to read.” —

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Conclusion It’s clear that we use lecture slides in ways that haven’t kept pace with the world from which our students come. But we also use them in ways that are counter-pedagogical. The good news is that it’s easy to fix: reduce visible text, increase use of apposite images. It isn’t PowerPoint; it’s how we use it. After all, you wouldn’t blame Microsoft Word for the production of a bad article.

David Roberts is Senior Lecturer in International Relations and can be reached on d.roberts@lboro.ac.uk

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SPECIAL ISSUE:

DECISION SCIENCES The emergence and rapid rise of big data, social media, cloud and mobile-based services have added to our present-day era of ever-increasing complexity within decision making. These complexities present massive challenges but also opportunities for government, organisations, society and individuals.

In light of this, the School of Business and Economics has developed a Decision Sciences research agenda which focuses on the development and application of rigorous methodologies that can support both private and public sector decision makers and policy makers in understanding, evaluating and improving the performance and choices of firms, organisations and individuals.This research agenda has emerged from the capabilities and strengths that the School has in this area, with strong international visibility and recognition in top Decision Sciences scientific fora. SBEâ&#x20AC;&#x2122;s Decision Sciences researchers are found across our seven discipline groups, conducting research in the public, private and third sectors, providing both theoretical and data-based tools for decision makers. Analytics for decision making specialises in the targeted collection and systematic analysis of data. Using modelling and prediction, we turn both

masses of everyday information, and more specialised quantitative and qualitative data, into actionable insights for decision makers. In addition, many strategic decisions involve high stakes, uncertainty, multiple stakeholders and conflicting objectives. Using robust and rigorous empirical and theoretical analysis and decision frameworks, we provide sound recommendations and strategic advice to policy and decision makers. The SBE is uniquely placed to help decision makers make better, more-informed decisions. We invite you to engage with us within the area of Decision Sciences and welcome the opportunity to discuss possible research projects that may benefit your organisation. The articles that follow all demonstrate different applications of Decision Sciences research.

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FROM DATA TO DECISIONS… AND THEN BACK We are inundated by data. Indeed, there are estimates that 90 per cent of all the data produced worldwide was generated in the past two years! Making sense of all this data has become critical to individuals and organisations – and this explains the rise of business analytics.

by Gilberto Montibeller 14

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Taking decisions nowadays, with all this data available, should have become easier, right? No. On the contrary, I would argue that making decisions continues to be challenging despite all this data – and indeed, has become more challenging given the ever-growing uncertainties that we experience and the complexities confronted by modern organisations, which have to balance multiple demands and consider issues of sustainability and corporate responsibility.

conflicting objectives. Think about the eternal conflict between work-life balance that professionals have to make. Or the compromise between quality and cost that companies have to consider when designing a new product, and consumers have to balance when buying goods. Again, values are required: different consumers have larger (or smaller pockets) and thus might be willing to pay more (or less) for goods, and thus need to prioritise quality against cost.

against pedestrians’ lives. This is an example on how we move back to data from decisions that need to be considered.

But hold on. Can’t we just automate all the decisions and relieve the pain of making them? Let us consider the case of driverless cars – the huge amount of data that they collect from multiple sensors, coupled with information processing computers and expert systems mean that they can outperform human drivers. Now imagine a scenario in which the driverless car’s speed is 40 mph and is 50 meters from a zebra crossing on a single lane motorway, fenced by guardrails on both sides, carrying one adult passenger (as there is no driver). The traffic light turns red (and green for pedestrians), so a little girl starts crossing the street. Suddenly the car breaks stop working. There are only two options available: either the car goes ahead and kills the little girl or it crashes on the guardrail and kills the passenger. Which option do you think the car should take? Think a bit before keep reading further: what would you do if you were the driver of a (standard) car.

Important decisions are usually made under uncertainty. A company doesn’t know if a new product is going to be successful, how much demand it will bring, how inflation might affect production costs, how importation rules might impact on its supply chains. Each decision therefore involves risks – and even a sound decision process might lead to a sour result if the negative scenarios happened. The crucial thing is to consider such risks and take informed and measured decisions, managing risks and maximising benefits. Judgments play a crucial role here: how much risk is a company willing to bear to heap large potential benefits?

Fortunately we have, in decision sciences, methodologies that can support decision making, providing robust and rigorous ways of representing the key components involved in every decision (objectives, uncertainties and alternatives) and aggregating the evidence from data. These methodologies are studied and developed by decision analytics, a thriving scientific field with relevant impact to organisations and societies.

Making this decision involves values: moral standards and preferences which should be used to assess outcomes and select an alternative. Some people may argue that the little girl should be saved, as she has a longer expected life than the car passenger. Others may argue that the passenger should be saved, as s/he bought the car and might have a family to care for… There is no right or wrong path in choosing one of these tragic choices. But they require the use of judgments – a cornerstone of decision making! This poses a huge challenge for the automation of sophisticated repetitive tasks – such as driverless cars – as engineers need to pre-program all the life-or-death decisions for any of these dreadful scenarios. And it means that we simply cannot automate non-repetitive and important decisions – they will always be made by humans making tough judgments. Most of the important organisational (and personal) decisions involve also multiple

So what is the role of data in our decision processes? Data is fundamental to assess the multiple consequences that each decision alternative might generate. It is crucial to help identifying trends and patterns, mapping out them against uncertainties, and in supporting the prediction of long-term consequences of decision alternatives. But values and judgments are what matter when companies or individuals make decisions: the objectives that they want to achieve, the priorities that they want to consider when balancing these objectives, the view of the future that they want to pursue and the amount of risk that they are willing to take. Moreover, we should strive for valuefocused data gathering to better support decision making: collect data about performances that are relevant for the individual or organisation, so they can assess to which extent each alternative fulfils their objectives. Consider again the case of the driverless car with faulty breaks. One piece of information that might be relevant in this scenario is the number of passengers, as well as their age and if they are bread winners. The same information is needed for the pedestrians. But this need is only clear once we know that, under this scenario, we will need to value passengers’

Evidence from behavioural decision research has shown that we become worse decision makers when confronted with a large number of options. So, rather ironically, the huge amount of data and the vast number of alternatives available nowadays has probably decreased the quality of decision making.

