8/6/20: 30 years of Financial Markets Manipulation

Students in my course Applied Investment and Trading in TCD would be familiar with the market impact of the differential bid-ask spreads in intraday trading. For those who might have forgotten, and those who did not take my course, here is the reminder: early in the day (at and around market opening times), spreads are wide and depths of the market are thin (liquidity is low); late in the trading day (closer to market close), spreads are narrow and depths are thick (liquidity is higher). Hence, a trading order placed near market open times tends to have stronger impact by moving the securities prices more; in contrast, an equally-sized order placed near market close will have lower impact.

Now, you will also remember that, in general, investment returns arise from two sources: 

1. Round-trip trading gains that arise from buying a security at P(1) and selling it one period later at P(2), net of costs of buy and sell orders execution; and 
2. Mark-to-market capital gains that arise from changes in the market-quoted price for security between times P(1) and P(2+).

The long-running 'Strategy' used by some institutional investors is, therefore as follows: 

Here is the illustration of the 'Strategy' via Bruce Knuteson paper "Celebrating Three Decades of Worldwide Stock Market Manipulation", available here: https://arxiv.org/pdf/1912.01708.pdf.

• Step 1: Accumulate a large long portfolio of assets;
• Step 2: At the start of the day, buy some more assets dominating your portfolio at P(1) - generating larger impact of your buy orders, even if you are carrying a larger cost adverse to your trade;
• Step 3: At the end of the day, sell at P(2) - generating lower impact from your sell orders, again carrying the cost.

On a daily basis, you generate losses in trading account, as you are paying higher costs of buy and sell orders (due to buy-sell asymmetry and intraday bid-ask spreads differences), but you are also generating positive impact of buy trades, net of sell trades, so you are triggering positive mark-to-market gains on your original portfolio at the start of the day.

Knuteson shows that, over the last 30 years, overnight returns in the markets vastly outstrip intraday returns. 

Per author, "The obvious, mechanical explanation of the highly suspicious return patterns shown in Figures 2 and 3 is someone trading in a way that pushes prices up before or at market open, thus causing the blue curve, and then trading in a way that pushes prices down between market open (not including market open) and market close (including market close), thus causing the green curve. The consistency with which this is done points to the actions of a few quantitative trading firms rather than

the uncoordinated, manual trading of millions of people."

Sounds bad? It is. Again, per Knuteson: "The tens of trillions of dollars your use of the Strategy has created out of thin air have mostly gone to the already-wealthy: 

• Company executives and existing shareholders benefi tting directly from rising stock prices; 
• Owners of private companies and other assets, including real estate, whose values tend to rise and fall with the stock market; and 
• Those in the financial industry and elsewhere with opportunities to privatize the gains and socialize the losses."

These gains to capital over the last three decades have contributed directly and signi ficantly to the current level of wealth inequality in the United States and elsewhere. As a general matter, widespread mispricing leads to misallocation of capital and human effort, and widespread inequality negatively a effects our social structure and the perceived social contract."

24/3/18: A Traders’ Nightmare: When all Risks Coincide

Really great analysis of recent volatility spike (early February correction) from the BIS Quarterly:

“The VIX is an index of one-month implied volatility constructed from S&P 500 option prices across a range of strike prices. …Because it is derived from option prices, theoretically the VIX is the sum of expected future volatility and the volatility risk premium. Model estimates indicate that the rise in the VIX on 5 February far exceeded the change in expectations about future volatility (Graph A1, centre panel). The magnitude of the risk premium (ie the model residual) suggests that the VIX spike was largely due to internal dynamics in equity options or VIX futures markets.”

“Indeed, the considerable expansion in the VIX futures market – market size (ie total open interest) rose from a daily average of about 180,000 contracts in 2011 to 590,000 in 2017 – means such dynamics are likely to have had a growing impact on the level of the VIX.”

And the dynamics were spectacular. Per BIS:

“Among the growing users of VIX futures are issuers of volatility exchange-traded products (ETPs). These products allow investors to trade volatility for hedging or speculative purposes. Issuers of leveraged volatility ETPs take long positions in VIX futures to magnify returns relative to the VIX – for example, a 2X VIX ETP with $200 million in assets would double the daily gains or losses for its investors by using leverage to build a $400 million notional position in VIX futures. Inverse volatility ETPs take short positions in VIX futures so as to allow investors to bet on lower volatility.” One that comes to mind immediately is XIV. 

