100 dollar money bill with face mask

Humanity+ Festival 07/08-Jul-2020: Max More on UBI

Sat 05-Sep-2020

In the past 2 years or so I have been increasingly interested in the transhumanist movement. Transhumanism has a bit of a mixed reputation in “serious” circles of business and academia – sometimes patronised, sometimes ridiculed, occasionally sparking some interest. With its audacious goals of (extreme) lifespan / healthspan extension, radical enhancement of physical and cognitive abilities, all the way up to brain uploading and immortality one can kind of understand where the ridicule is coming from. I can’t quite comprehend the prospect of immortality, but it’d be nice to have an option. And I wouldn’t think twice before enhancing my body and mind in all the ways science wishes to enable.

With that mindset, I started attending (first in person, then, when the world as we knew it ended, online) transhumanist events. Paradoxically, Covid-19 pandemic enabled me to attend *more* events than before, including Humanity+ Festival. Had it been organised in a physical location, it would have likely been the in US; even if it was held in London, I couldn’t take 2 days off work to attend it, I save my days off for my family. I was very fortunate to be able to attend it online.

I attended a couple of fascinating presentations during the 2020 event, and I will try to present them in individual posts.

I’d say that – based on the way it is often referred to as a cult – transhumanism is currently going through the first stage of Schopenhauer’s three stages of truth. The first stage is ridicule, the second stage is violent opposition, and the third stage is being accepted as self-evident. I (and the sci-fi-loving kid inside me) find many of the transhumanist concepts interesting. I don’t concern myself too much with how realistic they seem today, because I realise how many self-evident things today (Roomba; self-driving cars; pacemakers; Viagra; deepfakes) seemed completely unrealistic, audacious, and downright crazy only a couple of years / decades ago. In fact, I *love* all those crazy, audacious ideas which focus on possibilities and don’t worry too much about limitations.

Humanity+ is… what is it actually? I’d say Humanity+ is one of the big players / thought leaders in transhumanism, alongside David Wood’s London Futurists and probably many other groups I am not aware of. Humanity+ is currently led by the fascinating, charismatic, and – it just has to be said – stunning Natasha Vita-More.

Transhumanist movement is a tight-knit community (I can’t consider myself a member… I’m more of a fan) with a number of high-profile individuals: Natasha Vita-More, Max More (aka Mr. Natasha Vita-More aka the current head of cryogenic preservation company Alcor), David Wood, Jose Cordeiro, Ben Goertzel. They are all brilliant, charismatic, and colourful individuals. As a slightly non-normative individual I suspect their occasionally eccentric ways work can sometimes work against them in the mainstream academic and business circles, but I wouldn’t have them any other way.

During the 2020 event Max More talked about UBI (Universal Basic Income). I quite like the idea of UBI, but I appreciate there are complexities and nuances to it, and I’m probably not aware of many of them. Max More definitely gave it some thought, and he presented some really interesting thoughts and posed many difficult questions. For starters, I liked reframing UBI as “negative income tax” – the very term “UBI” sends so many thought leaders and politicians (from more than one side of the political spectrum) into panic mode, but “negative income tax” sounds just about as capitalist and neoliberal as it gets. More amused the audience with realisation (which, I believe, was technically correct), that Donald Trump’s USD 1,200 cheques for all Americans were in fact UBI (who would have thought that of all people it would be Donald Trump who implements UBI on a national scale first…? Btw, it could be argued that with their furlough support Boris Johnson and Rishi Sunak did something very similar in the UK – though these cheques were not for everyone, only for those who couldn’t work due to lockdown; so this was more like Guaranteed Minimum Income).

The questions raised by More were really thought-provoking:

  • Will / should UBI be funded by taxing AI?
  • Should it be payable to immigrants? Legal and illegal?
  • Should UBI be paid per individual or per household?
  • What about people with medical conditions requiring extra care? They would require UBI add-ons, which undermines the whole concept.
  • Should people living in metropolitan cities like London be paid the same amount as people living in the (cheaper) countryside?
  • How should runaway inflation be prevented?

Lastly, More suggested some alternatives to UBI which (in his view) could work better. He proposed an idea of universal endowment (sort of universal inheritance, but without an actual wealthy relative dying) for everyone. It wouldn’t be a cash lump-sum (which so many people – myself included – could probably spend very quickly and not-too-wisely), but a more complex structure: a bankruptcy-protected stock ownership. The idea is very interesting – wealthy people (and even not-so-wealthy people) don’t necessarily leave cash to their descendants: physical assets aside (real estate etc.) leaving shares, bonds, and other financial assets in one’s will is relatively common. Basically the wealthier the benefactor, the more diverse the portfolio of assets they’d leave behind. The concept of bankruptcy-protected assets is not new, it exists in modern law (e.g. US Chapter 13 bankruptcy allows the bankrupting party to keep their property), but to me it sounded like More meant it in a different way. If More meant his endowment as a market-linked financial portfolio whose value cannot go down – well, this can be technically done (long equity + long put options on the entire portfolio) – but only to a point. Firstly, it would be challenging doing it on a mass scale (the supply of required amount of put options could or could not be a problem, but their prices would likely go up so much across the board that it would have a substantial impact on the value and profitability of the entire portfolio). Secondly, one cannot have a portfolio whose value can truly only go up – it wouldn’t necessarily be the proverbial free lunch, but definitely a free starter. Put options have expiry dates (all options do), and their maturity is usually months, not years. Expiring options can be replaced (rolled) with longer-dated ones, but this would come with a cost. Perpetual downside protection of a portfolio with put options could erode its value over time (especially in adverse market conditions, i.e. underlying assets values not going up).