Teaching Application Here at the SBE our MSc in Business Analytics Consultancy extensively covers decision analytics, and we teach this content also to our undergraduate students and in our executive education courses. It’s a fascinating subject, which blends decision analysis, risk analysis, psychology and computing.

Gilberto Montibeller is Professor of Management Sciences and can be reached on g.montibeller@lboro.ac.uk

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By Simona Rasciute

CHOICE ANALYSIS We all make choices, for example, which of several competing products to buy and how to allocate our leisure time. Decision makers are not only individuals, but can also be firms and various institutions. Firms may decide which technologies to use in production or where to invest. Over the past twenty-five years, there has been a tremendous progress in developing methods of choice analysis. The way that researchers specify, estimate and interpret the models has changed.

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Current widespread media and policy debates in the UK have focussed on the UK’s decision to leave the EU and how it is likely to affect British and European economies. In particular, there is a discussion on how the dynamics of migration and capital flows are going to be affected. The UK’s Prime Minister, Theresa May, has recently announced that Article 50 of the Lisbon Treaty will be triggered by March next year, after which it is assumed the UK will be able to control immigration. Migration is a corollary of investment flows and, therefore, the change in migration will change firms’ choices about where to locate their investment. I am currently examining these issues, building upon previous research in which I applied discrete choice models to investigate the European locations selected by foreign firms for capital investment, and how these choices are affected by the ‘observed’ and ‘unobserved’ host countries, industry and firm characteristics. My research shows that different types of investors emerge, and they benefit from country-level factors to different extents. For example, the importance of market size increases with the investing firm’s size and scale, while proximity, as well as cultural and linguistic ties, appear to be more important for smaller firms. Working together with Professor Paul Downward from the School of Sports, Exercise and Health Sciences at Loughborough University, I have also applied discrete choice models attempting to disentangle the complexities of individual human choices. Neoclassical economic theory primarily studies the interactions of agents through results/outcomes that are mediated through impersonal and anonymous mechanisms such as markets. 18

The assumption that agents make decisions independently, i.e., immune to the influences of their peers and social interaction, is a critical enabling assumption in neoclassical models. However, in reality, individuals are constrained by habit; they adjust to circumstances and surroundings and may anticipate life events. They are also affected by the influences of their peers and social interactions. Therefore, modelling how leisure time is allocated by individuals and, consequently, how it affects subjective well-being, should take into account latent (unobserved) heterogeneity, anticipation, selection and adaptation effects, as well as peer behaviour and influence.

— “Discrete choice models open up opportunities of exciting applications in a wide range of areas.” — The choice of how leisure time is spent, for example, engaging in sports, arts and cultural activities, can have relational characteristics, and may not affect the subjective well-being of individuals in a simple way. Based on differences in the associative and instrumental disposition of individuals, the effects on well-being could vary across individuals even when undertaking the same sort of activity or sharing common socio-economic circumstances. Social interactions produce groupings of individuals according to their preferences for desirable experience – some individuals exhibit more of a tendency towards instrumental engagement while

others towards associational activity. For example, for more ‘instrumental’ individuals, income and working have a greater effect on well-being, as well as sports undertaken in a solitary way. In contrast, team sports, arts and attending cultural events as part of an audience as well as gambling at events are related to well-being in a more ‘associational’ way. Experience of associativity from activities, which can act as relational goods is an important feature of subjective well-being. Individual’s decisions to participate in physical activity is also affected by their peers, and Professor Downward and I have found that the role of the peer is potentially of greater relevance than an individual’s own habits and past behaviour, something that we have found to be of particular significance for women who are more likely to accommodate the interests of their peers. Our joint PhD student Nick Simmons also finds that individuals anticipate and adjust to the effects of certain life events on subjective well-being, but the speed of adjustment is different for males and females. For example, males appear to adapt more quickly to the positive effect of marriage and to the negative effect of widowhood and divorce, but more slowly to unemployment and separation. The negative effect of unemployment seems to be of greater magnitude for individuals with a university degree. The effects of life events on well-being are neither constant nor homogeneous. For example, owning a house has larger benefits for younger people, while marriage has a larger positive effect on well-being for older individuals. Discrete choice models consequently open up opportunities of exciting applications in a wide range of areas,

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About Simona including transport and environmental economics, international business, marketing, health and well-being, among others. The escalating marketisation of public services formerly delivered by the state is accompanied by a rhetoric that emphasises the role of consumer choice. For example, politicians tell us that parents want to be able to choose among different types of schools. Indeed, injecting competition into the delivery of public services and utilities is predicated on consumer choice. One of the first sectors in the UK to introduce competition was the energy utilities, and yet some 20 or more years later, one of the chief concerns of the regulator and competition regulator is that most consumers fail to exercise the choice to switch, which slackens the incentives for ‘keeping the suppliers honest’. Similar concerns are exercised regarding personal banking. Thus, this kind of work has increasing salience as we witness what has been called the marketisation of society.

Simona Rasciute is Lecturer in Economics and can be reached on s.rasciute@lboro.ac.uk

A Lecturer in Economics, Dr Simona Rasciute’s research is focused on the application of discrete choice models in two substantive areas of research. The first is international business and, in particular, the location choice of foreign direct investment (FDI). The second is in connection with the evaluation of public policy, specifically the impact of physical activity initiatives and behaviours on health and well-being.

Interested in a PhD? Simona is looking to supervise doctoral students interested in researching the application of discrete choice models. Please contact her if you would like to find out more.