And things went spectacularly South for these, once VIX started heading North.

“The assets of select leveraged and inverse volatility ETPs have expanded sharply over recent years, reaching about $15 billion at end-2017 (Graph A1, right-hand panel). …many market participants use these products to make long-term bets on volatility remaining low or becoming lower. Given the historical tendency of volatility increases to be rather sharp, such strategies can amount to “collecting pennies in front of a steamroller”.

“Even though the aggregate positions in these instruments are relatively small, systematic trading strategies of the issuers of leveraged and inverse volatility ETPs appear to have been a key factor behind the volatility spike that occurred on the afternoon of 5 February. Given the rise in the VIX earlier in the day, market participants could expect leveraged long volatility ETPs to rebalance their holdings by buying more VIX futures at the end of the day to maintain their target daily exposure (eg twice or three times their assets). They also knew that inverse volatility ETPs would have to buy VIX futures to cover the losses on their short position in VIX futures. So, both long and short volatility ETPs had to buy VIX futures. The rebalancing by both types of funds takes place right before 16:15, when they publish their daily net asset value. Hence, because the VIX had already been rising since the previous trading day, market participants knew that both types of ETP would be positioned on the same side of the VIX futures market right after New York equity market close.”

“The scene was set.” Or put differently, once information about leveraged funds having to go long at the end of the day became market information, arbitrage went to work like a sledgehammer over trading books. The impact risk, compounded by adverse price movements, went through the roof. The two key changes in trading environment were made even more egregious by the fact that intraday spreads are usually higher toward the day close, and risk of non-execution had become completely intolerable for the leveraged funds. Which means spreads ballooned. This was a classic trading nightmare:

“There were signs that other market participants began bidding up VIX futures prices at around 15:30 in anticipation of the end-of-day rebalancing by volatility ETPs (Graph A2, left-hand panel). Due to the mechanical nature of the rebalancing, a higher VIX futures price necessitated even greater VIX futures purchases by the ETPs, creating a feedback loop. Transaction data show a spike in trading volume to 115,862 VIX futures contracts, or roughly one quarter of the entire market, and at highly inflated prices, within one minute at 16:08. The value of one of the inverse volatility ETPs, XIV, fell 84% and the product was subsequently terminated.”

15/1/18: Of Fraud and Whales: Bitcoin Price Manipulation

Recently, I wrote about the potential risks that concentration of Bitcoin in the hands of few holders ('whales') presents and the promising avenue for trading and investment fraud that this phenomena holds (see post here: http://trueeconomics.blogspot.com/2017/12/211217-of-taxes-and-whales-bitcoins-new.html).

Now, some serious evidence that these risks have played out in the past to superficially inflate the price of bitcoins: a popular version here https://techcrunch.com/2018/01/15/researchers-finds-that-one-person-likely-drove-bitcoin-from-150-to-1000/, and technical paper on which this is based here (ungated version) http://weis2017.econinfosec.org/wp-content/uploads/sites/3/2017/05/WEIS_2017_paper_21.pdf.

Key conclusion: "The suspicious trading activity of a single actor caused the massive spike in the USD-BTC exchange rate to rise from around $150 to over $1 000 in late 2013. The fall was even more dramatic and rapid, and it has taken more than three years for Bitcoin to match the rise prompted by fraudulent transactions."

Oops... so much for 'security' of Bitcoin...

15/10/17: Concentration Risk & Beyond: Markets & Winners

An excellent summary of several key concepts in investment worth reading: "So Few Market Winners, So Much Dead Weight" by Barry Ritholtz of Bloomberg View.  Based on an earlier NY Times article that itself profiles new research by Hendrik Bessembinder from Arizona State University, Ritholtz notes that:

• "Only 4 percent of all publicly traded stocks account for all of the net wealth earned by investors in the stock market since 1926, he has found. A mere 30 stocks account for 30 percent of the net wealth generated by stocks in that long period, and 50 stocks account for 40 percent of the net wealth. Let that sink in a moment: Only one in 25 companies are responsible for all stock market gains. The other 24 of 25 stocks -- that’s 96 percent -- are essentially worthless ballast."

