HIIT Counter for Apple TV (tvOS)

This app provides a timer for High Intensity Interval Training. It allows you to select the training time and resting time for each set, and allows you to select the sets per exercise. The timer provides both a visual countdown display of the time remaining, for each set, and an audible beeping for the last five seconds of each set. You therefore know when a set ends without even looking at the display, allowing you to focus on your training.

There are displays for the number of exercises, and sets, as well as the total time of exercising. There is a start and pause button, and you can change the training time and resting time while exercising, allowing you to mix and match exercise and resting times.

HIIT Counter for iPhone and iPad (iOS)

This app provides a timer for High Intensity Interval Training. It allows you to select the training time and resting time for each set, and allows you to select the sets per exercise. The timer provides both a visual countdown display of the time remaining, for each set, and an audible beeping for the last five seconds of each set. You therefore know when a set ends without even looking at the display, allowing you to focus on your training.

There are displays for the number of exercises, and sets, as well as the total time of exercising. There is a start and pause button, and you can change the training time and resting time while exercising, allowing you to mix and match exercise and resting times.

  

 

ML Camera for iPhone and iPad (iOS)

This Artificial Intelligence Camera uses Machine Learning models to classify images real time from your iPhone camera. You don’t need to take a photo, you just point your iPhone camera at any object or scene, and the classification happens instantaneously. I’ve provided five different machine learning models available for iOS, including: GoogLeNet Places (for classifications of scenes), Inceptionv3, VGG16, Resnet50, and SqueezeNet (all trained to classify objects across 1000 categories). In addition to classifying things, it also provides the probability of the classification label given.

This is a simple but very powerful tool that demonstrates how far artificial intelligence and machine learning has come, especially the power of deep neural networks and specifically, convolutional neural networks.These models are all freely available from the internet under the licenses provided by the links below.

GoogLeNetPlaces: Creative Common License. More information available at http://places.csail.mit.edu

Resnet50: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE

Inceptionv3: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE

VGG16: Creative Commons Attribution 4.0 International(CC BY 4.0). More information available at https://creativecommons.org/licenses/by/4.0/

SqueezeNet: BSD License. More information available at https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE

Beyond 2020 (Artificial Intelligence)

STANLIB MULTI-MANAGER MINDSET – Q3 2017

BY JOAO FRASCO, CHIEF INVESTMENT OFFICER, STANLIB MULTI-MANAGER

One of my favourite TV shows growing up was an Australian TV show called “Towards 2000”, which later changed to “Beyond 2000” for what I hope are obvious reasons.

The program explored how companies and other institutions (e.g. research labs at universities) were developing technologies at the “bleeding edge” (a play on “leading edge” that is so far on the edge that it is invariably traumatic). Many people may not remember the pain of dialling up to the Internet at a baud rate of 300 kbps and waiting endlessly for a very small graphic to download. Most people probably don’t even remember the internet before the World Wide Web (www) and hypertext transfer protocol (http). While many of the technologies explored in the TV show didn’t make it into our everyday lives, most were surpassed. Many futurists will recognise that technology (or the impact on our environment) is impossible to predict, and the Internet is a great example of this. What’s next on the horizon?

It’s a brave new world

As cliché as this may sound, this time could be very different, perhaps for reasons that are not that obvious. Whenever facing the unknown, it is normal to react with fear and apprehension. Some of the brave in these encounters, live to tell the story, while others perish in the annals of history. Historically though, we’ve at least come to know the “unknown”, even if it took some time to discover. This time however, we may nd ourselves in the “dark” forever.

While most futurists think the “singularity” is a long time off (and some think it will never happen), there are some that think it is around the next corner. The “singularity” (a term hi-jacked from astronomy) refers to the point in time where humans will have developed a piece of technology (deliberately generic) that surpasses

human level general intelligence, creating an intelligence explosion where machines become super intelligent, leaving the human race forever behind.

In this article, I will talk to some of what is happening in tech that is noteworthy (in my opinion), and introduce some of the topics that my fellow colleagues will explore in more detail.

Technology in general

Everyone understands the importance of technology within our personal lives and our economy more generally, but few may appreciate just how big and important it has become. Globally, Apple Inc. became the biggest company in the world (by market capitalisation) a couple of years ago, and recently became the rst company to breach a market cap of $800 billion. Alphabet Inc. (holding company of Google) is not far behind at $682 billion, and is the second biggest company in the world. In case you were wondering, the next three biggest companies are also technology companies, namely Microsoft at $544 billion, Amazon at $476 billion, and Facebook at $442 billion (all numbers at 30 May 2017).

Closer to home, Naspers became the biggest share on the Johannesburg Securities Exchange (JSE) a couple of years ago, and has continued to dominate our market, all on the back of its holding in Tencent (itself in the top 10 largest companies in the world by market cap), another technology company. Many professional money managers are sceptical of technology company valuations, as they often trade on terrible value metrics (like the Price to Earnings (PE) ratio). Some money managers stay away because of the dot.com bubble where spectacular losses were made by companies that failed to monetise their value proposition. Unfortunately, these stocks can no longer be ignored. Warren Buffet (the sage of Omaha and CE of Berkshire Hathaway) and Charlie Munger apologised to investors at their latest AGM for a couple of their technology company blunders, including not investing in Google. They have recently invested in Apple, having stayed away from tech stocks for a long time.