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By Antuela Tako

Simulation in healthcare:

Supporting decisions through facilitated modelling Decision making is often a complex issue. How do managers make decisions? What tools can we provide to help their decision-making process? Given the myriad choices available in any given situation, here at the SBE we have a number of academics who investigate the use of simulation in order to develop useful tools that managers can use to make good decisions. Simulation modelling involves developing computer models that provide a representation of real-world systems in a dynamic fashion, showing people or objects moving through the system. In my research, I develop and use simulation models to support managers who wish to understand and improve their business processes.

of involving clients and their managers in the process of finding solutions to their problems or improving their systems through a collaborative approach. It ensures that the models developed are relevant and useful to the clients. This can in turn lead to the implementation of the findings and to impact generation. The challenge is to be able to discuss and present a simulation model in a non-technical way so that managers and users can engage in meaningful conversations about their systems and processes.

When developing a simulation model, besides coding the model on the computer, we have to decide what information to include in it, how to collect this information from the people that are part of the system, how models can help managers understand their problems, which alternative simulation approaches to use â&#x20AC;&#x201C; these are just some of the issues that have been the focus of my research. While these topics may seem different, the underlying theme is the same: trying to understand or improve existing simulation modelling practice.

My first encounter with facilitated simulation modelling was in 2009 where I worked on the PartiSim project, short for Participative Simulation. Together with my collaborator Kathy Kotiadis, we developed PartiSim with the view to improving the practice of simulation and making simulation relevant and fit for current working environments. It tackles the need to involve stakeholders in the process and thus offers a better alternative to the analyst-led mode of practice, where models and resulting recommendations from the study are given to the client.

One aspect of my research that I am passionate about is that of facilitated simulation modelling. Facilitated modelling entails developing and using simulation models in facilitated workshops with managers. The ethos of my research is based around the concept

The project involved working with two different NHS care trusts, where models were developed and used in facilitated workshops to consider how existing processes could be improved. Workshop participants were healthcare practitioners from different parts

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— “A direct outcome of this decision was the reduction of patient waiting times which ultimately translated to a better patient-centred service being provided.” — of the system. In these workshops, stakeholders, together with simulation analysts, can identify and solve problems that are relevant to them. The benefit of following a facilitated approach is that instead of presenting managers with an answer to their problems, the group goes through a process of idea generation and thinking creatively to reach commonly agreed and shared opinions about the action to be taken. Developing PartiSim has been very rewarding. Stakeholders find their involvement in the modelling process beneficial, with very positive feedback on how the evidence of the modelling outcomes is made transparent in face-to-face workshops. This enables a better communication between the analyst and the client and increases the chances of implementing the outcomes of the modelling work as the clients take ownership of the model and its outcomes. As a result of modelling workshops with stakeholders of an obesity service, the trust opened a new operating theatre, which increased the capacity of the service to provide more operations. A direct outcome of this decision was the reduction of patient waiting times, which ultimately translated to a better patientcentred service being provided. More recently, we used PartiSim in a Masters consultancy project with the East Midlands Ambulance Service (EMAS), which looked at how the organisation

could increase its effectiveness and reduce inappropriate attendances at A&E departments. The model we developed demonstrated that improvements could be made at the beginning of the call cycle where the team of paramedics and nurses intervene with calls in order to upgrade or downgrade the call or to offer advice to patients. The model showed that if scope or authority were increased at this point, time targets could be improved and A&E visits would decrease as more patients are treated or signposted to different services over the phone. The project was particularly successful in helping EMAS understand the route that patients take through their system and identifying ways of improving the patients’ experience, both of which are currently being looked at by their Executive Board. Building on my experience of developing and applying facilitated modelling, I am currently leading the second phase of the Simtegr8 project commissioned by the Leicestershire County Council and working in partnership with SIMUL8 Corp and HealthWatch. The project aims to support the provision of integrated health and social care services in the Leicestershire area as part of the Leicestershire Better Care Fund Programme and the Urgent Care Vanguard for Leicester, Leicestershire and Rutland to support patient health and well-being, with the ultimate view

to reducing A&E referrals. Four newly developed care services will be evaluated to test their impact and effectiveness through the application of facilitated simulation modelling, involving project leads and service users (patients) respectively. One of the services we have been working with is called Lightbulb, a service that aims to integrate practical housing support into a single service for vulnerable people. The models developed and discussed at the workshops have proved useful in validating the predictions made in the business case being put forward to secure funding for the continuation of this service. Hearing patients’ stories about how the service has improved their quality of life has given a different dimension to the project. Having been one of the first simulation modellers to have developed and used a facilitated approach, I have experienced first-hand the value placed by managers in implementing decisions that are meaningful and useful to their organisations.

Antuela Tako is Senior Lecturer in Operations Research and can be reached on a.takou@lboro.ac.uk

LOUGHBOROUGH UNIVERSITY

LETâ&#x20AC;&#x2122;S BE FAIR

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STAFF INTERVIEW: Nikolaos Argyris

Dr Nikolaos Argyris, a member of both the Management Science and Operations Management group and the Centre for Productivity and Performance, conducts research on fairness in decision making. How did you get started on your fairness research? Nikos: My research is in the area of decision analysis and decision theory, including methodological questions regarding how to provide support for decision makers who wish to solve particular problems. It just so happened, like with lots of things, that there was an opportunity to research this topic because of collaborators and my engagement with practitioners, particularly when I worked with a team to provide decision support for budget allocation to what used to be called Primary Care Trusts (they don’t exist anymore; they’re now called Clinical Commissioning Groups). Through that I became much more aware of the issue of inequality in the distribution of healthcare in a population, be it across socio-economic strata or genders or age groups and so on and so forth. My research began in the health sector, and I then took a step back to think with my collaborators about the theoretical and methodological aspects. I got inspired by encountering the problem in practice and then stepping back a bit and thinking about the theory. What sort of problems are you considering? Nikos: There’s this age-old question: ‘How do you distribute resources across a group of entities that each have a valid claim to those resources?’ It’s a very hard question, and many brilliant minds have considered it over a few centuries. So you might say it’s a philosophical question; but it’s also a practical question which arises very often in situations such as the provision of public services, where fairness is a salient objective. That’s for two reasons: the first is because the outcomes of public services and public

