Category: trading

Notes on The Man Who Solved The Market (Jim Simons)

Like seemingly everyone who works on the buy side, I have been reading the Zuckerman book about Jim Simons of Renaissance. The book spends a lot of time with the big personalities who have worked at Renaissance over the years. However there are some mathematical and technology hints:

Jim Simons’s academic field is geometric invariants in algebraic geometry.

Renaissance Technology worked with hidden Markov models, fit using the Baum-Welch algorithm. This algorithm has a Bayes update step, and a backward & forward process that feels like backprop. They also used high-dimensional kernel regression.

Henry Laufer worked with (vector) embeddings, very early. He also pushed for a single cross-asset and cross-asset-class model, so they could use all the cleaned “pricing” (market) data. They included assets with known bad data, but assets that nonetheless looked like existing assets in the model. This is maybe what we would call clustering, now-a-days. Everyone had access to the source code, even the administrative staff at first.

They tempted academics by working just one day per week, to see if they found trading interesting. They explicitly avoided trying to find economic sensibility for their strategies, but still followed a “scientific” method.

René Carmona imputed market data, which seems controversial.

Jim Simons invested in private companies alongside the systematic trading, especially in technology companies. This is probably because of the Nasdaq bubble.

They used a simple day-of-week seasonality model, at least for their futures trading.

They provided liquidity when “locals” de-risk, being in the “insurance business”.

They had a betting algorithm around the probability of moves in futures, not used for stocks at first. This was presented as opposed to statistical arbitrage with a factor model.

Their stock transaction cost model was the “secret weapon”. It was self-correcting, by “searching for buy-or-sell orders to nudge the portfolio back”. This increased their holding period to two days, on average. The strategy had very low capacity at first, however. In general they were not the best at trading, but at “estimating the cost of a trade”.

Around the time of the Nasdaq bubble bursting, they were trading 8,000 stocks. However this strategy was only 10% of the business. The futures strategy was still the mainstay, which was probably their chartist model.

Their use of basket options were 1) a tax optimization, but also 2) a way to cap downside, 3) isolate risk, and 4) increase leverage.

The internal Medallion fund is short-term, capped at $5b capital with all external clients eventually bought-out. The maximum capital is difficult to reconcile with Jim Simons’s compensation motivation, so it probably reflects limited capacity of the strategy, which means it trades in less liquid, smaller stocks and futures. In 2002 they were running 12x leverage in Medallion ($5b capital, $60b exposure given options).

They sought out astronomers because of their understanding of low signal-to-noise problems. Their named “Déjà Vu” strategy seems like a pairs or cointegration strategy.

Their strategy gets a 50.75% hit rate.

Why write this book now? Jim Simons is nearing the end of his career. Could also be transparency after the Mercers helped get Trump elected.

They at least use the terminology of risk factors and baskets: “RenTec decrypts them [inefficiencies]. We find them across time, across risk factors, across sectors and industries.”

The author uses strange terminology, suggesting that Zuckerman has actually not talked with many quants. For example, “trenders” for momentum style traders, and “data hunters” instead of “data scientists”.

Also the anecdote about Magerman unleashing “a computer virus that was infecting Renaissance’s computers” is clearly bullshit, and in a way that makes me doubt the author’s understanding of technology in general.

What is a Promise Worth?

How do you prevent hyperinflation without destroying the economy? The answer ain’t Bitcoin.

A virtual currency like Bitcoin uses a decentralized proof-of-work ledger (the block chain) to solve the the double-spending problem. “Satoshi Nakamoto” deserve serious accolades for this clever architecture, but Bitcoin has a few serious problems. The first is its lack of security. The infrastructure around the currency is shoddy and fragile. The website where 80% of Bitcoin trading currently occurs is called the Magic: The Gathering Online Exchange (a.k.a. Mt.Gox). Recently Mt.Gox has crashed and been cracked, and does not support easy shorting. More importantly, the Bitcoin system may never mature without a central authority spending a lot of (traditional) money to build-out the infrastructure, with negligible or negative financial return-on-investment. Without a social program, in other words.

Even if Bitcoins did have the infrastructure and liquidity of a traditional currency like U.S. dollars or Japanese yen, there is another more fundamental problem with Bitcoin becoming the money of the future. Bitcoins are intrinsically deflationary.

The future will always be in one of two states: Either Bitcoin miners are running up against the limits of Moore’s Law, and are unable to profitably mine new Bitcoins. Or some bullshit singularity has occurred, giving us all access to infinite computational power. In this state, we would run up against the Bitcoin architecture’s hard-coded monetary supply cap of twenty-one million Bitcoins.