If More had something even more innovative in mind then it could require rewriting some of the financial markets rulebook (why would anyone invest the old-fashioned way without bankruptcy protection when everyone would have their bankruptcy-protected endowments?). I’m not saying it’s never going to happen – in fact I like the idea a lot (and I realise how much different my life could be from the material perspective had I received such endowment when I was entering adulthood), I’m just pointing out practical considerations to address.

And one last thing: speaking from personal experience, I’d say that this endowment *definitely* shouldn’t be paid in full upon reaching the age of 18 (at least not for guys… I was a total liability at that age; I’d squander any money in a heartbeat); nor 21. *Maybe* 25, but frankly, I think a staggered release from mid-20’s to mid-30’s would work best.


Candle stick graph

Utilisation of AI / Machine Learning in investment management: views from CFA Institute, FCA / BoE, and Cambridge Judge Business School

Mon 31-August-2020

I spent a better part of the past 18 months researching Machine Learning in equity investment decision-making for my PhD. During that time two high-profile industry surveys and one not-so-high-profile were published (CFA / BoE, CFA Institute, and Cambridge Judge Business School respectively). They provided a valuable insight into the degree of adoption / utilisation of Artificial Intelligence in general and Machine Learning in particular in the investment management industry.

Below you will find a brief summary of their findings as well as some critique and discussion of individual surveys.

My research into ML in the investment management industry delivered some unobvious conclusions:

  • The *actual* level of ML utilisation in the industry is (as of mid-2020) low (if not very low).
  • There are some areas where ML is uncontroversial and essentially a win/win for everyone – chief among them anti-money laundering (AML), which I discussed a number of times in meetups and workshops like this one [link]. Other areas include chatbots, sales / CRM support systems, legal document analysis software, or advanced Cybersecurity.
  • There are some areas where using ML could do more harm than good: recruitment or personalised pricing (the latter arguably not being very relevant in investment management).
  • There is curiosity, openness, and appreciation of AI in the industry. Practicalities such as operational and strategic inertia on one hand and regulatory concerns on the other stand in the way. It’s not particularly surprising nor unexpected, and attitude towards this situation is stoical. Investment management has once been referred to as “glacial” in its adoption of new technologies – I think the industry has made huge progress in the past decade or so. I think that AI / ML adoption will accelerate, much like the adoption of the cloud had in recent years.
  • COVID-19 may (oddly) accelerate the adoption of ML, driven by competitive pressure, thinning margins (which started years before COVID-19), and overall push towards operational (and thus financial) efficiencies.

I was confident about my findings and conclusions, but I welcome three industry publications, which surveyed hundreds of investment managers among them. These reports were in the position to corroborate (or disprove) my conclusions from a more statistically significant perspective.

So… Was I right or was I wrong?

The joint FCA / BoE survey (conducted in Apr-2019, with the summary report[1] published in Oct-2019) covered the entirety of UK financial services industry, including but not limited to investment management. It was the first (chronologically) comprehensive publication concluding that:

  • Investment management industry as a subsector of financial services industry has generally low adoption of AI compared to, for example, banking;
  • The predominant uses of AI investment management are areas outside of investment decision making (e.g. AML). Consequently, many investment management firms may say “we use AI in our organisation” and be entirely truthful in saying so. What the market and general public infer from such general statements may be much wider and more sophisticated applications of the technology than they really are.

The CFA Institute survey was conducted around April and May 2019 and published[2] in Sep-2019. It was more investment-management centric than the FCA / BoE publication. Its introduction states unambiguously: “We found that relatively few investment professionals are currently exploiting AI and big data applications in their investment processes”.

I consider one of its statistics particularly relevant: of the 230 respondents who answered the question “Which of these [techniques] have you used in the past 12 months for investment strategy and process?” only 10% chose “AI / ML to find nonlinear relationship or estimate”. I believe that even the low 10% figure represented a self-selected group of respondents, who were more likely to employ AI / ML in their investment functions than those who decided not to complete the survey.

Please note that for any of the respondents who confirm that their firms use AI / ML in investment decision-making (or even broader investment process) it doesn’t mean that *all* of their firm’s AUM will be subject to this process. It just means that some fraction of the AUM will be. My educated presumption is that this fraction is likely to be low.

Please also note that both FCA / BoE and CFA Institute reports relied on *self-selected* groups of respondents. The former is based on 106 firms’ responses out of 287 the survey was sent to. 230 respondents answered the particular question of interest to me in the CFA Institute report – out of 734 total respondents the survey was sent to.

The Cambridge Judge Business School survey report[3] (published in Jan-2020) strongly disagrees with the two reports above. It concludes that “AI is widely adopted in the Investment Management sector, where it is becoming a fundamental driver for revenue generation”. It also reads that “59% of all surveyed investment managers are currently using AI in their investment process [out of which] portfolio risk management is currently the most active area of AI implementation at an adoption rate of 61%, followed by portfolio structuring (58%) and asset price forecasting (55%)”. I believe that Cambridge results are driven by the fact that the survey combined both FinTech startups and incumbents, without revealing the % weights of each in the investment management category. In my experience within the investment management industry, the quotes above make sense only in sample dominated by FinTechs (particularly the first statement, which I strongly disagree with on the basis of my professional experience and industry observations). I consider lumping FinTech’s and incumbents’ results into one survey as unfortunate due to extreme differences between the types of organisations.