policy decisions usually affect a very wide spectrum of the population, not just a few individuals; the second is that not everyone is affected uniformly. Some might benefit more, some might benefit less. This question has been studied from a theoretical perspective very much in the economics literature – where they have what we call theoretical or normative models about how to compare a particular distribution of resources with another distribution of resources. Two things come into play here. One is the issue of efficiency. By this we mean a sort of aggregate measure of how much societal benefit flows from a particular decision: e.g., the total increase in life-expectancy when implementing a specific health intervention. The second issue is that of fairness: so, for example, how balanced is the distribution of health or income across a population? To give you an example, in the health sector, some portion of money will be spent on palliative care or end-of-life care. Some people will feel very strongly about it when they are asked, that it is fair that we spent sufficient money making sure that patients feel comfortable during their final days of life. ‘How much?’ is a very difficult but important question. Now in many cases there will probably be no end to how many resources you need to put in place to extend a person’s life by even one more day. And there’s the question of efficiency: do you want to extend with the same amount of money one more day the life of someone who may possibly die the next day, or maybe a month or a year for people who are not terminally ill. So from an efficiency perspective, spending that money on palliative care may not be as efficient. It’s a tricky and very emotional question. 23

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— “My research considers how to perform the balancing act between what’s fair and what’s efficient in allocating a budget, by taking into account societal preferences.” —

What does your research involve? Nikos: It involves considering how to perform the balancing act between what’s fair and what’s efficient in allocating a budget, by taking into account societal preferences. There is a large body of literature and methods, particularly in economics, that considers the problem of budget allocation, especially in public spending. Most of this, e.g., cost-benefit analysis or cost-effectiveness analysis, considers the problem predominantly from an efficiency perspective. My research (and my collaborators’) is on how fairness considerations can be incorporated in these models and how to design decision-support frameworks that help decision makers and policymakers decide how to distribute resources by balancing fairness and efficiency. In doing so, and because fairness may depend on societal views, we are particularly looking at how existing models can be extended to incorporate value judgments elicited from the public or expressed by public officials acting on behalf of the public. These judgments relate to exploring societal values about the balance between fairness and efficiency. So you may, for example, ask individuals to compare two scenarios: one in which the overall health is greater in terms of some measure like life expectancy or quality of life (but with some groups, e.g., terminally ill patients, receiving a relatively smaller amount of care), and another where some of that total health is sacrificed for the benefit of achieving a better distribution of health across all groups of patients. In doing this, we are taking what you may call 24

a decision-analytic perspective. Decision analysis is based on what is known as the prescriptive approach to decision support: provide a recommendation by combining normative models with value judgments of decision makers. So you may also say that we are developing decision-analytic models for resource allocation by taking fairness into account. Are the public’s value judgments important to policy makers do you think? Nikos: I think it depends on whether they want to get elected or not (laughs). I think elected policy makers would probably want to show that they are taking the values of society as a whole, even from the cynical perspective of them making sure they will have a case for being re-elected. But even if you don’t take a cynical perspective and take instead a more aspirational one – they’re there because they want to act as agents of the population and therefore they can’t just be technocrats. They are agents between the technocrats and society. Can you give us some practical examples of where this research would be applied? Nikos: Firstly, think back to the example of a Clinical Commissioning Group: that is a body responsible for commissioning, i.e., purchasing, health services from healthcare providers for a local population. This is mainly consisted of GPs, public health specialists, etc – they look at their population and their available pot of money and ask: ‘What do we need in the next, say, three years? We will probably need to treat this many diabetics, this many cancer patients and so on’. Then they apportion their spending accordingly. Obviously their budget will not cover

giving absolutely everything to absolutely everyone, so it’s a balancing act. What healthcare do you purchase on the basis that you want to increase the aggregate health of the population as much as possible, but at the same time, balancing that with the distribution of healthcare across that population? In essence, this research is relevant to every decision relating to the provision of a public service. To give you a topical example, if you’re trying to site an airport, or decide which existing airports to expand, one might be best thinking in terms of overall income generation. At the same time, public money will be spent so you would want everyone to benefit – don’t want it to benefit a small selection of the public even if that would be best in terms of total income generated. I’ve spoken a lot about the public sector, but in the private sector these situations might also arise. This is particularly so when firms or organisations need to cooperate, so, for example, when companies might want to make a shared investment, and then it comes to a point where they need to share the resource or profits from that resource. You can think, for example, of a group telecoms companies, where they’ve invested in infrastructure, e.g., a new fibre optic network, jointly with help from the Government, and they need to share it. They use it to provide their service to their customers, and to each be able to do that they will need to work with each other. Here the issue of fairness versus efficiency arises again. We need to ensure an efficient network, say in terms of

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Nikolaos Argyris is Lecturer in Operational Research and can be reached on n.argyris@lboro.ac.uk

bandwidth achieved; yet everyone has spent money, and so bandwidth has to be allocated to everyone fairly. Another example is looking at equitable workload allocation amongst personnel at an organisation. Think of the case of a university. You might think that the best allocation would be to give the majority of the teaching load to the best teachers, but that wouldn’t necessarily be fair, would it? (laughs) Maybe for the students, but not for the employees! Your future research? I have a couple of papers in the pipeline. What I’m then hoping to do is go back to my contacts in the health sector and see whether I can get my hands on a dataset to help a Clinical Commissioning Group with this decision methodology to arrive at an allocation of their budget for the population.

