Quant Macro Investing

Risk Taking Disciplined

Ultimate Guide To Becoming A Quant By Mark Joshi

Ultimate Guide To Becoming A Quant By Mark Joshi

December 10, 2009

Very interesting overview of the quant world, if nothing else it will give you an overview of quant jobs and the lunacy or (brilliance depending your view) of wall st.

Click Here To Read:  Ultimate Guide To Becoming A Quant

What sorts of quant are there?

(1) Front office/desk quant
(2) Model validating quant
(3) Research quant
(4) Quant developer
(5) Statistical arbitrage quant
(6) Capital quant

A desk quant implements pricing models directly used by traders.Main plusses close to the money and opportunities to move into trading. Minuses can be stressful and depending on the outfit may not involve much research.

A model validation quant independently implements pricing models in order to check that front office models are correct. Plusses more relaxed, less stressful. Minusses model validation teams can be uninspired and far from the money.

A Research quant tries to invent new pricing approaches and sometimes carries out blue-sky research. Plusses it’s interesting and you learn a lot more. Minusses sometimes hard to justify your existence.

A Quant developer, a glorifed programmer but well-paid and easier to find a job. This sort of job can vary a lot. It could be coding scripts quickly all the time, or working on a large system debugging someone else’s code.

A Statistical arbitrage quant, works on finding patterns in data to suggest automated trades. The techniques are quite different from those in derivatives pricing. This sort of job is most commonly found in hedge funds. The return on this type of position is highly volatile!

A capital quant works on modelling the bank’s credit exposures and capital requirements. This is less sexy than derivatives pricing but is becoming more and more important with the advent of the Basel II banking accord. You can expect decent (but not great) pay, less stress and more sensible hours. There is currently a drive to mathematically model the chance of operational losses through fraud etc, with mixed degrees of success.

People do banking for the money, and you tend to get paid more the closer you are to where the money is being made. This translates into a sort of snobbery where those close to the money look down on those who aren’t. As a general rule, moving away from the money is easy, moving towards it is hard.

December 11, 2009 Posted by | Uncategorized | Leave a comment

Here’s How High Frequency Traders Dominate The Markets

Here’s How High Frequency Traders Dominate The Markets

Vince Veneziani|Dec. 7, 2009, 3:24 PM |

An interesting PDF concerning latency arbitraging in the world of HFT has been released by Themis Trading. In it, they explain just how HFT works with computers doing all the heavy lifting, though that quote about 60% of daily volume is way off. I’ve heard reports of 3 to 5-percent:

Themis Trading: Here’s an example of how an HFT trading computer takes advantage of a typical institutional algo VWAP order to buy ABC stock:

1. The market for ABC is $25.53 bid / offered at $25.54.

2. Due to Latency Arbitrage, an HFT computer knows that there is an order that in a moment will move the NBBO quote higher, to $25.54 bid /offered at $25.56.

3. The HFT speeds ahead, scraping dark and visible pools, buying all available ABC shares at $25.54 and cheaper.

4. The institutional algo gets nothing done at $25.54 (as there is no stock available at this price) and the market moves up to $25.54 bid / offered at $25.56 (as anticipated by the HFT).

5. The HFT turns around and offers ABC at $25.55 or $25.56.

6. Because it is following a volume driven formula, the institutional algo is forced to buy available shares from  the HFT at $25.55 or $25.56.

7. The HFT makes $0.01-$0.02 per share at the expense of the institution.

It is currently estimated that HFT accounts for 60% of all share volume.

December 9, 2009 Posted by | Hi Freq Trading (HFT), Uncategorized | Leave a comment

Even Better Than the Real Thing – 700% this decade

Even Better Than the Real Thing – 700% this decade

December 7th, 2009 by Mebane Faber

One of the best real time examples of using AlphaClone is comparing a clone to the underlying manager this year (which is real time and out of sample). In this case we take a look at the returns for David Tepper’s Appaloosa fund via Dealbreaker.  Year to date the fund is up a whopping 119.7% gross and 84.62% net (those pesky 2% and 20% fees!!).