Artificial intelligence

Technology by itself is not new (albeit interesting), as we have lived with tech for many years. What is far more interesting, is how the technology has begun transforming our lives and economy in ways that are different from any other time in history. Here, I’m talking about technology in its broadest sense i.e. the application of scienti c knowledge for practical purposes. We are making tremendous progress across many disciplines, and technology is the key enabler in all of them. I want to focus on arti cial intelligence (AI) generally, and speci cally a branch of AI called machine learning.

Like many technologies that have come before it, machine learning adoption is undergoing massive acceleration because of the intersection of three big “pieces” (the proverbial “perfect storm”). The rst is computing power (both in its magnitude and its cost) on the back of Moore’s law, which shows no sign of slowing down. We have unprecedented access to more computing power at lower costs than ever before. To make this “real”, my desktop PC runs at about 100 GFLOPS (100 000 000 000 oating point operations per second), where the Cray-2 supercomputer (from the 80’s) ran at about 2 GFLOPS (50 times slower for the world’s fastest supercomputer at the time).

The second is “big data”, or access to unprecedented amounts of data (both structured and unstructured). To make this “real”, and I am sure that you will look this up as the numbers are unbelievable, by some estimates, we now produce more data in two days, than we did in the entire history of humanity up until 2003 (and this is an old estimate dating back to 2010). Another estimate

by IBM, has us producing 2.5 quintillion bytes of data (that’s 18 zeros) every day, or 90% of the world’s data was created in the last two years. The Large Hadron Collider in CERN alone, produces about 25 petabytes of data every year (that’s fteen zeros, or 25000 1 terabyte hard drives every year).

The third and nal piece of the puzzle relates to the advances in the state of the art, which includes everything from brain power (the advances that have been made by individuals and groups), to the software that utilises the computing power and data referred to above (speci cally “deep learning” enabled by deep neural networks). A lot of this knowledge and software has been made available to the masses (democratised), allowing individuals and small enterprises to leverage the advances being made. Let’s brie y explore some of the things that this technology has enabled.

Applications of machine learning

Back in 1997, IBM’s supercomputer “Deep Blue” beat the reigning world champion (Gary Kasparov) at Chess by a score of 3.5 – 2.5. While many saw this coming given the progress that had been made over the years, and the limited number of moves in a chess game (albeit a very large number), it was still seen as a major milestone. Most people thought that the same feat would not be achieved for a very long time for the game “Go”, as the number of possibilities in this game is larger than the number of atoms in the universe, making a “brute force” solution impossible (which is essentially how the feat was achieved in chess, with Deep Blue able to calculate many move possibilities before every move). It was therefore a big surprise when AlphaGo (an AI computer program developed by Alphabet’s Google Deepmind) beat 9-dan professional, Lee Sedol, in 2016. More recently, AlphaGo beat the world number one ranked player, Ke Jie. In 2011, IBM Watson (another AI program able to process questions in natural language), beat two former winners of the quiz show “Jeopardy”.

Nowadays, we all have access to AI on our smart phones (Siri on Apple’s iPhone, and Google assistant on various platforms), where we too can pose questions in natural language. This is enabled through two technologies that have made signi cant progress in the last decade, all based on machine learning. The rst is speech recognition, and the second is natural language processing. Again, this has been made possible because of computing power and huge amounts of data. Another application that some of you may have tried, is translation (between languages), which can now be done in “real time” by pointing your smart phone at any written text and seeing the translation in place (using another technology called augmented reality).

At Google’s recent I/O (developer conference) the CEO, Sundar Pichai, announced that Google was changing from a “Mobile-First” company to an “AI-First” company, before introducing a slew of technologies and initiatives in support of this, including support for cloud machine learning using their TPUs (Tensor Processing Units – chips developed speci cally for machine learning).

Some of these applications may appear silly or gimmicky, but there are far more serious applications being developed. Machine learning in image recognition has taken even bigger strides in the last decade and is being used in healthcare for diagnosis. The “killer app” for image recognition from machine learning has to be self-driving cars. Various companies are developing this technology, including Tesla (founded by none other than South African born, Elon Musk). What makes their solution particularly interesting (compared to Google say), is they are achieving this without LIDAR (the equivalent of laser radar – or the spinning cylinder you see on top of Google’s cars). Tesla uses eight video cameras and one radar (and ultrasonic devices), and of course a supercomputer powering the AI.

Investments?

Most of the topics discussed above make news headlines and are therefore known to most people. What most people may however not be aware of, is how AI and machine learning is being used in investing. This is however not something new, although it will have bene tted more recently from the same technologies already discussed. The ash crash of 2009

which resulted from algorithmic trading performed by computers looking to exploit pricing inef ciencies (resulting from information inef ciencies because of their ability to gather trading information on order books on various exchanges before their human counterparts), is very much a part of the AI revolution.

Unlike in other elds already discussed above though, when it comes to investing very little is known about who is doing what, for obvious reasons (why tell the world about how you are exploiting technology to make excess returns, or worse still, share the technology and knowledge). So why does this interest us, and why should it interest you? Let’s explore a couple of the main reasons.

