The Impact of Large Institutional Investors on Financial Markets

This is the conclusion from, “The Granular Nature of Large Institutional Investors” by Ben-David, Franzoni, Moussawi and Sedunov, NBER May 2016.

In this study, we provide novel evidence that large asset managers have a positive causal impact on the volatility of the securities in which they invest. The result is economically significant: a 1% increase in stock ownership leads to an increase in stock volatility of about 12 to 18 basis points, relative to a daily average of 3.5%. This finding does not seem to only be the result of greater information production or faster price discovery. In fact, the presence of large institutions correlates with lower price efficiency, as the stocks in which they trade have higher absolute autocorrelations of returns. In addition, the stocks in the portfolios of large institutions display abnormal return co-movement.

In studying the origins of this effect, we provide evidence suggesting that the trading volume of large institutions generates a large price impact. Moreover, we find that large institutions’ trades are, on average, less diversified than the trades of a control group of smaller institutions with the same combined assets, which can explain their greater price pressure. Although large firms’ trades become less concentrated over time, the effect of interest remains significant even in the latest years of the sample. Finally, we show that the flows to the funds under the same institutional umbrella are more correlated than the flows to funds belonging to different families. This result provides one potential explanation for why the different units within an institution trade in a less diversified way than a set of independent institutions.

We believe that these results are informative for regulators. The evidence suggests that large institutional investors are more likely to destabilize financial markets than a set of small institutions that trade in a less correlated way. The effect that we find is likely to be exacerbated during times of financial crisis when large trades are executed in an illiquid market. Any policy prescription cannot, however, overlook the beneficial role played by large institutions in terms of economies of scale, information production, corporate governance, and liquidity provision. These other dimensions deserve further investigation to assess the overall impact of large financial institutions on financial markets. Hence, we see the main contribution of our empirical work as drawing attention to the special role played by large institutional investors in today’s economy.

Multi-factor Investing

A recent paper by Robert Novy-Marx discusses problems with multi-factor investment research. The author highlights how biases enter the research process by not accounting for the number of variations that were considered before arriving at a final model.

The key to eliminate this bias is to avoid incorporating future information. This is basic statistics 101. It’s easy to say go long Apple in 2006 with 10 years’ hindsight, less so with only the information at hand, at the time. Signals are similar: the question is whether you would put weight on a signal only with the information available at the time.

This requires a move away from the usual static weight approach to a more honest weights algorithm. As it’s almost impossible to go back in time and ignore all subsequent information to arrive at a gut-derived answer (the way it usually is arrived at) it must be quantified. Many algorithms are available (examples here), but the majority of them will probably rely on a combination of performance and risk to date, with either performance or risk getting greater emphasis depending on the algorithm. While some algorithms may be found that explicitly forecast factor reversal, the design of most factor investing is to be permanently on one side of the trade, which makes this a trend-following style, just on a different level of abstraction.

This offers a better solution than the main remedy in the paper. While increasing thresholds for t-statistics is one solution to go by, avoiding any peek-ahead in the selection and weight setting process in the first place is probably much better. Using ever evolving weights, the in and out of sample are always separated. However many versions of a signal you construct, you are free to select the best one and optimize the weight in the past, the true evaluation always happens out of sample with previously unseen data. Machine learning in time series analysis should follow a similar pattern.

Multi-factor investing is a positive thing. After decades of academic studies proclaiming that markets are efficient after taking (ever decreasing) transaction costs into account, you can avoid a big chunk of these by netting trades between strategies. At the same time, you can boost risk adjusted returns. You can dial up and down what you care about more (diversification or return) based on the weights algorithm you choose. You’ll likely end up in a better place than just using a single factor.

But wait a minute

Probability of The Unforeseen

Lord Mervyn King, the former governor of the Bank of England was once asked about the low interest rates and whether they were good or bad. He answered that they were good, because that meant young people can take out mortgages to buy houses. “But wait a minute”, was that what really happened? Not quite. As a result of the low interest rates non-young people started buying to rent and this drove the house prices even higher, putting the house market out of reach for the young people who were trying to get onto the housing ladder.

Was this an oversight by Lord King? Perhaps. It is hard to predict the future, but more often than not there are unforeseen consequences. Although we could not know beforehand what these might be, we should always assume the probability of the unforeseen happening to be not zero.

What is the usefulness of this assumption? It’s not as if we could prepare for the unforeseen or as the phrase often attributed to Donald Rumsfeld, “unknown unknowns”. Instead, by always having this at the back of our minds, hopefully our decisions are imbued with greater prudence and diligence, aware that the outcome of our decisions may materialise within a range and not in an accurate, specific bulls-eye way as we often wish it would.

Local versus Global

In a brief essay, Marti Leimbach writes about her hard life, arguing that privilege does not come automatically just by being born white. Despite sympathising with her I thought, “but wait a minute”, when making a case shouldn’t we first differentiate whether the points she makes are local or global?

Whereas her situation was due to bad luck and localised to her person, bad luck that could have fallen on anyone, the negative effects of racism (and yes, that includes lack of privilege), sexism and all other discriminatory ‘isms’ do apply universally based on colour, gender, sexual orientation or class independent of personal situations and luck. To conflate your own personal situation with society-wide challenges does not advance the discussion on the definition and exclusivity of ‘privilege’. People often try to pick out a single abnormality to disprove a whole case, especially those who write to distract the readers from the real issue.

Even in mathematics a distinction is made when describing local and global solutions. Every additional constraint which might appear as the problem demands, would require the narrowing down of the set to one or a few specific solutions of the formula, away from the global optimum. On the other hand, if you start from the vantage point of a local optimum, you may wrongly extrapolate that this is the global solution too.