That Cambridge Judge Business School publishes a report containing odd findings does not strike me as particularly surprising. It is, frankly, not uncommon for academics to get so detached from the underlying industry that their conclusions stand at odds with observable reality. However, the CJBS report has been co-authored by Invesco and EY, which I find quite baffling. Invesco is a brand-name investment management firm with USD 1+ Tn in AUM, which puts it in the Tier 1 / “superjumbo” category size-wise. I am not aware of it being on the forefront of cutting-edge technologies, but as is the case with US-centric firms, I may simply lack sufficiently detailed insight; Invesco’s AUM seem sufficient to support active research/implementation of AI. One way or another, Invesco should know better than to sign off on a report with questionable conclusions. EY is well on the forefront of the cutting-edge technologies (I know that from personal experience), so for them to sign off on the report is even more baffling.

Frankly, the Cambridge Judge report fails to impress and fails to convince (me). My academic research and industry experience (including extensive networking) are fully in line with FCA / BoE’s and CFA Institute’s reports’ findings.

The fact that AI adoption in investment management stands at a much more modest level than the hype would have us believe may be slightly disappointing, but isn’t that surprising. It just goes to show that AI as a powerful, disruptive technology is being adopted with caution, which isn’t a bad thing. There are questions regarding regulation applicable to AI which need to be addressed. Lastly, business strategies take time (particularly for larger investment managers), and at times the technology is developing faster than business can keep up. Based on my experiences and observation with cloud adoption (and lessons seemingly learned by the industry), I am (uncharacteristically) optimistic.

[1] https://www.fca.org.uk/publication/research/research-note-on-machine-learning-in-uk-financial-services.pdf

[2] https://www.cfainstitute.org/-/media/documents/survey/AI-Pioneers-in-Investment-Management.ashx

[3] https://www.jbs.cam.ac.uk/wp-content/uploads/2020/08/2020-ccaf-ai-in-financial-services-survey.pdf


Money laundering

Nerd Nite London – AI to the rescue! How Artificial Intelligence can help combat money laundering

15-Apr-2020

In April 2020, in the apex of the UK lockdown, I had the pleasure of being one of three presenters at online edition of Nerd Nite London. Nerd Nite is a wildly popular global meetup series, with multiple regional chapters. Each chapter is run by volunteers, and the proceeds from ticket sales (after costs) go to local charities. In this sense, lockdown did us an odd favour: normally Nerd Nites are organised in pubs, so there is venue rental cost. This time the venue were our living rooms, so pretty much all the money went to a local foodbank.

I had the pleasure of presenting on one of the topics close to my heart (and mind!), which is the potential for AI to dramatically improve anti-money laundering efforts in financial organisations. You can find the complete recording below.

Enjoy!


Solar Panels in a field

London Business School Energy Club presents: the renewables revolution

Thu 26-Sep-2019

As an LBS alumn (or is it “alumnus”? I never know…) I am a part of a very busy e-mail distribution list, connecting tens of thousands of LBS grads worldwide. LBS, its clubs, alumni networks etc. regularly organise different events, and I make an active effort to attend one at least every couple of months. I went to “the business of sustainability” a couple of months ago, so the upcoming “the renewables revolution” organised by LBS Energy Club (and sponsored by PWC) was an easy choice.

Renewable energy is not a controversial topic in its own right (unless you’re a climate change denier or a part of the fossil fuel lobby, especially on the coal side). It’s a controversial topic along the lines of disruption of powerful, established, entrenched industries (mostly mining and petrochemicals) and also along the lines of disruption of life(style) as we know it. Most of us in the West (the proverbial First World, even if it doesn’t feel like one very often) want to live green, sustainable, environmentally-friendly lifestyles… as long as the toughest environmental sacrifice is ditching a BMW / Merc / Lexus etc. for a Tesla, and swapping paper tissues for bamboo-based ones (obviously I am projecting here, but I don’t think I’m that far off the mark). Us Westerners (if not “we mankind”, quoting Taryn Manning’s character from “hustle and flow”) love to consume, love the ever-expanding choices, love all the conveniences we can afford – the prospect of cutting down on hot water, not being able to go on overseas holidays once or twice a year, or not replacing our mobiles whenever we feel like it, is an unpleasant one. Renewables, with their dependency on weather (wind, solar) and generally less abundant (or at least less easily and immediately abundant) output are an unpleasant reminder that the time of abundance (when, quoting Michael Caine’s character from “Interstellar”, “every day felt like Christmas”) might be coming to an end.

Furthermore, even for a vaguely educated Westerner like myself, renewables are a source of certain cognitive dissonance. On one hand we have several consecutive hottest years on record, floods, wildfires, disrupted weather patterns, environmental migrants, the prospect of ice-free Arctic ocean, Extinction Rebellion etc. – on the other hand we have seemingly very upbeat news like “Britain goes week without coal power for first time since industrial revolution”, “Fossil fuels produce less than half of UK electricity for first time”, or “Renewable electricity overtakes fossil fuels in the UK for first time”. So in the end, I don’t know whether we’re turning the corner as we speak, or not.

