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.

Differences in Usage Among ETF Investors

From DB Markets Research,

Institutional ETF Ownership nears 60% at the end of 2015
Institutional investors continue to jump into ETFs. As of the end of 2015, we estimate that 59.3% of ETF assets were owned by Institutional Investors up from 58.3% at the end of 2014, and up from 43% ten years ago. Investment advisers continue to dominate ETF usage with 29%, while Mutual Funds, Pension Funds, and Insurance Companies continue to increase their ETF utilization. Over 3,100 institutions had over $1.2 trillion invested in over 1,500 ETFs as of the end of last year. Our detailed ownership analysis reveals significant differences in usage among ETF investors.

Product usage suggests investors see multiple value propositions in ETFs
Not every type of investor uses ETFs for the same purposes. Some institutional investors use ETFs as a core strategic investment, others use them as a liquidity or hedging tool, and others may use them as a short-term access product to gain quick exposure or equitize cash.

Investment Advisers use ETFs mostly as core investment
Investment Advisers usually prefer very cheap products which usually track plain-vanilla equity and bond indices tracking major model portfolio building blocks. Investment Advisers tend to run simple asset allocation model portfolios which tend to be very scalable, and don’t experience significant turnover, therefore clean asset class exposure and cost tend to be key on the selection of products. The low product concentration relative to other institutional groups also suggests a very broad selection universe and satellite investment usage. Vanguard and iShares Core ETFs are very popular among this group.

Mutual Fund and Hedge Fund managers mostly use ETFs for operational efficiency
We believe that the most common usages of ETFs by Mutual Funds,in order of relevance, are: cash equitization, liquidity management, and core investment; while Hedge Funds mostly use ETFs for gaining quick and efficient access both on the long and short side to their desired asset class, similar to futures contracts. In general, the nature of these groups’ ETF usage leads to shorter holding period and higher portfolio turnover of ETF positions.

Pension Funds seem to be using ETFs for reducing the complexity embedded in multi asset global allocation mandates
ETFs are usually seen as a way to obtain access to different asset classes in an efficient way, and therefore ETFs tend to play a more strategic role in pension portfolios. Usually pension funds use ETFs as building blocks for more efficient financial markets, or markets that are difficult to access. Given the more strategic nature of ETFs in pension portfolios, turnover tends to be somewhere in between Investment Adviser and Mutual Fund. A combination of factors including exposure, liquidity, size, and cost is usually more important than any single selection criterion on its own.

Let’s Discuss Earnings

Price over earnings is the most common ratio quoted in valuing companies. Is it difficult to measure this ratio? After all, these figures are fully disclosed in earnings releases. The answer is, looking at the figures – just the figures – is not enough. When looking at earnings, we have to keep in mind two things, the intention of the discloser, and the quality of the earnings disclosed.

qualityearnings

Source: https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/12923/FAJ%20EQ%20WP.pdf

 

In Value Destruction and Financial Reporting Decisions, FAJ (Nov/Dec 2006), by John Graham, Harvey Campbell and Rajgopal Shiva,  found that “the destruction of shareholder value through legal means is pervasive, if not a routine way of doing business. Indeed, we assert that the amount of value destroyed by firms striving to hit earnings targets exceeds the value lost in these high profile fraud cases.”

There are many ways that managers can manipulate real activities of companies in order to avoid reporting losses. As per the model in Dechow et al. (1998), this is done through:

  • price discounts,
  • temporarily increasing sales,
  • overproduction to report lower COGS,
  • reduction of discretionary expenditures to improve reported margins.

In addition, Graham, Campbell and Shiva also included:

  • delaying or cancelling valuable investment projects,
  • cutting R&D,
  • shirking on maintenance expenses,
  • decreasing marketing expenditures.

The difficulty for outsiders in the midst of all this manipulation is therefore, how to determine the quality of earnings presented by insiders. On the flip side, how would managers (insiders) effectively communicate the quality of their earnings?