If human desire is infinite, then people will always want more money for goods and services. (All else equal, of course!) So we have an intrinsically fixed supply of a fungible good along with increasing demand. Therefore a Bitcoin is guaranteed to increase in value over time. Any fraction of a Bitcoin is guaranteed to increase in value over time. This may sound good if you happen to have a lot of BTC (Bitcoin) in your wallet. However at a macroeconomic level deflation is catastrophic, which I will explain.

A Hamburger on Tuesday
Would you trade something today that is certain to be worth more tomorrow? What about if the “something” is a currency, a good that has no intrinsic value other than it being money? (You cannot heat your house with the digital dollars in your checking account. Gotta pay the utility company first.) In an emergency you might spend your deflating currency, but in general you should hold onto your BTC as long as possible. And since there is uncertainty about the degree to which Bitcoin will deflate, the market will not instantly price BTC correctly. The BTC price of goods and services will not instantly adjust to match the level of computational power available to miners.

Some Bitcoin proponents think we can instantly discount the BTC price of all goods and services to sync-up with systematic BTC deflation, but this would need a seriously high-tech payment infrastructure. Square and Stripe are trying, but does anyone seriously believe the prices of all goods and services can be discounted in real-time by a macroeconomic indicator? We can’t even ditch the wasteful dollar bill!

The Bitcoin bulls also emphasize a currency’s dual role as a means of transaction and a store-of-value, but intrinsic deflation trashes both roles simultaneously. As a means of transaction, deflation makes allocating capital (money) across projects and activities difficult, and again, requires that perfect payment infrastructure. Since systematic deflation destroys every asset’s value and discourages economic activity, deflationary currencies do badly as stores-of-value. Less economic activity means GDP contraction and decreased livelihood. Yes, despite what Professor von Nimby may have spewed in your Postmodern Marxist Studies class, GDP is a very strong indicator for overall human happiness. Perpetual economic contraction makes your savings account irrelevant. You might have a zillion super-valuable BTC in your digital wallet, but you have nothing to spend them on. In other words, if you think (hyper-) inflation is bad, deflation is even worse…

Passing Notes
Let us go back to a few of the original Bitcoin goals. Bitcoin proponents want an efficient, liquid currency immune from the distortion caused by a government or central bank’s monetary policy. This is reasonable since inflationary monetary policy has a sad history of trashing peoples’ savings accounts, in places like the Weimar Republic or more recently in Argentina. So how can we build the decentralized, non-deflationary currency of the future?

Notes are an ancient monetary concept desperate for rethinking in the Internet age. At its most basic level, a note is a promise to exchange money, goods or services at some point in the future. However a note is not quite a futures contract, because the promise need not ever be exercised. And a note is not really an options contract, because a note need not ever expire. The most obvious form of a note is what a U.S. dollar bill used to represent when we were on the gold standard. It was a promise that the holder of the note (dollar bill) could exchange the note for a dollar’s worth of physical gold at any time. Notes are a lot easier to store and deal with than gold, and so they make a lot of sense for getting work done efficiently. We could also talk about the fungibility of notes, but that is less important at this point. And notes are definitely easier to move around than loaves of bread, head of cattle, barrels of oil, or other physical stuff with intrinsic value.

A hoard of notes would also be a decent store-of-value in your savings account, as long as the writer of the notes remains solvent and trusted. For example, a million dollars worth of U.S. gold-convertible notes is a great retirement nest-egg, since most normal people expect the U.S. government to honor its promises for a long time.

When the entity writing the note is trusted by just about everyone — expected to honor its contract — then the writer can declare the notes to be unconvertible, all at once. The notes become fiat currency, currency that is not explicitly backed by anything but the trust that the note writer will not issue too many notes and inflate away peoples’ savings.

Why does most global economic activity happen using a handful of fiat currencies, like the U.S. dollar or Euro? Nations have traditionally supported their (fiat) currencies through policy and war, because before the Internet trust did not scale. Imagine a small town. Mel and Stannis are neighbors in this town. Mel trusts Stannis to honor his promises, and accepts a note from Stannis in return for mowing Stannis’s lawn for the next year. Stannis’s note he writes for Mel says something like “Stannis promises to give the bearer of this note 100 loaves of bread, anytime.” Mel’s landlord Dave also trusts Stannis, and so he has no problem taking Mel’s note as rent. Stannis has essentially printed his own money that is a lot more convenient that baking 100 loaves of bread. Now in the next town over, no one really knows Stannis. Therefore Dave will have a hard time making use of Stannis’s note when he visits there to spend time with his grandparents. Dave and Mel trust Stannis, but the people living in the next town over do not.