The rst reason, is that we need to understand the world we live in, and the markets and companies that compete (do business) in that world. We need to understand the opportunities for investing, so that we invest in companies that not only provide great short-term returns, but will also survive longer- term and hence keep generating positive returns. If we understand the themes and transformative (or disruptive) power of technologies, we may be able to identify themes for longer term investing. We are entrusted by our clients to do all of this on their behalf, and cannot shirk the responsibility by simply passing the money to our underlying managers.

This leads us to the second reason. If we allocate capital (our clients’ hard earned money) to these markets through asset managers, we need to ensure that our managers also understand this world. It is pointless to allocate the money to people that don’t understand technology and therefore will never invest in technology companies (famous words from Warren Buffet). Some managers don’t invest in growth companies as they believe that they are invariably over- valued, again possibly missing great opportunities. We need to be able to assess whether managers have the skills and knowledge to exploit as many opportunities as possible as they arise, and not merely invest in “old world” companies.

The third and nal reason, is that we may ourselves be able to leverage these technologies in the work we do. We design and build solutions for many different clients, and machine learning may assist us with many parts of our investment process (from design to asset allocation, and from manager selection to portfolio construction). It is often the jobs / professions that we think are most secure from being automated by intelligent agents that will be the rst to go in the AI revolution. We are looking at all of these and many more applications (including robo tools powered by machine learning for advisers) to understand whether there are opportunities to meet tomorrow’s demands utilising these and other new technologies.

Conclusion

I think this is an exciting time to be alive and working in investments, but also believe that these technological disruptions will be very painful for many people and businesses. Despite these disruptions, we attempt to

have our eyes focussed on the horizon, with our feet rmly planted on the ground beneath us, thus ensuring that we make great investment decisions today for our clients for many years to come.

Technology broadly is changing our world, and AI/ machine learning is leading the charge. We can either embrace it with both arms or risk getting left behind.

In the articles in this edition, we will explore the topic of technology in investments in more detail by digging a little deeper into some of the things discussed above. In the accompanying podcast, I’ll go further down the “rabbit hole” and discuss the world of AI and machine learning in more detail, and how companies like Apple, Google and Facebook are investing heavily in these technologies in an attempt to capture the “hearts and minds” of their huge client / “follower” base.

I hope you enjoy and learn something new.

Understanding Goal-Based Investing (Financial Planning)

STANLIB MULTI-MANAGER MINDSET – Q2 2017

BY JOAO FRASCO, CHIEF INVESTMENT OFFICER, STANLIB MULTI-MANAGER

It is important to understand that we are only talking about the investment side of meeting goals here, and that financial planning is a much broader topic that will directly impinge on the ability to meet a specific goal if not directly addressed.

Let’s use a simple example to clarify

Imagine that a client identifies their goal as an income in retirement, and after careful consideration you establish that the income required is Rx per month after tax. If this client has not provided adequately for medical expenses in retirement (hopefully with medical insurance cover or medical aid), the income may be wholly inadequate if the client is faced with a major medical procedure. Financial advice can’t consider meeting goals with appropriate investments in isolation, but must cover all other aspects of financial needs, like appropriate insurance cover.

It is also important to note that in most cases, we will be dealing with constraints that make meeting financial goals less than certain. It would be wonderful if assets existed to meet all conceivable goals, but the reality is far from this. The best we can hope for is for some assets that meet some of the dimensions of goals (liabilities) that we want to achieve, but even this can be rare and often it is also very expensive. Let’s consider another simple example.

Imagine that an individual would like to save to buy a retirement home in the south of Portugal (the majestic Algarve) in 20 years’ time when they are planning on retiring. Portuguese inflation-linked bonds may seem like a good bet, but there are a couple of problems. Firstly, the Portuguese government does not issue inflation-linked bonds.

Secondly, even if they did exist (hypothetically), they may not have a maturity of 20 years, which would introduce either reinvestment risk (if maturity was less than 20 years) when they matured (and which will nevertheless exist on any coupon payments even if the maturity was 20 years), or market risk if the bonds would need to be sold before maturity (if maturity was substantially longer than 20 years). For clarity, market risk is the risk that the yield to maturity of the bond changes throughout its life (because of many factors that could affect the yield at different durations). The returns on bonds is therefore only somewhat known if they are held to maturity without default.

Thirdly, the Portuguese government could default on the (hypothetical) bonds before expiry, and the European Central Bank (ECB) may not provide any security on this default, in which case some of the capital invested would be lost (hopefully not all of it, although for governments this is a real possibility as bonds are not secured by assets).

Finally, and this is very important, house prices in the Algarve may not increase at the same rate as Portuguese inflation overall (which like all other country inflation is actually made up of a basket of goods consumed by an average household). In fact, given the demand for the majestic coast, house price inflation in the Algarve could be much higher. Clearly what started out as a simple goal, is actually not that simple and in actual fact can be very complex.

To explain goal-based investing, I will begin by describing the objectives of goal-based investing, and the theoretically optimal solutions used to achieve these objectives, before introducing various constraints that will lead into the final proposal. The reason for tackling the topic thus, is to demonstrate the complexities that exist, so that the final solutions are not misunderstood as being simplistic, but rather as practical. It does however also aid with the understanding of the complexity that exists and that will remain within the final solutions in resolving financial goals.