Here, most CFOs will insist that their earnings are revenues minus properly matched expenses. At first glance, emphasis on matching makes sense. However, standard setters believe that, “the idea that matching is important is somewhat misleading. Historical cost accounting necessarily involves allocating costs or benefits over some accounting period. However, we never do matching right. Most firms use straight-line depreciation. How can that reflect good matching?”¹

If the ideal solution lies in proper matching and we could never do matching right, or uniformly, or even objectively, hence, the quality of matching as a signal for quality earnings does not work in practice.

Then there is the question of whether we wish to take away managers discretion in reporting. There are two different directions which we could take from here, either to create yet more and more standards, or whether we should promote principles over rules-based accounting. Dichev, Graham and Shiva remarked in their paper, Earnings Quality : Evidence From The Field (2012), “81% of CFOs believe that the level of discretion today is lower than it used to be, suggesting that reporting discretion has been substantially reduced over time”.

There is no easy answer to how much discretion – too much, and it will be hard to judge the quality and open greater avenues for misreporting earnings, too little, and the report might as well be just a checklist of whether or not earnings meet the analyst consensus estimate.

According to Graham, Harvey and Shiva, “We would have thought that analyst consensus estimate would come out to be more important; and in the interviews, the CFOs told us that missing the consensus number leads to the largest stock price reaction”.²

What the earnings figure is benchmarked against is equally as important. The question of whether the huge stock price reaction is the market response to the information content in analyst consensus or the actual earnings figures, or the benchmarking process itself is still up in the ‘academic research’ air. It could very well be that institutional investors rely greatly on analyst consensus that any deviation from it, however slight, may move the market price.

misrepearnings

Source: https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/12923/FAJ%20EQ%20WP.pdf

 

Another metric to look at is cash flow. Eventhough for young companies sales growth may be more important than earnings, cash flow, is the lifeblood of any company be they young or old.

earnings

Source: https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/12924/Rajgopal_value.pdf

 

In addition, it could be that we not only have to look at figures in the reports, but also compare reports between similar companies for signs of anomalies. According to Sugata Roychowdhury, ceteris paribus, unusually low expenses or unusually high production cost compared to the others in the same industry can be useful flags.³

red flag

Source: https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/12923/FAJ%20EQ%20WP.pdf

 

As Shiva pointed out, are we being too myopic in emphasising earnings as the end all and be all metric? And does concentrating on frequent earnings reporting mean that we are encouraging managers to prioritise short-term over long-term projects? Should we instead focus on the integrity and process of financial reporting?

Myopic or  not, in light of how few metrics we can rely upon and systematically compare, any measure is a good measure if the alternative is none.

 

 

  1. Earnings Quality : Evidence from the Field
  2. Value Destruction and Financial Reporting Decisions
  3. Earnings Management Through Real Activities Manipulation

 

 

“In The Mood”

Reading BAML comments on the ECB “Rorschach test” yesterday,

It is even more ironic that this reaction occurred as Draghi acknowledged there were limits to how low negative rates could go before the banking sector would be harmed. Market commentary around negative rates had itself become quite negative in recent weeks, growing ever more vocal since Japan implemented NIRP. Draghi and colleagues surely felt they were doing the right thing in pivoting away from relying on ever more negative interest rates, and we agree. Yet it appears that this move was an important catalyst in the market selloff from around 2 PM London (9 AM NY) time.

Indeed, the selloff only makes sense if markets were pricing in a large positive boost from negative rates in the Euro area. And that may be one way to reconcile the euro and interest rate responses, assuming that more negative rates were already priced into the currency and the longer end of the yield curve. However, if markets did, in fact, believe that negative rates were a bad idea, then they should have celebrated the ECB’s confirmation that it would not perpetuate that “policy mistake,” not sold off.

So once again there is a puzzling contradiction within the market’s reaction. This leads us to suspect that markets were simply “in the mood” to sell off after the ECB’s announcements, and it did not take much of a catalyst to do so. In our view, it’s hard to argue that the program itself was a disappointment. True, it didn’t have everything the consensus was expecting — for example, the purchase period was kept “at least through March 2017” rather than extended for another six months as widely anticipated. However, objectively the program delivered a more-than-offsetting set of dovish surprises — which the initial market reaction confirmed.

