Johan Fourie's blog

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Posts Tagged ‘finance

How finance evolves

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Evolve

Why is it that stock markets tend to be depressed during winter months? Or that investors with too little emotional response (or too much) tend to be less profitable than those with just the right amount of emotion? Or that traders tend to make more money on days when their levels of testosterone are higher than average?

In a fascinating new book, Andrew Lo builds on the corpus of behavioural science research to outline a new theory of financial markets. His basic point: homo economicus is dead. The hyper-rational human that always optimized every decision, most famously portrayed in the Efficient Markets Hypothesis of Eugene Fama that has ruled the field of finance at least since the 1980s, does not exist. His new book, Adaptive Markets: Financial Evolution at the Speed of Thought, explicates his Adaptive Markets Hypothesis, first proposed in 2004 as a substitute for the Efficient Markets Hypothesis.

In short, the Adaptive Markets Hypothesis accepts that humans are biological beings, and that our biology limits our ability to optimize every decision as the Efficient Markets Hypothesis predicts. Most importantly, though, our ‘irrationality’ is not random. This means that we consistently make the same ‘mistakes’, something that behavioural scientists have known for quite some time. One of these mistakes, for example, is that we often link events together because they happen to occur close to each other. As Lo puts it: ‘We humans are not so much the “rational animal” as we are the rationalizing animal. We interpret the world not in terms of objects and events, but in sequences of objects and events, preferably leading to some conclusion, as they do in a story.’

Telling stories is one way we try to make sense of the world, even if those stories are sometimes false. We do this because, given the environments that we encountered, this was the most evolutionary successful behaviour. But that has consequences: If our environment change, our biological decision-making processes might not be equipped to deal with the new environment. In Lo’s words: ‘“Rational” responses by homo sapiens to physical threats on the plains of the African savannah may not be effective in dealing with financial threats on the floor of the New York Stock Exchange’.

Often the real world is not very different from the survival-of-the-fittest world our ancestors encountered on the African plains. Many times, humans do optimize their behaviour. This is why the Efficient Markets Hypothesis could hold for so long, treating ‘irrational’ behaviour as random outliers that will be averaged out in the marketplace. But as Low demonstrates in countless examples, often humans (and by implication traders) behave ‘predictably irrational’, reacting to fear systematically different than to reward, for example, and opening opportunities for windfall profits on the financial markets. That is why some famous investors, accounting for these predictably irrational heuristics of humans, can be consistently successful.

The good news, though, is that we are not like other animals. We do not have to wait for evolution to take its course, molding us to our environment through natural selection. We have the ability to learn and adjust through trial and error. High-frequency trading is a great example: speed is everything in financial markets, and automated trading programmes have replaced specialist human traders who are just too slow to recognize and respond to the predictably irrational human errors. But even this is changing, as Lo explains: ‘At first, these high-frequency traders made windfall profits, since human specialists were sluggish and inefficient in comparison. However, there ultimately came a point where high-frequency traders were mainly competing with each other. To succeed in this financial arms race, high-frequency trading firms had to invest in faster and more expensive hardware. At the same time, however, these firms were scouring the market for any trace of “juice” that might be left. In a very short amount of time, high-frequency trading was pushing against its natural evolutionary limits. It had unexpectedly become a mature industry, with low margins on trades and low overall profits.’ High-frequency trading is now on the decline, as more and more exchanges start implementing ‘no high-frequency trading zones’. The environment is changing, and those high-frequency traders that do not adapt, will perish.

The book presents not only a fascinating new theory that can explain why some investors continue to be successful despite the prediction of the Efficient Markets Hypothesis, but it also situates this theory within the context of broader developments in finance. We learn why the Efficient Markets Hypothesis was so appealing, why earlier attempts to use evolutionary thinking in finance never caught on, and what this new theory might say about the future of finance. It also has a cautionary word about how we train the next generation of finance gurus: ‘For the mathematically trained economist, it’s sometimes difficult to think in evolutionary or ecological terms, but sooner or later, this way of thinking will be domesticated (another biological metaphor), and will become another standard tool for economists to use, just as molecular biologists use it today.’

Just like the finance industry employed mathematically-inclined engineers and physicists in the last few decades, perhaps biology will be the training-of-choice for the next generation of investment firms. Perhaps. What we do know is that the environment is changing, and that means that traders will have to adapt too if they are to survive, and thrive. As Lo explains: ‘An evolutionarily successful adaptation doesn’t have to be the best; it only needs to be better than the rest.’ Let the games begin!

*An edited version of this first appeared in Finweek magazine of 7 September 2017.