Understanding goals

It is generally well understood that goals can differ substantially along many different and important dimensions. Let’s begin by exploring these to understand the complexity that this creates. A goal can be of a capital nature (a single payment at some future date), or of an income nature (a couple of, or even many, payments at various future dates). They could be in one of many possible currencies (e.g. rand, dollar, or euro). They could be nominal amounts i.e. fixed amounts whose value is known today, or real amounts i.e. an amount linked to inflation (not only consumer price inflation, but any possible measure of inflation, say medical inflation). The future date/s could be in the near future (say next year), or very far into the future (say 80 years hence for a 20 year old starting their career and wishing to save for retirement and an income for when they are a centennial). The amounts required (capital or income) could be gross or net of tax. Finally, any of the above dimensions could be known with certainty, or completely uncertain e.g. how long will I live and need an income for? Do I know what education inflation will be and how it will relate to consumer price inflation? Do I know what future tax rates will be? Do I know what the price of a life annuity (with an insurance company providing longevity risk cover) will be? And so the list goes on.

It is important to distinguish between the dimensions of the goal(s) listed above, and the investor preferences associated with the goals, and priorities. Different investors may give different goals different priorities, and it is important to understand and respect these when considering how to construct solutions to meet them. When certainty of meeting the specified goal is given the highest weighting (highest priority), the “best” solution will focus on minimising the risk of not meeting the goal. This solution is unlikely to be the same solution as when another dimension is given a higher priority e.g. maximising wealth or returns.

It is however appropriate to understand that the starting point to investing to meet a goal, is to find assets that best match the nature of the goals, as any deviation away from this introduces variability (uncertainty) in the outcomes. It is important to highlight that even the “optimal” solution would not be “risk-free” as uncertainty of meeting a specific goal could never be guaranteed (except in the simplest of cases). We’ll touch on this in more detail below in the section on funding goals, but it should already be evident from the examples given in the introduction above.

The above complexity and conflicting objectives pose a serious problem which is not trivial to solve. To meet the various goals of various investors, you would need a large number of solutions to meet all of the different dimensions and requirements. While enough solutions probably exist globally to get you to a good answer for each goal, you would potentially need to know about and monitor hundreds or even thousands of these solutions. This is clearly untenable, which is why we begin by simplifying the dimensions so that we end up with a manageable set of solutions that will approximately match the dimensions of most goals.

Funding goals

Given the above as a starting point, how can we proceed? There are generally two approaches.

The first approach, which relates back to the theoretically “optimal” solution, would be to model the investments (every asset class available for investing i.e. not just theoretically available) and the goals (liabilities) stochastically i.e. randomly (introducing their “known” uncertainty). We won’t go into the detail here around the modelling methodology, but it is important to know that modelling requires assumptions which are informed by historical data (or at least should be, and hence why I refer to it as “known”).

This means that the modelling considers all of the uncertainty around returns and correlations between asset classes, as well as the uncertainty of the liabilities and how they may change or evolve (and the correlation between the assets and the liabilities). The optimal solution to the problem is the investment (combinations of asset classes) that provides the best match to the liabilities (goal) i.e. the solution that minimises the uncertainty around meeting the goal, regardless of the cost. Investor preferences can then be factored into alternative solutions that deviate away from this based on other priorities.

The second approach, focuses on the traditional requirement for a discount rate to be used to equate the present value of the investments and goals (assets and liabilities), and does this deterministically i.e. not randomly. It is however important to note that even here stochastic modelling is used for the assets, but this is used to establish the expected risk of the assets and how to combine these efficiently (using a mean variance framework with expected return assumptions).

Mean-variance optimisation

Practically, this means building an “efficient frontier” i.e. a combination of assets that minimise variability (or variance/standard deviation) of returns for a given level of expected return. We actually do this in real return “space” (an abstract construct that looks at returns and variability in real terms i.e. after adjusting for inflation). This is straightforward when working in rand (because we can use SA CPI for inflation), but creates additional complexity when considering global goals and investments (which currency and inflation rate should be used?).

It should be obvious from the second approach, that you need a discount rate to equate assets (investments) and liabilities (goals). This creates the need for target returns in the solutions i.e. there is no alternative way (except for the first approach) to translate the goals into specific investment objectives.

Up to this point, things should be fairly clear. We’ve constructed an efficient frontier that provides us with the combination of assets to be used for a given level of risk or return (real). It is important to appreciate the sensitivity of the results obtained, to the assumptions made (and methodology adopted), so that we don’t get too comfortable with the “preciseness” of the numbers i.e. it would be wrong to think of this efficient frontier as being “certain” in any meaningful way as one of the dimensions actually represents uncertainty (risk).

The portfolios on the efficient frontier will then have a corresponding asset allocation to the asset classes used in the modelling, and this is used as the strategic asset allocation (SAA) for the corresponding solution. All that is left to do, is decide on how many solutions are needed, and exactly where along the frontier should these be selected from, and we will consider this next.

Mean-variance optimisation may appear dated, but it remains a useful and powerful tool in understanding how to build portfolios under certain assumptions. It can incorporate Monte Carlo simulations using historical data, or parametric distributions based on historical data. It can incorporate historical or expected returns, and can even incorporate stochastic covariances (correlations) between the various asset classes i.e. uncertainty can be introduced into the various dimensions of interest.

