The Giffen Good Concept Applied To Investments

Editor’s Note: A version of this post appeared in the San Antonio Express News “So…Money” column.

The only “C” I got in college was in Intermediate Macroeconomics, but I remember one economics term that I really loved — the “Giffen Good.”

With ordinary, rational, economic behavior, we expect that when prices go up, people buy less, and when prices go down, people buy more. We buy more things, for example, at Wal-Mart and Costco because of their low prices. We buy fewer things at Nordstrom because of their higher prices. Makes sense, right?

sir_robert_giffen
Sir Robert Giffen

A Giffen Good — named for a 19th Century Scottish economist named Sir Robert Giffen — is an odd thing. It’s something that people buy more of as the price goes up. With a Giffen Good, people act in exactly the opposite way we would normally expect them to in response to the price of things.

When you look up Giffen Good in Wikipedia — as I just did to refresh my memory — you read that little evidence exists for Giffen Goods in the real world, and people do not generally purchase more of something when the price goes up.

When it comes to our investments, however, I totally disagree with Wikipedia.

Ever since learning about Giffen Goods, I see them everywhere, as well as what’s known by analogy as “Giffen Behavior.”
Outside of the investing world, I remember reading with much interest the story of a guy trying to get rid of his mattress. He posted a “Free Mattress, Used” notice on Craig’s List, and got no responses. When he posted “Mattress, used, just $10,” he had to turn away interested buyers who lined up with their trucks to try to take advantage of a great bargain. That’s a Giffen Good.
Here’s an example of a Giffen Good from the art world: Imagine if I landed on Earth knowing nothing about art and somebody offered me the Edvard Munch painting “The Scream” for $1,000 to hang in my living room.

The_Scream_giffen_good
I’d offer you $75 for this, because I love a bargain.

I don’t know about you, but I might just think, “Whoa, that’s kind of a lot of money, and although there’s something neat about the painting, it’s still a bit creepy.”
And then I might think, “How about I give you $75 for it?” Because I love a bargain.

Of course, knowing that somebody else paid $120 million for it last year changes its attractiveness to me. Would I sell every single one of my worldly possessions right now to own “The Scream?”

Duh. I’m a finance guy. Of course I would. That painting is the ultimate Giffen Good.

Shifting from the absurd to the irrelevant, a concept like Bitcoin suddenly became everybody’s most desired tulip bulb last year when the price starting shooting upward, making it the Giffen Good of 2013.

And now lets return to the core of ordinary investment behavior: Discretionarily-managed equity mutual funds typically charge 0.75 to 1.5 percent management fees, while equity index mutual funds typically charge one-third of that amount in management fees, despite offering the same long-term results, according to every academic study that’s ever been done. Like, ever.

Most investors figure — wrongly — that if the fees on the discretionarily managed equity funds are higher, they must be a better product. The lower-priced index mutual funds just seem less attractive. That’s a Giffen Good.

In fact, much of the time, the entire stock market is an example of a Giffen Good. We really don’t want to own stocks when they fall in price. On the other hand, we really, really, really get interested in stocks after they’ve jumped 10 to 15 percent a year for a couple years in a row. This is madness, of course, but it’s also exactly what drives much investing activity.

active_vs_index
Most of the time, indexing wins

Beware of your own Giffen Behavior.

Final note: Real, live economists reading this may object to my imprecise adaptation of an economic term for the popular illustration of a personal finance concept. In anticipation of their objection, I can only show them my previously mentioned “C” on my college transcript. Also, lighten up, dismal scientists.

 

Please see related post: Guest Post by Lars Kroijer – Agnosticism over Edge

A book review of Investing Demystified by Lars Kroijer

 

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Options Part III – Delta Hedging

Please see related posts

Options Trading Part I – NFW Edition

Options Trading Part II – The Currency of Options Trading

 explain_delta_hedging

For the next little bit I’ll use a specific made-up example, of a fictional pet insurance company[1] with ticker symbol PAWS.[2]

Let’s say PAWS shares trade at $100 per share, and I am planning to sell 1,000 3-month puts struck at $90 for $2.70. I bank $2,700 (1,000 shares x $2.70), and I give the put buyer the option to sell me 1,000 shares at $90 a piece at any time in the next 3 months. [For a definition of what a put is, I recommend starting here.  and then read here.]

As a retail investor, I’m hoping the shares stay roughly where they are, and certainly above $90 per share for the next 3 months. If they drop below $90/share, I am forced to buy them at a price above the market.

Without getting into heavy math, we can see intuitively why an option on a stock would be more valuable, or cost more, for a more volatile stock. If you have a 3 month option on a stock, and the stock moves only slightly during those three months, the owner of the option will have no opportunity to profit. The more dramatically the stock moves during those three months, the more the owner of the option may profit.

Perhaps not intuitive at first glance, however, is the idea that an options trader (the pro, not you and me) cares almost exclusively about the volatility of the stock – the frequency and magnitude of price changes during the time period of his option – and not about the price direction of a stock, up or down.

An options trader trades “volatility” for a living rather than stocks, and the value of all calls and puts in an option trader’s portfolio fluctuates with the rise and fall of volatility, rather than the price of stocks.

In practice, that means that if the trader owns an option, he hopes (or can be said to have ‘bet’) that the stock becomes more volatile. Conversely, if he has sold an option, he hopes (or can be said to have ‘bet’) that the stock becomes less volatile over the time horizon of the option.

In practice the professional options trader – or “vol trader” as he or she may be known – has a portfolio which is net long or net short volatility. And just importantly, in most cases the vol trader will seek to be “flat” or neutral with respect to the underlying stock, or stock market, or whichever market he or she[3] trades.

Hedging market exposure to the stock in order to isolate volatility exposure

Do you want to go deeper down the options trading rabbit hole with me? Why not? Let’s hum a few more bars of the volatility tune to learn about what options traders actually do for a living.

When an options trader buys my 1,000 puts on PAWS struck at $90 per share he typically will want to leave his portfolio exposed to volatility, but not exposed to the underlying stock. After all, he’s a ‘Vol trader,’ with a  view to the historical, present, and future value of volatility, but typically without any particular responsibility for a view on the historical, present and future price of the underlying stock. But initially, at least, he’s a little bit exposed to the price of the stock.

