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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Square’s Small Business Lending – Innovative?

square_capital_lending_to_small_businessA friend sent me a link this morning to an article about Square Capital’s small business cash advance business. The credit card processing company Square claims to use credit card receipts data to prompt it to advance money – within as quickly as 24 hours from the request – to existing small business customers, even before they ask.

My first reaction, of course, is that we should beware banks bearing gifts of “easy, fast” money. That’s the style of pay-day lenders and its never a good thing. I have received more than my fair share of business credit card solicitations in the mail, with high interest and hidden fees, to remain skeptical of this kind of innovation.

On the other hand, Square Capital’s money seemingly comes with

1. A 10% fixed fee (high, but a reasonable annual rate when it comes to small businesses);

2. Flexible payment terms. Merchants are expected to pay on their own time frame, out of ongoing credit card receivables;

3. No paperwork or waiting, which is pretty rare in the small business lending space.

For a certain type of high-growth small business customer, I can imagine the appeal of the Square Capital offer.

Although the article in Wired emphasizes the innovative aspect of Square’s use of Big Data to identify potential customers for their cash advance, the core of what they’re doing is really a version of “factoring,” the oldest type of commercial lending in the world. Instead of purchasing future receivables at a discount, Square will simply ‘factor’ the receivables by charging an extra 10% fee on top of the amount of repayment. While traditional factoring, or nouveau factoring like Square Capital isn’t particularly new, it can fill business’ need for fast money at a time of high growth.

Small businesses without deep pockets often have few choices about where to raise capital.

There’s the most expensive way: Selling equity to friends and relatives.

Then there’s the next most expensive way: High-interest credit card debt.

And then there’s the least expensive, but slow way: Apply for, and receive, a traditional bank loan.  This rarely works.

I have claimed in the past (and still basically believe) that, despite their rhetoric, the vast majority of banks are not in the small business lending business at all.  Banks would prefer to lend against real estate or cars because both types of loans can be offloaded from their balance sheet through securitization. All other types of loans have been effectively replaced by personal or business credit cards.

To the extent Square Capital can update an old lending model – factoring – with a new data-rich approach to get small businesses money quickly, the innovation sounds worth knowing about.

 

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Video: Cool Multiplication Math Trick

multiplication_with_linesThe following video will do nothing to improve your understanding of finance (my primary Bankers Anonymous goal) but if you, like me, occasionally have the opportunity to teach math to 8 years olds, you might find this as cool as I do.

I had never seen the ‘visual way’ to teach and/or do multiplication until I saw this on my Facebook feed. Also, I have no particular idea why it works, but would be happy to learn, in case any readers can shed some light.

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Some Terrible Financial Advice: The “Emergency Fund”

sacred_cowIf you frequently read articles, books and blog posts about personal finance – as is my unfortunate wont – you quickly stumble upon one of the sacred cows of the genre: “The Emergency Fund.”

“The Emergency Fund,” the grown-ups tell us (as in this post that showed up in my inbox yesterday,) consists of 3 to 6 months of wages, socked away in a safe CD or savings account at the bank, untouched by regular expenses. Only when you stumble to the emergency room for your uninsured, unplanned, uninvited $5,000 appendectomy, or only when you lose your job and take 6 months to land a new one, are you allowed to dip into this account.

This sacred cow of personal finance, however, deserves to be cow-tipped at midnight. Because, mostly, its a complete load of bullcrap.

I’m not saying it’s a bad idea to have some ready money in the bank. Of course it is a good idea. Money in the bank is lovely. The idea is fine. But it stinks as a piece of personal finance advice.

In reality, there are three types of people, and none of these three types need the ‘Emergency Fund’ sacred cow advice.

Group One – You have money in the bank, (or stocks in the market, or a trust fund annuity, or whatever) without having been told. Maybe you were a fortunate beneficiary of the genetic lottery and tax code (The first $5.5 million inherited is tax free!), or maybe you just have a squirrel-like capacity for storing nuts. Good for you, but you really don’t need to be told about the Emergency Fund rule. The advice is irrelevant. You’re past that, you got that covered.

Group Two – On the edge of solvency, trying to make it through every month with all bills paid, but occasionally slipping into deficit. This includes about 50% of all Americans and 90% of Americans under the age of 30. This is the group to whom the “Emergency Fund advice” gets directed by the concerned grown-ups with a furrowed brow.

Now, this Group Two might, theoretically, be interested in the advice, but it really doesn’t even make financially savvy sense to follow it.

Here’s the mathematics of why. If you’re at break-even financially, with occasional monthly deficits, then you’ve got some credit card debt. You’re like 55% of Americans who carry a balance from month to month. You might pay the national average of 12% on that credit card principal balance. If you’ve got a checkered pay history you’re looking at 18% to 29% interest on the balance.

emergency_fund

To make the math easy, let’s assume you have $5,000 in credit card debt, on which you pay 15% per year in interest, which totals $750 per year in interest.

