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Algorithmic Trading has changed the world the way the Traders trade and Trade Support supports. There is a Brave New World happening with the "hands on" Trading evolving into "hands off" Algo Trading. Not all trades need to be made in ultra low latency timing. Future trading will rely on a broader set of data which will be mined for relevance. For example, an important series of XBRL Financial Reporting events are happening throughout the world and especially in the USA. A critical mass of financial data will be ready for mining which will be a boon for transparent "low touch" fundamental style algorithmic trading.
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Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Financial Data Mining with Algorithmic TradingRobert Golan
DBmind Technologies, Inc.Please Note: This is the view of DBmind only which may not
pertain to DBmind’s Client Views
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Financial Data Mining with Algorithmic TradingAlgorithmic Trading has changed the world the way the
Traders trade and Trade Support supports. There is a Brave New World happening with the "hands on" Trading evolving into "hands off" Algo Trading. Not all trades need to be made in ultra low latency timing. Future trading will rely on a broader set of data which will be mined for relevance. An important series of XBRL Financial Reporting events are happening throughout the world and especially in the USA. A critical mass of financial data will be ready for mining which will be a boon for transparent "low touch" fundamental style algorithmic trading. Also, "low touch" trading such as program trading & direct market access (DMA) will evolve into advanced Algo Trading strategies. Stock and economic indicators combined with XBRL will add value for Algo Trading. This is about a well thought out strategic high latency trading strategy with data mining discovering the governing rules while adding the expert rules with validation. Yes, the trader is still the key to making this all happen. Both fundamental and technical trading rules need to be combined with the expert rules, the data mined rules, and most importantly the regulatory environment rules. RegNMS in the USA and MiFID in Europe have indirectly helped the adoption of electronic trading and it is important to integrate the GRC related rules in an agile way. Agility is the key and thus the rules need to be placed into a rules engine and managed by the experts for proper compliance, risk management, and governance. Japan, China, and the Netherlands with regards to XBRL are ready to be data mined with Algo Trading now. A XBRL US survey is indicating at least 340 of the estimated 500 public companies that the SEC requires to begin filing in XBRL format in June 2009, have already converted their financial statements into XBRL. XBRL US, is the non-profit XML standard setter that developed and maintains the US GAAP taxonomy used by filers to comply with the SEC mandate. Almost $7 trillion in market capitalization will be represented by this XBRL financial data which is over 50% of the total market cap for all publicly traded companies reporting to the SEC. As this XBRL Financial Data ripens, a wonderful harvest awaits us data miners which will enhance the current Algo Trading strategies which use this data.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Algorithmic trading http://en.wikipedia.org/wiki/Algorithmic_trading
• In electronic financial markets, algorithmic trading, also known as algo, automated, black box, or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on certain aspects of the order such as the timing, price, or type (market vs. limit, or buy vs. sell) of the order. It is widely used by pension funds, mutual funds, and other institutional traders to divide up a large trade into several smaller trades in order to avoid market impact costs or otherwise reduce transaction costs. It is also used by hedge funds and similar traders to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information.
• Algorithmic trading may be used in any market strategy, including market making, intermarket spreading, arbitrage, or pure speculation (including trend following) to make the complete decision on entering trades and electronically executing the trade with no human intervention, other than in writing the computer program.
• In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and equity markets generally have a higher proportion of algo trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algo trading (about 25% of orders in 2006). Futures and options markets are considered to be fairly easily integrated into algorithmic trading, and bond markets are moving toward more access to algorithmic traders.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Citadel Investment Group
Interactive Brokers
Credit Suisse
Deutsche Bank
Goldman Sachs
Lehman Bros.
