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.
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.
DIGITAL FINANCE: UNLOCKING NEW CAPITAL IN DISRUPTED MARKETS
Krishnan Raghunathan, Head of Finance & Accounting Services at WNS, explores how a digitally transformed finance department can give enterprises the ability they need to improve cash flow and revenue through better use of data and improved analytics-driven visibility.
Businesses everywhere are scrambling to recover lost revenues and protect cash flow. But as countries globally grapple with a dreaded second wave of the pandemic, imposing far more stringent localised lockdowns and new restrictions, it is set to be the hardest winter in living memory for many sectors.
The likelihood of winter peaks, so often the saviour of sectors such as travel and hospitality, benefitting businesses is diminishing rapidly. While many have pivoted to a greater or lesser degree, few have been able to offset the impact of falling revenues on cash flow. Even retail, riding an e-commerce boom in many regions, is finding itself in choppy waters, with 17 percent of consumers switching brands due to the economic pressures and changing priorities caused by the pandemic.
As one McKinsey article notes, “With some companies losing up to 75 percent of their revenues in a single quarter, cash isn’t just king – it’s now critical for survival”. Where then do businesses find new sources of cash to sustain their operations through the coming months?
Tapping Overlooked Cash Opportunities
For many, the answer could depend on whether they have digitally transformed their finance department. Why? Because many organisations are sitting on unidentified opportunities, funds that could be vital in shoring up businesses over the next few months or plugging the gap between operating costs and government bailouts. Yet those that have been slow to start their digital transformation journey are at a disadvantage;. At the same time, it is possible to identify these hidden seams in an analogue organisation, the process is time-consuming, manually intensive and, without the right digital tools, prone to human error.
Where deploying digital tools helps is by bringing speed, automation and reliable data to the fore. Connecting them with digital finance and accounting systems can give businesses clear insights into how money is being spent, where wastage is occurring, and where opportunities for optimisation exist.
It might be something as simple as automating the accuracy checking, issuing and chasing of invoices and late payments. This could reduce errors and invoice disputes and ultimately lead to faster payments. Accuracy and organisation are also important in billing – better records enable faster billing for work completed, and in turn, should deliver quicker payments.
It could also be around having the ability to review the supply chain and procurement data and identify where a supplier is subsidising a larger customer’s product line through drawn-out payment terms, or where a variety of vendors are on different terms across the business. Using that data and overall knowledge of the business to negotiate better terms that work for both supplier and customer can create new opportunities. It could even be to identify late-paying customers, determine the reason for late payments, and use that intelligence to develop products or financing solutions that continue to support those customers (and improve loyalty) without increasing the burden on the balance sheet.
Generating Reliable Insights for Faster Decision-making
To do any of these manually would take months, generating data slowly that would quickly go out of date. But digital finance departments have evidence they can trust to inform business decision-making. That’s because old, manual processes built around Order-to-Cash lack the flexibility and agility that businesses require in today’s markets. The fact is that even before the global pandemic crisis, the pace of digitisation across all sectors was demanding new approaches to finance and book balance.
The opportunities are significant – from cognitive credit and improved forecasting accuracy to enhanced customer analytics. All use similar tools, based on artificial intelligence and quality, trusted data. Cognitive credit can be deployed to quickly make decisions on whether to advance or restrict credit, based on individual company positions and available data. Doing so enables businesses to either capitalise on opportunities (for instance, agreeing credit for a supplier that has run out but is a supportive and integral partner) or avoid risk (in the cases where a business might be in administration).
With more accurate forecasts, businesses can better manage their currency purchases and deposits, selling currency that is not required or buying more where predictions identify an upcoming demand.
It is the same with customer analytics – with a greater understanding of customer needs, businesses can make decisions based on the right mix of the product (and how it meets demand) and supply chain suitability (such as production costs and location in relation to customers).
In many ways, the events of the past year have accelerated the process. In doing so, the problem is the pandemic has also accelerated the speed at which failure to act can lead to obsolescence. Therefore, it is vital that businesses, and more particularly their finance and accounting departments, kick start their digital transformation. This will enable them to deploy the tools and analytics that is needed to capture data, generate insights and drive fast, accurate decision-making to uncover previously untapped sources of cash and reverse revenue degradation.
The Importance of Digitally Enabled Finance Teams
Forward-thinking CFOs have already begun the process of digitising their departments, but for those that have been slow to start, now is the time to push forward. It is only through digital tools and analytics that finance leaders can identify both the internal and external opportunities to recover revenue and improve cash flow. Whether that’s releasing working capital, minimising revenue loss and accelerating revenue recovery, reducing total cost of ownership or enhancing customer retention – only digitally enabled finance teams will be in a position to capitalise and, ultimately, bolster business performance during what will be a trading period like no other.
About the author: Krishnan Raghunathan
Krishnan Raghunathan is the head of Finance & Accounting (F&A) practice and operations at WNS. He also leads the international delivery locations in China, Costa Rica, Spain, Sri Lanka, Romania, The Philippines, Poland and USA.
Prior to this, Krishnan was Chief Capability Officer for WNS, in that role he headed Horizontal practices across Finance & Accounting, Customer Interaction Services and Research & Analytics, Transformation & Process Excellence, Program Management (Transitions) and Solutions development.
He has more than 27 years of experience across Finance & Accounting, Business Process Management, Sales Solutions and Capability functions including 7 years in Accounting practice.