INFORMS Finalist This November, Nikos was announced as a Finalist in the Decision Analysis Society Publication Awards – an award that is given annually to the best decision analysis journal article or book as judged by the awards committee – for his paper, “CUT: A multicriteria approach for concavifiable preferences”.

Just recently, I have been awarded some Newton funding through the British Council to work with a colleague in Turkey to, among other things, apply this research to fairness in the allocation of aid for helping refugees, in the content of the current refugee crisis. This is extremely topical of course, and I am very excited about starting this new stream of research.

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FORECASTING UNDER UNCERTAINTY by Kavita Sirichand

Decision makers within the financial world, be they policy makers, wealth managers, private investors or central bankers, all have to make decisions under uncertainty because decisions are made in relation to a future that we cannot predict with complete accuracy. For example, central banks have to decide if and how the base rate should change, and investors have to decide how to allocate their portfolios, based on current and projected economic and financial conditions. Those involved with modelling and forecasting are continuously looking for ways to improve forecast accuracy, the objective being to minimise the errors associated with their projections. This is of huge importance to decision makers because at the heart of decision making are the forecasts used to inform these decisions. Hence the way the forecasts are prepared, the information they convey and how this information is utilised to inform decisions will directly impact the decisions made and the resulting benefits and costs. A key question here is, what tools can we develop and use in order to make the best possible decisions we can, given we are operating in an uncertain environment? My research is concerned with macroeconomic and financial decision making under uncertainty. This uncertainty could stem from how economic relationships should be modelled, the accuracy with which these models are estimated, the uncertainty relating to unpredictable future events that may come to pass and the uncertainty due to changes over time in the relationships we are modelling. 26

I am a financial economist and I am interested in how modelling and forecasting techniques can be used to deal with these types of uncertainty, and whether accounting for these uncertainties leads to better decision making within the context of asset allocation. These statistical and mathematical modelling tools are not just restricted to use in finance and economics, but are of importance to anyone interested in forecasting â&#x20AC;&#x201C; be it forecasting stock returns, exchange rates, inflation, rainfall or the demand for electricity. Further, it is essential for those using forecasts to know the best way to assess forecast accuracy â&#x20AC;&#x201C; how can we determine whose forecasts are the best? Specifically, decision makers may have several different forecasts generated by different models for the same variable, so how can they assess these forecasts and determine which one(s) to use? Conventionally, statistical metrics are used. However, given that forecasts inform decisions, it is fundamental to use evaluation criteria that reflect the

objectives of the user and assesses forecasts in the context in which they will be used. So for instance, an investor wants to know how she can maximise her wealth and would be interested in using these forecasts to assist her in allocating her portfolio. In this case the investor wants to know the value, in terms of potential gains in wealth, of a set of asset price forecasts, and further, the expected gains in wealth if she bases her decisions on a particular set of forecasts. Broadly, my work considers: (1) the interactions between the macroeconomy and the financial markets (using modelling techniques that accommodate the time variation in the economic relationships we seek to model); (2) the uncertainty about the forecasts we make; and (3) evaluating these forecasts in the decision-making context for which they are intended, and how these will (a) affect the decisions we make and (b) enable us to make better decisions. In particular, I use statistical and mathematical techniques to model

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government bond yields and stock market returns employing a selection of models. These models range from a simple, purely statistical model to a theory-informed model that embeds economic theory. The forecasts generated from these different models are then assessed using both statistical measures and in a decision-making context, which in this case is an investor seeking to optimally allocate her portfolio. Evidence from the asset price forecasting literature suggests that sophisticated theory-informed models do not necessarily do a better job of forecasting compared with simple statistical models, when statistical forecast evaluation methods are used. This raises two questions. First, why would one undertake the estimation of more labour-intensive sophisticated theoretical models when a simpler statistical model would serve the same purpose? Second, would this result hold true if forecasts were assessed in terms of worth to the end user of the forecast? For example, would statistical models still perform better if we assessed them in terms of how much expected wealth their forecasts would generate? Interestingly, I find that those theoretical models do embed in them some valuable information that the statistical models do not. In other words, those more sophisticated models do tend to perform better when assessed from a wealth point of view. Given that the motivation behind developing these models and generating forecasts is for the generated forecasts to be ultimately used in decision making, it seems apt to assess these models in terms of how they would be used rather than from a purely statistical standpoint. In short, this line of research is of both academic and practical relevance because forecasts are required to inform decisions, but it is impossible to always forecast with complete accuracy, so decisions are made under uncertainty. In which case, the examination of the factors that influence decision making under uncertainty serves to provide key insights into how decisions can be made optimally.

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Some current work of mine analyses the impacts of the recent global economic and financial crisis on international yield curves. A notable observation from the crisis is the interrelatedness between the financial sector and the wider economy, both nationally and internationally. We saw how what started as a subprime mortgage crisis in the US escalated into an international financial crisis and global recession. One of the things that become apparent from analyses of this crisis is the need to acknowledge the interdependence between the financial markets and the wider economy, both nationally and internationally. Such acknowledgements are particularly important in empirical, financial or macroeconomic investigations, especially if when seeking to inform policy.

— “What has become apparent from analyses of this crisis is the need to acknowledge the interdependence between the financial markets and the wider economy.” —

Another interesting aspect of the work is that although monetary policies of these areas were similar in response to the crisis, which suggests that one can expect and observe a positive comovement, our finding of decreasing and even negative comovements between Canadian, UK and US yields with those of the Euro area suggest that changes in investors’ risk perceptions is likely to have been the dominant channel through which the crisis has impacted comovement. In particular, the troubling levels of sovereign debt of some Euro area member states, and the ensuing implications for the future of the Union, unsurprisingly altered investors’ perception of the relative riskiness of this sovereign debt. We posit a ‘flight to safety’ as a reason for the observed negative yield comovements between the Euro area and the other areas. Notably, our findings suggest that from an investment point of view, in relation to portfolio diversification and rebalancing for effective risk management, investors’ perception of a low-risk investment before the crisis appears to be quite different to that after the crisis.