At the moment he begins to own my puts – with the right to sell me some 1,000 PAWS shares at $90 – he becomes ‘long’ volatility but slightly ‘short’ some notional amount of PAWS shares.

pet_insurance

The notional ‘short’ PAWS shares needs some explanation. You see, he’s slightly short PAWS shares despite the fact that he hasn’t sold any yet.

Even though he hasn’t sold me any, there is some non-zero probability that he will end up selling me PAWS shares 3 months from now, so he has a contingent future short exposure to the stock, the contingency being that PAWS shares drop below $90 per share.

This positive probability of selling shares in the next 3 months makes the vol trader somewhat exposed to the direction of the market. And, generally speaking, a vol trader doesn’t want to be exposed to the direction of the market.

Notional market exposure and “the Delta”

Let’s assume the vol trader knows – and in fact he would know based on the measure of the historical volatility of PAWS shares – that there’s a 20% probability that PAWS stock goes below $90 in the next 3 months. That makes the options trader 20% “short” 1,000 shares of PAWS, on a probability-weighted basis.

In the options trading world this notional market exposure is known as the ‘Delta.’

The delta is used in practice to calculate how the options trader can hedge his market exposure to PAWS shares, which he doesn’t want. With a 20% short position on 1,000 shares, the right thing for the options trader to do is to purchase 200 shares of PAWS (20% of 1,000) at the market price of $100 per share.

This purchase – assuming he’s calculated the delta correctly – leaves him ‘market neutral’ with respect to the future price of PAWS but ‘long’ volatility with respect to future fluctuations – in either direction – in PAWS stock.

He’s long puts on 1,000 shares, and he’s also long enough shares to cover – on a probability-weighted basis – the expect amount of shares he may sell.

Now he’s good. And ready.

Trading the delta

The interesting part for an options trader begins as soon as he’s isolated his exposed to volatility only, so next I’ll describe good scenarios for the options trader.

Let’s assume PAWS drops the next day to $90 per share.

paws_insurance
My fictional pet insurance company

For the next part I’m mostly going to ignore the option seller’s situation (my situation) however because – as noted earlier – no retail investor should be doing this.[4]

Our options trader, who is long the 1,000 puts and long 200 PAWS shares as a hedge, now has a great opportunity based on the market’s dramatic move downward. The delta of a $90 put with the market at $90 per share will be roughly 50%, meaning the trader is now 30% under-exposed to the underlying stock.

The delta changes, remember, because it reflects the probability-weighted exposure for an options trader to the stock market price over the remaining three months. Once the stock has dropped to $90, we can assume that there’s roughly a 50-50 chance that these puts will be exercised – meaning a 50-50 chance the trader will sell 1,000 shares to the put seller at $90, three months from now.

Our vol trader can, and should, purchase an additional 300 shares of PAWS to remain ‘market neutral’ to PAWS shares.

Once he buys 300 shares to add to his original 200 shares, he owns 500 shares total, and he owns 1,000 puts on PAWs with a Delta of 50. Once again, he’s good and ready.

He’s long volatility, but neutral to PAWS, exactly how a ‘vol’ trader should be.

The next day, PAWS rockets back upwards to a price of $100 per share.

Then what happens?

The original put seller (still me, I guess?) lets out a big sigh of relief that his puts are back ‘out of the money.’[5]

Interestingly, however, our options trader is also made happy by the quick move. He couldn’t care less that the puts he owns might expire unexercised, because he cares instead that the volatility of the stock has spiked.

Why is volatility so good for him?

The delta of the PAWS shares at $100 shifts back in this example to something close to 20%, leaving the trader ‘long’ PAWS shares by about 300 shares. The options trader, in order to shift back to ‘neutral’ on the PAWS stock, gets to sell 300 shares at $100.

This part is kind of cool, if you’re an options trader long ‘vol’ on PAWS.

In the course of two trading sessions, our options trades has bought 300 shares at $90 and sold 300 shares at $100, pocketing the riskless difference of $3,000. [300 shares x $10 price move.]

An options trader who is ‘long’ volatility will always have the happy circumstance of buying low and selling high in the course of ‘delta-hedging’ his exposure to the underlying stock.[6]

If PAWS shares go up again, to $110, his delta shrinks further and he will sell some of his original 200-share delta hedge at an even higher price. If PAWS shares drop, he will buy low at the new low share price to hedge his delta. All the while remaining ‘market neutral.’

The more the shares move over the course of the next three months the more the vol trader delta hedges profitably. Wash, Rinse, Repeat.

On the other hand if PAWS never moves over the three month period in our example, our options trader loses the money he spent on the premium.

That, in super-simplified form, is how options trading works.

The retail investor speculating in options rarely delta hedges or even understands how to calculate volatility, putting him at an extraordinary disadvantage with this type of speculating.

You can still get lucky with a leveraged long or short position, and everybody knows it is better to be lucky than good.

But again, I would only wish this type of retail speculating on my worst enemy.

 

Please see related posts:

Options Trading Part I – NFW Edition

Options Trading Part II – The Currency of Options Trading

 

 

[1] Here’s how I imagine pet insurance working: You pay $10/month to PAWS, and in return little Fifi gets medical costs covered up to a certain amount, plus some lump sum ($10K?) to compensate you in case Fifi goes missing or gets hit by a truck. I’m making this business up but I’m certain many dog owners would be willing to buy this type of insurance.

[2] I learned after I wrote this that there is an actual penny stock with ticker symbol PAWS and I’d recommend getting involved with that penny stock even less than I would recommend selling puts. Run away!

[3] Apologies in advance, I’m going to go all gender-specific in my pronouns for the rest of the post so I don’t have to keep adding “or she” to every clause. This is just to say that I’m sorry about this and I hope to make it up to you some day.

[4] But understand that the option seller (me in this example) begins to wet his pants because losses start quickly from here.

[5] Importantly, he has time to step away from his day-trading desk to change his pants.

[6] Of course this cuts both ways – an options trader who is ‘short’ volatility will be in the uncomfortable delta-hedging position of buying high and selling low if the underlying stock makes volatile moves over the life of the option.

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Batman strikes – Some Thoughts on Short Sellers

Gotham_City_Hedge_FundIn the bad old days of the 2008 Crisis, a casual reader of the financial news might have been fooled into thinking that “short-sellers,” those financial firms that bet on the price of some financial instrument (like a stock, or bond, or currency, or commodity) going down, rather than up, ranked on the financial attractiveness scale somewhere between Renee Zellwegger and Quasimodo – simpering, disfigured, unpatriotic, and untrustworthy.