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Can Twitter predict the markets?

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Twitter-PPC

Ask anyone about the pitfalls of Twitter, and they might point to recent gaffs by a prominent South African politician as evidence that the dangers outweigh its benefits. But such warnings have not stopped many others, most notably the president of the United States, from tweeting on a regular basis: Twitter’s user base creates more than 500 million tweets a day, and it has added about 2 million new users in the last quarter of 2016.

Presumably this wealth of information must have some value. Twitter, sadly for its shareholders, struggles to turn such growth into profit: in the last quarter of 2016, revenue growth was only 1%. But because it captures public sentiment at a very granular level, it has attracted the interest of both scientists and entrepreneurs hoping to turn this information into public or private benefit.

The use of social media for prediction is, of course, not a recent phenomenon. Google Flu Trends, founded in 2008, used Google’s search engine to track the spread of flu in 25 countries. But excitement about the project waned as it struggled to make accurate predictions. A 2014 Nature paper noted the value of social media ‘Big Data’, but warned that ‘we are far from a place where they can supplant more traditional methods or theories’.

Twitter, though, seems to attract increasing attention. A 2014 paper uses Twitter to predict crime. A 2015 paper show how psychological language on Twitter predicts heart disease mortality. Another 2015 paper show how Twitter sentiment predicts enrollment of Obamacare. A 2016 paper show how Twitter could be used to predict the 2015 UK general elections.

But it is, understandably, the financial markets that has attracted the most attention. A 2016 paper by Eli Bartov (NYU Stern School of Business), Lucile Faurel (Arizona State University) and Partha Mohanram (University of Toronto) shows how Twitter can predict firm-level earnings and stock returns. They use a dataset of nearly a million corporate tweets by 3662 firms between 2009 and 2012, all tweeted in the nine-trading-day period leading to firms’ quarterly earnings announcements. The authors find, unsurprisingly, that the tweets successfully predicts the company’s forthcoming quarterly earnings, but find, more surprisingly, that the tweets predict the ‘immediate abnormal stock price reaction to the quarterly earnings announcement’. These findings are more pronounced for firms in weaker information environments, such as ‘smaller firms with lower analyst following and lower institutional ownership’, and are not driven by concurrent information from sources other than Twitter, such as press articles or web portals.

It makes sense that corporate communication provides information, but can public sentiment on Twitter also inform market activity? A 2017 NBER Working Paper, by Vahid Gholampour (Bucknell University) and Eric van Wincoop (University of Virginia), answers this question by looking at the Euro/Dollar exchange rate. They start with all Twitter messages that mention EURUSD in their text and that were posted between October 9, 2013 and March 11, 2016. There were 268 770 of these messages, for an average of 578 per day. What they hope to do, is to identify whether informed opinions about future currency changes can actually predict actual currency changes, so they eliminate all tweets that do not express a sentiment about the future behaviour of the two currencies. This reduces the sample to 43 tweets per day, or 27 557 in total. They then classify each of these tweets as positive, neutral or negative using a detailed financial lexicon that they develop to translate verbal tweets into opinions, and create a Twitter Sentiment index for each day. They also split the sample in two: those opinions expressed by individuals with more than 500 followers, which they call the ‘informed opinion’, and those with fewer than 500 followers, which they call the ‘uninformed opinion’.

So what do they find? It turns out that the 633 days of data they have is too short to calculate the Sharpe ratio, a measure of the risk-adjusted return. The annualized Sharpe ratio based on daily returns is 1.09 for the informed group and -0.19 for the uninformed group. The Sharpe ratio of 1.09 for the informed group is impressive, but it has a large standard error of 0.6. The 95% confidence interval is therefore very wide, ranging from -0.09 to 2.27. They then construct a model with a precise information structure, estimate the parameters and then recalculate the Sharpe ratio to average at 1.68 with a 95% confidence interval between 1.59 and 1.78. Success: ‘the large Sharpe ratios that we have reported’, they conclude, ‘suggest that there are significant gains from trading strategies based on Twitter Sentiment’.

If all this sounds terribly complicated, that is exactly the point. Translating opinions into numbers is not an easy undertaking, and discerning the ‘informed’ opinions from the noise is even less so. But there is no doubt that Twitter does offer some useful, perhaps even lucrative, insights. Whoever can exploit that knowledge first, stand to benefit most.

*An edited version of this first appeared in Finweek magazine of 20 April.

Written by Johan Fourie

May 16, 2017 at 06:24

Do CEOs deserve their high salaries?