Which brings us to the key concepts related to this observation:

1. Concentration risk: This an obvious one. In today's markets, returns are exceptionally concentrated within just a handful of stocks. Which puts the argument in favour of diversification through a test. Traditionally, we think of diversification as a long-term protection against risks of markets decline. But it can also be seen as coming at a cost of foregone returns. Think of holding 96 stocks that have zero returns against four stocks that yield high returns, and at the same time weighing these holdings in return-neutral fashion, e.g. by their market capitalization.  
2. Strategic approaches to capturing growth drivers in your portfolio: There are, as Ritholtz notes, two: exclusivity (active winners picking) and exclusivity (passive market indexing). Which also rounds off to diversification. 
3. Behavioral drivers matter: Behavioral biases can wreck havoc with both selecting and holding 'winners-geared' portfolios (as noted by Rithholtz's discussion of exclusivity approach). But inclusivity  or indexing is also biases -prone, although Ritholtz does not dig deeper into that. In reality, the two approaches are almost symmetric in behavioral biases impacts. Worse, as proliferation of index-based ETFs marches on, the two approaches to investment are becoming practically indistinguishable. In pursuit of alpha, investors are increasingly being caught in chasing more specialist ETFs (index-based funds), just as they were before caught in a pursuit of more concentrated holdings of individual 'winners' shares.
4. Statistically, markets are neither homoscedastic nor Gaussian: In most cases, there are deeper layers of statistical meaning to returns than simple "Book Profit" or "Stop-loss" heuristics can support. Which is not just a behavioral constraint, but a more fundamental point about visibility of investment returns. As Ritholtz correctly notes, long-term absolute winners do change. But that change is not gradual, even if time horizons for it can be glacial. 

All of these points is something we cover in our Investment Theory class and Applied Investment and Trading course, and some parts we also touch upon in the Risk and Resilience course. Point 4 relates to what we do, briefly, discuss in Business Statistics class. So it is quite nice to have all of these important issues touched upon in a single article.

16/5/17: Insiders Trading: Concentration and Liquidity Risk Alpha, Anyone?

Disclosed insiders trading has long been used by both passive and active managers as a common screen for value. With varying efficacy and time-unstable returns, the strategy is hardly a convincing factor in terms of identifying specific investment targets, but can be seen as a signal for validation or negation of a previously established and tested strategy.

Much of this corresponds to my personal experience over the years, and is hardly that controversial. However, despite sufficient evidence to the contrary, insiders’ disclosures are still being routinely used for simultaneous asset selection and strategy validation. Which, of course, sets an investor for absorbing the risks inherent in any and all biases present in the insiders’ activities.

In their March 2016 paper, titled “Trading Skill: Evidence from Trades of Corporate Insiders in Their Personal Portfolios”, Ben-David, Itzhak and Birru, Justin and Rossi, Andrea, (NBER Working Paper No. w22115: http://ssrn.com/abstract=2755387) looked at “trading patterns of corporate insiders in their own personal portfolios” across a large dataset from a retail discount broker. The authors “…show that insiders overweight firms from their own industry. Furthermore, insiders earn substantial abnormal returns only on stocks from their industry, especially obscure stocks (small, low analyst coverage, high volatility).” In other words, insiders returns are not distinguishable from liquidity risk premium, which makes insiders-strategy alpha potentially as dumb as blind ‘long lowest percentile returns’ strategy (which induces extreme bias toward bankruptcy-prone names).

The authors also “… find no evidence that corporate insiders use private information and conclude that insiders have an informational advantage in trading stocks from their own industry over outsiders to the industry.”

Which means that using insiders’ disclosures requires (1) correcting for proximity of insider’s own firm to the specific sub-sector and firm the insider is trading in; (2) using a diversified base of insiders to be tracked; and (3) systemically rebalance the portfolio to avoid concentration bias in the stocks with low liquidity and smaller cap (keep in mind that this applies to both portfolio strategy, and portfolio trading risks).

19/12/16: Market Anomalies and Data Mining: Some Pretty Tough Love from Data

Investment anomalies (or in other words efficacy of exogenous factors in determining abnormal returns to investment) are a matter of puzzle for traditional investment analysis. In basic terms, we normally think about the investment as an undertaking that offers no ‘free lunch’ - if markets are liquid, deep and, once we control for risk factors, taxes and transaction costs, an average investor cannot expect to earn an above-market return. Put differently, there should be no ways to systematically (luck omitting) beat the market.