Building solutions

So how do we move from the optimisation work, and the resultant possible solutions, to a range of portfolios to meet a varied range of individual goals? One obvious extreme method would be to include just one portfolio (somewhere on the frontier), and force everyone to use this portfolio for every goal.

Clearly this is not very client-centric, and appears to be a little too extreme in terms of simplification. Another less obvious extreme may be to have many portfolios (say 50) all along the frontier, hoping to provide a lot of granularity in meeting various risk and return requirements. We hope that it is obvious that this is not practical or necessary, and actually highlights a lack of understanding of the uncertainty present in modelling and dependence on the assumptions.

So, having thrown out the extremes, we can focus on finding a suitable compromise, but let’s consider the compromise I’m discussing in more detail first. Too many portfolios are extremely costly to manage (on various cost dimensions, including indirect costs related to governance), and we therefore want to minimise the number of portfolios to minimise these costs, costs which will need to be passed on to clients. Too few portfolios on the other hand, don’t provide enough granularity in terms of meeting different risk and return requirements. This is what we will need to balance, and find a reasonable compromise around.

On the lower limit, we could build just two portfolios (100% local cash, and say 100% local equities), and every client could be given a combination of these two to meet their specific requirements. It is important to understand the limitations of this possible solution. The first, is that it is sub-optimal in a mean-variance sense (i.e. the combination will not lie on the efficient frontier except for the two extreme cases) because it doesn’t make use of all available asset classes, which provide diversification benefits. The second, is that it could be sub-optimal from a tax and cost perspective because it would require constant rebalancing to maintain a fairly constant allocation to cash and equities.

If we consider 1% real return increments from 1% for local cash to 7% for equities (approximately our long-term real expected return assumptions), we could end up with five multi-asset class portfolios ranging from 2% to 6%, giving us a good range of portfolios to meet most investors’ risk and return requirements (in addition to cash and equities at the two extremes for investors looking for something more). Some people may argue for even greater granularity (i.e. more portfolios at say 0.5% increments), but the above proposal already introduces spurious accuracy i.e. there is already so much uncertainty around what each portfolio will deliver over various time frames.

It is important to understand that there is no “correct” or “optimal” number of portfolios, or where they should be positioned on the efficient frontier. If we are given a specific utility function that captures the client’s preference with reference to competing constraints, it is fairly simple to point to an optimal solution, but generally this is derived through a conversation with clients around the priority of the competing objectives and constraints. To suggest otherwise demonstrates a lack of understanding, and is simply misleading.

Mapping goals to solutions and understanding the limitations

Now that you have a range of portfolios along the dimensions of expected risk and return, you need to decide on which portfolio to use to meet each specific goal. I will deliberately sidestep the issue of whether investments should be considered separately for each individual goal (as opposed to collectively which is actually more optimal) as this remains a contentious issue and difficult for many to grasp.

This is where the traditional approach of “risk-profiling” investors enters the advice framework, although I think this will ultimately evolve away from this (another contentious issue I will avoid in this article). The traditional approach considers three dimensions of risk, which includes risk capacity, risk required and risk tolerance (the dimension where psychological questionnaires are used to establish attitude to and appetite for risk).

It is critical for the investor to understand risk as uncertainty at this point, as many investors may believe that this approach to investing for meeting goals removes all uncertainty, where nothing could be further from the truth. The methodology actually enables a discussion around the dimensions of goals and investments, and their attendant uncertainties, so that an appreciation of the complexity can be reached. Financial advisers will be doing their clients a great disservice if they don’t use the opportunity to have this discussion upfront as they may learn later when their clients become disgruntled by “poor” performance.

This is where the use of great tools/aids can assist financial advisers and their clients in understanding these dimensions and risks, and graphical representations of the evolution of the investment and the goal can be very enlightening. Scenario analysis and hypotheticals are two more great tools to help in this mammoth task e.g. showing how the investment would have performed through the global financial crisis (GFC). If a client is uncomfortable with the level of drawdown through the GFC, they should seriously consider lower risk portfolios as this scenario could easily repeat in the lifetime of the goal.

A tool that allows an adviser to flex (change) various dimensions associated with the investor, their goal, and possible solutions, is extremely powerful in trying to find a suitable investment to meet an investor’s very specific requirements. The investor should be able to see (visually as well as understand) the impact of changing the investment or consumption horizon, the initial and ongoing investment contributions (if any), the expected risk and return assumptions, and the certainty (probability of achieving the goal), on the goal value. The investor should then be able to change the question around to ask what the impact would be on any of those same dimensions, if the goal value were changed e.g. if the investor wants a higher amount at retirement, how much longer should the investor work before retiring?

Evaluating performance and ongoing investment advice

Once all of the above has been adequately covered, with the investor demonstrating a good understanding of the methodology and how it will assist in meeting their specific goals, a record of advice can be produced for both the investor and the financial adviser. It is critical that this record includes the uncertainty discussed as this is one of the most important dimensions of the exercise and will represent the most discussed issue in the annual review of how the investment is tracking against the goal. It would be simple if the trajectory of the investment progressed smoothly along the expected return path, but this is not only unlikely, but actually practically impossible.

