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THE ADOPTION OF MACHINE LEARNING IN THE WORLD OF QUANTITATIVE FACTOR INVESTING

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Imran Lakha, Senior Advisor, Vanguard Capital AG & CEO and Founder of Options Insight, Financial Markets Training

 

What is factor investing?

Factor investing has been around for over a decade and has been especially popular among quantitative equity investors.  Essentially, it is a way of filtering stocks based on specific types of factors such as Value, Momentum (both price and earnings), Quality, Riskiness and Size to mention a few.  This enables one to explore companies from different industry sectors and regions through an alternative lens and identify relative value trading opportunities.  Also known as smart beta, these quantitative styles are supposed to allow investors to extract some risk premium from the market which over time should generate excess returns.  Analysing a portfolio in terms of its quant factor exposure can help investors to identify biases or risks that they might have otherwise missed and enables better risk management of their assets.

However, as I mentioned earlier, these are not ground-breaking or new approaches and given the sheer amount of AUM that goes into these strategies nowadays I would argue that their value proposition is becoming less clear.  It is often now the case that broad market indices  don’t move much but the factors (often referred to as the market internals) are having extremely large moves relative to each other, creating all kinds of pain for quant investors.  The defined nature of what they should or shouldn’t be investing in (especially around MSCI index rebalances) leads to position crowding which almost always creates pain and sub-optimal returns.

 

Imran Lakha

Why use machine learning?

The leading practitioners in this space, often from hedge funds or maybe more surprisingly pension funds, have realised that to maintain an edge, they need to innovate and find factors that are not so commonly followed.  This is where machine learning and artificial intelligence comes in.  In this context, ML is basically where a massive amount of past stock return and company data is given to an algorithm, and its job is to use statistical analysis in a looping trial and error process to spot patterns in the data that can be used to make money.  With the explosion in data availability and processing power, computers are now able to perform a vast number of trails and finds patterns and dependencies in data relatively quickly, something a human would never be able to do efficiently.

The hunt for alpha has also led some firms to use alternative data sources and NLP (natural language processing) with the most sophisticated deep learning techniques to perform analytics and extract meaningful insights.  Deep learning refers to the number of layers in a neural network which makes it possible for an algorithm to learn in an unsupervised manner as opposed to supervised ML where it is given targets to help train and optimise itself.  It is the unstructured nature of the data being used which requires the use of ML and extreme computational power.

 

Combining man and machine

Despite this seeming dependence on artificial intelligence, it is also well understood that market domain knowledge is still crucial and human oversight from experienced professionals must be used to ensure sensible investment strategies are being employed.  A machine learning algorithm could find some obscure non-linear relationship in the data, but if it doesn’t make any sense from a rational economic perspective then it shouldn’t get implemented.  A common sense approach is key and handing over all the controls to the AI and trusting it blindly is obviously not the way forward.

Also worth considering is the quality of the data source, which is often the determining factor in the success of this type of investment strategy.  This is another reason why human intervention makes sense for logically pre-processing the data and spotting any biases that may exist before allowing the model to start training. There is no escaping the fact that the quality of a model is dependent on the quality of the data used to build it.

All the institutions with genuine experience using these technologies, see ML as an addition to the investment toolkit and something that assists them in making their trading decisions rather than making the decisions for them.  Whilst the large scale adoption of ML and AI in finance is inevitable, it is clear to me that the winners will be the firms that utilise the technology to augment the human capital they already have which has successfully been generating alpha for many years.

 

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OPTIMISING DIGITAL EXPERIENCE IN AN INTERNET-RELIANT FINANCIAL SECTOR

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Tony Finn, EMEAR Lead, ThousandEyes

 

It would be unfair to say that the events of the last year have started a wave of digital change in the financial services (FS) sector. Speak to any leader within the industry and, although digital transformation looks different to each organisation, it was already high on the business agenda. That said, the pandemic has undoubtedly fast-tracked many FS firms to a complete digital overhaul. In fact, according to data, business adoption of digital services was propelled forward five years in a matter of weeks. For all FS organisations who have made the transition to remote sales and services, it has evoked a re-evaluation of everything from people and property, to technology.