In this parochial example, trust has not scaled across the network of transactions and relationships. The money Stannis created, the note he wrote, is not all that useful to Mel. Instead she could insist on being compensated by a note from an entity more trusted the world over, say the First Bank of Lannister which has a branch in both towns. Mel, Stannis, Dave and his grandparents all probably trust the First Bank of Lannister to pay its debts.

If Dave wants to spend Mel’s note written by Stannis in the next town over, he can ask a third party to guarantee or sign-off on the note. This can be done by exchanging Stannis’s promise for a promise by the First Bank of Lannister, which is more trusted throughout the realm. The First Bank of Lannister would be compensated for extending its trust by taking a cut of the promise from Stannis.

So before he leaves on his trip, Dave takes his rent check (note) from Mel into the First Bank of Lannister. They write a new note saying “The First Bank of Lannister promises to give the bearer of this note 95 loaves of bread, anytime” and gives this note to Dave in exchange for the note written by Stannis. The bank has decided to take responsibility for chasing down Stannis if he turns out to reneg on his promise, and in return they are compensated with the value of five loaves of bread. Here the Bank of Lannister has also issued its own currency, but more as a middle-man than someone doing economic activity like Mel’s lawnmowing or Dave’s landlording.

This middle-man role is very important but also difficult to scale across a physical economy. Eventually someone refuses to trust the First Bank of Lannister, and then the chain of economic activity halts. This is why the world’s global economy has consolidated onto a few currencies, for reasons of both efficiency and trust.

The Internets
In the age of the Internet and pervasive social networks like Facebook and Linkedin, everyone is connected in a global network. This is the famous degrees -of- Kevin Bacon or Erdös Number concept. Any two people are connected by just a few steps along the network. Most of Stannis’s friends on Facebook would be willing to accept a note or promise from Stannis, and the same holds true for Dave, Mel and the First Bank of Lannister’s social networks. Since the whole of humanity is probably connected in a trust network, software can automatically write those middle-man notes along the chain of connections. Therefore any two people can automatically find a chain of trust for spending money.

Back to our example, but in the age of the Internet. Mel, Dave and Stannis all trust each other, since they are Linkedin contacts. Peter reneged on a note a few months ago, so no one really trusts Peter except Stannis. Everyone unfriended Peter but Stannis, so Peter has a very isolated social network. This time around we do not need to care about geography and small towns, since everyone is connected via the Internet and social networks. Let’s say Peter wants to buy an old iPad from Dave, and Dave thinks the iPad is worth about a hundred loaves of bread. Peter could try to write a note promising a hundred loaves of bread, but Dave would not accept this note since he does not trust Peter. Now for the cool part.

Peter goes to a notes exchange website (NoteEx), and asks for a hundred-loaf note that Dave will trust. The website knows that Stannis trusts Peter, and that Dave trusts Stannis. (See the triangle?) Through the website, Stannis writes Peter a note for one hundred loaves of bread that Peter gives to Dave in exchange for the iPad. Dave has a note he trusts in exchange for his good, at the price he wanted. Similarly Stannis receives a note written by Peter, whom he trusts. This note might be for 105 loaves of bread, giving Stannis a little cut in exchange for trusting the dodgy Peter. This five loaf interest, cut or edge is Stannis’s compensation as a middle-man.

This can all be done automatically by the NoteEx server with a list of middle-men volunteers. People volunteer to be middle-men up to a maximum amount of exposure or risk (i.e. one thousand loaves of bread total). Or middle-men could even offer to guarantee up to two degrees of Kevin Bacon away, for a much higher cut. After a bunch of people volunteer to be middle-men in the NoteEx process, all economic activity could be subsumed, with social networks ensuring that you only ever receive payment (promises) from people you trust. A NoteEx transaction could have more than one middle-man, up to the six degrees of Kevin Bacon maximum that we assume connects all people.

Ironically, the good or service underlying the notes is not all that important, since notes are very rarely redeemed. In the same way that powerful governments can support fiat currencies backed by nothing, fiat notes backed by loaves of bread will not actually turn everyone into a baker. Usually notes are exchanged with their value being the trusted promise, but not necessarily the realization. Heavy stuff here.

Decentralized Bakery
The NoteEx website would be built atop an open and standard protocol, and competing notes exchanges could borrow from the Bitcoin architecture to be decentralized (i.e. the shared ledger). More importantly, there would be a natural level of inflation in the system as the cuts or interest that middle-men demand increase the total value of all promises across the economy. And of course, notes are an excellent store-of-value because who would you trust more to support you in an emergency or retirement than your tightest friends & family?

So! We have a theoretical monetary system free from government interference, and one that encourages economic activity through modest and natural inflation.

Under the Hood of Buying & Selling Predictions

How do those futures markets like Betfair and Intrade work?