How about following Tepper on AlphaClone?  Taking his top 10 picks, equally weighted and rebalanced quarterly, would be up 126% through the end of November.  That not only beats Tepper’s gross returns, but also goes  to show how nicely it replicates the fund’s performance.  You’d certainly be bank heavy with some of his positions like BAC, C, FITB, VCI, and BC.

So to my readers who are institutions, family offices, endowments, and money managers – doesn’t this sound a lot better than dealing with all the lockups, headaches, K-1’s, tax inefficiency, career risk, fraud, and transparency risk of allocating to these funds?

And check out the returns since 2000 – beats the market by a mile.  While the S&P had negative returns over this period, following Tepper resulted in a 7 bagger.

December 9, 2009 Posted by | Indicator setup | Leave a comment

Goldman Claims Momentum And Value Quant Strategies Now Overcrowded, Future Returns Negligible

Goldman Claims Momentum And Value Quant Strategies Now Overcrowded, Future Returns Negligible

Even as momentum buyers keep driving the market to new 2009 highs today on a worse than expected ISM numbers (more Obamoney coming), none other than Goldman Sachs head of quant strategies Robert Litterman says that with everyone on the same side of the trade in momentum and value quant strats, the returns to these strategies are rapidly becoming negligible due to overcrowding. Of course, what happens when the crowds disperse is anyone’s guess, although if Obama had anything to say about it, the exit would be cool, calm and collected. Obviously it will be anything but. And very limited upside also means very unlimited downside. Yet let he who wants to fight the Marriner Eccles lunatics cast the first short.

More from Reuters:

Computer-driven hedge funds must hunt for new areas to exploit as some areas of making money have become so overcrowded they may no longer be profitable, according to Goldman Sachs Asset Management. Robert Litterman, managing director and head of quantitative resources, said strategies such as those which focus on price rises in cheaply-valued stocks, which latch onto market momentum or which trade currencies, had become very crowded.
Instead he said opportunities could come in areas such as event-driven strategies — which focus on special events such as mergers or restructuring — and catastrophe reinsurance, although he added they can just as quickly disappear.

He also pointed to credit, emerging markets, volatility trading and commodities.

That’s all we need: computers trading on event situations, where the first three letters of the headline will be sufficient to throw any thinly traded stock into a parabolic rise or drop. Whatever happened to good old fashioned humanitarian trading?

Yet isn’t it ironic that none other than Goldman which lost billions in August 2007 when the quants went haywire most recently, should be warning about the dangers of overcrowded groupthink?

“You have to adapt your process,” Litterman said at the Quant Invest 2009 conference. “What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.” Computer-driven or quantitative hedge funds attempt to make money by quickly exploiting trends or anomalies in markets such as equities, government bonds or currencies.
However, some funds such as Goldman’s controlled a large share of some markets in summer 2007 and many were caught in a vicious circle of selling. “I think the world has fundamentally changed for quants,” he said, adding that his funds now allocate a greater share of assets to newer strategies since that crisis.
“We’re putting together data that’s not machine-readable, finding databases that haven’t been explored nearly as well as others, identifying linkages across companies and industries and finding patterns in the data that are not as well known.”

Yeah right, things are so different than August 2007, when insane parabolic melt ups each and every day were so unique and so completely different to those from… today. The poetic justice of Goldman’s trading P&L imploding after several “unique” haywire strategies end up losing the firm a cool couple of billion will be unsurpassed. Not only that, but the statistical garbage that is GS’ VaR will finally be exposed for the sham mathematical artefact it is. Until then we can all wait and hope.

December 7, 2009 Posted by | Uncategorized | Leave a comment

Barron’s Red Flags: Do They Actually Work?

Barrons Red Flags: Do They Actually Work?