There is no shortage of credible statistics out there – it’s quite a challenge for a non-energy expert to understand them. According to BP, renewables (i.e. solar, wind and other renewables) accounted for approx. 9.3% of global electricity generation in 2018 (25% if we add hydroelectric). Then, as per the World Bank (spreadsheets with underlying data from Renewable Energy), in 2016 all renewables accounted for approx. 11% of global energy generation (35% if we add hydroelectric). Then, as per IEA, in 2018 renewables accounted for measly 2% of total energy production (rising to 12% if we add biomass and waste, and to 15% if we add hydro).

2% looks tragic, 9.3% looks poor, 25% or 35% looks at least vaguely promising – but no matter which set of stats we choose, fossil fuels still account for vast majority of global energy generation (and the demand is constantly rising). Consequently, my anxiety remains well justified. It was the reason I went to the event in the first place – to find out what the future holds.

The panellists were:

  • Equinor, Head of Corporate Financing & Analysis, Anca Jalba
  • Glennmont Partners, Founding Partner, Scott Lawrence
  • Globeleq, Head of Renewables, Paolo de Michelis
  • Camco, Managing Director, Geoff Sinclair

The panellists made a wide range of observations, depending on their diverse geographical focus and nature of their companies. You will find a summary below, coupled with my personal observations and comments. I intentionally anonymized the speakers’ comments.

One of the panellists remarked that in the last decade a cost of 1MW of solar panels went from EUR 6-8m to EUR 3.5m to EUR 240k, and at the same time ESG went from being a niche area in investment management to being very much at the core (I echo the latter from my own observations). At the same time, according to research, in order to meet Paris Accord targets, by 2050 50% of global energy will need to come from renewables. So no matter which set of abovementioned statistics we choose, we’re globally nowhere near 50%.

The above comments are probably fairly well known, sort of goes without saying. However, the speakers made a whole lot of more targeted observations.

The concept of distributed renewables (individual households generating their own electricity, mostly using solar panels on their roofs, and feeding surplus into the power grid) was mentioned. This is being encouraged by some governments, and the speakers noted that governments are the key players in reshaping the energy landscape. They were also quite candid on there being a lot of rent seeking behaviour in the (established) energy sector (esp. utility companies). Given the size and influence of the utility sector, it is fairly understandable that they may have mixed feelings towards activities that may effectively undercut them. At the same time, one would hope that at least some of them see the changes coming, and appreciate their necessity and inevitability by adapting rather than opposing. Interestingly, emerging markets where energy infrastructure and power generation are not very reliable were mentioned as an opportunity for off-grid renewables.

We were also reminded that electricity generation is just part of the energy mix. It’s a massive part, of course, but there is also automotive transport, aviation, and shipping – all of which consume vast amounts of energy, with very few low-carbon or no-carbon options. Electric vehicles are a promising start (not without their own issues though: cobalt mining), but aviation and shipping do not currently have viable non-fossil-fuel-based options (except perhaps biofuels, but I doubt there is enough arable land in the whole world to plant enough biofuel-generating crops to feed the demands of aviation and shipping).

The need for (truly) global carbon tax was also raised. I think (using tax havens as reference) it may be challenging to implement, but, unlike corporate domicile and taxation, energy generation is generally local, so if governments would tax emissions physically produced by utility companies within their borders, that could be more feasible. Then again, it could be quite disruptive and thus challenging politically (think the fight around coal mining in the US or gillet jeunes in France as examples).

On the technical side, intermittency risk is a big factor in renewables, and energy storage is not there yet on an industrial scale. It is a huge investment opportunity.

In terms of new sources of renewable energy, floating offshore wind farms were mentioned as the potential next big thing, even though it is currently not commercially viable. My question about the panellists’ views on feasibility of fusion power was met with scepticism.

In terms of investment opportunities, one of the speakers (prompted by my question) mentioned that climate change adaptation is also one. This echoes exactly what Mariana Mazzucato said at the British Library event some time ago (pls see my post “Mariana Mazzucato: the value of everything” for reference), so there might be something there. More broadly, there seemed to be a consensus among the speakers once subsidies disappear, only investors will large balance sheets and portfolios of projects will be in the position to compete, given capital-intensive nature of energy infrastructure.

I ended by asking a question about the inevitability and scale of impact of the climate change on the world as we know it and on our lifestyles. I didn’t get a very concrete reply other than there *will be* impact, and adaptation will be essential. It hasn’t lifted my spirit, but I don’t think I was expecting a different answer. In the end, it looks like the renewables are currently more of an evolution than revolution. Evolution is better than nothing; it might just not be enough.


Support Vector Machines

#articlesworthreading: Dr. Kyoung-jae Kim “Financial time series forecasting using support vector machines”

Wed 21-Aug-2019

I don’t know (beyond some extreme examples, like “The Magical Number Seven, Plus or Minus Two” or “Computing Machinery and Intelligence”) what qualifies a journal article as seminal. However, if there is such thing as a seminal article in the field of Machine Learning-powered prediction of financial markets, then Kyoung-Jae’s 2003 article is definitely it (1,300 citations on Google Scholar and counting).

The author introduces then (relatively) unknown Support Vector Machines (a subset of supervised learning AI models) into financial prediction domain. Using 10 years of historical data of Korean KOSPI index, the author is comparing the predictive power of SVM’s against a traditional backpropagation neural network. In his brief and elegantly written article, the author is very clear about the end goal: prediction of index’s direction on the following business day (i.e. whether it’s going to go up or down). SVM’s are critical and essential, but the article isn’t about SVM’s in their own right – it’s about SVM’s being able (or unable) to predict the direction of KOSPI.

Kim’s article is worth reading for a number of reasons.