The managers of a prediction market decide upon a finite number of prediction contracts. A contract is essentially a description of some hypothetical future event. For example, a contract might be “Fadebook trades at least $50 per share in 2012.” Another important aspect of the contract specification is the anticipation and preemptive resolution of ambiguity. If Fadebook does a 2:1 split, does the event become “…at least $25 per share?” What if the asking price for Fadebook shares reaches $50, but the bid price does not? Contracts also specify an expiration date, a time by which the event must occur. In the case of the Fadebook contract, the obvious expiration date would be January 1st of 2013. For other contracts, the expiration will be arbitrary to allow for a final decision on the event’s occurrence or nonoccurrence.

Virtual Currency
A prediction is a single user’s opinion on the likelihood of the contract’s event occurring. The prediction market encourages users to form opinions about contract events, and encourages users to wager virtual currency on a contract. Users purchase virtual currency (henceforth “credits”) with real money, or perhaps are granted an amount of virtual currency in a freemium offering.

Contract Size
Each contract also specifies a size, which is both the minimum number of credits a user can wager on a contract, and the marginal increase in the size of a wager. If the prediction market choose a size of “5 credits” for the Fadebook contract, then an individual user can wager only 5, 10, 15… credits on the contract. Contract sizes are necessary in order better match both sides of the wager. Somewhat confusingly, experienced traders refer to each increment of the contract size as “a contract.” If I wager 15 credits on the Fadebook contract with a size of 5 credits, then I am said to be wagering “3 contracts.”

Direction
When making a wager on a contract, the user specifies the number of credits she will risk, in contract size units, and the direction of the wager. If a user believes the event is certain to occur and the user is eventually proven correct, she will profit from buying a contract whenever its likelihood is less than 100%. If a user believes an event is certain not to occur and turns out to be right, the user will profit from selling the contract when its likelihood is greater than 0%. The buyer of a contract is said to be long and the seller of a contract we call short.

Payoff
When the contract’s event occurs, the long side earns the contract size in credits for each contract, less the likelihood level where she initially make the wager. So if Fadebook’s stock hits $50 in October of 2012 while I am long 3 contracts bought at 25% likelihood, then the prediction market would immediately close the contract and credit my account with 5 credits (size), times 3 contracts, less 25% of the total, or 11.25 credits. The opposite also occurs for the short side. In this case, the 3 contracts the short side sold are closed out worthless at 0%, and the short’s account is reduced by 5 credits size, times 3 contracts, times 25% of the total. Again this is 11.25 credits but deducted from the short side of the wager.

If the contract expires as the year 2012 comes to a close, and I shorted 2 contracts at 60% back in February of 2012, then I will earn a profit of 5 credits size, times 2 contracts, times 60% or 6 credits. This is exactly what the long side of those 2 contracts would lose. The long side may be 1 user, or 2 different users each long 1 contract.

By definition, the long and short sides of a contract will always balance. A user is not able to be long a contract unless another user is short. This would be similar to a real futures contract traded in Chicago or New York, but where the long side is committed to “buying” the event for 100% if it occurs.

Orders
The current likelihood (henceforth “price”) of a contract is determined by looking at the contract’s order book on the prediction market. Order books are how buyers and sellers of contracts are matched, and indicate a prediction contract’s current price or likelihood. An order book is an ordered list of buying and selling prices. If there is no market in a contract and a user wishes to make a wager, her estimated likelihood becomes the best buying or selling price for the contract. From now on we drop the price or likelihood’s percent sign for brevity.

Say the Fadebook contract has just been listed and publicized on the prediction market. If I believe the event is very likely to occur, then I might offer 99 to anyone willing to sell at 99. If I am correct, then I will eventually earn 1 credits per contract for my trouble. I will have paid 99 for something that will earns me 100. Though perhaps I want to leave myself more room to profit, and instead offer 25 to anyone willing to sell at 25. This would mean a profit of 75 per contract when it expires. The prices at which every user is willing to buy forms one side of the order book, the buying prices or bids.

A similar process happens on the selling side. I want to be short a contract when I do not believe the event will occur. So I would sell to anyone for more than 0 likelihood or price. If I end up correct in my prediction and the event does not occur when I went short at 90 likelihood, then I earn 90% of the contract size for each contract. The collected prices at which all users are willing to sell forms the other side of the order book, the selling prices or asks.

Market Orders
When a wager occurs at a price matching one of the buy or sell orders currently available in the market, the order is instantly made complete by matching a long and a short side of a wager. If I want to buy the Fadebook contract at 30 or 35 and there is already a user with a selling price or ask of 32, my wager will be immediately matched. In this sense, my order never actually appears in the prediction market order book for the contract, since the wagers are instantly matched.