Ok, now let’s say you somehow, despite paying significant interest on your debt burden, manage to accumulate a $5,000 Emergency Fund, just like they told you to. Congratulations. Now the adults convince you to ‘do the right thing’ and put it in an untouchable savings account.

Here’s some more easy math: You can safely earn up to1% annually on that Emergency Savings, or $50 per year.

So, in sum, the sacred cow advice is to pay $750 per year in interest while earning $50 in interest? Let’s just lock in a $700 loss per year! So even for this group, to whom the advice always gets passed, it doesn’t make sense.

What makes financial sense for Group Two, instead, is to have close to zero savings, but also to have close to zero credit card debt, with open lines of credit to be drawn on in an emergency. In that scenario, you neither earn interest nor pay interest, and you’re certainly not safe and comfortable, but at least you don’t lock in an annual $700 loss on your money, due to bad advice.

Because let’s face it: If you’re in Group Two, the choice isn’t between having an Emergency Fund or not. The choice is between having high-interest debt on the one hand and low-interest savings account on the other while paying the difference to your bank(s), or having neither and keeping the money yourself.

In a related story, nobody in Group Two actually has an Emergency Fund.

Group Three – Totally indebted, with no prospect of savings. This includes the chronic under- or unemployed, anyone whose house is in foreclosure, or is bankrupt, or not paying their credit card bills. At any point in time this is going to include about 25% of all Americans.

Yes, an Emergency Fund would be fabulous, but it’s totally irrelevant for this group.

I know I’m being flip and overly simplistic about this, and for the five readers who are about to write in to tell me about their emergency fund and what a great thing it has been for them, I apologize in advance.

You know who really likes the ‘Emergency Fund’ advice? Those five people. The ones who already have one.

You know who else really likes the ‘Emergency Fund’ advice? Banks, because they can earn the interest rate spread between your debt and your savings.

But for the 299,999,995 other people who have done the math on the classic Emergency Fund advice and agree with me: Respect.

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Time out for Poetry: Ithaka

The students in my Personal Finance course took their final exam yesterday, and many of them will graduate from college very shortly.

One of the themes of the course, of course, was ‘getting wealthy.’ In addition, that’s the theme of the book I’m trying to write.  One of my friendly early readers of my book proposal draft mentioned a poem this week that seems apt: For college graduates starting on a journey, for people trying to get wealthy, and for anyone else.

Constantine_Cavafy
Constantine Cavafy

Ithaka

When you set out for Ithaka
ask that your way be long,
full of adventure, full of instruction.
The Laistrygonians and the Cyclops,
angry Poseidon – do not fear them:
such as these you will never find
as long as your thought is lofty, as long as a rare
emotion touch your spirit and your body.
The Laistrygonians and the Cyclops,
angry Poseidon – you will not meet them
unless you carry them in your soul,
unless your soul raise them up before you.

Ask that your way be long.
At many a Summer dawn to enter
with what gratitude, what joy –
ports seen for the first time;
to stop at Phoenician trading centres,
and to buy good merchandise,
mother of pearl and coral, amber and ebony,
and sensuous perfumes of every kind,
sensuous perfumes as lavishly as you can;
to visit many Egyptian cities,
to gather stores of knowledge from the learned.

ithaka_map
When you set out for Ithaka

Have Ithaka always in your mind.
Your arrival there is what you are destined for.
But don’t in the least hurry the journey.
Better it last for years,
so that when you reach the island you are old,
rich with all you have gained on the way,
not expecting Ithaka to give you wealth.
Ithaka gave you a splendid journey.
Without her you would not have set out.
She hasn’t anything else to give you.

And if you find her poor, Ithaka hasn’t deceived you.
So wise you have become, of such experience,
that already you’ll have understood what these Ithakas mean.

–Constantine P. Cavafy

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Guest Post: The Simplest Investment Approach, Ever

Editor’s Note: Lars Kroijer is a former hedge fund manager, and the author of two books. He previously posted here on the advantage of conceding ‘edge’ in personal investing. I appreciate his debunking the value of high-cost financial services.

 

You probably can’t outperform the market – here is how you should invest once you accept that

As investors we are bombarded with stock tips about the next Apple or Google, read articles on how India or biotech investing are the next hot thing, or are told how some star investment manager’s outstanding performance is set to continue.  The implicit message is that only the uninformed few fail to heed this advice and those that do end up poorer as a result.  We wouldn’t want that to be us!

do_you_have_an_investing_edge
Do YOU have an edge investing? Doubtful.