For a brief time in the midst of the October 2008 panic, the financial regulators nearly outlawed short-sellers, as if there was some moral difference between short sellers and their counterparts “long buyers.” (We don’t refer to them of course as “long buyers” but rather ”investors,’ but finance folks do use the ‘shorts’ and ‘longs’ monikers when describing market participants.)

Only people who have never participated in financial markets could reasonably argue that ‘short selling’ has any better or worse effect on markets than ‘long buying.’ In fact, brokering most markets absolutely requires short selling, both to offer a product to a client that the broker does not currently have in inventory, as well as to hedge purchases from a client that a broker can not immediately dispose of in the market.

When a client needs to sell a block of a stock, or a pile of bonds, the broker will often sell (sometimes selling short) a similar-characteristic block of stocks or bonds right away, to minimize the market-directional risk of holding the client’s recently dumped position. The ability to sell short, for a hedge, is a key tool in the arsenal of brokers.

Short-selling by hedge funds

The ability to short sell is also the fundamental differentiating tool of 80% of hedge funds vis-a-vis mutual funds: Namely, the former can sell short a stock (or bond, or currency, or commodity) whereas a traditional mutual fund may only deploy money on the ‘long’ side, by buying a financial product. Financial products tend to go both down and up, but your typical mutual fund may only be able to deliver a positive return when the markets go up in aggregate.

A hedge fund by contrast – in theory at least – can deliver positive results regards of the direction of securities or the market as a whole. Or, more frequently, a hedge fund may seek to smooth out investment results through a combination of shorts and longs – achieving an acceptable positive return while delivering a ride with less volatility. In that sense your hedge is acting like a Lexus in city traffic – you won’t necessarily get there any faster but the shock absorbers will deliver a much less bumpy and therefore more pleasant ride along the way. At a much higher cost, of course.

Short-sellers as heroes

All of this explanation I intend as prelude to this week’s story about the Spanish tech company Gowex, in which dedicated short-sellers actually prove themselves not only on a moral plane with the Clinton-formulation abortion (“legal, safe and rare”) but actually clever, necessary, and heroic.

gotham_city_hedge_fund

Enter Gotham City Research LLC, a hedge fund dedicated to short-selling as a primary strategy. As the Wall Street Journal reports, these guys – and similar-strategy firms like them such as Jim Chanos’ Kynikos Associates that took down Enron, and Carson Block’s Muddy Waters Research that took down Sino-Forest Corporation – look to sniff out frauds and bet heavily against them through short-selling.

Gotham City swooped down like the caped avenger and exposed the Gowex fraud in Spain.

As recently as July 1st Gowex was a Spanish high technology of the markets, providing free Wi-Fi to municipalities. They boasted the following credentials:

  • A $2.6 Billion valuation in April 2014
  • In May 2014 Gowex won the top prize from a Spanish marketing association
  • Gowex Founder Jenaro Garcia was the 39th richest Spaniard, with an (on-paper) net worth north of $240 million.

After Gotham City released its report July 1st, claiming 90% of Gowex’ revenues were nonexistent and that Gowex is ‘too good to be true,’ the firm’s stock began to nosedive. After briefly denying the reports and accusing Gotham City of trying to benefit through short-selling, Garcia resigned and asked for forgiveness. Gowex filed for bankruptcy by July 6th.

That is some Batman-style, hard-core, swift justice.

People don’t generally love dedicated short-sellers, because they  profit when other people lose money. The Gotham City vs. Gowex story this week is a great example of why we need these mysterious caped avengers in the cityscape, broodingly seeking out wrongdoers.

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Book Review: Diary Of A Very Bad Year


I’ll admit to two large biases before praising Diary of a Very Bad Year: Confessions of an Anonymous Hedge Fund Manager.

First, I prefer a personal account by a financial practitioner, rather than a financial journalist’s perspective, nearly every time. This preference, after all, underpins my idea with the Bankers Anonymous site itself.

The acronymic jargon of an ordinary financial practitioner’s presentation, however, typically overshadows his story for the lay reader. Who, except the specialist, can unpack the Re-Remics from the Reverse Repos, the positive carry from the negative basis trades, or FX forwards from a commodity curve in backwardation? Only the rare financier knows his craft so well that he can explain complexity while using language we can all understand.

Second, the Anonymous Hedge Fund Manager (HFM) featured in the book was a client of mine for a short while when I sold bonds on the emerging markets desk for Goldman. His clear language and thinking made a strong impression at that time.

Which explains why, when I read a review of this book a few years ago, I immediately thought of my ex-client. Was he the unidentified HFM? An email query and reply a few hours later confirmed it, yes.

Through a series of interviews with a journalist, HFM gives a wide-ranging but personal perspective on his experience between September 2007 and August 2009, covering the periods of the deepest dive and steepest financial recovery. His interests, while inescapably specific and technical, frequently veer to the philosophical and big picture.

In the free-fall period of late 2008 – when even the most plugged-in hedge fund manager was overwhelmed with unexpectedly bad developments – we experience a real existential question for financial markets: If all private banks were at risk of implosion without the backing of the US Government, what happens when the US Government defaults? Who is insuring it and how do you hedge that risk? Martians were not offering credit default swaps to earthlings.

Diary of a Very Bad Year will not tell you everything you need to know about the Credit Crisis of 2008. It will tell you what a large hedge fund manager experienced, in real time, in a way no journalist on the outside could ever tell you.

It’s the best book I’ve ever read on the Crisis.

I read this a few years ago but was reminded of it because my wife just read Diary of a Very Bad Year this past week. She found it somewhat technical for the non-finance expert – as terms like leverage, credit default swaps, FX crosses, and even ‘hedge fund,’ get thrown around without explanation or definition. But she also appreciated the brilliance and humor of HFM in describing those two awful years, in real time.

The final chapter reveals HFM’s plan to quit New York City and move to Austin, TX with his fiancé.

He’s burned out on the stress of the Crisis and the responsibility of managing a large team and complex portfolio at his New York hedge fund. He dreams of eliminating his management responsibilities, simplifying his life, and shifting his balance, away from working, and more toward living.