Anomalies represent the case where some factors do, in fact, generate such abnormal returns. There is a range of classic anomalies, most commonly known ones being Small Firms Outperform, January Effect, Low Book Value, Under-dogs or Discounted Assets or Dogs of the Dow, Reversals, Days of the Week, etc. In fact, there is an entire analytics industry built around markets that does one thing: mine for factors that can give investors a leg up on competition, or finding anomalies.

One recent paper have identified a list of some 314 factors that were found - in the literature - to generate abnormal returns. As noted by John Cochrane: “We thought 100% of the cross-sectional variation in expected returns came from the CAPM, now we think that’s about zero and a zoo of new factors describes the cross section.”

A recent paper published by NBER and authored by Juhani Linnainmaa and Michael Roberts (see link below) effectively tests this Cochrane’s proposition. To do this, the authors “examine cross-sectional anomalies in stock returns using hand-collected accounting data extending back to the start of the 20th century. Specifically, we investigate three potential explanations for these anomalies: unmodeled risk, mispricing, and data-snooping.” In other words, the authors look into three reasons as to why anomalies can exist:

1. Unmodeled risk reflects the view that some of risk premium paid out in the form of investment returns is not captured by traditional models of risk-return relations;
2. Mispricing reflects the view that markets’ participants routinely and over long run can misplace risk; and
3. Data-snooping view implies that anomalies generate returns in the historical data that do not replicate in forward-looking implementation because these anomalies basically arise from ad hoc empirical data mining.

The authors argue that “each of these explanations generate different testable implications across three eras encompassed by our data: (1) pre-sample data existing before the discovery of the anomaly, (2) in-sample data used to identify the anomaly, and (3) post-sample data accumulating after identification of the anomaly.”

In their first set of tests, the authors focus on profitability and investment factors, because prior literature shown that “these factors, in concert with the market and size factors, capture much of the cross-sectional variation in stock returns.”

Finding 1: the authors “find no statistically reliable premiums on the profitability and investment factors in the pre-1963 sample period… Between 1963 and 2014, these factors average” statistically and financially significant returns on average of “30 and 25 basis points per month, respectively.”

Finding 2: “The attenuations of the investment and profitability premiums in the pre-1963 data are representative of most of the other 33 anomalies that we examine. Just eight out of the 36 (investment, profitability, value, and 33 others) earn average returns that are positive and statistically significant at the 5% level in the pre-1963 period.

Finding 3: All of the measures of abnormal returns used in the study generate premiums that “decrease sharply and statistically significantly when we move out of the original study’s sample period by going either backward or forward in time.” In other words, anomalies tend to disappear or weaken every time the authors significantly broaden time horizon beyond that which corresponds to the time horizon used in the original study that uncovered such an anomaly.

As authors note, “these findings are consistent with data-snooping as the anomalies are clearly sensitive to the choice of sample period."

How? "...If the anomalies are a consequence of multidimensional risk that is not accurately accounted for by the empirical model (i.e., unmodeled risk), then we would have expected them to be similar across periods, absent structural breaks in the risks that matter to investors. Similarly, if the anomalies are a consequence of mispricing, then we would have expected them to be larger during the pre-discovery sample period when limits to arbitrage, such as transaction costs, were greater.”

But there is a note of caution due. “Our results do not suggest that all return anomalies are spurious. The average in-sample anomaly earns a CAPM alpha of 32 basis points per month (t-value = 10.87). The average alpha is 13 basis points (t-value = 4.42) per month for the pre-discovery sample and 14 basis points (t-value = 4.06) for the post-discovery sample. Although these estimates lie far below the in-sample numbers, they are highly statistically significant.”

The kicker is that “investors, however, face the uncertainty of not knowing which anomalies are real and which are spurious [or due to data mining], and so they need to treat them with caution. …because data-mining bias affects many facets of returns—averages, volatilities, and correlations—it is best to test asset pricing models out of sample," or absent such opportunity (perhaps due to tight data) - by selecting a model / factor that "is able to explain half of the in-sample alpha".




Full paper: Linnainmaa, Juhani T. and Roberts, Michael R., The History of the Cross Section of Stock Returns (December 2016). NBER Working Paper No. w22894. https://ssrn.com/abstract=2880332