Wisdom of Crowds and Corporate Strategy

Product Differentiation

From Stock markets, wisdom of crowds, and corporate strategy,

We consider the particular case of product differentiation choices by firms. By differentiating their products from rivals, firms can gain market shares and increase their value (see, for instance, Tirole 1988). We show, however, that differentiation can bear an informational cost, as it makes the information that a firm’ manager can extract from its stock price less informative. The reason is that while stock prices aggregate investors’ private information they also typically contain noise, which limit their how informative they are. We show that it is easier for investors to filter out the noise from stock prices when they observe the prices of several firms whose cash flows load on the same fundamentals (e.g. they are exposed to the same demand shocks). As a result, the stock prices of firms following similar product market strategies are collectively more informative (i.e. contain less noise). One direct consequence is that a firm constrains its ability to learn from the stock market if it differentiates too much from its rivals. Overall, the equilibrium levels of product differentiation and stock price informativeness in the economy are jointly determined.

One implication of our theory is that firms’ incentive to differentiate from rivals should increase when they publicly list their share on stock markets. In this case, managers switch from an environment in which they cannot learn from their own stock price to an environment in which they can. Thus, at the margin, an initial public offering mitigates the informational cost of differentiation, and therefore increases firms’ incentive to differentiate and opt for more unique product market strategies.

Earnings Differentiation

From Earnings Increases as a Type-Revealing Signal,

We argue managers use earnings increase as a signal to distinguish themselves from
their counterparts when sales are decreasing. Managers increase earnings by making changes to the firm’s business and through real and accruals earnings management all costly actions suitable for the production of a credible signal. That is, prior work has shown that when faced with increased demand uncertainty and demand shocks, managers adjust the firm’s cost structure or manage earnings. Thus, when managers want to attain earnings targets (avoid losses, reach analyst forecasts), they decrease cost stickiness; adjust slack resources downward quicker (e.g., Kama and Weiss, 2013); when faced with a negative demand shock they adjust their cost structure and engage in real earnings manipulation (e.g., Bourveau, 2015).

However, for the earnings increase signal to be credible, it needs to be costly. We posit that reporting earnings increases when sales are declining is both counter-intuitive and costly. It is counter-intuitive because a decrease in sales should be accompanied by a higher percentage decrease in earnings, given non-zero fixed costs. It is costly, because increasing earnings on decreasing sales requires managers to have operational and financial reporting flexibility, and such flexibility is limited in resource-constrained firms with deteriorating prospects.

 

A Question of Law and Wealth

In What is a Safe Asset?I wrote,

Not to be underrated is the role of law in defining and nurturing safe assets. […] The law not only upholds rights, but also ensures that the value creating activities of the asset continue to be legal and valid. A long list of other instances where without the backbone of the law, the safety of assets become a pointless study. The law, and hence, the institution that upholds the law, is important for the existence of safe assets.

This LSE public lecture discusses the issue between four experts, which I highly recommend listening for retail investors and professionals alike.

http://www.lse.ac.uk/newsAndMedia/videoAndAudio/channels/publicLecturesAndEvents/player.aspx?id=3421

The law plays a crucial part in the creation, proliferation, and distribution of wealth. Through private law institutions such as contract and property, but also through the criminal law (consider the numerous offences pertaining to wealth, such theft, fraud, money laundering) the law creates and regulates the categories making possible the exclusive relations between us and the world. In doing so the law also, at least indirectly, shapes social relations.

Questions of wealth creation and distribution have become particularly urgent since the beginning of the ongoing financial crisis. This also puts to the question the way in which law regulates wealth. Are corporations and financial markets sufficiently regulated? Is it even possible to regulate them by law? What protection does the law offer to the worse-off and especially those who financially depend on creditors? What role can the criminal law play in hindering aggressive corporate conduct especially in conditions of globalisation?