At each review, the financial adviser can therefore consider how far above or below the trajectory the investment is progressing, and whether any corrective action should be taken. There are many things to consider in this process, so I will not be tackling them here, but it again represents a wonderful opportunity for adviser and investor to have a discussion around the initial process and their shared understanding of how the investment would evolve. By spending adequate time doing this, it should prevent any short-term irrational decisions that could be detrimental to the long-term success of meeting goals, which was the initial intention of following this methodology.

Conclusion

It is important to recognise what goal-based investing aims to achieve, and the idealised solutions that would theoretically be employed to meet them. It is equally important to understand the practical considerations that are needed when arriving at real world solutions, and the limitations and compromises that have been made to arrive at these. It is then fairly easy to understand why the solutions look the way they do, and how that can be integrated into an advanced financial advice framework. Without this understanding, it is easy to criticise the solutions as simplistic, and advisers should be aware of this, so that they can defend the methodology and approach to their clients.

I have taken care to articulate these complexities and discuss appropriate ways of addressing them before presenting a solid foundation for the methodology and recommendations made. I would urge all stakeholders to put sufficient emphasis on this understanding, before embarking on this methodology of investing to meet goals. I think that clients want to see consistent and integrated thinking and advice, and goal-based investing is well positioned to provide it, but requires a deep understanding of the complexity and the time to get the client to a good level of understanding.

The time invested upfront will be worth it as the adviser meets with clients annually along the journey, comparing how the investments are tracking relative to the goals. This presents a wonderful opportunity to stop the “short-termism” prevalent in the industry as investors chase the best past performers according to some survey or peer group ranking tables, in the belief that past performance may in fact be a good guide to future performance, despite all the “health” hazards communicated around this.

It is important to recognise what goal-based investing aims to achieve, and the idealised solutions that would theoretically be employed to meet them. It is equally important to understand the practical considerations that are needed when arriving at real world solutions, and the limitations and compromises that have been made to arrive at these.

An Alternative Introduction (Alternatives)

STANLIB Multi-Manager Mindset – Q1 2017

By Joao Frasco, Chief Investment Officer, STANLIB Multi-Manager

As far as years go, 2016 will not quickly be forgotten by the people of today and the history books of tomorrow.

The rise of populism on the back of the Global Financial Crisis (GFC) and the plight of the proletariat dealing with high unemployment and stagnant real wages (while the rich get richer), has led to some significant and surprising elections around the world. The two most significant were of course the British vote to exit the European Union, and the US presidential vote for Donald Trump.

Much of the blame for populism has been placed on globalisation and specifically the free movement of labour. Although many people will recognise that globalisation has lifted most of the global population significantly out of abject poverty, many do not care as they have not been the direct beneficiaries of this. They see the rich getting richer and being bailed out when they make catastrophic mistakes, while the poor or middle class are being left behind.

As far as “movements” go, this tide is unlikely to turn very quickly and we will probably continue to see the swell of populism rising for years and decades to come. We therefore need to think about what this means to all of us as citizens and managers of capital in this “new world order”.

Our theme for this quarter is therefore in many ways apropos. Given the changing landscape, we may need to rethink the investment opportunities of tomorrow. More specifically, alternative investments may actually help to address many of the issues that we are grappling with globally.

Let us not waste a great opportunity to create and shape our destiny!

An alternative definition

In investment terms, what are alternatives exactly? Like many other abstract concepts in investments, you will fail to find a single answer, or to get everyone to agree on a single definition. You can however ensure that everyone knows what you mean by a term you use, by clearly defining it upfront. People can then disagree with your definition, but still understand the discussion and arguments presented.

So let me define what I mean by alternative investments before discussing them in more detail. The most basic way of defining alternatives, is to include everything that is not considered “traditional”.

One of the first points of contention is usually whether it refers to asset classes only, or to investment strategies as well e.g. hedge funds (which are definitely not an asset class). I will define alternatives to include alternative investment strategies, which are substantially different from traditional investment strategies. As with any exercise attempting to label “things”, when the number of labels is limited you will always have difficulties. Let us not allow this to stall the discussion here.

Another point of contention is the label for real estate (property) as an alternative asset class, with many correctly arguing that real estate was in fact the first (and hence “most” traditional) asset class.

Most contemporary investors are typically more comfortable including it in their alternatives bucket, especially when it is in the form of direct ownership of land and buildings (instead of wrapped in a listed form).

If you therefore consider that anything other than listed company (including property companies) shares (equity), listed corporate, government bonds, and money market instruments, is defined as alternatives, you will see that the range of investment possibilities is indeed very broad. Alternatives is such a large category, it can itself be sub-classified into more traditional alternatives (like the unlisted equivalents of the traditional asset classes e.g. unlisted or private equity and bonds), and more alternative alternatives: art, wine, and other collectibles (perhaps record labels, stamp and coin collections or even first edition comic books). Other asset classes like land, direct property, and commodities (e.g. precious and industrial metals, energy, food etc.) probably lie somewhere in the middle of the spectrum.

An alternative context and history

Wealthy individual investors have invested in alternatives for a very long time, but institutional investors have been much slower to adopt them for some very important reasons. There is actually an interesting life cycle to markets and investments, whereby alternatives will dominate markets without deep and liquid listed markets (which define traditional investments). As markets become more developed (liquid and deep), more money chases these traditional investments because of the benefits they provide. As these markets become increasingly efficient, many investors look for alternatives to provide some diversification and potential for higher returns (and access to new market segments e.g. emerging technologies). Alternative strategies on the other hand, trade in traditional asset classes (in many instances), but in novel and complex ways, requiring even more mature and developed markets (the derivatives market being a great example of this).

