Cracking the Code: Data Science Tackles Investment Management
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The document discusses how data science can be used to enhance investment management operations. It describes how machine learning algorithms can be used to power robo advisors that provide tailored investment recommendations to clients based on their risk tolerance, behavior, and preferences. Neural networks can also be used for fraud detection by analyzing customer behavior and transactions to identify suspicious activities. Predictive analytics uses historical data to build models to analyze current data, while scenario-based analytics considers alternative future outcomes. The document also discusses how data science can help reduce cognitive biases that investors tend to have.
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Cracking the Code: Data Science Tackles Investment Management
- 1. 11 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Cracking the Code: Data ScienceTackles Investment Management Sharala Axryd, Founder and CEO of The Center of Applied Data Science
- 2. 22 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted We need to understand the business goals ‒ What are your strategic KPIs ‒ What are the challenges you are facing as a business ‒ How can data and analytics support delivering these objectives We explore the data and analytic capability we have to deliver these insights ‒ Do we have the data ‒ Can we get the data ‒ How sophisticated do we need to be with the analytics We consider do we have the people to deliver this capability ‒ Can we train our people ‒ How do we augment capability with external resource ‒ Are we leveraging skills across the organization Finally, we look at the tools ‒ Do these align with our business goals ‒ Are they complimentary with the skills and talent we haveWhat does it mean to be data-driven? Advanced Analytics: A Powerful Edge As innovation triggers an explosion of options, expertise becomes paramount
- 3. 33 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Smart advisors (robo advisors): • Relies on algorithms for management strategies. • Considers various customer data – risk tolerance, behaviour, legal benchmarks, preferences – and make recommendations. • Allows portfolio managers to suggest tailored investment plans to clients in both B2B and B2C operations. Fraud detection powered by neural network • Anti-money laundering and fraud- detection models that helps identify suspicious activities. • The system is trained and developed that can track and assess the behaviour of all the individuals involved in the process. • Detects fraud and implicit link between customer-potential fraud. Predictive / Scenario-based analytics • Predictive analytics - historical data to determine the relationships of data with outputs and build models to check against current data. • Scenario-based analytics - analyse possible future events by considering alternative possible outcomes, presents several alternative future developments. Data Driven Asset Management Here are the main operations that can be enhanced with a data driven approach
- 4. 44 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted The Rise and Rise of Data Investors will be more regularly informed, analysis will be more granular, and investment opportunities will ultimately be unearthed more frequently. Alternative Data – Don’t have to rely so heavily – or perhaps even at all – on traditional, cyclical financial data. The very fact that we could have fresh data consistently at our disposal that allows us to make assessments in real-time, rather than periodically, is a profound evolution. Through increasingly sophisticated techniques that can capture huge quantities of data, we now have the means to reveal behaviour, trends, and patterns of enormous relevance when gauging the appeal of a potential investment.
- 5. 55 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 6. 66 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Using Big Data to Avoid Bias Machine learning is opening up new avenues in behavioural finance – but human curiosity and intelligence are still essential Behavioural finance theory has identified three main types of bias, which can explain many central facts about markets, including average returns, time series, predictability, momentum and reversals, bubbles, and trading volume: Over-extrapolate the past, putting too much weight on the recent past (such as recent performance) while making decisions and forming beliefs about the future. Over-confident in our own ability over other people’s, as well as in the accuracy of our beliefs. More sensitive to the prospect of loss over gain, and to give too much weight to low- probability outcomes. Another bias is the influence of narratives and their contagion. The current biggest influencer on the US stock market is the narrative of Donald Trump as cutter of taxes and regulations giving a boost to earnings, despite a rational understanding being that these effects will only be temporary.