When I checked in with him for lunch in Austin a few years ago, he had followed his plan exactly.

diary of a very bad year

 

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Book Review: Inside the Black Box

I read Rishi Narang’s Inside the Black Box as a kind of primer on quantitative trading, in advance of reading Michael Lewis’ Flash Boys.

Need for a primer on quantitative trading

I need a primer before reading Flash Boys for several reasons.

I was an ‘over-the-counter’ institutional bond guy, rather than a participant in the exchange-traded stock markets that form the core of many quantitative trading strategies.  In addition, my Wall Street experience is a decade old at this point, (I left Goldman in 2004) and the development of dark pool trading, high-frequency trading (HFT), and alternate electronic exchange trading has proliferated since then.

Whereas the traditional stock exchanges with real-live human specialists (like the New York Stock Exchange – NYSE) was already in decline by the time I left Wall Street, the vast majority of stock-trading volumes now happen electronically on alternate exchanges and dark pools away from the traditional NYSE.

As I noted a few years ago, most stock-trading volume is entirely devoid of human decision-making, which is to say, computer-programmed algorithms determine what trades happen on a second-by-second, moment-by moment basis.

Finally, I have a few friends in the quantitative trading world and one of them reached out to me shortly after the publication of Lewis’ book and said there are laughably wrong statements and implications in Flash Boys. In the interest of having an intelligent dialogue with him, to figure out if Lewis really screwed up, I needed some background.

 

Different Audience for the Books

Narang’s audience for his book differs from Michael Lewis’ audience for Flash Boys. We rely on Lewis for funny anecdotes and characters, and enough financial sense to give the general public an accurate, entertaining, and non-technical narrative.

By contrast Narang’s book targets non-expert practitioners who need more quantitative trading knowledge such as: new hires into the quantitative trading world – or current and prospective investors/asset allocators to quant-trading funds. Narang currently is an investor/allocator to quantitative strategies, who previously founded, and ran, a quantitative trading firm directly.

Narang provides a simplified ‘how to understand’ or ‘how to evaluate’ quantitative trading firms, for readers without specialized trading and computer programming background.

When I say ‘simplified’ I don’t mean the book will be ‘simple’ for people outside finance. Narang’s language is clear and precise, even full of useful analogies for explaining concepts, but it’s academic in style. He systematically reviews, engineer-like, the common components and varieties of quantitative strategies.

I learned a ton, but folks without a need to know about quant trading will not rip through this like they would a Michael Lewis book on finance.

I had a number of questions about quant trading before I read Inside the Black Box and Narang sheds light on my questions.

rise_of_the_machines

What is quantitative trading and what should we know about it?

Quant trading, in its simplest definition, can be distinguished from discretionary trading by the role of human decision-making. In traditional, discretionary, investing we decide to sell or buy a financial instrument for any number of good or bad reasons. With quant trading, by contrast all of the human inputs occur ahead of time, that is to say, through the programming of a trading algorithm to look for and respond to the optimal opportunities to trade. The actual order to sell or buy occurs in response to the signals, rather than any in-the-moment human decision.

How big is quant trading compared to discretionary trading in the markets?

Narang cites a study from 2009 that indicated 60% of trading volume in US equities came from quantitative trading, as well as 45% of trading volume in European exchanges. So if 6 billion US shares changed hands per day, then Hal9000 algorithm-based trades accounted for 3.6 Billion of those, leaving only 2.4 Billion shares traded by human discretion. In addition, over 90% of commodity trading firms (CTAs) are quant-trading firms, rather than discretionary traders.[1]

So how do programmers and traders set up the trading algorithm to order trades ahead of time?

Naturally, the next question is about how this programming gets done, and answering this question forms the bulk of Inside the Black Box.

Narang comprehensively reviews and explains both the common elements of building a quant trading strategy, as well as the wide variety of approaches used by the entire spectrum of quant trading firms.

Typically, programming a successful trading algorithm depends on reviewing extraordinary amounts of historical data on observed trading responses of financial instruments, and then seeking patterns among the noise. If a quant trader can find predictable enough responses, then it becomes possible to test if trading on an expectation that the pattern continues could be profitable over time. If his algorithm recognizes the pattern well enough, he can, in effect, ‘see the future’ enough to trade predictably and profitably.

 

So, it’s pattern-seeking?

A few simple examples of patterns may be helpful here to give an introductory idea. Some stocks – often from the same industry – predictably track each other’s direction, such that a directional move from one typically will be mirrored by a directional move in the other. A similar pattern could hold for a single stock and a ‘basket’ of similar enough stocks.

Volume data on stocks – how much is being traded over any given time period – may predictably be followed by moves in the stock or financial instrument.

Some directional trends in financial instruments could indicate a predictable pattern of a continuation of that trend, or just as likely (and somewhat paradoxically) the likely reversal of a trend.

The common denominator in this type of trading, however, posit that past trading data indicates somehow what will happen in the near future.

To the extent that markets behave in patterned ways – and in fact most of the time financial markets do behave in recognizable historical patterns, or at least recognizable to quants and their computers – quant traders can make money with trading algorithms.  This doesn’t have to happen every time, with every trade or every day. In fact, quant strategies depend not so much on winning on every trade, but winning on enough trades to overcome the losses on other trades, as well as the costs, or ‘trading friction,’ of executing many trades simultaneously.

Some of the time, of course, market regimes may shift and past patterns become poor indicators of future moves. At that point, the human-element decision becomes whether to lower the trading exposure based on the algorithm, to shut down the algorithm altogether, or to reprogram the algorithm in response to updated market conditions and patterns.

My sense from reading Narang’s book, as well as from talking to friends in the business, is that quant traders must engage in constant vigilance around their algorithms:  Algorithms may cease to generate positive returns as competition eliminates the opportunity, or competitors’ speed eliminates the previous advantage, or patterns simply stop working.

Research and development to find new ‘signals’ inside the noise of past trading data requires constant work.

summer_glau
Summer Glau, my favorite terminator ever

Besides pattern-seeking and constant R & D, what are other elements common to the wide variety of quant traders?

DATA – The acquisition of historical data, the scrubbing of data to extract useful information, and the testing of hypotheses (will following that pattern actually make money?) using the historical data are common to all quants.