Firstly, it is one of the first articles to bring SVM’s into financial time series prediction discussion, and one of the most influential ones by far (judging by number of citations). Kim posits that SVM’s should be more accurate than neural networks as they require only one or two parameters to tune and are less prone to overfitting. His experiment tests this assumption.

The author attempts prediction of Korean KOSPI index using out-of-sample training data. He is not attempting to predict exact index level, only day-on-day direction change (whether it’s going to go up or down compared to the current day). This simplified approach is still a perfectly legitimate investment strategy – plus it is very easy to quantify using straightforward statistics (i.e. simple % accuracy).

Furthermore, he is comparing and contrasting SVM’s with more mainstream backpropagation neural networks (BPNN’s) as well as less popular case-based reasoning (CBR), enabling like-for-like accuracy analysis using the same input data. The input data is just a series of technical indicators using OHLC (open /high/low/ close) daily time series. In this sense the experiment is using one of the most limited input data sets of all – notably it doesn’t even use volume data (the majority of other experiments do). One could argue that the data series is *too* limited and adding more input variables (e.g. volume) could increase predictive accuracy of the model.

Experiment results are very interesting, if not sobering. Using market data which certainly hadn’t been subjected to any “AI arbitrage” (because it dates back to the period when AI was not deployed in the real markets), the results are only slightly above a random 50/50 guess. In the most successful configuration (and Kim tested many), SVM’s achieved 57.83% prediction accuracy on out-of-sample testing data (in some other configurations it was only marginally above 50%, but never below). While it was higher than BPNN (with 54.73%) and CBR (with 51.98%), this result is still poor. While theoretically prediction accuracy even marginally above 50% should ensure virtually infinite profit over sufficiently long a time frame, that won’t hold in practice. If we factor in trading costs, as well as standard overheads of a fund, we need accuracy much higher than 50% in order to generate a net profit (the author didn’t factor in trading or any other costs).

However, Kim’s conviction that SVM’s may be a useful tool in financial time series prediction has largely proven itself to be correct. SVM’s are one of the most popular Machine Learning approaches in financial predictions (probably second after neural networks) and are widely believed to be among the most accurate ones. His brief and elegant introduction of this ML technique remains a huge contribution to academic works nearly 2 decades later.

You can find the article through Google Scholar. Please note that it may be behind a paywall.

*Full citation: K.-j. Kim, „Financial time series forecasting using support vector machines,” Neurocomputing, no 55, pp. 307 – 319, 2003.


#articlesworthreading - Neural Network Techniques for Financial Performance Prediction

Sun 24-Mar-2019

In my work and studies I consume – voraciously if not somewhat compulsively – vast (and I mean vast) amounts of content: academic papers, online articles, talks, events, and meetups. While it can get intense and a bit FOMO, I consider myself really fortunate to be exposed to so much truly brilliant, cutting-edge thinking by people so much smarter than I am.

I try to record, save, bookmark and Evernote quality content, but I thought it would be a good idea to also share some of it with my readers. To that effect, I am launching a series titled #articlesworthreading, in which I will present articles which made an impact on me.

In the inaugural post, I will present a 2003 article by Dr. Monica Lam from California State University titled “Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis”*.

Dr. Lam’s article is worth reading for a number of reasons.

Firstly, there is the subject matter. Dr. Lam took on a very ambitious task of training her AI (to be more exact: artificial neural networks, ANN’s) to predict which stocks in her selected universe will be in the top third (33%) of performers (defined as % return). There are hundreds of articles in which authors attempt to train their AI’s to predict the performance of entire markets (proxied by their equity indices), but there are far fewer experiments in which AI is actively constructing some sort of bespoke portfolio.

Secondly, it is one of a few articles which isn’t purely focused on AI / neural network architecture and performance. Many similar articles treat market data just like any other dataset and approach their research purely as experiments in pattern recognition and prediction. These articles tend to focus on the technical angle, such as architecture of the neural networks, algorithms, training functions etc. – they are much computer science- and data science-focused. They are of somewhat limited value to investment professionals considering adoption of AI in the investment decision process.

Thirdly, the research was conducted at the time when anything to do with AI was decidedly *not* en vogue. It wasn’t any of the officially recognised “AI winters”, but – after short-lived mini-hype of neural networks in the late 1990s, and, more importantly, shortly after dot-com bubble burst – there wasn’t much interest nor excitement around the topic. In the first decade of the 21st century the tech world and the (Western) world, in general, were focused on exponentially growing Web, e-commerce, online banking, MP3’s, mobile connectivity and social media. So Dr. Lam was definitely going against the tide.

Last but not least, Dr. Lam performed a thorough investment performance analysis – this is what made her article really stand out for me. She compared % returns of AI-selected top performers against actual top 33% of equities in the sample in different neural network configurations. In some of her experiments, ANN’s were working off historical time series only (making it essentially a technical analysis-based approach), in some, she enriched the time series input with several fundamental inputs.

While AI-selected top performers underperformed actual top performers, they *outperformed* the investment universe as a whole. Moreover, this outperformance was consistent and statistically significant. So AI did manage to beat the market – and that is an exciting and promising conclusion for future research (and we need to remember that the article is over 15 years old).

You can find the article on Google Scholar. Please note that it may be behind a paywall.

*full citation: Lam, Monica. “Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis.” Decision Support Systems 37.4 (2004): 567-81. Web.