- 7. 77 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 8. 88 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 9. 99 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted How millennials are changing the face of finance & investing
- 10. 1010 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted PI 2018702181™
- 11. 1111 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Characteristics of different maturity model Strategy Analytics Talent Mature data and strategyMature in data and technology These organisations rely heavily on technology to deliver their services. As a result have access to large quantum of data. Being technology led, they are slow to movers from a strategy, organisational culture change and analytics adoption perspective Organisations that operate in heavily regulated industries are motivated to manage and use their data as an asset but at the same time crippled by legacy systems and data regulations. Typical sectors: Retail banks, Utilities. • Data • Mature across all areas • Newly formed in the information age with little or no legacy systems and regulations. • Innovation based on data and analytics culture drives the organisation. • Typical sectors: Online gaming, consumer services (Uber etc). Typical sectors: Telco, media. Mature strategy Traditional monoliths with large amount of data. The appetite to understand and make use of the data is there but the execution at the organisational level is low. Limited internal technology knowledge is seen as a limitation Typical sectors: Healthcare, Newsprint. © 2016 ANSYS SDN BHD; the operating company for ADAX. All rights reserved.
- 12. 1212 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted CADS Approach Analytics Framework Data Management Is data available? Diagnostic Analytics Why did it happen? ValuetoBusiness Source : Adapted from Gartner’s Analytics Ascendancy Model Difficulty Descriptive Analytics What happened? Predictive Analytics What will happen? Prescriptive Analytics How can we make it happen? Hindsight Identify Revenue Shortfall Insight Revenue Shortfall caused by shortage of key inventory Insight Forecast future supply and demand of inventory Foresight Optimize pricing Visualize your Data Get Hindsight Insight Foresight Make Fact Based Decisions Collect data Maintain dataValidate data Store data
- 13. 1313 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — AlvinToffler, American Writer, Futurist, Businessman
- 14. 1414 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted ThankYou
Editor's Notes
- Example #1 - When JCPenney reported its financial results for 2Q 2015, it came as quite a shock to the market. The US retail giant outperformed most analyst expectations, which subsequently led to a 10% surge in its stock price over the next 2 days. But not everyone was caught off guard… RSMetrics, a Big Data intelligence firm for businesses and investors, used satellite imageryof JCPenney parking lots during the quarter to confirm that traffic into its stores across the country was in fact increasing. The firm’s clients (mostly hedge funds) who paid to obtain this satellite imagery could thus deduce, virtually in real-time, that JC Penney’s performance was on the up. And many of them ultimately capitalized on this information by buying JCPenney stock well before the release of the company’s Q2 report in August – and well before the 10% price jump. The example illustrates just how useful – if not vital – data such as satellite imagery is now proving in exploiting investment opportunities well ahead of the rest of the market. Such data was simply not being identified a few years ago; indeed, financial analysts using traditional financial models wouldn’t even have acknowledged its existence. Thankfully today, we don’t have to settle for just relying on theory. As well as financial modeling has served us over decades, we now have something more granular, more attuned to the real world, and more prescient than any chart or news eventually reveals. Example #2- After major online retailer Wayfair published stellar quarterly results back in 2015, many investors were pleased with what they read and promptly purchased stock in the company, which duly sent the price of Wayfair shares soaring. Some fund managers, however, expected Wayfair to deliver a solid performance long before the release of those financial figures. That’s because they had previously managed to obtain company data on Wayfair that showed that its mobile app had experienced a massive boost in downloads over a short period of time, in addition to a marked increase in the number of reviews the app was receiving. As such, while some investors simply used the tried-and-tested method of looking at financial results to make their investment decisions, others had managed to gain an edge by accessing more obscure data on the company. Why will data science become a permanent feature of the investment landscape? Because it outperforms humans in at least three areas: Unbiased Analytical Thinking: Using machines to make investment decisions minimizes human error and cognitive biases. Investment professionals may use a number of techniques to recognize and minimize them, but we can’t eliminate them. Many of these are “hardwired” into our brains as established neural pathways. In contrast to humans, AI-based algorithms have no egos. They are agile, they can quickly absorb new information and make course corrections. Any data can be used to generate insight. AI can learn and evolve from changes in its environment. Unlike static quantitative models with limited shelf lives, AI-based systems are “alive.” 2. Processing Power: When it comes to information processing, humans are no match for machines. They can out-analyze us. Think of IBM’s Deep Blue supercomputer defeating grandmaster Garry Kasparov at chess in 1997, or Google’s AlphaGo AI outplaying the world’s top-ranked Go player in 2017. And this edge goes beyond analytical thinking. Machines also have us beat in the more subtle associative thinking, a skill long thought to be exclusive to humans. In 2011, IBM’s Watson defeated the top human Jeopardy! champions by a wide margin. For me, this was the moment that redefined my view of analytical thought, artificial or not. In their current form, machines like Siri and Alexa already understand human speech and can learn, process, and analyze the entire history of a human-produced domain knowledge. If this trend continues, machines will become capable of intelligent investment and resource-allocation decisions with minimal human input. 3. Software Economics: From a purely economic point of view, the value of an employee is a function of his/her contribution to the bottom line. Software that can replicate an employee costs a fraction of what firms may spend on their new hires. This threat is especially pronounced for college graduates whose starting jobs consists of collecting, organizing, and analyzing analytical data.