COSTS – Some strategies require thousands or even millions of trades, so costs associated with trading matter a lot.  Quants have to include in their model expected trading costs – not only commissions paid to brokers or exchange – but the ‘slippage’ that occurs due to trading. The scientific method analogy to this ‘slippage’ here would be the “Hawthorne Effect” or “Observer Effect,” meaning it’s difficult-to-impossible to collect ‘profit’ from the market without affecting the market opportunity itself.

When a quant finds a financial instrument momentarily ‘cheap’ according to some historical pattern, the act of purchasing the instrument to take advantage of the cheapness may cause the price to rise.

The larger the trade that the quant algorithm demands to take advantage of that momentary cheapness, the larger the ‘slippage’ due to the trade itself will actually eliminate the opportunity. Any historical model that fails to take into account this cost element will likely fail to work.

SCIENTIFIC METHOD – Quant-trading, more than discretionary trading, depends on a ‘scientific method’ approach to investing. While a discretionary trader may depend on fuzzy thinking and ‘gut feeling’ to guide his buy and sell signals, quant trading eliminates this part of trading in favor of a rigorous Hypothesis à Test à Hypothesis à Test continuous feedback loop, with consistent profitability (in trading-speak, the generation of “Alpha,” or excess return over risk) the primary indicator of whether the hypothesis has any validity in real markets. Like scientific hypotheses, the trading algorithm can never become ‘proven’ or ‘permanent law.’ In fact, many current predictors of market moves may be shown to be poor predictors in the future when markets change. A continuous testing of hypotheses about patterns through the scientific method helps distinguish quants from discretionary traders.

How different is High Frequency Trading (HFT) from other types of quant trading, and what should we know about the subset of quant trading known as HFT?

Narang devotes the final chapters of this book to explaining HFT. Narang suggests that the trading ideas of HFTs can often be much simpler than other quant trading – but depend on faster execution of the simpler idea.

Narang provides a relatively easy illustration of a typical, simple, HFT idea.

The S&P500 index consists of shares from the 500 largest market-capitalization US companies, in known relative weightings. Naturally whenever the price of an individual stock in the index changes, the value of the S&P 500 index as a whole changes slightly as well. An exchange traded fund (ETF) like the SPY, or an S&P500 futures contract (ES), both of which are meant to track the index precisely, should exactly mirror the price changes in the underlying stocks.

If, however, one of the underlying stocks goes up in value and the SPY value does not change precisely, instantaneously, to a new value based on the moves in the underlying stocks, a super-fast arbitrage can be had by buying the SPY and shorting the stock. These arbitrage opportunities are either rare or exist for very brief instances – all sorts of traders would look for precisely this simple risk-less opportunity – but trading speed makes exploiting a price difference like this possible.

Neo_dodging_bullets
Keanu: “Whoa”

The best way to explain this is that only a computer programmed to move as fast as Neo in that scene dodging Mr. Smith’s bullets, could hope to profit from the S&P/ SPY arbitrages. Nobody else has a chance.

The basic trading idea here is super-simple. On the other hand the technology involved in calculating the precise price discrepancies, in the right weightings, based on the right data feeds, and doing it faster than anyone else in the marketplace, is not easy.

The S&P 500 trade described above is probably version 1.0 of HFT trading, and most HFT firms seek out similar price opportunities with fewer competitors and less efficient instruments in myriad markets around the world.

HFTs may seek to use more complicated algorithms than this example, but at a certain point the computing time required to sift through the market signal may slow down the process, so that they begin to resemble other quant traders.

By keeping the trading idea dumb and simple, the HFTs retain the advantage that comes from technology. HFT is the Keanu Reeves of quant trading, dumb and dependent on Speed to keep the bus above 88 mph to prevent everything from exploding.

Conventional quants may have harder-to-explain patterns that they recognize and exploit, but probably do not depend on execution speed to the same extent as HFTs in order to be profitable.[2]

How likely, and with what severity, could we experience a destructive flash-crash catastrophe driven by the present and future take-over of markets by quant trading? In other words, should we the public (and regulators) be much more actively regulating SkyNet?

Narang addresses a version of this question in Chapter 10, “Risks Inherent to Quant Strategies,” although his goal is limited to showing – persuasively in my opinion – that large market blow-ups in the past cannot be blamed on quants.

The Long Term Capital Management (LTCM) blowup of September 1998, for example, may be described by some as a quant-fund disaster that fundamentally shook capital markets, and might have gotten worse without a Federal Reserve-Inspired ‘Bail-in’ of LTCM by Wall Street firms.

Narang points out, however, that while LTCM used sophisticated quantitative models in their trading, their strategy remained fundamentally discretionary – meaning humans made the calls on what went into and out of their portfolio.

The 2008 Mortgage-bond inspired crisis, in addition, cannot reasonably be linked to quant trading firms. While the creation and trading of highly-leveraged mortgage and default-swap CDOs involves sophisticated mathematics and modeling, the billions in losses owed more to a combination of leverage (debt), human error, poor risk-modeling, the connectivity of markets and counter-parts, and misunderstood incentives among disparate parts of the mortgage-bond origination chain than anything to do with quantitative trading – as explained well in Michael Lewis’ The Big Short.

Arguably, the most significant systemic market risk crash to date that could be blamed on quant-like trading is the October 1987 market crash, in which computer-based “portfolio-insurance” algorithms each triggered a cascade of automatic selling, independent of human judgment. The Crash of 1987 occurred prior to the rise of quant trading as we know it, so ‘blaming’ quant trading can really be done only by historic analogy. It’s the kind of crash we could imagine from algorithmic trading in which the size of computer trading orders, added to the overwhelming one-sidedness of the computer signal (a crowded trade) causes the devastation.

The actual quant trading blowups to date, as Narang explains, have caused localized losses without inflicting much observable or systemic market disruption.

During a few weeks in August 2007, for example, a majority of algorithmic traders simultaneously experienced sudden losses, as the trends suggested by historic patterns of trading relationships reversed. It turns out that the similarities in quantitative strategies at that time meant that as some quants lost money and unwound their portfolios they caused a cascade of losses in other quants’ positions.

In the meantime, the non-quantitative investment community – people who just own stocks or mutual funds, or non-quant hedge funds – barely noticed any particular change in market prices.

The market as a whole neither soared nor crashed. Only the historic patterns of short-term trading relationships broke down and then suddenly and severely reversed. As a silent market-crisis, only experienced by the quant community, August 2007 may be a harbinger of the kind of localized pain that non-quants can safely ignore. Or conversely, the August 2007 crisis indicates the growing power and ‘crowded trade’ nature of quant trading that regulators should worry about which could freeze up or destroy orderly markets in the future.