More specifically, in some recent work with my colleague Dr Simeon Coleman, we analyse sovereign bond yields for the UK, the US, Canada and the Euro area. We analyse the extent to which the yields of these major economic areas comove (move in the same direction) before and during the crisis. We distinguish between the yields of AAA-rated Euro area government bonds (i.e., debt securities with the most favourable credit risk assessment) and bond yields for all Euro area member states. This allowed us to identify differences between core and peripheral countries in the Euro area. Typically, the sovereign debt of developed countries has been considered to be low risk, and in 1997 many of the Euro area member states had AAA-rated debt; however it is interesting to note that only five states’ debt had the top level AAA rating in January 2014. We find that the Canadian, UK and US yields comove with each other, but they do not comove with the Euro area to the same extent. Moreover, we find that an increasing divergence unfolds with the crisis.

Kavita Sirichand is Lecturer in Financial Economics and can be reached on k.sirichand@lboro.ac.uk

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THE LINK BETWEEN MENTAL HEALTH AND INFORMATION OVERLOAD In today’s digital competitive environment, it is evident both professional and personal survival in modern society clearly depends on individual’s abilities to absorb increasing amounts of information. The amount of information created every two days is equivalent to that created from the dawn of civilisation until year 2003, proving one of the fastest-growing quantities on this planet is the amount of information being produced.

by Tom Jackson 28

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— “With multifunctional smartphones enabling workers to be accessible 24-hours a day unlike ever before, it is likely that there will be an increase in stress levels.” —

There is a common perception that the younger generation, ‘Digital Natives’, have grown up engaging with modern technologies at a younger age and have greater understandings of the concepts and are more adaptive. This results in fewer chances of information overload due to naturally developed skills and intuitions of interacting with information, since technologies are an integral part of their daily lives. The other common perception is that the older generation within the workplace struggle to maximise technology due to adopting later in life to modern technologies and suffer with information overload (because they acquire skills cognitively before effectively applying them towards their information responsibilities). The general feeling is that the older generation are more experienced, conscious and more attentive to detail, but require greater time to adapt to change. However, are these simple common perceptions hiding something a little more sinister? Is there a link between information load and mental health, in other words how we process information interruptions. The number of 15- to 16-year-olds frequently feeling anxious or depressed has doubled over the last 30 years, increasing the need to determine the causes of poor mental health. There are many factors that affect mental health, but I believe one of them is how today’s younger generation have an insatiable thirst to be connected to the digital world 24-7. The younger generation are addicted to their mobile devices and are constantly attached to them awaiting the next message from the ever-increasing number of communication applications. Therefore the user is in a constant state of alertness, multitasking between information interruptions and rarely having the time to switch off from the demanding social world. From previous research we know the impact this can have on employees within the workplace,

but we are yet to study teenagers. For example, multitasking email alongside other communication media, such as phone and face-to-face meetings, increases the risk of becoming stressed. In our research the results showed the majority of participants (92 per cent) displayed an increased stress response, with many recording elevated blood pressure and heart rate readings during email and phone use. With multifunctional smartphones enabling workers to be accessible 24-hours a day unlike ever before, it is likely that there will be an increase in stress levels. Another concerning aspect is that many employees do not realise that they are stressed. In our study users perceived themselves not to be stressed when the physiological findings showed their bodies were under increased stress. This would indicate that employees might find it difficult to self-regulate their use of communication media to ensure they do not become overwhelmed by stress. This signifies that long-term short sharp increases such as this can lead to long-term chronic health conditions (such as hypertension, thyroid disease, heart failure and coronary artery disease), as well as can impact upon mental health.

If you are interested in obtaining any of the references for this article, please contact Tom.

Tom Jackson is Professor of Information Management, Associate Dean [Research] and Director of the Centre for Information Management. Tom can be reached on t.w.jackson@lboro.ac.uk

So the key question is: Does this information multitasking have a greater effect on teenagers as they are yet to develop the skillset to manage the volume of multitasking requests? Earlier indications show that the older generation are actually better equipped to deal with information multitasking, but how they gained that skillset is still to be determined. So the proposition here is not that all social media is bad, as there is research that shows that interacting with social media can help reduce stressful situations. It is, however, to determine if there is any correlation between poor mental health and multitasking between information interruptions in the teenage population. 29

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By Andrew Vivian

PATTERN PREDICTIONS My research is broadly around asset pricing and international investments, and one strand of work I’ve been involved with recently has been looking at predicting and forecasting stock returns in a range of different European markets using a variety of different predictive variables. Using a range of macroeconomics variables (fundamental variables like earnings-to-price ratio and some technical trading variables) we try to predict the stock market index return in those countries. We then take those forecasts and predictions in real time and look at whether they would have helped an investor to tilt their portfolio to stocks or to the money market based on those predictions, hoping to answer the question, ‘Could these forecasts be used to time the market and generate larger returns than using just a passive strategy wherein you have a fixed 30

proportion in the equity market and a fixed proportion in the money market’. And the answer is yes; we’ve seen a pattern. What we tend to find is that by amalgamating the different forecasts from the different models into a single forecast, you can consistently help to beat the market. So in essence, if an investor had followed this strategy over the last 10 years, he or she would have done better than just holding a fixed proportion in the market. Aside from looking for possible outliers in the data that may bias the results, we