- Data-driven asset management: Smart advisors (or robo-advisors): These advisors have been around for almost a decade and have now become the hottest personalisation trend in the financial management industry. The algorithms consider various customer data – risk tolerance, behaviour, legal benchmarks, preferences – and make recommendations based on this data. Robo-investing entered the market in the aftermath of the 2008 financial crisis as a response to the major changes in the industry. In particular, investors desired to manage their assets in a personal way. Currently, robo-investment services manage more than $60 billion in assets worldwide. That number is projected to reach $2 trillion by 2021. Robo-advisers are most common among American investors, but they have a growing presence elsewhere. By combining multiple data sources, one can increase the dimensionality of models and solve complex optimisation problems that account for hundreds of individual portfolio factors. This allows portfolio managers to suggest tailored investment plans to clients in both B2B and B2C operations. Because of robo-advisors’ technology-driven automated processes, they don’t have the high overhead costs that traditional advisors have. As such, robo-advisors can pass these savings on to clients: robo-advisors typically charge between 0.2% and 1% annually, and often don’t have other fees. That costs 2-3% less than a Unit Trust from a bank in Malaysia. That’s 2-3% more in returns just by paying less in fees. 2. Fraud detection powered by neural networks: Another emerging trend in financial management are anti-money laundering and fraud-detection models that are powered by neural networks and help in identifying any suspicious activities. The system is trained and developed in a way that it can track and assess the behaviour of all the individuals involved in the process. The systems use and apply deep neural networks to detect any fraud by analysing both structured and unstructured data that include all kinds of online footprints. The strong neural networks efficiently detect any implicit link between the customer and any potential fraud. The Capgemini insights, for instance, show the following fraud-detection opportunities: 50-90 percent increase in revealing scams; Up to 90 percent fraud-detection accuracy improvement; Investigation time reduction up to 70 percent; Real-time fraud detection; Neural networks can be continuously improved by learning from new data and the history of successful/unsuccessful detection cases. 3. Predictive analytics: Predictive analytics uses historical data to determine the relationships of data with outputs and build models to check against current data. Stocks, bonds, futures, options, and rates movements form the stream of billions of deal records every day, which make for non-stationary time series data. These often become complex problems for financial analysts because conventional statistical methods fall short both in terms of prediction accuracy and speed. There are three approaches to combat these data. Machine learning methods: Models are trained on short-term historical data and yield predictions based on it. Stream learning: A predictive model is continuously updated by every new inbound record, which provides better accuracy. Ensemble models: Multiple machine learning models analyse incoming data, and the predictions are based on consolidated forecasting results. 4. Scenario-based analytics: The method lets financial managers to analyse possible future events by considering alternative possible outcomes. Instead of showing just one exact picture, it presents several alternative future developments. Computing power and new data processing packages have made building stress models for company operations and stock market performance possible. With this method, one can test millions of scenarios accounting for hundreds of unique market conditions. Potential Beyond Solving Investment Problems Investors can also use machine learning to develop better algorithms that help portfolio managers and traders decide what to trade, when to trade it and where to trade it. By continuously evaluating feedback from trades, algorithms can be adjusted dynamically to conduct transactions at the best prices for clients. Risk-management applications have potential, too. Imagine an automated risk manager that can systematically crawl through a targeted set of data and information sources around the world, process the findings, and highlight specific strategies and holdings that might be at risk from overnight developments. Those timely insights can then be passed on to the risk-management team to discuss and debate with the investment teams. Data science can also help us understand organizational risks, including monitoring for anti–money laundering compliance and offering insights on the impact of new regulations. There’s a lot of spending on data science among large investment managers, but that doesn’t guarantee that money is being applied to the right priorities or structures. There’s still a lot of hype relative to substance, which we expect to continue for the next few years or so. A shakeout period is likely to follow. Some firms will become less enamored with data science for two key reasons. First, alternative data sets are very hard to work with, and if everyone’s doing the same thing, it can seem harder to be different. Second, some machine learning and AI techniques don’t apply to our investment problems; these techniques will fail, and people won’t be able to explain why. We’ve seen limitations in our own research with machine learning techniques: financial data have an extremely low signal-to-noise ratio.