Underpinning it all is the core focus on how to deliver an always on digital experience. Whether you’re a payment provider or stock brokerage, ‘business as usual’ is now dependent on this, without delay or failure.

That said, keeping customers happy and employees productive is increasingly difficult, with remote business adding another layer of complexity to an already intricate puzzle. Providing a good digital experience now relies on an intricate web of Internet, cloud and SaaS services – with many infrastructures lying outside of an organisation’s view and subsequently their control. With remote business here to stay, many are realising the need for increased control over both customers’ online experience and employees’ ability to access now business-critical SaaS applications.

 

Conservative to cutting-edge

According to EY’s UK Banking Cloud Adoption Index, before the pandemic hit, the majority of UK banks (80%) had moved less than 10% of their infrastructure to the cloud. As a highly regulated sector, financial services have traditionally been conservative about diving headfirst into new technologies.

Enter COVID-19. National lockdowns meant that a new approach to technology, particularly in IT, was necessary. With offices and branches closed, never before had FS organisations had to face almost all of their customer base and workforce accessing services and products online – and perhaps most importantly, outside of their IT perimeter.

Over a year has passed and a by-product of the pandemic is that many FS organisations are rethinking their entire business operations. At the end of last year, Capital One became the first major bank to exit all of its data centres, completely overhauling its IT environment in favour of AWS’ public cloud services.

But it’s not just technology choices that have irrevocably changed. Changes are also being made to both organisations’ real estate footprint and remote working policies, with banks already making work-from-home options permanent. As a result of the latter, we’ll see hybrid work strategies emerge with different category employee personas, including field, fixed, and flexi  workers – those who return to the office, those who continue to work remotely and then a combination of both.

 

An IT blindness dilemma

Navigating new employee preferences and consumer expectations for digital banking requires a  complex service delivery ecosystem, hinging on a multitude of external components including public and hybrid cloud, SaaS applications and the Internet. Finding the source of any performance and availability issues amidst a maze of internal and external dependencies is almost an impossible task. However, gaining visibility of what’s occurring within these networks is critical for businesses to achieve that all important user experience.

The challenge is that traditional monitoring tools can’t identify the problem quickly or provide insights into what’s going on outside an organisation’s four digital walls. You certainly can’t fix what you can’t see so, more often than not, IT teams are left scrambling to troubleshoot the issue. What’s more, customers and employees don’t see or appreciate this internal battle so a lack of sight into the root cause often leads to blaming of the product, rather than the network. Not only can a potential outage cause immediate problems in the form of lost employee productivity but it can result in more harmful damage to a FS organisation’s reputation, and ultimately its bottom line.

 

Getting digital experience right in the “next normal”

So, what’s the solution for FS businesses? To optimise digital experience, it’s all about understanding the health of global Internet networks, employee and customer applications, and everything in between. Financial services navigating the digital era post COVID, will need new solutions that provide the reach, visibility, and insight they need to get a holistic view of their entire digital service delivery ecosystem.

End-to-end visibility ensures that issues – happening both in and out of a business’ control – can be quickly pinpointed and mitigated. Sometimes this can take place even before customers are aware or are impacted. This level of visibility empowers IT teams to avoid any finger-pointing and quickly decipher root cause and have purposeful discussions with relevant parties such as service providers. What’s more, there are also advantages to this approach from a network planning perspective – something which many FS organisations will need to include in their IT strategies. Visualisation and scoring of performance across applications, groups of users and locations allows a better understanding of how critical employee services are performing from a benchmark perspective.

The pandemic has undoubtedly turned the financial services sector on its head. With restrictions in the UK slowly easing and optimism around the vaccine rollout, we will see employees return to offices and customers visit branches again this year. That said, some aspects of remote business are here to stay forever and we’re already seeing the impact of this on organisations’ priorities in relation to property, technology and people. Ultimately, success in this new digital-led future will depend on a business’ ability to provide a first-rate digital experience.

 

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Finance

CAN THE CLOUD REVOLUTIONISE FINANCE?