Proper Sample Size

“But wait a minute” thinking may also help to prevent us from jumping to conclusions. Let’s say that a person attends an interview equipped with high recommendations from previous employers and a nearly flawless record performance of many years. The interviewer for some reason or another then summarily dismisses the person based on this single interview. Is this outcome correct?  Can suitability for a job be determined based on one interview?

Alternatively, if someone is recruiting an athlete and dismisses him as a candidate based on a single field performance, we would say “but wait a minute” that’s ridiculous, that’s not enough observation to know whether he is a good athlete or not. Some would even say that this is not fair, we have to see more of him on the field. It could be that that day he was ill or still recovering from an injury.

To come to the right conclusion and therefore outcome, we need to have a proper sample size suited to the situation. Here, I’m reminded as well of a lecturer at the MIT who acknowledges this by allowing his students to take the better marks of the two major exams, saying that “Everyone has a bad day!”.

In case you wonder, Google, who is well known for measuring everything, found from their research that the marginal benefit of an additional job interview diminishes after the fourth, so maybe there is value to making the intangibles measurable after all.

At this point you might think “but wait a minute” is just a disguise for adopting good mathematical practice in your thinking, and indeed you may be right. Leonardo Da Vinci said, “No human investigation can be called true science without passing through mathematical tests; and if you say that the sciences which begin and end in the mind contain truth, this cannot be conceded, and must be denied for many reasons.”

‘But wait a minute’ thinking as the phrase implies, is about taking a pause after we have come to a conclusion and questioning whether it was the right one. This one minute of self-check is perhaps sixty seconds longer than most people would ever give themselves the luxury to ponder.

Ghost in The Shell

A series of tweets by Jon Tsuei:


I’ve been seeing a lot of defenses for the ScarJo casting that seem to lack a nuanced understanding of a Ghost In The Shell as a story.

The manga came out in 1989, the first film 1995. An era when Japan was considered the world leader in technology.

Everything hot in that era came out of Japan. Cars, video games, walkmans, all of that. Japan was setting a standard.

This is a country that went from poised to conquer to the Pacific to forcibly disarmed. They poured their resources into their economy.

And as a country that was unable to defend themselves, but was a world leader in tech, it created a relationship to tech that is unique.

Ghost In The Shell plays off all of these themes. It is inherently a Japanese story, not a universal one.

This casting is not only the erasure of Asian faces but a removal of the story from its core themes.

You can “Westernize” the story if you want, but at that point it is no longer Ghost In The Shell because the story is simply not Western.

Understand that media from Asia holds a dear place in the hearts of many Asians in the west, simply because western media doesn’t show us.

Ghost In The Shell, while just one film, is a pillar in Asian media. It’s not simply a scifi thriller. Not to me, not to many others.

Respect the work for what it is and don’t bastardize it into what you want it to be.



Coaling China

From the Economist,

FIRST a tsunami of steel—next a flood of what? Industrialists all over the place might look nervously at China’s cooling economy and ask that question. The global glut in steel is most alarming because China’s industry dwarfs all others and its mills could easily produce more. Yet other sectors also have existing or looming gluts.

One is coal. Thanks to a massive expansion now under way, China’s coal industry could have 3.3 billion tonnes of excess capacity within two years, reckons Fitch, a rating agency; domestic consumption is less than 4 billion tonnes a year and dropping. Traditionally China has imported, not exported, coal—but that could change. Shenhua Energy, the country’s biggest coal miner, says it might export 10m tonnes soon, up from 1.2m tonnes last year.

A report by JP Morgan states,

The Chinese government is working to reduce coal’s share in its powergen mix by one percent to 63% this year. It is also taking steps to curtail as much as a billion tonnes of excess coal production capacity over the next few years. However there is about a billion tonnes of new capacity expected to come online over the next two years likely offsetting the capacity cuts. Though we see the anti emissions policies as real and China recently halted 15 “under construction” coal-fired power plant projects in Mongolia and Shanxi. Another emerging risk for the seaborne thermal market is of rising Chinese coal exports. Shenhua is considering exports of as much as 10mt of coal to Korea and Japan which, given lower freight costs, could hurt coal demand from Australia and Indonesia.

Maybe that’s why…

Lessons from Recent Market Turbulence

From Gavyn Davies, Lessons from recent market turbulence,

What, then are the main lessons from this turbulent period? Several spring to mind.

The global economy is not quite as weak as it appeared early in 2016, though this needs continuous assessment;

The Federal Reserve can readily be dissuaded from tightening monetary policy if global economic conditions deteriorate, or market turbulence returns;

The Fed is not “out of ammo” because actual and forward nominal interest rates are positive and can be impacted by policy statements;

Chinese foreign exchange rate policy may be particularly important in affecting the Fed’s thinking, though it is highly unlikely that any formal “agreement” has been reached between the US and China on currency policy;

Japan is now facing a crisis of confidence in Abenomics that needs to be quickly addressed through monetary, fiscal and exchange rate policy;

The ECB may find it quite difficult to banish deflation risks, since markets no longer seem to be impressed by increases in asset purchase programmes by central banks, at least when bond yields are below zero;

There are very serious disadvantages with negative interest rate policies, which can actually be counter-productive;

Expansionary fiscal policy, and maybe even helicopter money, would probably creep onto the policy agenda if there were a renewed economic downturn in the eurozone and Japan.

It is very good that we are able to look back and learn some important lessons on coordination and spillovers, but now we must look ahead and form prudent strategies, keeping in mind inter-generational effects and global contagion risks.