Tim Loughran
University of Notre Dame

Bill McDonald
University of Notre Dame

November 20, 2009

Abstract:
Investors are often concerned that managers might hide negative information in the maze of mandated SEC filings. With advances in textual analysis and the availability of documents on EDGAR, individuals can quite easily search for phrases that might be red flags indicating aggressive accounting practices or poorly monitored management. We examine the impact of 13 suspicious corporate phrases identified by a recent Barron’s article in a sample of 50,115 10-Ks during 1994-2008. There is evidence that red flag phrases like related party and unbilled receivables signal a firm may subsequently be accused of fraud. At the 10-K filing date, phrases like substantial doubt are linked with significantly lower filing date excess stock returns, higher stock return volatility, and greater analyst earnings forecast dispersion.


http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1510188

December 7, 2009 Posted by | Uncategorized | Leave a comment

Thomson Reuters woos algo traders with machine-readable company events

Thomson Reuters woos algo traders with machine-readable company events

01 December, 2009 – 11:01

Reflecting the growing influence of computer-driven trading strategies, Thomson Reuters has expanded its machine-readable news offering to include real-time analysis of company events.

The news and information group says the automated process will scan and automatically extract critical pieces of information from corporate announcements for clients to use in a machine readable format.

The information will be delivered in an XML format via Thomson Reuters NewsScope Direct, the firm’s low latency news distribution platform. In addition, clients will have access to nearly seven years of historical data, enabling them to back-test in their trading and investment strategies.

Initially, NewsScope Company Events will focus on US newswires before expanding to Canada and Europe.

The provision of market-moving news and information in a machine-readable format has become a key battleground for market data firms as increasing volumes of stocks are executed by computer algorithms.

Late last month, Deutsche Börse acquired machine-readable data specialist Need to Know News, in an effort to raise the profile of its market data and analytical offerings with algorithmic traders.

Thomson Reuters to offer company events data in real time

By Davis D. Janowksi
December 1, 2009

Advisers using the Thomson Reuters’ Quantitative and Event Driven Trading solution set will soon have one more morsel of data coming their way to help them gain an edge in their trading strategies.

Thomson Reuters today announced that it has expanded its machine-readable news offering to include real-time analysis of company events.

Within the company’s wide array of market data offerings, users will find these particular new points of information in the feeds from NewsScope Direct, which is delivered in XML format.

The company events data are being culled from U.S. company press releases. The info is then scanned by computer.

Initial focus will be on US newswires. Thomson Reuters plans to expand its coverage to Canadian and European company news in coming weeks and months.

Pricing and availability specifics were unavailable at press time

http://thomsonreuters.com/products_services/financial/financial_products/quantitave_event_driven_trading/

http://thomsonreuters.com/products_services/financial/financial_products/quantitave_event_driven_trading/high_frequency

December 7, 2009 Posted by | Uncategorized | 2 Comments

Currency Carry Trade Regimes: Beyond the Fama Regression

Currency Carry Trade Regimes: Beyond the Fama Regression

http://www.nber.org/papers/w15523

We examine the factors that account for the returns on currency carry trade strategies. Using a dataset of daily returns spanning 18 years for 5 different long – short currency carry portfolios, we first document a robust empirical relationship between carry trade excess returns and exchange rate volatility, both realized and implied. Specifically, we extend and refine the results in Bhansali (2007) by documenting that currency carry trade strategies implemented with forward contracts have payoff and risk characteristics that are similar to those of currency option strategies that sell out of the money puts on high interest rates currencies. Both strategies have the feature of collecting premiums or carry to generate persistent excess returns that unwind sharply resulting in losses when actual and implied volatility rise.