- “Your kings of the universe are no longer the folks wearing suits and going to galas. It’s the folks that are crunching Python and going to meet-ups. These are becoming the new masters of the universe.” Gene Ekster, CFA; originator of the term “alternative data”. Data-Driven investing builds on what models can achieve by enabling investors to achieve significantly more granularity from their analyses. Through increasingly sophisticated techniques that can capture huge quantities of data, we now have the means to reveal behavior, trends, and patterns of enormous relevance when gauging the appeal of a potential investment. Known as alternative data when applied to investing, there’s seemingly no limit to what kinds of information can be extracted. Whether it’s credit card data allowing us to verify what consumers are purchasing; geolocation data that can track cell phones, or data scraped from airline websites that can tell us whether or not to invest in the travel industry, these non-traditionally sourced datasets are facilitating much greater insights into potential investment targets. Alternative data is an umbrella term for information that is not already part of the core currency of investment research, being, broadly, everything that is not company accounts, security prices or economic information. Because alternative data is often unstructured, it may need considerable processing before it can yield meaningful conclusions. A cadre of data science professionals has emerged to meet this challenge and fashion the necessary techniques to handle large data sets. Good data scientists have several distinct qualities. A sound knowledge of maths, statistics, programming and algorithms is essential. But a firm understanding of the security under consideration, where the data is from and the way in which it is being applied are equally valuable for understanding what really matters. Combining an investor’s deep knowledge of the market and securities with skilled data scientists, whose specialised work becomes part of the investment process, is optimal. “Alternative data enriches the structured data sets already acquired by investment management firms, fueling the potential for information advantage and providing a distinct differentiator in terms of speed and knowledge.”
- What is Alternative Data? Alternative data is data from non-traditional data sources. What counts as alternative data will depend upon your industry and the traditional data sources that are already widely used by you and your competitors. The value is simple: the use and the analysis of alternative data drives unique insights and actions for your business beyond those that regular data sources are capable of providing. Alternative data can therefore be a very important competitive differentiator. Alternative data is always changing, you need a strategy now We are in the midst of a data revolution and the data that you use to power your business is at least as important as the technology that stores, processes and analyzes it. Technology is always changing and in order to remain competitive, you know that your technology needs to be constantly updated and improved. The same is true of the data that you use with that technology. Over time what was once considered alternative, non-traditional data becomes widely adopted by all companies, while new sources of alternative data are constantly emerging. It is important that you grasp the opportunity and begin to form an alternative data strategy today or risk being left behind. Alternative data and artificial intelligence Artificial intelligence is the next lever of business automation and over the coming years the development of new products and services across businesses in all industries is going to be driven by AI. Where the development of AI products is concerned, what really matters is the data that you have available to train your machines. Today’s data is driving tomorrow’s products. In the new world of artificial intelligence, product and service innovation depends on you having a data-edge as well as a technological-edge over your competitors and that means using alternative sources of data that others are not using.