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Whitey Basson

Late last year, Bloomberg reported that South African Chief Executive Officers earn the 7th most of any country in the world – a whopping R102 million per person per annum. This was equivalent to 541 times the income of an average South Africa. The study, understandably, caused some outrage.

These numbers have been disputed, mostly because they include CEOs who earn in foreign currency. A study by 21st Century Consultants found that the CEO salary of the median large cap South African firm in 2016 was less than R6 million, roughly 5% of the Bloomberg average. PwC found the median between R3.1 and R7.7 million.

But even at these lower levels, many ask whether CEOs deserve what they earn. Do the value that they add outweigh the millions spent on salaries and bonuses? This, of course, is an incredibly complex question. Economists have no laboratory where they can randomly assign CEOs their salaries, and see what the likely outcome might be. Instead, we have CEOs that respond to the firm’s internal and external demands in various ways, planning, strategizing, meeting and organizing. Which of these activities adds more value seems impossible to determine.

That is, until now. A new study by four economists – Oriana Bandiera (LSE), Stephen Hansen (Oxford), Andrea Prat (Columbia Business School) and Raffeala Sadun (Harvard Business School) – measure the behaviour of CEOs in Brazil, France, Germany, India, the UK and the US, and compare these measurements to their firm’s performance. They do this using a two-stage method: first, they collect the weekly diaries of 1114 CEOs in the six countries. These diaries include detailed information about the hourly activities of each CEO: with whom they met, the number and duration of plant/shop-floor visits, business lunches, how many people joined, and the functions of these participants (whether they were in finance or marketing, for example, or clients or suppliers).

Their finding is that CEO activities differ remarkably across firms. While CEOs spend most of their time in meetings, they ‘differ in the extent to which their focus is on firms’ employees vs outsiders, and within the former, whether they mostly interact with high-level executives vs. production employees’.

The authors then use a machine learning algorithm to create an index of CEO behaviour. At low values of the index, CEOs spend more time with production and in one-on-one meetings with employees and suppliers, and at high values CEOs spend more time with executives and in meetings with more participants.

The authors note that there is no theoretical reason for one type of behaviour to lead to better outcomes. That such different types of behaviour exist may just be a consequence of the fact that firms require different types of CEOs, i.e. some firms will do better with a low-index CEO while others would do better with a high-index CEO. When CEOs are perfectly matched – or ‘assigned’ – to the type of firm that suit their style, there should be no correlation between the index-value of a CEO and the firm’s performance. In other words, a low-index CEO matched to a firm that will benefit from a low-index CEO style would perform just as well as a high-index CEO matched to a firm that will benefit from this CEO style type.

The results, however, shows the opposite. High values on the CEO index are strongly correlated with higher firm productivity, a measure of firm performance. CEOs who spend most of their time in meetings with senior executives, engage in communication (phone calls, videoconferences, etc.), bring together inside and outside functions, and bring together more than one function of a kind are also more likely to lead more productive firms.

Their results also show that CEOs are often not matched to the right firm: “Our estimates indicate that, while low-index CEOs are optimal for some of the sample firms, their supply generally overstrips demand, such that 17% of the firms end up with the ‘wrong’ CEO.”

More importantly, it is in the two developing countries in their sample – Brazil and India – where this matching is especially bad: 36% of firms in those countries end up with the ‘wrong’ CEO compared to the only 5% in the four developed countries. “The productivity loss generated by the misallocation of CEOs to firms equals 13% of the labour productivity gap between high and low income countries”.

The authors do not speculate on why this difference exists. One likely reason is weaker competition for top jobs within a thinner talent pool owing to the unequal levels of education in these countries. Another may be that appointments happen for reasons other than merit.

What the study does show, though, is that the choice of CEO is critical for firm success. Appoint the wrong type of CEO, and productivity is likely to decline. Although some firms benefit from a CEO who frequently has one-on-one conversations and visits the production floor, most firms benefit from a CEO who spend their days leading large meetings with top executives from different fields.

That helps to explain the high salaries for CEOs in South Africa too. A mismatch between CEO and firm is costly and seems to happen quite frequently. The small talent pool means that most firms are willing to pay exceptional salaries to those rare individuals with a high CEO index-value. If they don’t, the firm is likely to suffer far more costly productivity losses.

It also points to the dangers of policies that hope to place an upper-bound on managerial remuneration. Lower levels of remuneration will likely lead to fewer CEOs with high index-value, and to higher levels of mismatch between CEOs and firms. That, as the authors show, will be devastating for firm-level productivity, and economic development. Beware the unintended consequences of policies made with good intentions.