Natural Language Generation

Natural Language Generation (NLG) is coming to asset management

Sun 17-Mar-2019

Natural Language Processing (NLP) is a domain of artificial intelligence (AI) focused on, well, processing normal, everyday language (written or spoken). It is used by digital assistants such as Siri or Google, smart speakers such as Google Home or Alexa, and countless chatbots and helplines all around the world (“in your own words, please state the reason for your call…”). The idea is to simplify and humanize human-computer interaction, making it more natural and free-flowing. It is also meant to generate substantial operational efficiencies for service providers, allowing their AI’s provide services that were either previously unavailable (human-powered equivalent of Siri – not an option), or costly (human-powered chats and helplines).

Natural Language Generation (NLG) is an up-and-coming twin of NLP. Again, the name is rather self-explanatory – NLG is all about AI generating text indistinguishable from what could be written by a human author. It has been slowly (and somewhat discreetly) taking off in journalism for a couple of years now.1 2 3

NLG is far less known and less deployed in financial services (and otherwise), but given potential for operational efficiencies (AI can instantly, and close to zero cost produce text which would otherwise take humans much more time to prepare, and at a non-negligible cost) it makes an instant and strong business case. There are areas within asset management whose primary (if not sole) purpose is the preparation of standardised reports and summaries: attribution reports, performance reports, risk reports, or periodical fund/market updates. Some of these are so rote and rules-based that they make natural candidates for automation (attribution, performance, perhaps risk). Fund updates and alike are much more open and free-flowing, but still, they are rules- and template-driven.

AI replacing humans is an obvious recipe for controversy, but perhaps it is not the right framing of the situation: rather than consider AI as a *replacement*, perhaps it would be much better for everyone to consider it a *complement* or even more simply: a tool. You will still need an analyst to review those attribution reports and check the figures, and you will still need an analyst to review those fund updates. And with the time saved on compiling the report, the analyst can move on to doing something more analytical, productive, and value-adding. At least that’s the idea (QuantumBlack, an analytics consultancy and part of McKinsey, calls this “augmented intelligence” and did some research in this field which they shared during a Royal Institution event in 2018. You can watch the recording of the entire event here – the key slide is at 16:44. There is some additional reading on Medium here and here).

Some early adoption stories begin to pop up in the media: SocGen and Schroders (who, with their start-up hub, are quite proactive in terms of being close to the cutting edge of tech in investment management) are implementing systems for writing automated portfolio commentaries4. No doubt there will be more.

Disclaimer: this post was written by a human.


https://www.fastcompany.com/40554112/this-news-site-claims-its-ai-writes-unbiased-articles
https://www.wired.co.uk/article/reuters-artificial-intelligence-journalism-newsroom-ai-lynx-insight
https://www.wired.com/2017/02/robots-wrote-this-story/
https://www.finextra.com/pressarticle/75910/socgen-to-use-addventa-ai-for-portfolio-commentary


United states of crypto crazy

 

Sat 02-Mar-2019

Some assorted reflections on cryptocurrencies a little more than a year since the peak (Dec-2017). Hindsight is always 20/20, but I never was on the crypto bandwagon (and there are timestamped records to prove it…), so I believe I have the right to a little bit of “told you so” smugness.

A number of friends and colleagues have in recent days mentioned the FCA cryptocurrency assets consultation paper, which made me reflect. That FCA is on top of fintech developments is in itself great; regulators haven’t historically always been known for being ahead of the curve, but in recent years there has been marked improvement (nb. FCA isn’t the only regulator proactively looking into cryptocurrencies – regulators in many jurisdictions including USA, German, France, China, Australia, Japan and EU (ESMA) published guidelines, consultation papers, or cautions pertaining to investing in coins and tokens).

Reading the FCA paper I recalled an article in Wired magazine (UK edition) published more or less exactly a year ago, at a time when bitcoin was only beginning the precipitous slide off its all-time peak of nearly USD 20,000 (which happened in Dec-2018), all things crypto were still the hottest topic in fintech, utilities and services were meant to become a better and less-centralized, and nothing could have possibly gone wrong. And there was plenty of money being thrown at crypto. PLEN-TY.

While the article was measured and not too hype’y, it still struck me as a little less critical than I’d expect from Wired. But that in itself is probably a reflection of the time it was written in: it was such a frenzied and insane period, even measured journalism would still reflect a little bit of that insanity, it had to (my favourite quote: “<<We had all the money we needed to build the software,>> block.one CEO Brendan Blumer told me. <<All the money that comes from the token sale will be block.one’s profit.>>” – I mean, that level of crazy puts the “AAA” CDO’s of the aughts to shame).

One thing that stood out factually in the article is that coins and tokens were referenced synonymously, while they shouldn’t be. I would have never picked up on it had it not been for a very useful session at Clifford Chance in Jun-2018, and that difference is useful to know: while the entire ecosystem is ultra-fluid (as you’d expect given that it’s entirely digital), coins are generally a medium of exchange native to given chain and do not represent any claims or assets, while tokens tend to represent claims against the issuer or some sort of rights. So it’s really not the same thing, with tokens falling quite closely under the definition of a security.

What was symbolic to me in this “what a difference a year makes” story is that *the* crypto investor extraordinaire, Brock Pierce featured in the Wired story has been a subject of a super-scathing expose by John Oliver (a part of entire episode-length scathing expose of crypto in general and bitcoin in particular) and the 2 ventures he’s been associated with are yet to revolutionise the world (I’m not saying they can’t or won’t, I’m saying that they haven’t as yet), while the other, seemingly much more measured crypto venture from the same article, Dovu, appears to still be out there, but (see above) is yet to deliver anything I would want to use.