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— “What we tend to find is that by amalgamating the different forecasts from the different models into a single forecast, you can consistently help to beat the market.” —

also looked at whether the results were impacted by the financial crisis period, but we found that the results are fairly stable across both calmer periods and the more recent period that included the financial crisis. Can we predict the market? Not exactly. We all know that forecasting is incredibly difficult, and I wouldn’t necessarily say that we can predict market conditions, but we have methods that are adaptable and can cope with whatever conditions are thrown at us. Thus, we can do consistently well on average regardless of what the conditions tend to be. Although the improvements in forecast accuracy are relatively small, we are able to reduce forecast errors by 5 to 10 per cent. Typically, you could get up to maybe 5 per cent in some cases, depending on the market, but there is a lot that can’t be predicted: natural disasters, an earthquake in Japan, are completely random and have a big impact on market prices – they’re impossible to forecast. So 90 per cent of errors still remain, but if you apply these practically, it could lead to investors generating substantially larger returns than they otherwise would have done. We think that investors – both individuals and institutional – and practitioners would be interested in this type of research and that it might help inform some of their tactical allocations as to how they might for example choose to split their portfolio across different European markets using these kinds of forecasts. In addition to my research, I teach on the finance suite of Masters programmes here at the School. Many of our graduates from those programmes will go into investment analysis and investment management roles, especially from the Finance and Investment MSc, and into trading roles.

But there is a wealth of different areas for graduates to go into. As part of the programme, I’m involved in teaching a module on global investments, and some of this research does feed into that, especially as to how tactical allocations could be made in a global context. In addition, there’s a module taught by my colleague Dr Alper Kara on financial trading where he draws on his professional background when he was an active trader. Another area of research I am currently involved in is on commodity markets and commodity returns that have been increasingly included into investors’ portfolios. One piece of research that we think is important is looking at whether there are common factors (i.e., marketwide factors that impact all of the individual commodities) that are priced in commodity returns and how important these common factors are for explaining the fluctuations in returns. Within this strand of research, we have looked at more than 30 individual commodities where the pricing data was available (oil, gold, corn, aluminium, soy beans, to name a few). We split them into categories like grains, cereals, precious metals, industrial metals, livestock etc. and investigated how important the market, the sector and the individual component were.

return of the asset, and so this increase in the importance of the market factor which can explain up to 20 per cent of the variation in the commodity returns suggests that they can be viewed more of an asset class now and be treated together as a group of commodities rather than as separate and being looked at separately in different sectors. Interestingly, but not surprisingly perhaps, we find that the common factor has become much more important over the last ten years.

Andrew Vivian is Senior Lecturer in Accounting and Financial Management and can be reached on a.j.vivian@lboro.ac.uk

Essentially, we extract a common trend from these 30-plus commodity returns and we refer to this as the common factor, and this should be capturing the marketwide fluctuations. But the way we generate this is by using a statistical approach that separates the common factor from the sector factor and the individual factor. In asset pricing, traditionally you’d expect that the market factor should be of key importance in determining the expected 31

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STAFF INTERVIEW: Giovanni Calice

THE LONG AND (BIG) SHORT OF ASSET PRICING Dr Giovanni Calice is a Senior Lecturer in Finance, and his research spans several areas, including financial markets and institutions, financial innovation and financial stability. His recent research focuses on tests of idiosyncratic sovereign risk, analysis of systemic risk in the financial sector, market liquidity and asset pricing. His research on asset pricing aims to help regulators understand the role of markets for financial innovation (in particular, credit derivatives markets) for the stability of the financial system. The two main areas of Giovanni’s research within asset pricing focus on 1) the 2008-2009 financial crisis and 2) the Eurozone sovereign debt crisis. Giovanni's research is empirical. The aim is to investigate the relationship between instruments of financial innovation (e.g., credit default swaps) and systemically important financial institutions (e.g., Goldman Sachs, JP Morgan, Morgan Stanley) and how these instruments may affect financial stability. These institutions became, especially prior to the 2008-2009 financial crisis, the leading players in the global CDS market. “A credit default swap (CDS),” explains Giovanni, “is a contract designed to transfer the credit risk associated to a particular fixed-income product (such as a corporate or a sovereign bond) between two or more parties. Hence, a CDS acts as an insurance policy since it hedges out against the risk of default of a bond or a loan.” Do credit default swaps ring a bell? They should. Although this is controversial, many economists and 32

analysts believe it’s what, among other factors, brought the US economy (and thus the global economy by and large) to its knees in 2008. Giovanni’s research found some strong evidence that the CDS market may have acted as a propagatory mechanism of distress across the financial system. “It is now clear that in a context of inadequate underwriting practices in the US subprime mortgage markets and excessive granting of loans by non-regulated entities, financial innovation based on CDS was at the heart of the 2008-2009 global financial crisis. I think the problem has to do with the incentives that these institutions built into their investment culture. I don’t think financial institutions were making proper use of the instruments and were promoting more speculative uses. In fact there is evidence that in some instances they had been used for speculative purposes rather than for hedging risks.” In his recent research, Giovanni has examined the credit and liquidity interactions between the sovereign CDS market and the sovereign bond market for Eurozone countries. The resulting study has been provided as written evidence to the House of Lords Treasury Select Committee and

has been cited in the Financial Times, in an article entitled “CDS: Modern day weapons of mass destruction” which was based on a paper Giovanni co-authored with Jing Chen (Cardiff University) and Julian Williams (Durham University): “Liquidity Spillovers in Sovereign Bond and CDS Markets: An Analysis of The Eurozone Sovereign Debt Crisis”. Giovanni says: “The use of sovereign CDS has increased dramatically during the last decade. They represent key instruments for transfer credit risk related to corporate or sovereign entities. “Using an empirical model of cross liquidity and price discovery in parallel markets, we find strong evidence of substantial liquidity spillover effects from the sovereign CDS market to the sovereign bond market for a group of European countries during the Eurozone sovereign debt crisis. “The implications of our results for policy makers and practitioners are significant. For instance, our results suggest that at the height of the Eurozone sovereign crisis (in 20092010), in the absence of a coordinated action of the relevant authorities (International Monetary Fund, European Commission and European Central Bank), the explosive trend in the CDS