We next also document significant volatility regime sensitivity for Fama regressions estimated over low and high volatility periods. Specifically we find that the well known result that a regression of the realized exchange rate depreciation on the lagged interest rate differential produces a negative slope coefficient (instead of unity as predicted by uncovered interest parity) is an artifact of the volatility regime: when volatility is in the top quartile, the Fama regression produces a positive coefficient that is greater than unity. The third section of the paper documents the existence of an intuitive and significant co-movement between currency risk premium and risk premia in yield curve factors that drive bond yields in the countries that comprise carry trade pairs. We show that yield curve level factors are positively correlated with carry trade excess returns while yield curve slope factors are negatively correlated with carry trade excess returns. Importantly, we show that this correlation is robust to the current crisis and to the inclusion of equity volatility in the model. What distinguishes carry trade returns in the current crisis from non crisis periods is not changed loading on yield curve factors but a much larger loading on the equity factor.

This paper is available as PDF (718 K) or via email.

December 3, 2009 Posted by | Case Study | Leave a comment

Quant hedgies must fish in fresh waters-Goldman

Tue Dec 1, 2009 1:01pm EST

* Older strategies like value, momentum getting overcrowded

* Eyes newer areas of event-driven, catastrophe reinsurance

By Laurence Fletcher

PARIS, Dec 1 (Reuters) – Computer-driven hedge funds must hunt for new areas to exploit as some areas of making money have become so overcrowded they may no longer be profitable, according to Goldman Sachs (GS.N) Asset Management. Robert Litterman, managing director and head of quantitative resources, said strategies such as those which focus on price rises in cheaply-valued stocks, which latch onto market momentum or which trade currencies, had become very crowded.

Instead he said opportunities could come in areas such as event-driven strategies — which focus on special events such as mergers or restructuring — and catastrophe reinsurance, although he added they can just as quickly disappear.

He also pointed to credit, emerging markets, volatility trading and commodities.

“You have to adapt your process,” Litterman said at the Quant Invest 2009 conference. “What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.” Computer-driven or quantitative hedge funds attempt to make money by quickly exploiting trends or anomalies in markets such as equities, government bonds or currencies.

However, some funds such as Goldman’s controlled a large share of some markets in summer 2007 and many were caught in a vicious circle of selling. “I think the world has fundamentally changed for quants,” he said, adding that his funds now allocate a greater share of assets to newer strategies since that crisis.

“We’re putting together data that’s not machine-readable, finding databases that haven’t been explored nearly as well as others, identifying linkages across companies and industries and finding patterns in the data that are not as well known.” (To read the Reuters Hedge Fund Blog click on blogs.reuters.com/hedgehub; for the Global Investing Blog click here) (Editing by Jon Loades-Carter)

December 3, 2009 Posted by | ABCs for Quant | 1 Comment

Trading – mental strategy

NYT

http://www.nytimes.com/2009/11/30/business/30kiev.html?_r=1&hpw

Ari Kiev, a Psychiatrist, Dies at 75

Ari Kiev, a psychiatrist whose early work on depression and suicide prevention led to a career helping athletes and Wall Street traders achieve peak performance, died Nov. 18 in Manhattan. He was 75 and lived in Park Ridge, N.J.

… His work with athletes caught the attention of Steven A. Cohen, the founder of the hedge fund SAC Capital Advisors, who hired Dr. Kiev in the early 1990s to coach his traders and help them deal with the stress and uncertainties of financial markets.

Dr. Kiev translated his Wall Street experiences into best-selling books on stock trading, notably “Trading to Win” (1998), “Trading in the Zone” (2001) and “Hedge Fund Masters” (2005).

… Mr. Cohen, who saw parallels between the challenges faced by top athletes and Wall Street traders and hired Mr. Kiev in 1992 to coach his employees.

Dr. Kiev helped traders develop techniques to shift abruptly from moments of extreme exertion to relaxation, as he had done with athletes, and to manage the stress that comes with uncertainty — or, rather the certainty that even good traders can expect to be right only a little more than half the time.