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By Walter Heck, CTO, HeleCloud 

 

The scale of the Cloud revolution that businesses have gone through over the last few years can’t be overstated. Across almost every industry, businesses that have migrated to the Cloud have seen increased revenues, higher productivity and were more prepared to face the challenges of the pandemic than those relying on legacy infrastructure.

However, one industry that has been slow to realise the potential of the Cloud has been finance. PwC found that 81% of banking CEOs were ‘concerned’ about adopting digital tools too quickly however,  even though 91% of hedge fund executives who adopted Cloud solutions stated that their chosen cloud solutions performed ‘better than expected’. Those sitting on the fence when it comes to the cloud can afford to do so no longer. The speed, security and efficiency offered by the cloud is already changing the face of finance, as it has so many industries before it.

 

How Cloud can help Finance?

Compliance continues to be an area that financial institutions of all shapes and sizes are spending an increasing amount of time and money on. The majority (71%) of large firms are cutting the size of their compliance departments while GDPR, Brexit and increased global economic sanctions make even simple tasks regulatory headaches. Compliance is also costing the finance sector more every year. Since the financial crash, Deloitte estimates Deloitte that compliance costs have increased by as much as 60% for retail and consumer banks.

Migrating to the Cloud can solve many of these compliance issues for financial service institutions. For instance, by leveraging modern technologies on the Cloud, such as Artificial Intelligence (AI) and Machine Learning (ML), organisations can ensure financial activities remain compliant with local regulations, no matter where the data is stored. AI can also process this data far quicker and more effectively than humans, ensuring compliance matters are solved quickly and with little room for error.

With companies downsizing their expensive compliance departments, while at the same time regulation increase, the role of Cloud-based automation in compliance is set to become even more important to the financial sector.

Financial institutions that utilise Cloud-based automation allow themselves the peace of mind that they are less likely to be faced with sanctions from regulators for unforeseen or unknown infractions when carrying out day to day activities. With the cost of non-compliance running into the billions every year, neutralising this threat has the potential to save significant amounts of money for the financial institutions who make the move to the Cloud.

 

Security

Data security is vital to the survival of financial institutions. With strict rules in place, and punishments for breaches from regulators and governments increasingly common. As the number of cyber-attacks continues to increase, and costly ransomware continues to put companies out of business, it is imperative that financial institutions take the necessary steps to secure their data.

Traditional on-premises storage and data management solutions of the type utilised by many financial institutions are frequent victims of various types of cyber-attack. Gartner research has shown that up to 60% fewer attacks occur on Cloud structures when compared to on-premises alternatives.

There are many reasons for this but one of the simplest is remote access. An IBM study highlighted that 95% of security failures at companies are due to human error. This can be anything from employees using unapproved third-party applications to being the victim of ‘spill over’ malware for an attack on a different company that bleeds onto another’s on-premises infrastructure. With data being stored and managed remotely, the Cloud offers fewer direct contact points between employees and valuable company data.

However, not all Cloud solutions are created equal and when going alone companies can often find themselves under-utilising the security benefits of the Cloud and leaving themselves vulnerable to threats. Selecting the right Cloud service provider is vital. Storing sensitive data on a Cloud service enabled and managed by an experienced, trustworthy partner, ensures that client and customer data remains safe and accessible without the litany of security issues that come with on-premises infrastructure.

 

Partnering with a Cloud enabler

The Cloud is already revolutionising finance in the way it has so many other industries. Big players such as JP Morgan and Goldman Sachs have started migrating core applications to the Cloud and setting up Cloud hubs in major American cities. Almost half (43%) of financial services decision makers have stated their intent to increase their reliance on the Cloud over the coming year as more and more finance professionals see the benefits that larger competitors are reaping from Cloud migration.

In periods of great change and uncertainty, it can be tempting to bury your head in the sand and stick to the way things are already being done. However, those who ignore the Cloud revolution leave themselves vulnerable in a rapidly changing and unforgiving business climate. An experienced Cloud services partner can help guide a business on its Cloud journey and ensure they receive all the security and productivity benefits the Cloud offers. With more and more major players moving processes and workflows onto the Cloud, it is up to each finance decision maker to change now, or be overtaken by their forward looking and savvy competitors.

 

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