Morgan Stanley
Susquehanna Investment Group
UBS
Some of the Algo Players
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Statistical Arbitrage in the U.S. Equities MarketMarco Avellaneda† and Jeong-Hyun Lee
First draft: July 11, 2008This version: June 15, 2009
AbstractWe study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as mean-revertingprocesses. This leads naturally to “contrarian” trading signals.The main contribution of the paper is the construction, back-testingand comparison of market-neutral PCA- and ETF- based strategies applied to the broad universe of U.S. stocks. Back-testing shows that, afteraccounting for transaction costs, PCA-based strategies have an average annual Sharpe ratio of 1.44 over the period 1997 to 2007, withmuch stronger performances prior to 2003. During 2003-2007, the averageSharpe ratio of PCA-based strategies was only 0.9. Strategies basedon ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, experiencing a similar degradation after 2002. We also introduce a method to account for daily trading volume information in the signals (which is akin to using “trading time” as opposed to calendar time), and observe significant improvement in performance in the case of ETF-based signals. ETF strategies which use volume informationachieve a Sharpe ratio of 1.51 from 2003 to 2007. The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in detail the performance of the strategies during the liquidity crisis of thesummer of 2007. We obtain results which are consistent with Khandani and Lo (2007) and validate their “unwinding” theory for the quant funddrawdown of August 2007.Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, N.Y. 10012USA†Finance Concepts, 49-51 Avenue Victor-Hugo, 75116 Paris, France.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
XBRL Basics
XBRL is XML
It is Extensible
There is an XBRL specification – tells you how to use XBRL
Hinges on taxonomies – the dictionary of terms for business reporting – which includes financial statements
Copyright ® 2009, SAS Institute Inc. All right s reserved.
XBRL TaxonomyCreated by XBRL Consortium
Consumed
Rendered
XB
RL
Cre
ati
on
XBRL DocumentCreated by Preparer
TAGGING
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Validation
Standardization
CalculationCash = Currency +
Deposits
CalculationCash = Currency +
DepositsFormulas
Cash ≥ 0Formulas
Cash ≥ 0Contexts
US $FY2004
Budgeted
ContextsUS $
FY2004Budgeted
LabelcashCashEquivalentsAndShortTermInvestment
s
LabelcashCashEquivalentsAndShortTermInvestment
s
ReferencesGAAP I.2.(a)Instructions
Ad Hoc disclosures
ReferencesGAAP I.2.(a)Instructions
Ad Hoc disclosures
PresentationCash & Cash Equivalents
PresentationCash & Cash Equivalents
XBRLItem
XBRLItemXMLItemXMLItem
XBRLItem
XBRLItem
PresentationComptant et Comptant
Equivalents
PresentationComptant et Comptant
Equivalents
PresentationGeld & Geld nahe Mittel
PresentationGeld & Geld nahe Mittel
PresentationKas en Geldmiddelen
PresentationKas en Geldmiddelen
Presentation现金与现金等价物
Presentation现金与现金等价物
Presentation現金及び現金等価物
Presentation現金及び現金等価物
PresentationДеньги и их эквиваленты
PresentationДеньги и их эквиваленты
PresentationГроші та їх еквіваленти
PresentationГроші та їх еквіваленти
Copyright ® 2009, SAS Institute Inc. All right s reserved.
The Business Reporting Supply Chain
ExternalFinancialReporting
BusinessOperations
InternalFinancialReporting
Investment,Lending, andRegulation
Processes
Participants
AuditorsTradingPartners
Investors
FinancialPublishersand Data
Aggregators
Regulators
Software VendorsSoftware Vendors
ManagementAccountants
Companies
XBRL XBRLXBRL
XBRLFinancial StatementsXBRL-GL
TheJournal
Standard
Transaction Standards
Collaboration is KEY!!!
Copyright ® 2009, SAS Institute Inc. All right s reserved.
USUS
UKUK
JPJPESES
SESE
CNCN
ZAZA
AUAU
DEDEDKDK
Financial Banking Regulators PilotPilot Committ
ed
Committed
KRKR
SGSG
FRFR
NZNZ
NLNL
LuXLuXPortug
al
Portugal
BEBEEU CEBSEU CEBS
Copyright ® 2009, SAS Institute Inc. All right s reserved.