Before joining WNS in 2013, Krishnan led several challenging roles at Genpact, supporting strategic deals and consultative selling. In addition, Krishnan was also the business leader for a number of industry verticals at Genpact, including hospitality, transportation, logistics, media and professional services
Krishnan is a Chartered Accountant, a Certified Six Sigma Green Belt and a trained Six Sigma Black Belt
DATA DILEMMAS IMPACTING ESGS
Mario Mantrisi, Chief Strategy and Knowledge Officer, Kneip
It’s been well documented over the past few months that the COVID-19 pandemic has had a positive impact on ESG funds. ESG funds are typically portfolios where environmental, social and governance factors have been considered as part the investment process. Research from Morningstar shows that globally investors poured $45.6 billion into sustainable funds in the first quarter of 2020. In comparison, the overall fund industry saw outflows of $384.7 billion.
This trend is predicted to continue. Worldwide, attitudes are changing and a younger crop of ‘conscious investors’ with strong ethical views are now increasingly influential. Today, you’re less likely to see someone invest in an oil company. Instead, they are looking for innovative technology companies which fall under the ESG bracket, and more companies are entering this space. For example, United Nations Principles of Responsible Investment, which launched in 2006 with 100 signatories, now has more than 3,000 supporters, with a combined $100 trillion of assets under management.
With data now playing a fundamental role in the way funds, both ESG and typical, are managed, what role will data play in accelerating growth in this space? Although ESGs are doing well, we are seeing a critical issue which will determine their future growth – and it stems from data.
Across the board, ESG scores vary, and despite increased regulations from the UN, EU and individual countries’ regulatory bodies, there is no unified definition on what constitutes an ESG. This is why you’ll occasionally see oil companies pop up in an ESG fund. Tied into this, the way a lot of companies analyse data is biased toward larger companies who publish more data about themselves and are therefore likely to score higher in a fund manager’s ranking.
It’s clear changes need to be made to make it easier for fund managers to convert the interest investors are expressing in ESGs into proactive investments. The first change to be made is better sustainability reporting from companies. The second is improving data measurement and reporting. By making changes to these areas we will be able to accelerate the growth of investment in ESGs.
Let’s start with what we need from companies. Currently, most reporting on sustainability is aimed at stakeholders such as NGOs, which isn’t most relevant to investors. However, data management platforms can dissect and digest these reports to provide a reliable assessment of ESG performance. The state of play is rapidly improving, for example there are various EU directives and UK laws that require companies of a certain size to report non-financial information on an annual basis, but is this enough to attract conscious investors, driven by a sustainability motive?
Currently, many companies are missing out on potential investment from a host of conscious investors. To make themselves more desirable as a viable ESG option there are several steps that they can take to improve their reporting. Recent research from Harvard Business Review recommended the following:
- Articulate your purpose: Companies should demonstrate their purpose within a society, not just their profits. When reporting they should clearly explain how they produce profits by providing a solution to problems people and the planet face. The easiest way to articulate this is by producing a Statement of Purpose
- Improve engagement with stakeholders: Company reports should include an analysis that identifies the ESG issues that affect financial performance. Such a report is an effective way to demonstrate to shareholders and other stakeholders that the company recognises its role in society
- Improve measurement in reporting: Investors want to know how ESGs affect society as a whole and are committed to investing in these impacts. However, most companies aren’t demonstrating the positive impact they’re having. For some, this will be because of a lack of framework available to report this. The UN’s Sustainable Development Goals (SDGs) provides a reliable list of objectives that companies should recognise when preparing stakeholder reports. The SDGs recognises 17 goals that the UN identified as necessary for a sustainable future, including eradicating poverty and hunger, ensuring responsible production and consumption, and promoting gender equality.
We are also seeing moves toward data standardisation when it comes to ESG reporting. Standards such as the Sustainability Accounting Standards Board (SASB) and the Task Force on Climate-Related Financial Disclosures (TFCD) leading the way. The European Commission has also blazed trail here, in pushing for the standardisation of ESG data, from the Non-Financial Reporting Directive, which entered into force in 2017, to the EU Action Plan on Sustainable Finance, which will impose ESG reporting obligations on European investors from 2021.
However, it will take time – possibly years – before we see more companies begin reporting around ESG in a structured and standardised way. Until then, fund managers wanting to satisfy the increasing appetite from investors for ESG, will need to find ways efficient ways to make sense of disparate pieces of information and spot ESG opportunities for their clients.
Tech and innovation needs to be at the forefront of how reporting platforms support fund managers so they can effectively advise their clients.
A combination of data-driven processes is needed to measure and analyse the complex and unstructured ESG data that is available today. Technologies such as artificial intelligence and expertise in handling Big Data make it easier to analyse ESG data. In addition, machine learning and natural language processing (NLP) allows for algorithms to infer context as they sift through a variety of sources, such as annual reports, NGO reports, academic papers, regulatory and legal disclosures to assess a company’s sustainable credentials and performance. Furthermore, using these technologies removes biases and allows fund managers to review a much broader range of sustainable funds available to them.
It’s clear that ESG growth is going to continue to rocket as investors across the world become more conscious of their impact and look for ways to invest money more sustainably way. Smart fund managers will augment their own skills with the right data management platforms so that they and their clients can ride this wave of growth.
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