Often a user will want to buy or sell at whatever likelihood or price the market is currently offering. In this market order, the user takes whatever happens to be the best price available at the time. Market orders are risky because the user may not know the exact price at which she commits to the wager. Market orders are also said to leduce liquidity in the market, and may be considered less healthy for the prediction market than regular limit orders.

Trading Out Early
The fundamental advantage to prediction markets over traditional oddsmaking is the option for a user to exit out of a profitable or loser wager early, before the contract event’s expiration. A user who is currently long or short a contract is free to trade the position to another party at any point, even if the user has only held the contract for a few minutes. This is advantageous for a user who wants to cut their losses early on a losing contract, or wants to take their profit early on a contract that becomes profitable.

If I went long 5 of the Fadebook contracts at 35 a few months ago, but that contract now has an order book centered around 50 bid/ask, then I can sell the 5 contracts for a 15 profit per contract right now. I do not need to hold my Fadebook contracts until 2013. This is not going short the contract, but selling to a willing buyer in order to net out my position with a profit.

Liquidity & Accurate Predictions
The more users are actively participating in wagering on a contract, the more accurate the collective estimate of likelihood. The most recent trade price or likelihood of a very busy and popular contract is an excellent estimate of the real likelihood of the contract event occurrence. If a single user believes a contract has a 35% likelihood, but a thousand other users are trading that contract around 75% likelihood, chances are the first user is wrong!

Prizes & Incentives
Prediction markets are more powerful when users are incentivized with real compensation. Therefore even if the prediction market’s virtual currency simplifies the regulatory aspects of the project, credits need to be closely tied to real money or prizes, so users assume actual risk when making wagers. Also any prizes need to incentivize users to make careful wagers and not necessarily “swing for the fences” on each wager. In other words, each bit of virtual currency must contribute to winning prizes.

Users who risk nothing of value when making wagers will not turn out to form a particularly accurate prediction market.

A Different House Hedge

Where do stock market winners buy houses?

There are many ways to predict how the price of an asset will change in the future. For stocks, one approach is based on fundamental analysis and another approach uses portfolio diversification theory. A third approach to predicting stock movement is so-called “technical analysis,” which is too silly for more than a mention. There are also statistical arbitrageurs in the high-frequency market-making and trading arms race, who make minute predictions thousands of times per day. If we pretend real estate acts as a stock, we can stretch the analogy into a new mathematical tool for hedging house prices.

Fundamentalism

Fundamental analysis is usually what people think about when picking stocks. This is the Benjamin Graham philosophy of digging into a company’s internals and financial statements, and then guessing whether or not the current stock price is correct. The successful stock picker can also profit from an overpriced share by temporarily borrowing the stock, selling it, and then later buying it back on the cheap. This is your classic “short,” which may or may not be unethical depending on your politics. Do short trades profit from misery, or reallocate wasted capital?

Fundamental analysis is notoriously difficult and time-consuming, yet it is the most obvious way to make money in the stock market. Fundamental analysis is also what private equity and venture capitalists do, but perhaps covering an unlisted company or even two guys in a garage in Menlo Park. When you overhear bankers talking about a “long/short equity fund” they probably mean fundamental analysis done across many stocks and then managing (trading) a portfolio that is short one dollar for every dollar it is long. This gives some insulation against moves in a whole sector, or even moves in the overall economy. If you are long $100 of Chevron and short $100 of BP, the discovery of cheap cold fusion will not trash your portfolio since that BP short will do quite well. However for conservative investors like insurance companies and pension funds, government policy restricts how much capital can be used to sell assets short. These investors are less concerned about fundamental analysis, and more about portfolio diversification and the business cycle.

Highly Sensitive Stuff

If a long-only fund holds just automobile company stocks, the fund should be very concerned about the automobile sector failing as a whole. The fund is toast if the world stops driving, even if their money is invested in the slickest, most profitable car companies today. Perfect diversification could occur if an investor bought a small stake in every asset in the world. Though huge international indices try to get close, with so many illiquid assets around, perfect diversification remains just a theory. How can an investor buy a small piece of every condominium in the world? How could I buy a slice of a brand like Starbucks? Even worse, as time goes by companies recognize more types of illiquid assets on their balance sheets. Modern companies value intellectual property and human capital, but these assets are difficult to measure and highly illiquid. What currently unaccounted-for asset will turn up on balance sheets in 2050?