Other flash crashes, such as the May 2010 Flash Crash or other mini-crises such as happened in August 2012 that brought down Knight Trading, may fit the pattern of acceptable, localized, market blips.  Optimistically, programming errors like those are just a Darwinian way of punishing the inept and incentivizing the careful and level-headed to take advantage of others’ mistakes.

Or, they could be the early tremors in market-structure fault-lines created by quant trading that will eventually cause an irreversible earthquake in world markets. I don’t want to be alarmist on a topic where I’m an amateur, but I hope some regulators have taken on the ‘seismic geologist’ role of figuring out how to predict and prevent ‘The Big One’ caused by quant trading, before it happens.

I mean, we’ve all seen 2001 Space Odyssey, Tron, Terminator, and The Matrix, right, so we know this doesn’t bode well for algorithmic trading. Even Aliens and Planet of the Apes are basically warnings about quant trading, aren’t they? Wait, am I overthinking this?

Hal9000
Open the pod bay doors, please, Hal

Narang does not particularly address this much more troubling, forward-looking question, of whether and to what extent quants will significantly blow up markets in the future. His primary response in Chapter 16 to this worry or criticism points out that human errors have more frequently caused losses or mini-crashes in the markets than computer glitches. No doubt this is true, but that’s a bit like saying we shouldn’t worry about tornadoes because hurricanes have caused more damage up until now.

As we learned from the 2008 Crisis, markets have a way to experiencing the near-statistically impossible, stunningly unlikely, “6-sigma” event, or “1,000-year flood” just about every decade or two, causing massive wealth destruction in new and unpredictable ways.

Often we can see, in retrospect, that the root cause of a market crash has something to do with the rapid advance of a profitable financial technology far ahead of our ability to control all the risks of the financial technology or regulate the inequities or societal costs of the financial technology.

I’m not in a great position to evaluate the probability and consequences of a future algorithmic-trading driven market catastrophe, and perhaps Narang is not either, although he’s certainly better positioned than most.

I do remain worried about a future SkyNet-driven Crash.

 

Is quantitative trading in general, or HFT in particular (as the rapidest version of quantitative trading is known) inherently unfair, unethical, or taking advantage of other market participants?

I have not read Flash Boys yet, but I know enough about the plot line to see Michael Lewis’ book as a call to action about the inequities inherent in HFT, a subset of quantitative trading. Whether he’s right or not, regulators are already responding to their perception that HFTs are gaming the system in a way that’s harmful to markets overall.

Narang clearly feels that quantitative trading is an interesting and honorable way to make a living. In the final section of the book, he describes the methods and implications of High Frequency Trading, with noted contributions from his brother Manoj Narang, the CEO of a HFT firm Tradeworx and sometime commenter on the industry.

Given Narang’s experience and connection with the industry, we would not expect him to point a finger at quants or HFTs and declare them inherently unfair. What he does try to do instead is educate the non-expert in how quants may make money, and why HFTs in particular feel the need for speed.

To give one example of this need for speed, his brother’s firm Tradeworx researched the profit difference between placing purchase or sale orders first, versus placing them later in the queue for order filling. Inherent to equity trading, he explains, is the rule that when buy or sell orders get placed on an exchange at the same price, the first order placed gets filled first. The value of being first with certain types of orders, according to Tradeworx, turns out to be approximately 1.7 cents per share.

With other kinds of orders, they have found that immediate, speedy, fulfillment of the trade reduces the cost or ‘slippage’ due to trading. In a strategy dependent on a massive amount of trading to capture momentarily fleeting price reactions, the high speed fulfillment of orders matters tremendously.

How do HFTs get so fast?

In the same chapter, Narang mentions the advantage of locating HFT trading servers at, or near, market exchange servers, a major theme of Flash Boys.

Along a similar vein, Narang reports the lengths and costs endured by trading firms to reduce data transmission times between major trading centers such as New York, Chicago, and London. HFT trading firms will sign long-term, multi-million dollar contracts for fiber optic access in order to shave off five milliseconds (there are 1,000 milliseconds in every second, so five milliseconds is 5 thousandths of a second!) in data transmission times between New York and London, as an indication of the importance of being the first to post, or first to cancel, transaction orders.[3]

HFT firms, Narang points out, monitor data and order trades in tenths of a millisecond, creating myriad technical challenges, between hardware, software and network engineering.

Another speed bump in the way of the highest frequency trading, Narang notes, has been the regulatory rule adopted in July 2011 that US equity brokers must enact a ‘risk check’ for every trade, in order to ensure that the size and capacity of the customer and the trade are valid – meaning not too big, which might indicate a trade made in error.

The brokerage software required to do this risk check slows down every trade, which could dampen HFTs speed by 0.5 milliseconds. In response, some HFT created their own brokerages, in order to reduce the time-lag of using someone else’s risk check system.

 

Where do I differ from Narang’s perspective?

1. This is a bit nitpicky, but I have a philosophical issue with Narang’s definition of ‘value’ and the role of ‘timing’ in investing.  He argues in both Chapter 3 and Chapter 16 that all investors, all the way from HFTs on the one side of the spectrum to long-term investors such as Warren Buffett on the other side of the spectrum, depend on timing to beat the market. He means that investors have to execute their purchase (or sale) in advance of others’ execution, and it’s this earlier-than-others’ action that creates value.

In Narang’s presentation, the good investor buys first, others follow suit later, and the original investor can then sell at a higher price based on the fact that others have driven up the price of the instrument.

Here I think Narang’s missed the point of value-investing as advocated by Buffett: Value is created by purchasing future cash-flows at an attractive price, and value investors are mostly indifferent to other investors’ subsequent actions. If you conceive of stock ownership with a perpetual time horizon, as a value investor like Buffett seems to, and that value derives from those future cash flows purchased at a  good price, then timing falls out nearly completely as a consideration.

With a quant’s focus on short-term price changes rather than the cash-flow potential of financial instruments, I can see why Narang views timing as a key to all investing, but I think that focus leads to inaccurately describing other types of long-term investing.