He also zeroed in on behavior patterns and subconscious fears that limited or even subverted investment goals. Part of his work, he often said, was to force traders to see their tendency toward denial and rationalization.

“An athlete who wants to run a four-minute mile can work backward and establish a regimen to attain that,” said Matt Grossman, who worked with Mr. Kiev at SAC and now runs his own hedge fund, Plural Investments. “Ari applied this concept to investing: set a target, then design an approach that gives you a high probability of achieving that target.”

… His book “The Mental Strategies of Top Traders” is scheduled to be published in December by Wiley.

December 2, 2009 Posted by | Tools | Leave a comment

The Lunar Cycle and Stock Returns

CXO

http://cxoadvisory.com/blog/internal/blog11-06-08/

November 6, 2008 – Update: The Lunar Cycle and Stock Returns

Does the lunar cycle affect the behavior of investors/traders, and thereby influence stock returns? In the August 2001 version of their paper entitled “Lunar Cycle Effects in Stock Returns” Ilia Dichev and Troy Janes conclude that: “returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. This pattern of returns is pervasive; we find it for all major U.S. stock indexes over the last 100 years and for nearly all major stock indexes of 24 other countries over the last 30 years.” To refine this conclusion and test some recent data, we examine U.S. stock returns during intervals relative to the dates of new and full moons since 1990. When the date of a new or full moon falls on a non-trading day, we assign it to the nearest trading day. Using dates for new and full moons for January 1990 through October 2008 as listed by the U.S. Naval Observatory (233 full and 233 new moons) and daily closing prices for the S&P 500 index over the same period, we find that:

The following chart summarizes average S&P 500 index returns over the 11 trading days (about half a month) centered on new moons or on full moons over the entire sample period, during the 1990s and during the 2000s. Results are in rough agreement with the conclusion of the study cited above, with intervals centered on new moons outperforming those centered on full moons. The difference for the 1990s is, however, small.

Can we refine the interval of new moon outperformance?

The next chart compares average S&P 500 index returns over the entire sample period for three intervals relative to new or full moons: (1) the five trading days just before full or new moons; (2) the five trading days centered on new or full moons; and, (3) the five trading days just after new or full moons. Results suggest that the outperformance of intervals around the new moon comes from returns after, rather than before, the new moon.

The standard deviation of 11-day returns over the entire sample period is 2.64% (3.13%) for new (full) moons, large compared to the difference in average returns.

Might any lunar effects stem from the waxing or waning of the moon rather than new or full moons?

The next chart compares average S&P 500 index returns for the intervals of waxing and waning between new and full moons over the entire sample period, during the 1990s and during the 2000s. Results consistently indicate that the waxing moon (new-to-full) interval on average outperforms the waning moon (full-to-new) interval by about 0.25%.

The standard deviation of returns over the entire sample period is 2.78% (2.93%) for waxing (waning) moons, large compared to the difference in average returns.

Can more granular data help explain why intervals around new moons and during waxing moons outperform those around full moons and during waning moons?

The final charts present the average daily detrended S&P 500 index returns from 11 trading days before new and full moons to 11 trading days after new and full moons. The total interval covered in each chart is roughly a month, but mismatches between lunar and monthly cycles introduce differences between them. We detrend by subtracting the average daily return for the entire sample period from the raw average returns for each trading day in the intervals tested. These charts provide some insight into the less granular results above. However, the lack of systematic variation in daily returns casts doubt on the lunar cycle as the explanation of new-full and wax-wane differences in average returns.

Physicist Charles Pennington employs a quite different approach using a Fourier transform, concluding that lunar cycle effects on SPY are, if they exist, very small.

In summary, the U.S. stock market since 1990 performs better on average around new moons than full moons, and during waxing moons than waning moons. However, the levels of relative outperformance are small compared to market variability, so trading these differences is very risky.

For related research, see Blog Synthesis: Calendar Effects and the Trading Calendar.

December 2, 2009 Posted by | Case Study | Leave a comment