More broadly one can’t help but notice that despite the hype, the interest, the obscene amounts of money, and genuinely innovative technology there hasn’t been a genuine game-changing disruption use case as yet; JP Morgan and its blockchain-based cross-country payment project may be one exception, the Japanese project of improving efficiency of the power grid may be another, but even those are still pilots / POC’s – definitely not verified success stories (at least not yet).


What's wrong with my LinkedIn?

 

Sat 09-Feb-2019

LinkedIn has a ton of information about me. A ton of specific, structured, and organised information about me. How come it has been consistently unable to populate my feed with anything remotely relevant to me?

Every weekend I promise myself to take a stroll down my LinkedIn feed and every weekend I fail. Today I actually did it, for the first time in forever, and I just don’t get it. LinkedIn probably has slightly less info on me than Google or Facebook, but it’s very focused and factual info, as opposed to a little bit of everything that Google and FB have. Also, LI itself is very focused: it’s about maintaining professional presence and visibility. It’s *not* about cats / dogs / other animals / prank videos / hot guys (hi!) / food porn etc. I scrolled down my feed for 30 mins flat and barely found 1 or 2 (re)posts that were of any relevance to me. The rest was inspirational quotes and bumper sticker wisdoms (just shoot me), promoted posts of companies that have no interest and no tangent with me, events of no interest / relevance / tangent with me, and a number of recruitment posts – again, for roles I am not even vaguely qualified for. The recruitment posts are my biggest dilemma: on one hand there is karma etc. and being grateful for seeing *any* roles in my feed (I remember when it was very, very different), on the other there is this profound frustration: is my profile not clear enough? Have I inadvertently used the wrong keywords? Is there – at the time of lowest UK unemployment in decades – an abject shortage of roles that match my profile? Let me be clear, I’m not actively (nor passively) looking, but it’s just baffling to see roles for Senior Java Developers in Hertfordshire or lower-mid-level retail banking roles in Sheffield while seeing *nothing* in one of my areas of expertise in London.

Separately I can’t help noting that the smartest roles I see being recruited for (senior development roles, quants, modelling, etc.) appear to be clearly underpaid vs. more general roles on the same level of seniority, I don’t get it at all.

Separately still, side-by-side Facebook, which profiles me along multiple (hundreds I’m guessing – Alex Nix would know ? ) attributes is giving me soooooooooooo much more relevant posts on technology, science – even financial services; while also giving me kind people / kind animals / kind people being kind to animals / hot guys (hi ? ) as filler.

LI is running on an AI (obviously…), and you’d expect that with LI’s profile, user base, scale etc. it should be doing a pretty good, targeted job. Alas it’s not, nowhere near…

Any thoughts? I haven’t been this confused since I was 15!


The growing prominence of ESG in investments

 

Fri 23-Nov-2018

The first time I heard the abbreviation “ESG” was about a decade ago at Bloomberg. It was part of a test for a specialist role in the Analytics department (the <help><help> guys). I had no idea what it meant, which meant that for a little while longer I remained a generalist. I would say that my knowledge of ESG was fairly representative of financial services at the time.

Fast forward to present day, and it’s practically a brave new world. The financial crisis is over, the AI is coming (for our jobs…), and ESG leapt from something you’d put on a glossy (non-recyclable…) annual statement as a “nice-to-have” to a “business-as-usual-goes-without-saying”.

 

While the term itself doesn’t have an unambiguous definition, most people (finance professionals and general public alike) seem to have a good, organic understanding thereof: broadly defined ethical, environmentally-friendly investing. The width of the spectrum will differ among individuals: some will exclude industrial animal farming, some will not; some will exclude tobacco and alcohol, some will not; many will exclude hydrocarbons; and everyone will exclude assault weapons or landmines.

Change begins with awareness. 30 years ecology was either unheard of entirely or – at best – considered a fad. Today most people have a level of environmental awareness. There have always been activists who tried to raise awareness of inconvenient truths, but it took the explosion of social media to democratize previously unwelcome content (with the obvious flipside being fake news) and gave general public the opportunity to educate themselves on the environment, sustainability, corporate governance, animal welfare etc.

The next step from awareness is action, which isn’t always easy. Consumers in the developed world are not taking very kindly to the idea of “do without” (author included). Instead, they (we…) want ever more stuff – but this time environmentally-friendly, sustainable, ethical stuff (the prodigy architect Bjarke Ingels called this philosophy “hedonistic sustainability”). With our natural-born, neoliberal, capitalist awareness, we – the consumers – know all too well that brands and corporates depend on us for their survival. It is therefore unsurprising that different shades of consumer activism erupted in recent years: we want the manufacturers of our trainers to pay their labour force in South-East Asia living wages; we want cobalt in our consumer electronics to come from conflict-free mines; we want our coffee to be Fairtrade and the milk we add to it to be organic. Alongside all this activism there is also naming and shaming: of corrupt defence contractors; of polluting coal mines; of clothing manufacturers ignoring health and safety of their seamstresses etc. etc.

Financial services (especially banks; asset managers have reputationally fared much, much better) are not always synonymous with high ethical conduct (and I’m being really charitable here…). The list of prosecuted cases, no contest settlements, and plain lack of ethics of the past decade alone will feature many of the largest players in the industry (some of them included on multiple counts). On the upside, the broad social/regulatory climate has also changed in recent years and (unabashed) greed is no longer good. One hopes that increasingly high costs of misconduct will turn out to be the best, the most effective nudge financial services could ask for.