2. I cannot tell from what I read and really this book doesn’t say – whether a constant stream of high frequency orders and cancelled orders constitutes a confusing ‘spamming’ of equity market exchanges, to the detriment of slower investors.  Rapid fire orders, the non-quant critics say, create the illusion of market liquidity where it doesn’t really exist, as it may disappear quickly when non-quants actually seek to trade.

I can understand the way in which HFTs seeking arbitrage opportunities between linked instruments (like the S&P500 and the SPY) create liquidity and efficiency to some extent, in a way that may benefit the smooth running of markets.

In addition, Narang claims that rapid order cancellation is a necessary component of HFT strategies[4] and he claims that no evidence supports the complaints from non-quants about damage done by this type of activity.

But do millisecond orders and cancelled orders jam up the orderly execution of others? Does algorithmic swarming of orders around non-quant investment flow hurt the non-quants in a fundamentally unhelpful or unfair way?

Clearly some non-quants traders and investors believe they are harmed by excessive order-making and order-cancellation, when it comes to trading. Are the non-quants being overly paranoid or jealous of HFT profits, or is there actual harm done? Basically I need a non-quant trader’s opinion here, and I don’t think Narang sufficiently addresses this type of externality of HFT activity.

3. I’ve already mentioned that his book does not dwell on the potential for future catastrophic losses, nor did I expect it to, as the future is unpredictable and Narang would be engaging in speculation about the unknown.  On the other hand, we’ve had enough mini-shocks in the market attributable to algorithmic trading that I hope someone, somewhere (ideally in a government regulatory office) is thinking deeply about ways to prevent The Big One from happening.

Please see related book review: Flash Boys, by Michael Lewis

as well as Flash Boys – Not So Fast by Pete Kovac

Please see related post:  The Rise of The Machines

 

[1] People, what I’m trying to say is: SkyNet already took over the commodities and equity markets. Good thing there’s some real live ex-bankers who still write finance blogs. Ha ha. Or maybe that’s just what I want you to think.

[2] Maybe in this analogy traditional quant trading is a more complex George Clooney-character? I don’t know, still working on it.

[3] I don’t generally begrudge hedge fund traders anything, but now I’m starting to think they never have to wait those annoying 10 seconds for Game of Thrones to load up on Amazon Prime, like I typically do. Damn, I hate those guys. If I was a well-compensated hedge fund trader right now I would happily pay 12 million dollars just to eliminate that delay.

[4] to avoid a problem he explains well, which is the ‘negative selection’ of passive orders on an exchange.

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Interview: Lars Kroijer (Part II) – On Having An Edge In The Markets

Please see earlier podcast Interview with Lars Kroijer Part I – on the importance of Global Diversification

And my earlier book review of Lars Kroijer’s Investing Demystified.

Lars_Kroijer

In this discussion with author Lars Kroijer we talk about the main assumption of his book Investing Demystified – which I happen to completely endorse – which is that ‘beating the market’ lies somewhere between highly unlikely and impossible. The goal for individuals should be, instead, to earn market returns. Common behaviors that most investors do, like

1.  Paying extra management fees to an active portfolio manager, or

2. Stock picking yourself in order to ‘beat the market’

is a fool’s game, and will ultimately prove unnecessarily costly.

Later in the interview I asked Kroijer to describe his earlier book, Money Mavericks.

 

Lars:                Everyone’s got sort of their angle. My angle is really to start with asking a question of the investor, which is; do you have edge? Are you able to beat the markets? I don’t even make that call for you but I try to illustrate it is incredibly hard to have edge, and that most people have no shot in hell whatsoever of attaining it. Incidentally, that means that people like you are I are not necessarily hypocrites because it’s entirely consistent with our former lives to say we worked in the financial markets; we’ve bought and sold products, and as well informed as anyone. So if we didn’t have edge, edge doesn’t exist.

You could say I’m a hedge-fund manager, and I sold edge for a living, and I certainly thought I had it. But that doesn’t mean that most people, or even that many people have it. I start with the premise in this book of saying do you have it. Then I go on to explain it’s really bloody hard to have it. If you don’t have it, which most people don’t, what should you do?

Essentially, this is a book written for my mom. It kind of is. You wouldn’t believe, but as a former hedge-fund manager, every time I talk to my mom, who’s a retired schoolteacher, she’d always say which stock should I buy. I’d say mom, you could buy an index. And she’s like no, no. Then she’d say stuff like Dansker bonds have done so well, I should be buying it. And I’d be like no, don’t do that. She’s certainly not alone in that position.

Michael:          I completely agree with you, and when I think about how your book lays out four simple rules, starting with the one that you should be exposed to the broadest, most global index portfolio, and I have not done that, in terms of I am US-centric and small-caps centric, so I don’t have the broadest exposure. On the other hand, there is no gap between what you advocate, in terms of can you beat the market, and the way in which I invest, which is always I assume from the get-go ‑‑ and this is why that part of it resonated with me ‑‑ so clearly I, like you, say you can’t beat the market. The goal should not be to beat the market. The goal is to expose yourself to the appropriate allocation to risky markets, appropriate to your own personal situation. And then get the market return.

Lars:                You want to capture the equity-risk premium.

Michael:          The entire finance-marketing machine is about can you beat the market. Beating the market is a complete fool’s game. I think it’s particularly interesting, the other reason I wanted to talk to you, is because you’ve worked in the hedge-fund world, you’ve been a hedge-fund manager, an advisor to hedge funds. I worked on Wall Street. I founded my own fund, and it’s all about that theory that you can, in a sense, have an edge in the market. Yet, the more you know about how it actually works, the more extremely bright people, with the highest powered computing power and the most cutting-edge ideas ‑‑ and you think about the power they had, and we had, and the chances of any retail investor or in fact any of those investors themselves beating the market, or as you say, having edge, is just impossible.

Lars:                Add to that they’re at a huge cost disadvantage, informational disadvantage, analytical disadvantage. It’s so unlikely, and this is why always start with you’ve got to convince people they can’t. That’s actually probably the toughest thing. You’re fighting not only against conventional wisdom, but you’re also fighting this almost innate thing we have, that you somehow have to actively do something. You somehow have to pick Google or whatever.

You have to have a view, and you’re smart, you’re educated, doing something to improve your retirement income, or whatever you’re doing. What you and I are advocating is essentially do nothing. Admit you can’t. I think that rubs a lot of people the wrong way.

Michael:          You need to bring humility to the situation. I cannot do better than the market. I can do the market but I can’t do better. It’s a very hard, humble approach, but in my opinion and in your opinion it’s the correct approach.