Smart fund managers understand that perfect diversification is impossible, and so they think in terms of a benchmark. A fund benchmark is usually a published blend of asset prices, like MSCI’s agricultural indices. The fund manager’s clients may not even want broad diversification, and may be happy to pay fund management fees for partial diversification across a single industry or country. Thinking back to our auto sector fund, they are concerned with how the fortune’s of one car company are impacted by the automobile industry as a whole. An edgy upstart like Tesla Motors is more sensitive to the automobile industry than a stalwart like Ford, which does more tangential business like auto loans and servicing.

Mathematically we calculate the sensitivity of a company to a benchmark by running a simple linear regression of historic stock returns against changes in the benchmark. If a company’s sensitivity to the benchmark is 2.5, then a $10 stock will increase to $12.50 when the benchmark goes up by one point. A sensitivity of 0.25 means the stock would just edge up to $10.25 in the same scenario. A company can have negative sensitivity, especially against a benchmark in another related industry. Tesla probably has a negative sensitivity to changes in an electricity price index, since more expensive electricity would hurt Tesla’s business. No sensitivity (zero) would turn up against a totally unrelated benchmark. Sensitivity has a lot in common with correlation, another mathematical measure of co-movement.

One type of sensitivity is talked about more than any other. “Beta” is the sensitivity of a stock to the theoretical benchmark containing every asset in the world. Data providers like Bloomberg and Reuters probably estimate beta by regressing stock returns against one of those huge, international asset indices. An important model in finance and economics is called the Capital Asset Pricing Model, which earned a Nobel Prize for theorizing that higher beta means higher returns, since sensitivity to the world portfolio is the only sort of risk that cannot be diversified away. Though the CAPM beta is a poor model for real-life inefficient markets, sensitivities in general are a simple way to think about how a portfolio behaves over time. For instance, it turns out that sensitivities are additive. So $100 in a 0.25 sensitive stock and $50 in two different -0.25 sensitive stocks should be hedged against moves in the index and in the industry the index measures.

Back to Real Estate

Prices in certain local real estate markets are bolstered by a rally in the stock market. The recent murmurings of another IPO bubble suggest that newly minted paper millionaires will soon be shopping for homes in Los Altos Hills and Cupertino. We can put numbers behind this story by calculating real estate price sensitivity to a stock market benchmark. If we choose the S&P 500 as the benchmark, the sensitivity number will be a sort of real estate beta. Since real estate is far less liquid than most stocks, I regressed quarterly changes in our Altos Research median ask price against the previous quarter’s change in the S&P 500. Historically speaking, those real estate markets with a high beta have gotten a boost in prices after a good quarter in the stock market. Those markets with a low, negative beta are not “immune” to the stock market, but tend to be depressed by a stock market rally.

Below is a map of the Bay Area’s real estate betas. These numbers were calculated using prices from Altos Research and benchmark levels from Yahoo! Finance. The darker red a zipcode, the greater an increase in the market’s home prices after a quarterly stock market rally. As we might expect, the betas in Silicon Valley are above average. However there are also some surprises in Visalia and Wine Country.

Real Estate Beta, Bay Area

Our hypothesis for positive real estate beta is easy: those IPO millionaires. But what could cause a real estate market to tank after a good run in the stocks? Perhaps negative real estate betas are in more mobile labor markets, where stock market wealth triggers a move away from home ownership. Or maybe negative real estate betas turn up in markets where the condo stock is higher quality than single-family homes, like in some college towns. Remember the betas mapped above are based on only single-family home prices.

Real estate remains a difficult asset to hedge, an asset almost impossible to short by non-institutions. This is unfortunate, because a short hedge would be a convenient way for people with their wealth tied up in real estate to ride out a depressed market cycle. However like long-only fund managers, real estate investors could benefit from thinking in terms of benchmark sensitivity. If we choose a benchmark that represents the broader real estate market, we could hedge real estate buy purchasing non-property assets that have negative real estate betas. You would want your value-weighted real estate beta to net out to about zero. Now there is a plethora of problems and assumptions around making investment decisions with a crude linear sensitivity number, but at least real estate beta gives us another tool for thinking about housing risk.

(An abbreviated version of this post can found be at http://blog.altosresearch.com/a-different-house-hedge/ on Altos Research’s blog)

Case-Shiller April Forecasts

Another finger in the air, in the beginning of the month lull.