*An edited version of this first appeared in Finweek magazine of 6 April.

Written by Johan Fourie

May 1, 2017 at 05:57

Is our financial sector too big?

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Roads to prosperity? Sandton is the hub of South Africa's financial industry

Roads to prosperity? Sandton is the hub of South Africa’s financial industry

Most economists would agree that a growing economy requires a well-functioning financial system that is able to move capital between its owners and those who need it. The larger the financial sector, the argument goes, the more likely it is that capital will be efficiently allocated, and the better for the economy. Of course, the same is true for other intermediate services, from law and consulting to auditing and marketing, which performs intermediate services that helps firms to specialise, and flourish.

But a new working paper by economists Stephen Cecchitti and Enisse Kharroubi at the Bank for International Settlements questions this logic. They argue instead that a too-large intermediate sector (they specifically refer to finance) can actually hurt growth. Neoclassical theory argues that mergers and acquisitions (M&As) create value through the takeover of undervalued products, as typically recognized through stock market valuations. The larger the financial sector, the more resources are available for these transactions to take place.

There are two caveats to this. First, instead of focusing on the long term value of a firm, executives often embark on M&As to further their own short term gain, e.g. prestige and increased compensation of managing a large firm. Second, and independent of M&A activity, the larger the financial sector, often the more complex it becomes and the more resources must be spent to analyse and understand it. And sometimes, despite these resources, it still spins out of control, as in 2008.

Cecchitti and Kharroubi finds that there is a threshold beyond which growth of the finance industry actually reduces total factor productivity growth. All developed economies are already beyond this threshold, they find, and provide evidence of a clear negative correlation between financial sector growth and R&D-intensive industries. One mechanism through which this happens is that finance consumes resources that could have been utilised more productively in other sectors. A complex financial system needs highly-qualified engineers, for example, clever people that could have been employed in research industries that would have had a bigger impact on society.

This is worrying for a country like South Africa where financial and other intermediate services are, like the US, a large part of the economy. The more finance and other intermediate service firms employ our smartest students (a precious resource), the fewer there are of them to start their own businesses producing stuff that we can export, or doing research that can invent new things. I’ve seen this myself: the largest consulting firms pilfer our best graduates (promising the incomes and status that come with these jobs – and the luxurious Sandton offices) at the expense of far less appealing jobs in industries that our economy desperately need. Who wants to work in a factory anyway?

In their book Concrete Economics, Brad Delong and Stephen Cohen explain why the finance industry grew so rapidly, from roughly 3% in 1950 to almost 9% of US GDP today. It happened as a result of the deregulation that already began in the 1970s but intensified in the 1990s. Some of this was good, like the innovation of low-cost brokerages and low-cost investment funds, just like the deregulators had hoped. Unfortunately, these were the exceptions rather than the rule. Financial intermediaries soon realised that it is much easier to promise clients that ‘they could beat the market and become rich’ than provide value to their clients by ‘soberly matching risks to risk-bearing capacity’. And so, instead of charging lower fees which would benefit investors, a freer market made financial intermediaries move into fancy office blocks, recruiting the smartest minds, and charging higher fees as a signal that their portfolios are the ones with the best returns.

In South Africa, I would venture that this also happened in other intermediate sectors, like auditing and consulting. Between 1981 and 2006, our service sector increased by 42%. Finance may have benefited from deregulation, but the tightening of accounting standards and other types of well-intentioned regulation to safeguard businesses from fraudulent practices meant that these highly concentrated industries had a captured market for their services.  High prices – and Sandton office towers – followed.

But, as DeLong and Cohen aptly summarise, ‘nobody eats the advice of M&A strategists’ (or the audits of accountants, or the powerpoints of consultants). Our large intermediate services sector means that we have fewer innovative firms that can produce products and services to sell to a global audience. Our best minds should be developing new genetically-modified crops or mobile apps, not more complex financial instruments.

How we fix this is a more difficult question. It is unlikely that change will come from within these firms; in fact, expect lobbying for more rules and higher standards which require bigger teams of experts selling better advice. Why kill the goose that lays the golden eggs? A concerted effort by government is instead necessary to reduce the demand for and market power of these intermediate services firms. Reducing excessive bureaucratic red tape can help with the former. Competition policy can help with the latter.

Perhaps the emphasis should instead be on growing other sectors, specifically manufacturing. But what regulators should realise is that, unlike fancy office towers, bigger is not always better when it comes to finance and other intermediate service industries.

*An edited version of this first appeared in Finweek magazine of 5 May.

Written by Johan Fourie

May 26, 2016 at 07:22