Lars:                Yeah. And I also think we’re extremely guilty of selective memory. We remember our winners. That adds to the feeling. It’s a bit like when you ask guys whether they’re an above-average driver. 90% will say they’re above average.

If you ask stock pickers whether they do better than average, 90% of stock pickers would say yes, even more than that. I think there’s a lot of that, a huge degree of selective memory. It’s a shame because I think it really hurts people in the long run.

Michael:          It makes conversations along the lines of what you mentioned with your mother conversations with me and other friends and retail investors in stocks ‑‑ hey, I’ve got this great new stock. I’m such a bummer when I talk to them because I say really? I don’t know what to say.

Lars:                The alternative is to say you don’t know what you’re talking about, which is not an all together pleasant thing to say. It’s not how you make friends. Certainly not when you’re moving to a new town, like you did.

Michael:          I’ve written about this on my site before, but essentially when somebody talks about individual stocks, to me what I’m hearing is I went to Vegas. I put money on 32 and 17 on the roulette wheel. Look how I did. I just don’t know how to respond to that. That’s fabulous, you hit 17 once. I don’t know what to say.

Lars:                This is conventional wisdom because to most other people that person will sound smart and educated. They will say here’s why I found this brilliant stock and here’s why it’s going to do great. Most people in the room will consider that person really smart, educated, and someone who’s got it. They’ll sound clever about something we all care about, namely our savings. And you think if I could only have that, I’d want that. It’s tough to go against that.

This is why I think the biggest part of this book is if I could get people to question that. Maybe even accept they can’t beat the market. Then that would be the greatest accomplishment. I think a lot of the rest follows. I haven’t come up with any particularly brilliant theory here. It’s sort of academic theory implemented in the real world and that’s pretty straightforward.

Michael:          It cuts against the grain of what I call on my site “Financial Infotainment Industrial Complex,” which is there’s a lot of people invested in the idea that markets can be beaten, that individual investors can play a role.

Lars:                Think of how many people would lose their jobs?

Michael:          Yeah, it’s an entire machine around this idea. It’s very hard to fight against that. It’s very boring to fight against that. I joked about it in my review; your book is purposefully hey, I have some boring news for you. Here’s the way to get the returns on the market and sleep better at night.

Lars:                I sort of compared going to the dentist. You really ought to do it once in a while and think about it. I completely agree with you. I mentioned in the book ‑‑ when I thought about writing this book, it was one of these things that slowly took form, but there’s this ad up for one of these direct-trading platform websites. And there was a guy who was embraced by a very attractive, scantily dressed woman. He was wearing Top Gun sunglasses, with a fighter jet in the background. It said something like “Take control of your stock market picks.”

I thought fuck; are you kidding me? Really? Whoever falls for that, I’d love to sell them something.

Michael:          Oh yeah, they’d be a great mark.

Lars:                You also hear a lot about the quick trading sort of high-turnover platforms. It’s something like 85-90% of the people on there will lose money. You have a lot of these companies, their clients, 85-90% will lose money.

It’s almost akin to gambling. You can argue is it gambling, which is a regulated industry in a lot of countries, for good reason, because it costs you a lot of money. And I think certain parts of this circus is the same. But it’s very tough to regulate, and I’m not saying you should. But it could cost a lot of people a lot of money.

I feel very strongly about this. I’m not saying edge doesn’t exist. I’m saying it’s really hard to have it. And you’ve got to be clear in your head why you do, and what your edge is.

Michael:          I have not read [Kroijer’s previous book] Money Mavericks but give me a preview so when I do read it, what am I going to get?

Lars:                It’s a very different book. Money Mavericks is essentially the book of how someone with my background, a regular kid from Denmark ends up starting and running a hedge fund in London, and all the trials and tribulations, humiliations and all that you go through in that process. I thought when I wrote it lots of things have been written about hedge funds, and a lot of it’s wrong. Namely this whole idea that we’ll all drive Ferraris and date Playboy Bunnies and do lots of cocaine.

I thought very little was written from a first-hand perspective, someone who’s actually set up a fund and gone through the fund raising and trying to put together a team. And the humbling failures, and successes, so I thought let me try to write that. I did. I found myself enjoying the process of writing it, which I guess was part of the reason I did it. But then it got published, and it ended up doing really well.

I was actually kind of pleased about that because I thought it’s very nonsensationalist. We didn’t make billions, we didn’t lose billions. No one defrauded us and we didn’t defraud anyone. So those are the four things you normally think about when you think of hedge funds.

Michael:          If you’re trying to sell books, yes.

Lars:                Yeah, so this is none of that. It’s just a story of some guy starting a hedge fund, how it all worked out, all the little anecdotes. I was really pleased that resonated. In fact, the best feedback I got was from people in the industry who were like yeah, that’s exactly what it was like. I’m sure you would appreciate it because you’d have lived a lot of it. Begging for money.

Michael:          No, that’s my second [imaginary] book. My first [imaginary] book is personal finance. My second book is gonna be that experience, your books in reverse.

Lars:                I think you’d enjoy it. That resonates with a lot of people, including what you also would’ve experienced, this whole undertone of anyone can start a hedge fund; I’m going to quit my job and raise 50 million dollars. I’m going to build a track record and then raise another couple of hundred million. Then I’m going to be rich and happy. The number of times I’ve heard some version of that makes me want to puke. When you’re actually doing it you realize how incredibly hard it is.

Michael:          Very stressful.

Lars:                It impacts your health, your life, your family, all of that. Then that’s before you try to make or lose money.

Michael:          You actually have to do it, get a return that people are happy with, and they’re happy to stick with you. Does your fund exist still?

Lars:                No, it’s just my own money. I had incredibly fortuitous timing. I returned all capital in early ’08. But no skill, it was for mainly my own reasons, sanity, health, and family. I’ve been lucky.

Michael:          As we always say better lucky than good. That’s more important.

Lars:                For me there was a big part of that. I thought let’s quit while you’re ahead. To be honest, I have yet to wake up one day where I miss it. I get to wake up one morning where I wish I was heading to Mayfair to turn on to Bloomberg and be at it.

 

Please see related podcast Interview with Lars Kroijer Part I – on the importance of Global Diversification

Please see related book review on Investing Demystified by Lars Kroijer

 

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