XBRL Jurisdictions
UKCA
SPUS
AU
NZ
IR
JPKR
BE
VZ
CO
BR
AR
PT
RU
SG
HK
NOSE
PL
FI
IT
CN
IN
LB
CZ
UA
LUIASB
AE
NL
TR
GR
MTCH
FR
SI
HU
AT
Established Jurisdictions
Provisional Jurisdictions
in Construction & in Project
DE
DK
ZA
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
CACA UKUK
IEIE
AUAU
NONO
JPJP
NZNZ
NLNL DEDE
CNCN
Tax Authorities PilotPilot Committed
Committed
Tax XML Technical Committee recommends use of XBRL (Oasis-OECD)29 Tax authorities
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Exchanges & Equity Regulators
Sao PauloSao Paulo
NZSENZSEASXASXJohannesburgJohannesburg
ShenzenShenzen
EuroNextEuroNextKOSDAQKOSDAQ
TokyoTokyo
SingaporeSingapore
SWXSWXLuxLux
Pilot LiveEval
TSXTSX
OBXOBX
LSELSECSECSE
DeutscheBörse
DeutscheBörse
Taipei
SECSEC
Korea
Korea
ShanghaiShanghai
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Grant Boyd, [email protected] Technical Manager – XBRL, AICPA•http://www.icgfm.org/XBRLPresentations.htm
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Data Mining Approaches
Three Basic Approaches to Data Mining
• Mathematical-based methods,
• Distance-based methods, and
• Logic-based methods
Methods may use supervised or unsupervised variable
• Supervised – induction rules for predefined classifications
• Unsupervised – rules and classifications determined by data mining method
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Mathematical-based Methods Neural Network
• Network of nodes modeled after a neuron or neural circuit
• Supervised learning
• Weighted values at different nodes
• Mimics the processing of the human brain
• Form of Artificial Intelligence
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Mathematical-based Methods
Discriminant Analysis• Similar to multiple regression analysis uses a non-
continuous dependent variable
• Approach identifies the variables (features or cases) that best explain the classification
• Supervisory learning approach
• Loses effectiveness with large complex data sets
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Logic-Based Approach
Tree and Rule Induction • Supervised Learning
− Uses an algorithm to induce a decision tree from a file of individual cases
− Case has set of attributes and the class to which it belongs • Decision tree can be converted to a rule-based view. • Major advantage is ability to communicate and understand
information derived from this approach. • Prior research addressed audit areas of:
− bankruptcy, bank failure, and credit risk
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Distance-Based Method
Clustering• Data mining approach that partitions large sets of data
objects into homogeneous groups
• Uses unsupervised classification where little manual pre-screening of data is necessary –
− useful in situations where there is no predefined knowledge of categories
• Classifications based on an object’s attributes
• Most commonly used in field of marketing but could be used in auditing
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Selecting Data Mining Approach
Criteria:• Scalability - how well data mining method works
regardless of data set size • Accuracy - how well information extracted remains
stable and constant beyond the boundaries of the data from which it was extracted, or trained
• Robustness - how well the data mining method works in a wide variety of domains
• Interpretability - how well data mining method provides understandable information and valuable insight to user
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Continuous Auditing, XBRL and Data Mining
Presenters:
Jennifer Moore, Lumsden & McCormick, LLP
Karina Barton, Canisius College
Dr. Joseph O’Donnell, Canisius College
New York State Society of Certified Public AccountantsTechnology Assurance Committee
June 15, 2004
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Prime Services http://en.wikipedia.org/wiki/Prime_brokerage
• Prime Brokerage is the generic name for a bundled package of services offered by investment banks to hedge funds. The business advantage to a hedge fund of using a Prime Broker is that the Prime Broker provides a centralized securities clearing facility for the hedge fund, and the hedge fund's collateral requirements are netted across all deals handled by the Prime Broker. The Prime Broker benefits by earning fees ("spreads") on financing the client's long and short cash and security positions, and by charging, in some cases, fees for clearing and/or other services.