- What is the HiPPO Effect? Avinash Kaushik was the first to coin the term HiPPO in his book Web Analytics: An Hour a Day. When a HiPPO is in the room and a difficult decision needs to be made but there’s not data or data analysis to determine the right course of action one way or another, the group will often defer to the judgement of the HiPPO. HiPPOs usually have the most experience and power in the room. Once their opinion is out, voices of dissent are usually shut out and in some cases, based on the culture, others fear speaking out against the HiPPO’s direction even if they disagree with it. When Ron Johnson, former head of retail at Apple who was responsible for the highly profitable Apple Stores, took over as CEO at J.C. Penney, he suffered from the HiPPO Effect. Without reviewing the existing data or investing in new data about the very different retail store he was now leading, he went full throttle ahead on his strategy for the department store chain. When his strategy was launched and he checked in to see if it was working, few had the courage to give him the unvarnished truth and be labeled as a resistor. Needless to say, his strategy wasn’t succeeding with J.C. Penney’s customers. https://finance.yahoo.com/news/jc-penneys-controversial-former-ceo-is-unsure-if-the-retailer-will-be-around-in-5-years-145259863.html The Harvard Business Review found that while 80 percent of survey respondents rely on data in their roles and 73 percent rely on data to make decisions, 84 percent still said managerial judgment is a factor when making key decisions. If you are the HiPPO, follow the example of Alfred Sloan, long-term president, chairman and CEO of General Motors who “had a strong belief about making decisions; they shouldn’t be made until someone had expressed why the ‘preferred’ option might not be the right one.” Invite disagreement; make yours a culture that you seek multiple opinions and even ask someone to play devil’s advocate prior to an important decision being made.
- The other reason that should also be considered for making the shift are the millennials. They are not just digitally savvy but are also potentially rich. Just to give a sense, the millennials will soon make for the largest part of the workforce and also stand a strong chance to inherit ancestral wealth, which could approximately be $15tn in the U.S. and $12tn in Europe over the next 15-20 years, Create Research said. With all that money and digital savviness, financial advisors should equip themselves to stand a chance in the growing competition. In its wealth-management survey for millennials, consulting firm Deloitte said millennials would be the largest client group and were, therefore, driving many wealth managers to assess their business models, as well as the way they interact with clients. Two-thirds of the global millennial adult population are from Asia itself. “Until 2020, the aggregated net worth of global millennials is predicted to more than double from 2015, with estimates ranging between US$19 trillion and US$24 trillion, ” said Deloitte. Five keys to positioning for the impending massive wealth transfer Millennials are the first digital native generation. They have a distinct set of expectations, such as enhanced communication, transparency, convenience, and readily accessible products. Furthermore, millennials generously share private information and expect in return a customized experience at low cost, if not free. 2. Millennials have a collective inherent distrust of banks, partially due to witnessing pivotal financial moments like the Great Recession, the bursting of the first technology bubble, and the Madoff Ponzi scheme. A better digital experience with more transparency and customer-centric models are characteristics that will be necessary to engage the massive opportunity with millennials. 3. Traditional financial advisors will be supplanted. The emergence of robo-advisors was an early signal that crowdsourced information will be key to engaging millennials. For example, communal discussions are seen as providing a more intimate investing experience to a generation comfortable with (over)sharing. Millennial investors seem to prefer information received from social media, which means they can participate without relying on traditional financial outlets, a financial advisor, or an institutional analyst’s view of the market. 4. Online investment clubs and social trading are being embraced, as ways to help millennials collaborate and navigate the challenging wealth management landscape. Social trading differs slightly from robo-advising, as it allows users to automate trades to follow individual traders based on performance, investment style, or relationship. Millennial investors appear to value crowdsourcing and the validation that comes from transparency and peer review. 5. Millennials’ lifestyle priorities will challenge traditional advisor models. This group’s savings objectives are far different from those of other demographics and appear eager to pursue goals that are less focused on wealth accumulation. Plus, major life choices such as marriage, children, and college funding are being pushed to later in life, so it may be some time before millennials prioritize savings. These preferences will defer the need for traditional financial advice. It may be early days, but it is critical to engage the millennial group and make inroads as early as possible. To do so, incumbents will have to understand these preferences and, in response, create a more human and credible marketplace position by using the tools this demographic prefers.
- A college degree at the start of a working career does not answer the need for the continuous acquisition of new skills, especially as career spans are lengthening. Vocational training is good at giving people job-specific skills, but those, too, will need to be updated over and over again during a career lasting decades. – The Economist Fortunately it doesn’t take much time or money to boost your skills to make you more competitive. You just need to have a strategy for ensuring that your knowledge and skills are always up-to-date. Even if you aren’t in a technical job, technical skills like software and social media help everyone. Creative skills like graphic design and photography are also useful in a variety of jobs. Skills like project management, team leadership, and conflict resolution are critical to anyone’s success. In short, knowledge work is an area that will continue to grow; career options will become more varied and require ongoing education to remaining current.
- 2nd last slide. Final slide will be the same as the 1st slide.