My forecasts for the March, 2011 Case-Shiller index levels were quite rushed. They were released quickly so I could publicly compare the forecasts with the CFE futures contracts about to expire. However, since the statistical models use active market data, there is no mathematical reason to wait on our forecasts until the end of the month. The April, 2011 index levels will be released on June 28th, but here are my forecasts given what the real estate markets were doing a few months ago:

City Confidence Forecast Predicted HPI
Minneapolis, MN +1 -10.52% 94.46
Phoenix, AZ +1 -2.85% 97.42
Las Vegas, NV +3 -1.56% 95.67
Atlanta, GA +2 -1.45% 96.93
Boston, MA 0 -1.32% 145.42
Los Angeles, CA -2 -1.22% 165.73
Seattle, WA +3 -0.46% 132.35
New York, NY -1 -0.21% 163.15
San Francisco, CA -3 -0.20% 129.56
Chicago, IL +2 -0.06% 110.50
San Diego, CA -3 +0.18% 154.16
Detroit, MI 0 +0.41% 67.34
Charlotte, NC 0 +0.50% 107.50
Miami, FL 0 +1.01% 138.66
Dallas, TX +1 +1.62% 114.72
Cleveland, OH +1 +2.12% 98.85
Denver, CO 0 +2.27% 123.29
Tampa, FL +1 +2.28% 129.98
Portland, OR +1 +4.71% 138.92
(The confidence score ranges from negative three for our weakest signals, up to positive three for strength. Unfortunately I am still sorting out a bug in our Washington, DC model.)

Housing Finger in the Air

The March, 2011 Case-Shiller numbers will be released this Tuesday, but the CME’s May futures contracts expire tomorrow. Some of the real estate transactions that will be summarized in Tuesday’s numbers are up to five months old, where our data is at most one week old. This is why Altos Research calls its statistics “real time,” since it is an order of magnitude more current than the benchmark in real estate data.

Below is a table of our forecasts for six of the Case-Shiller futures contracts. Check back in a few days, when I will compare with the actual March, 2011 numbers.

Metro Area Feb-2011 CS HPI Forecast Signal
Boston, MA 149.86 -2.33% 111bps below the future’s spot bid price
Chicago, IL 113.26 -1.28% in the spread
Denver, CO 121.26 -3.31% 64bps below the future’s spot bid price
Las Vegas, NV 98.28 -3.26% 96bps below the future’s spot bid price
Los Angeles, CA 168.25 -8.64% 763bps below the future’s spot bid price
San Diego, CA 155.05 +1.66% 209bps above the futures spot ask price
(all spot prices as of 10:30am PST on 26-May-2011)

Banks from the Outside

How do you identify the big cheese at a bank, the decision maker you should sell to? It’s not as easy as it sounds.

Investment banks are notoriously opaque businesses with a characteristic personnel and power structure. Still, there is plenty in common across investment banks and a few generalizations an outsider can make when trying to deal with an investment bank.

The “bulge bracket” are the large investment banks. Bank pecking order and prestige is roughly based on a bank’s size and volume of transactions. Banks who do the most deals generate the highest bonus pool for their employees. The pecking order since the credit crisis is probably:

This list is obviously contentious — though Goldman Sachs and JPMorgan are the undisputed masters, and Citibank and BofA are both the train wrecks. BofA is also known as Bank of Amerillwide, given its acquisitions. Bear Stearns opted out of the 1998 LTCM bailout, which is probably why they were allowed to fail during the credit crisis. Lehman Brothers had a reputation for being very aggressive but not too bright, while Merrill Lynch was always playing catchup. NYC is the capital of investment banking, but London and Hong Kong trump in certain areas. I’ve indicated where each of the bulge brackets are culturally headquartered. Each bank has offices everywhere but big decision-makers migrate to the cultural headquarters.

Investment Bank Axes

There are two broad axes within each bank. One axis is “front office -ness” and the other axis is “title” or rank. The front office directly makes serious money. The extreme are those doing traditional investment banking services like IPO’s, M&A, and Private Equity. And of course, traders and (trading) sales are also in the front office. Next down that axis are quants and the research(ers) who recommend trades. Then the middle office is risk management, legal and compliance. These are still important functions, but have way less pull than the front office. The back office is operations like trade processing & accounting, as well as technology.

This first front office -ness axis is confusing because people doing every type of work turn up in all groups. JPMorgan employs 240 thousand people so there are bound to be gray areas. An M&A analyst might report into risk management, which is less prestigious than if the same person with the same title reported into a front office group.

The other axis is title or rank. This is simpler, but something that tends to trip up outsiders. Here is the pecking order:

  • C-level (CEO, CFO, CTO, General Counsel. Some banks confusingly have a number of CTOs, which makes that title more like:)
  • Managing Director (“MD”, partner level at Goldman Sachs, huge budgetary power, the highest rank we mere mortals ever dealt with)
  • Executive Director or (just) Director (confusingly lower in rank than an MD, still lots of budgetary power)
  • Senior Vice-President (typical boss level, mid-level management, usually budgetary power, confusingly lower in rank than a Director)
  • Vice-President (high non-manager level, rarely has budget)
  • Assistant Vice-President or Junior Vice-President (“AVP”, rookie with perks, no budget)
  • Associate or Junior Associate (rookie, no budget)
  • Analyst (right out of school, no budget, a “spreadsheet monkey”)
  • Non-officers (bank tellers, some system administration, building maintenance)

Almost everyone at an investment bank has a title. Reporting directly to someone several steps up in title is more prestigious. Contractors and consultants are not titled, but you should assume they are one step below their boss. If someone emphasizes their job function instead of title (“I’m a software developer at Goldman Sachs”), you should assume they are VP or lower. Large hedge funds and asset managers mimic this structure. So to review, who is probably a more powerful decision maker?