• The following "core services" are typically bundled into the Prime Brokerage package:
− Global custody (including clearing, custody, and asset servicing)− Securities lending− Financing (to facilitate leverage of client assets)− Customized Technology (provide hedge fund managers with
portfolio reporting needed to effectively manage money)− Operational Support (prime brokers act as a hedge fund's primary
operations contact with all other broker dealers)
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Hedge Fund
Hedge Fund
Hedge Fund
Exchange Clearing Settlement
Trading
Position
keeping
Clearing
And
Settlement
Prime
Services
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Hedge Fund
Mutual Fund
Hedge Fund
Exchange Exchange Exchange
Algorithmic
Trading
System
Prime Service Provider
Ice Berg
VWAP
TWAP
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Client
Client
Client
Exchanges Clearing Settlement
Equities
Derivatives
Fixed
IncomeReference
Data
Validation
and
Enrichment
Risk
Management
Financial
Control
Clearing
and
Settlement
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Date: 15 May 2007Produced by: Chris Swan
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Overview of stock exchanges The main stock exchanges in the world include:
America• American Stock Exchange
• NASDAQ
• New York Stock Exchange
• São Paulo Stock Exchange
Europe• Euronext
• Frankfurt Stock Exchange
• London Stock Exchange
• Madrid Stock Exchange
• Milan Stock Exchange
• Zurich Stock Exchange
• Stockholm Stock Exchange
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Listing requirements
LSE — main market has requirements for a minimum market capitalization of £700,000, three years of audited financial statements, minimum public float of 25 % and sufficient working capital for at least 12
months from the date of listing NASDAQ — to be listed a company must have issued at least 1.25
million shares of stock worth at least $70 million and must have earned more than $11 million over the last three years
NYSE — a company must have issued at least a million shares of stock worth $100 million and must have earned more than $10 million over the last three years
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Overview of stock exchanges
Australia/Asia/Africa• Australian Stock Exchange
• Bombay Stock Exchange
• Hong Kong Stock Exchange
• Johannesburg Securities Exchange
• Korea Stock Exchange
• Shanghai Stock Exchange
• Taiwan Stock Exchange
• Tokyo Stock Exchange
• Toronto Stock Exchange
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Participants Broker — an individual or firm which operates between a buyer and a seller and usually
charge a commission. For most products a licence is required.
Dealer — an individual or firm which buys and sells for its own account.
Broker/dealer — an individual or firm buying and selling for itself and others. A registration is required.
Principal — a role of broker/dealer when buying or selling securities for its own account.
Market maker — a brokerage or bank that maintains a firm bid and ask price in a given security by standing ready, willing, and able to buy or sell at publicly quoted prices (called making a market). These firms display bid and offer prices for specific numbers of specific securities, and if these prices are met, they will immediately buy for or sell from their own accounts.
Specialist — a stock exchange member who makes a market for certain exchange-traded securities, maintaining an inventory of those securities and standing ready to buy and sell shares as necessary to maintain an orderly market for those shares. Can be an individual, partnership, corporation or group of firms.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Prototypical trading systems Call (periodic) auction — selling stocks by bid at intervals throughout
the day. The orders are stored for execution at a single market clearing price.
Continuous auction — buyers enter competitive bids and sellers place competitive offers simultaneously. Continuous, since orders are executed upon arrival.
Dealership market — trading occur between principals buying and selling to their own accounts. Firm price quotations are available prior to order submission.
Auction markets are concentrated and order-driven
Dealership markets are fragmented and quote-driven
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Examples
NYSE — opens with a periodic auction market and then
switches to a continuous auction. Same for Tokyo Stock Exchange.
NASDAQ and International Stock Exchange (London) are quote-driven systems (continuous dealership market).
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Examples
Euronext Paris — the market is segmented into a number of different groups of stocks based on size and liquidity. The trading mechanisms vary depending on the segment.
Euronext 100, Next 150 ,CAC40 indices and stocks which have more than 2,500 order book transactions per year — continuous auction.
Other stocks — call auction twice a day.
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Order types
Market order — immediate execution at the best price available when the order reaches the marketplace
Limit order — to execute a transaction only at a specified price (the limit) or better
Stop order
Good till cancelled
Fill-or-kill
All or None
Day order
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Algorithmic Trading: An Overview of Algorithmic Trading: An Overview of Applications And Models.Applications And Models.
Ekaterina Kochieva
Gautam Mitra
Cormac Lucas
Copyright ® 2009, SAS Institute Inc. All right s reserved.
Summary Definitions and examples have been given about XBRL &
Financial Data Mining.
Key components to make this work are the global adoption of a Business Reporting language. This includes consistent standards being set by both XBRL US & XBRL International.
All companies need to be held responsible for their reporting with the current Reg rules adjusted. A global certification process is needed with the proper GRC engagement model followed. The good news is that there will be plenty of data to be mined.
How we mine and integrate this into our Trading Strategies is up to us. This is the "special sauce" which will make or break how our Trader trades & Trade Support supports.