The US alternatives market is amongst the most developed globally, and many prominent investors have had great success by investing in this part of the market (the very large pension funds and university endowments are an example of these). This has led to research investigating the case for alternatives, and the slow adoption by other market participants. The Myner’s Report (in the UK to HM Treasury in 2001 on institutional investors) was a great example of this, and found that advisers (e.g. pension fund trustees) may not have been acting in the beneficiaries’ best interest by not dedicating enough resources into evaluating the case for unlisted equity.

As markets become increasingly efficient, many investors want to look for higher returns in segments of the market with less competition and greater opportunities. The efficient allocation of capital is also an important consideration as listed companies, through their monopoly on raising capital in the listed market, may allocate their assets in less efficient ways (bad projects), and investors need alternatives to penalise these companies and reward those who are allocating capital efficiently.

As bond yields have turned negative, and the economic outlook globally looks muted, investors may feel that traditional assets are overvalued and will want to look elsewhere, for themselves or for those for whom they act as fiduciaries, for higher returns. Sometimes this is as simple as looking at what fewer people are doing (so the implication being that there is more opportunity for information asymmetry on which to act), or where others will not have the ability or appetite to invest. Chris Roelofse and Richo Venter will explore this in more detail in their articles in this publication.

The above, and many other factors, have led to an increase in the allocation to alternatives globally, and although there has been some bumps along the way (GFC was one), the trend of increasing allocation remains strongly in place.

But what about South Africa?

An alternative South Africa

Fortunately, South Africa has a long and very distinguished financial services market, including a stock exchange dating back to 1887 (130 years). It is however a fairly small market by some international standards (it is the 19th largest stock market globally by market cap), although large by African continent standards (where it is the largest). It also has a well- developed derivatives market (ranked sixth and ninth for single stock futures and currency derivatives traded respectively in 2012).

This has meant that the need to develop an alternatives market has generally had less priority, although alternative markets have existed in South Africa for some time. The uptake from institutional investors has therefore been fairly slow. It has not helped that the regulatory environment for institutional investors (specifically retirement funds) has not been particularly conducive to investing in alternatives. The fact that boards of trustees in South Africa have predominantly been constituted by individuals without investment expertise has also not helped.

As in the rest of the world, the biggest opportunity from alternatives is for high returns, made possible because of the limited amount of investment capital available. This means that investors can generally be very selective in investing in only the opportunities that have a great chance of success for high returns. This is exactly the opposite of what we find in the listed space, where most professional money managers feel that the highest quality companies are significantly overpriced and therefore offer no margin of safety and hence high risk.

Fortunately, alternatives received a substantial boost a number of years ago with an update to Regulation 28 (of the Pensions Fund Act dealing with prudential limits relating to asset classes, issuers and instruments), where various alternative investments were specifically named and their limits increased above their previous classification in “other assets” which had a limit of 2.5%.

This was all placed at risk recently when national treasury in draft papers began pushing a “passives” agenda and “war on fees” with little recognition that fees on their own offer little in the way of protection of savers, and less in the way of value! From a return perspective, it is net returns that are important, not fees, contrary to the narrative being pushed by passive product providers. More importantly however, are the other benefits that are on offer from alternatives, including less risk (if listed equities have been pushed to valuations that are not sustainable), and the opportunity to re-allocate capital from existing companies in sunset industries to the companies and industries of tomorrow e.g. technology (bio, nano, hardware, software, internet, artificial intelligence, machine learning, predictive analytics etc.). Fortunately, this seems to have been put back on track with the latest draft papers following an outcry from the industry.

The massive opportunity that exists by moving the ow of capital from traditional to alternative investments, lies in the opportunity to develop these markets and create employment and other social benefits, in addition to higher returns which is non-negotiable for most investors. With official unemployment figures for South Africa as high as 27% (the unofficial numbers being well above 50%), this provides a spotlight on the old (and seriously awed adage) that markets are a “zero sum game”.

By allocating resources and exerting effort on this important segment of the economy, we can truly transform a country for generations to come. Although investors are right to be fearful of government intervention in driving this agenda (because of their lack of credibility across many initiatives), this doesn’t make the case for alternatives any less exciting. We would be serving our investors and the general public and country well by taking these matters into our own hands.

A couple of themes that will resonate with most South Africans today, include investing in energy (rolling blackouts), specifically clean energy (wind farms and solar, on the back of global warming), water/dams (droughts and oods, again on the back of global warming), other infrastructure (roads, bridges, airports, trains/rail, ports), and agriculture (again on the back of global warming).

An alternative challenge

Unfortunately, the best things in life are not free, and this is where the balance between costs, fees, and value is important. In this case, the adage that “price is what you pay and value is what you get” is apropos. Alternatives are generally expensive and there are often very good reasons for this. It is very insightful to understand this in detail because it often forms the basis of decision making. I sometimes find that people make bad decisions because they are acting on bad information.