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— “It is now a widespread belief that following the recent financial crisis all the previous paradigms of economics have to be reconsidered.” —

DECISION SCIENCES

LOUGHBOROUGH UNIVERSITY

— “Our results suggest that CDS markets may induce distress on the financial system. So it is important to investigate the link between new products of financial innovation and measures of systemic risk.” — market would have resulted in a complete market failure for the trade in sovereign debt instruments for several Eurozone countries. “Our empirical evidence demonstrates substantial spillover effects,” he says, “between the two markets during the 2009-2010 Eurozone sovereign debt crisis.” Giovanni’s final line of investigation within this area focused on studying the CDS term premium as potential early warning indicators of financial distress. In fact, the CDS term premium can be interpreted as a forward-looking measure of idiosyncratic sovereign credit risk as perceived by financial markets. Specifically, it tracks the investors’ assessment about the likelihood of a particular country experiencing a financial crisis. “Market participants typically look at the spread in the sovereign bond market (and CDS market) – when the spread increases, the levels of financial distress increase and a country’s health looks more fragile. “The main argument that we make in this line of research is that the evolving pattern of the sovereign CDS term premium can provide the relevant monetary authorities with detailed information on financial market perceptions of the vulnerabilities in sovereign debt markets, as well as on the sources of propagation of those vulnerabilities.” Is this research important for society? “Yes,” says Giovanni, “most certainly. Our results suggest that CDS markets may induce distress on the financial system. So it is important to investigate the link between new products of financial innovation and measures of systemic risk. “Our analysis is important for both regulators and policymakers, and our 34

findings have significant implications for the optimal design of financial regulation and for supervisory scenario stress tests. For example, our research can help policymakers monitor default risk and the distance to specific capital thresholds of individual financial institutions at a daily frequency by testing the extent of co-movements between North American and European CDS markets conditions in normal, as well as in stressful, periods. “However, it is important to acknowledge that by definition every model is built upon assumptions. Hence, our estimation approach has its own inherent limitations. Therefore, we urge caution when attempting to infer the economic interpretation from our results.

The Big Short The title of this article is a direct reference to the 2010 book (and subsequent film), The Big Short: Inside the Doomsday Machine, which sought to explain how the 2008 global financial crisis began, controversially naming and shaming big financial institutions and their unregulated use of credit default swaps.

“To date there is no clear consensus on the implications of these instruments for the welfare of the global financial system. Even leading economists do not have a convergent view on CDS and their implications for financial stability. Economics is not a perfect science. It is now a widespread belief that following the recent financial crisis all the previous paradigms of economics have to be reconsidered. “In the aftermath of the financial crisis, the market for financial innovation has had a long period of decline, tarnished by tougher regulatory rules for large internationally important banks. In fact, these institutions are now more regulated and as a result possibly more conservative in their investment strategies. “Nevertheless, recent market data suggests that CDS markets are experiencing renewed growth, and I think that, as new institutions have recently entered the market (e.g., large, global asset management firms), this trend is likely to continue also in the future.”

Giovanni Calice is Senior Lecturer in Finance and can be reached on g.calice@lboro.ac.uk

INSPIRE

SCHOOL OF BUSINESS AND ECONOMICS

BUSINESS INSIGHT by Jeff Prestridge

Strictly FinTech The financial services industry is changing rapidly. On the whole for the better, but occasionally for the worse. Overall, two steps forward, half a step back. The main driver, of course, is technology and the internet. It is radically altering how the industry interacts with its customers. Less personal, more impersonal. Some customers like it, others love it. A few yearn for the past – and always will do – but time marches on. FinTech is the future. The relentless advance of technology is everywhere in the personal finance space. As customers, it means more online banking – despite cyber hacking issues – and less reliance on the High Street branch. More investing via online platforms (think Hargreaves Lansdown) rather than through a friendly stockbroker. It also means buying insurance over the internet rather than from a local broker – and saving via a phone app rather than rocking up at your local building society with a cheque to deposit in your cash account, only to be thwarted by a queue stretching out of the door. It is liberating and empowering in so many ways, providing greater choice and a smoother customer journey, although there always will be a place for the ‘personal’, especially in the ‘high net worth’ space. There will always be

demand for a Coutts or a C Hoare & Co and a financial planner who is prepared to sit down with you and discuss your finances in intimate terms. Indeed, FinTech has spawned new financial industries, such as peer-topeer lending, and new ways of doing things, such as so-called ‘robo-advice’ where complex algorithms are used to generate investment solutions. These are forces for good, rather than for bad. Given the birth of the FinTech revolution, it is no surprise that the financial services industry (old as well as new) is slowly warming to the use of decision sciences in order to deliver better outcomes for customers. Be it in improving customer service (still the Achilles heel of many companies), identifying problems more quickly and solving them straightaway, and responding to customer needs. After all, FinTech and decision sciences are natural bed partners and should thrive off each other. There is no doubt in my mind that the financial services industry needs to use decision sciences to its advantage (and that of customers) as a matter of urgency. For too long, it has suffered reputational damage as a result of episodes of rampant mis-selling and sloppy service (as I have already mentioned). It has also led to a damaging disconnect between those who run financial companies and their customers. If decision sciences can address some of these issues, it will be to everyone’s benefit. For example, wouldn’t it be

great if it could be used to help snub out potential mis-selling problems before they became real ones? Think of the benefits – no consumer detriment, no future quagmires for financial businesses to sort out (using up invaluable company resources that could be better employed elsewhere) and no regulatory fines for offending companies. Maybe in time it could help the financial services industry shed its bad boy image and customer trust will return. It is a nirvana I crave. Decision sciences. Bring it on. A step at a time.

Jeff Prestridge is a Distinguished Alumnus and Personal Finance Editor of The Mail on Sunday. He can be contacted via Twitter @jeffprestridge 35

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Loughborough University School of Business and Economics