  • A. an MD in IT at BofA, based out of Los Angeles -or- B. an ED in Trading also at BofA, but based in Charlotte (highlight for the answer: B because front office wins)
  • A. an MD in Risk Management at Morgan Stanley in NYC -or- B. a SVP in M&A also at Morgan Stanley in NYC (A because title wins)
  • A. a Research Analyst at JPMorgan in NYC -or- B. a Junior Vice-President in Research at Citibank in London (A because NYC and front office wins)
  • A. a VP Trader at Morgan Stanley in Chicago -or- B. an SVP in Risk Management at UBS in London (toss up, probably A since traders win)
  • A. an Analyst IPO book runner at Goldman Sachs in NYC -or- B. an Analyst on the trading desk at JPMorgan in NYC (toss up, probably A because Goldman Sachs wins)

Sour Grapes: Seven Reasons Why “That” Twitter Prediction Model is Cooked

The financial press has been buzzing about the results of an academic paper published by researchers from Indiana University-Bloomington and Derwent Capital, a hedge fund in the United Kingdom.

The model described in the paper is seriously faulted for a number of reasons:

1. Picking the Right Data
They chose a very short bear trending period, from February to the end of 2008. This results in a very small data set, “a time series of 64 days” as described in a buried footnote. You could have made almost 20% return over the same period by just shorting the “DIA” Dow Jones ETF, without any interesting prediction model!

There is also ambiguity about the holding period of trades. Does their model predict the Dow Jones on the subsequent trading day? In this case, 64 points seems too small a sample set for almost a year of training data. Or do they hold for a “random period of 20 days”, in which case their training data windows overlap and may mean double-counting. We can infer from the mean absolute errors reported in Table III that the holding period is a single trading day.

2. Massaging the Data They Did Pick
They exclude “exceptional” sub-periods from the sample, around the Thanksgiving holiday and the U.S. presidential election. This has no economic justification, since any predictive information from tweets should persist over these outlier periods.

3. What is Accuracy, Really?
The press claims the model is “87.6%” accurate, but this is only in predicting the direction of the stock index and not the magnitude. Trading correct directional signals that predict small magnitude moves can actually be a losing strategy due to transaction costs and the bid/ask spread.

They compare with “3.4%” likelihood by pure chance. This assumes there is no memory in the stock market, that market participants ignore the past when making decisions. This also contradicts their sliding window approach to formatting the training data, used throughout the paper.

The lowest mean absolute error in predictions is 1.83%, given their optimal combination of independent variables. The standard deviation of one day returns in the DIA ETF was 2.51% over the same period, which means their model is not all that much better than chance.

The authors also do not report any risk adjusted measure of return. Any informational advantage from a statistical model is worthless if the resulting trades are extremely volatile. The authors should have referenced the finance and microeconomics literature, and reported Sharpe or Sortino ratios.

4. Backtests & Out-of-sample Testing
Instead of conducting an out-of-sample backtest or simulation, the best practice when validating an un-traded model, they pick the perfect “test period because it was characterized by stabilization of DJIA values after considerable volatility in previous months and the absence of any unusual or significant socio-cultural events”.

5. Index Values, Not Prices
They use closing values of the Dow Jones Industrial Average, which are not tradable prices. You cannot necessarily buy or sell at these prices since this is a mathematical index, not a potential real trade. Tracking errors between a tradable security and the index will not necessarily cancel out because of market inefficiencies, transaction costs, or the bid/ask spread. This is especially the case during the 2008 bear trend. They should have used historic bid/ask prices of a Dow Jones tracking fund or ETF.

6. Causes & Effects
Granger Causality makes an assumption that the effects being observed are so-called covariance stationary. Covariance stationary processes have constant variance (jitter) and mean (average value) across time, which is almost precisely wrong for market prices. The authors do not indicate if they correct for this assumption through careful window or panel construction.

7. Neural Parameters
The authors do not present arguments for their particular choice of “predefined” training parameters. This is especially dangerous with such a short history of training data, and a modeling technique like neural networks, which is prone to high variance (over-fitting).