Consider two managers each charging 50 basis points (0.5% per annum) for managing a listed equity mandate. Assume now that one manager has R200 billion in assets under management, while the other manager has R2 billion. The annual fee for the larger manager is R1 billion, while for the smaller manager it is only R10 million. Both managers could be analysing the same shares, and have similar teams of portfolio managers and analysts, yet they make a very different amount of money. Why is this? Surely the fee would be similar in rand amount if it was based on the cost of the underlying function (i.e. managing the money). It is however rather based on the value provided i.e. the opportunity to make returns is shared between the investor and the manager. It is important to note that this is not entirely accurate, as we haven’t addressed why the fee was set at 50 basis points. Clearly this is a function of the potential assets that could be managed, as well as the competition in the market. Great managers can charge more, and managers operating in smaller markets can charge more, than their counterparts in both cases.

This is the same in alternatives, to the extent that the market is much smaller, the fees need to be higher to attract market participants (the money managers), or to make the same rand amount. To the extent that the value provided is much higher, the fees can also be much higher. This is simply the result of demand and supply in free markets. A great example can be found in Renaissance Technologies (manager of the Medallion Fund) which charges a fixed fee of 5% and a performance fee of 44% (numbers that are literally off the charts). Most investors would think that these numbers are ridiculous, and would not invest with this manager if they had the opportunity. Unfortunately, most investors will not have the opportunity as the manager is only open to employees.

Now consider an investor considering the above and the allocation of an additional R1 million. If they gave this money to the large or the small equity manager, do they think that the manager would undertake any additional work to invest the R1 million? Surely no additional research is required as the managers have already researched all the shares of interest to them. The money is invested without much further consideration into the existing shares held by the manager.

What would this look like for an additional R1 million invested in alternatives? Although it may vary significantly from one alternative proposition to the next, the reality is that the marginal investment in alternatives will need to find new opportunities, along with all the other new ows to alternatives. This could be deployed into new infrastructure or new employment – or both. What value is therefore created with this transaction, even before any return is realised?

The rest of the articles in this publication will explore the challenges in a lot more detail.

And finally… an alternative conclusion

It is important for investors and their advisers to realise that the world is changing, and the pace of change is accelerating. Business as usual and investing as usual, will not be appropriate and you could correctly argue that it has never been appropriate which is why the world has continued to change to re ect these imperatives. We are however seeing some significant moves that will present new challenges and opportunities, and alternatives may be poised to benefit from many of these.

Investors should therefore resist the urge keep things the same, and begin questioning their service providers around solutions for tomorrow. In turn, service providers as experts with the combination of skills and information, should be proactive in finding solutions of tomorrow for their clients. To the extent that these service providers can stand back and appreciate the bigger opportunities, they will realise that the opportunities go way beyond just great returns for their clients, but a better future for everyone.

It is important for investors and their advisers to realise that the world is changing, and the pace of change is accelerating. Business as usual, and investing as usual, will not be appropriate, and you could correctly argue that it has never been appropriate which is why the world has continued to change to re ect these imperatives.

For the full publication, visit STANLIB Multi-Manager!

MakeIcons for Mac (macOS)

MakeIcons is a very simple to use app for macOS that will produce a range of different size icons for Xcode developers to use in their app development. Simply create a large version of your icon using your favorite graphics app, and drop it onto the canvas. You can then either click a button to produce iOS and watchOS icons, or another button for macOS icons. You can also create any size icon you want as Apple introduces new icon sizes, without waiting for the latest version of the app to include these new sizes. It is fast and simple, and I have created an extra bonus whereby you can also create TabBar item icons. iOS icons are produced for more than 10 different sizes, at x1, x2, and x3 resolutions, and watchOS and macOS get x1 and x2 resolutions. You can then simply drag and drop all of the icons into Xcode, and they will all go into their appropriate image slot (you may need to make a few minor additions).

LearnGraphs for iPhone and iPad (iOS)

This app is great for scholars and parents wishing to understand graphs in mathematics. While there are many graphing calculators that allow you to graph many complex functions, this one focuses on teaching simple grade 10 math graphs in a really simple and visual way. Using sliders to change the values of variables, you can see the impact the variable has on the overall equation or function, as well as other interesting things like x and y intercepts and asymptotes. You will quickly grasp just how simple graphs are by watching them change shape and move around as the value of a variable becomes larger or smaller or goes from positive to negative, or zero. Even parents will wish they had this app when they went to school, as they too will grasp just how easy and fun math is.

Specifically, the following graphs are included:
1. Lines of the form y = mx+c or ax+b
2. Parabolas of the form y = ax^2+bx+c
3. Hyperbolas of the form y = a/(x+b)+c
4. Exponentials of the form y = ab^x+c
5. Trigonometric function (sin, cos, and tan) of the form y = a*sin(bx)+c or a*cos(bx)+c or a*tan(bx)+c

You can also pan around the graphs and pinch to zoom in and out, and just tap the graph to reset the pan and zoom.

Enjoy learning math graphs, and drop me an email if you would like me to include a couple of other graphs that you or or children are struggling with.

Speedometers for iPhone and iPad (iOS)

Speedometers is a really simple and beautiful speedometer for your truck, car, motorbike, or bicycle. It uses GPS on your iPhone or iPad to establish your speed, and shows this information graphically in a simple user interface. You can change the units from miles per hour to kilometres per hour and back, and change the top speed of the speedometer reading. You can also let your speed guide what the top speed shown should be.