What if we started with a very different premise?  The premise that markets are actually quite efficient.  Even if some people are able to outperform the markets, most people are not among them.  In financial jargon, most people do not have edge over the financial markets; they can’t consistently outperform the market by picking different securities / sectors / geographies from the market as a whole, especially after costs.  Nor are they able to pick which of the thousands of fund managers have the ability to do it for them.  Accepting, embracing, and acting on this absence of edge should in my view be a key moment in most investor’s lives.

The absence of edge does not mean that you should avoid investing.  Doing so would exclude you from potentially exciting long term returns in the equity markets, or benefitting from the security of highly rated government bonds.  Also, what else were you going to do – leave your money under the mattress or in a bank at zero interest?  Instead we should assume that the current market prices of securities capture all available information and analysis, and that the price reflects that security’s future risk/return profile.  In equities we should then pick the broadest possible selection of stocks because just like we don’t know which one stock will outperform, we don’t know which sector or geography will outperform.

And what is broader than an index that track equities from all over the world in the proportion of value that market forces have already put on them?  With a world equity index tracker we maximize diversification and minimize exposure to any one geography, sector, or currency.  And since we simply track an index (like the MSCI All Country World, etc.) it is very cheap to put together for a product provider like Vanguard, iShares, etc., and thus cheap to us.  If an all equity exposure is too risky, you can combine this world equity portfolio with government bonds in the proportions that suit your risk profile.  The lower the risk desired, the more bonds you want.

global_etf_investing
How much of the world equity market to invest in? All of it

So my key takeaways to most investors can be summarized as follows:

  1. You almost certainly do not have edge in the financial markets.  That’s ok.  Most people don’t, but you should plan and act accordingly.
  2. There is an easy and cheaply constructed portfolio which is close to optimal.  It combines the highest rated government bonds in your currency with the most diversified possible world equity portfolio.  Get close to that in the right proportions, which depend mainly on your risk tolerance, stick to it and in my view you are doing better than 95% of all investors.  That’s it – two securities: one being an index tracker of world equities and the other a security that represent government bonds of maturity and currency that match your need.  Both equity and bond exposure perhaps via an ETF.  Simple perhaps, but you capture an incredible diversification of exposures via the equities and the portfolio is at your risk appetite when you incorporate the bonds in a proportion that suit your risk.  You can add other government and diversified corporate bonds if you have appetite for a bit more complexity in your portfolio, but the portfolio is very powerful even without those.
  3. Your specific circumstances do matter a great deal.  Think hard about your risk appetite and optimizing your tax situation.  But also pay attention to your non-investment assets and liabilities – many people already have a disproportionate exposure to their domestic economy through their house and some sector via their jobs.  Don’t add to this concentration risk with your investment portfolio.
  4. Be a huge stickler for costs, don’t trade a lot, and keep your investments for the very long run.  The portfolio above should only be implemented via extremely cheap index tracking products that charge 0.25% per year or less.

Follow these steps and I think you will have a personal portfolio strategy that lets you sleep well at night, knowing that you have created a powerful and diversified portfolio cheaply, tailored to your risk appetite.  To emphasize the point of costs, suppose you are a frugal saver who diligently put aside 10% of $50,000 annual income from the age of 25 to 67 that you invest in world equities.  Further assume markets return 5% real per year in line with historical returns (ignoring taxes).  Considering a typically 2% annual cost difference between an index tracking product and an actively managed fund (potentially in addition to the cost of an advisor), as you get ready to retire at age 67 the difference in the savings pot is staggering.  You are left better off by perhaps $250,000 in today’s money simply by investing with an index fund as opposed to an active manager.

porsche_savings_from_low_cost_mutual_funds
Can index funds save you enough to afford this?

If you think you have great edge in the market and think you could easily make up this 2% annual cost difference then by all means pick an active manager or your own stocks.  If not, then the sooner you shift out of the expensive investment products or active stock picking and into cheap index tracking products the better off you will be.  To put things in perspective consider that these additional and unnecessary fees for just one saver over their investing lives could buy 6 Porsches.  And paradoxically this is money paid to the finance industry from a saver who could typically not afford to drive a Porsche.

 

Lars Kroijer is the author of Investing Demystified – How to Invest Without Speculation and Sleepless Nights from Financial Times Publishing.  He founded and ran Holte Capital, a London based hedge fund in 2002.  You can follow him on Twitter @larskroijer.

Please also see related posts:

Agnosticism Over Edge Can Earn You 7 Porsches

Book Review of Investing Demystified

Podcast Part I – Lars Kroijer on Global Diversification

Postcast Part II – Lars Kroijer on Having Edge

Lars on CNBC discussing his book Money Mavericks

 

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