There’s a well-known saying that no man is an island; likewise, no business is an island which can exist ignoring changes in their customers’ lifestyles, values, and preferences (at least not for very long). Consequently, financial services had to start taking notice of pressures, trends, and opportunities in the ESG space. On the banking side that comes down to good, old-fashioned lending and project finance; and when a wind or solar farm begins to look competitively (or even favourably) compared to another mine or oil rig, then financing is a matter of common business sense, with huge intangible benefits in the shape of PR, publicity, investor relations, etc. etc. On the investment side certain assetscan – over a relatively short period of time – become highly unfashionable. Big Tobacco was first, around 1980’s / 1990’s, followed in more recent years by cases of high-profile (albeit sometimes delayed, and not always full) gradual divestment from fossil fuels (Norwegian sovereign wealth fund being the best-known example).

A unique problem of ESG is the difficulty of monitoring and scoring entities and investments, especially multinationals operating in multiple markets. Glock and Kalashnikov are fairly unambiguous, but what about EADS and Boeing with their defence and missile arms? Coca-cola seems rather neutral in terms of its environmental or social impact, but what about the impact of corn (for corn syrup) plantations or contribution to the obesity epidemic? Environmental and social impacts can at least be *somewhat* quantified, but what about governance? What defines good governance? Consistently beating quarterly EPS expectations? Low employee turnover? Paying high taxes? Avoiding high taxes? That problem isn’t new, it’s just becoming increasingly prominent – and with an investment decision being binary (you either invest in something or not; you can’t half- or three quarters-invest) there is no immediate solution in sight. London Business School’s Associate Professor of Strategy and Entrepreneurship Ioannis Ioannou eloquently captured this conundrum during a recent sustainability event: “I’m an academic researching sustainability and governance, I can see my pension portfolio on my mobile, but I can’t see its ESG breakdown”.

There are competing analytics vendors in purely commercial space, but there is also one alternative, more “grassroot’y” approach: B Corp. Awarded by non-profit B Lab organization, B Corp certification (B standing for “beneficial”) is a quantitative (score-based) measure of given company’s accountability, sustainability, and value added to the society. As of 2018, it’s still a somewhat niche designation, but it’s highly recognised in the ESG circles. It also carries a certain cachet which organisations increasingly see as an exclusive and prestigious differentiator. Furthermore, certification is inexpensive, which means low barrier for small organisations and reduced conflict of interest for large ones (they can’t be accused of buying a B Corp certification – with the cost being low, it’s more a matter of earning than buying it). Another interesting initiative is Natural Capital Coalition, which is a business management framework taking into account both impacts and dependencies on nature. For a small organisation NCC has been really successful at signing up large corporates such as Burberry, EY, Deloitte, Credit Suisse, (part of) university of Cambridge, Nestle or – somewhat unobviously – Royal Dutch Shell (in all fairness, it’s just implementation of the framework, which may or may not inform future actions, but still, it’s a promising start).

Still, despite certain “fuzzy logic” issues, many investment decisions can be made with a degree of confidence based on company profile, its core activities, its industry, and lastly its reputation. Overall business environment also seems to be moving, fairly quickly, towards increased adoption of ESG metrics and/or principles.

Earlier this year European Commission released first proposals of EU-wide framework facilitating sustainable investment (with a proposal to develop a clear ESG taxonomy being an added bonus and proposal to link remuneration to sustainability targets a literal one) while Bank of England issued recommendation for climate risks to be factored into broader credit risk framework. On the business side, there are almost daily developments, with: UBS Asset Management recently rolling ESG data for all its funds (May-2018), Nutmeg (UK’s largest robo-advisor) doing the same in Nov-2018, or fund giant BlackRock adding 6 UCITS funds to its growing family of sustainable iShares (Oct-2018).

The push for wider adoption of ESG investments and metrics is not going without some hurdles. A number of industry bodies (Alternative Investment Management Association [AIMA], ICI Global, European Fund and Asset Management Association) pushed back on European Commission’s recommendations. The pushback focuses on competitiveness, demand and materiality and relevance of ESG disclosures. It’s a slightly peculiar situation where many firms openly advocate and push ESG agenda while trade bodies speaking on their behalf are much less enthusiastic. It may be that industry-wide consensus is not exactly here yet; it may also be that some asset managers feel that they have no choice but to be (officially) ESG advocates, while in private they do not necessarily share the sentiment quite as much. Secondly, there is no clear conclusion as to whether ESG funds outperform, underperform, or perform at par with their non-ESG counterparts; there simply isn’t enough historical data to make a conclusive and statistically meaningful determination.

My little foray into ESG ended on an unexpectedly profound and emotional note. London’s Science Museum (alongside Royal Institution and Patisserie Valerie one of my happy places) held a special one-off screening of Anote’s Ark, a documentary chronicling titular character (Anote Tong, then-president of Kiribati) crisscrossing the globe and walking the corridors of power looking for practical solutions for Kiribati and its 110,000 nationals as their small island state is being gradually submerged and deprived of fresh water by not-so-gradually rising ocean levels. Seeing this spectacularly beautiful, benign and extremely vulnerable island paradise – and, more importantly, a home to its inhabitants – literally disappearing underwater was profoundly upsetting and put all things ESG in a completely different perspective.