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Trading system transformation in global markets (Review Report)Published by: Datamonitor Published: Dec. 6, 2006 - 75 Pages Table of ContentsDATAMONITOR VIEW 1 CATALYST 1 SUMMARY 1 EXECUTIVE SUMMARY 2 Introduction 2 The adoption of algorithmic trading methods (Market Focus) 2 The evolution of algorithmic trading (Strategy Focus) 2 Key technology considerations for algorithmic trading capability (Technology Focus) 3 US algorithmic trading spend (Databook) 3 European algorithmic trading spend (Databook) 4 Asia-Pacific algorithmic trading spend (Databook) 4 Global algorithmic trading spend (Databook) 4 Total algorithmic trading spend (Databook) 5 THE ADOPTION OF ALGORITHMIC TRADING METHODS (MARKET FOCUS) 10 Summary 10 The need for better execution is driving a renewed focus on front office trading tools 10 Explosion of market data is putting pressure on achieving best execution 10 Growth of electronic channels is permitting greater automation in the trade execution process 11 Algorithms are increasingly being seen as a necessary tool to help achieve best execution 14 Algorithms the key to optimally slice and dice orders 14 Close management of algorithms is necessary to help traders achieve efficiency goals 15 Algorithms need to be adapted to suit individual market conditions to be successful 17 Certain factors of the US market facilitate algorithmic use 17 European and Asian markets must overcome structural and regulatory factors that inhibit algorithmic growth 17 Regulation acting as a catalyst for algorithm growth 18 Traditional buy-side managers are demanding greater levels of control over execution 19 Asset managers move onto the algorithmic path 19 THE EVOLUTION OF ALGORITHMIC TRADING (STRATEGY FOCUS) 21 Summary 21 The growth of algorithms is driving new responsibilities for the buy-side in the trading process 21 Technology is gradually shaping a new role for the sell-side 21 The abundance of algorithms is placing increased focus on analyzing transaction costs 23 Choosing between algorithms from one provider 23 Choosing between different providers' algorithms 24 Newer products will follow a different evolutionary cycle to that of equities 24 Different products are emerging on the algorithmic use but progression must be hesitant 25 The next generation of algorithms will target greater intelligence to combat competition 26 Customised algorithms 27 Portfolio algorithms 27 Algorithms adapting to events 28 Advanced algorithms moving up the value chain 28 Alternatives to using sell-side institutions are gaining in popularity for algorithm access 28 KEY TECHNOLOGY CONSIDERATIONS FOR ALGORITHMIC TRADING CAPABILITY (TECHNOLOGY FOCUS) 30 Summary 30 An increased amount of focus is now being placed on effective algorithm implementation 30 Key requirements 31 Demand is increasing for enhanced trading desktop functionality 31 Firms are turning to real-time data analysis tools to enhance execution quality 32 A shift in the use of TCA is occurring 33 The demand for increased latency is driving investment in data infrastructure 34 Architectural structure considerations 36 Greater technological intelligence will be required to obtain superior trading performance 36 Innovative algorithms are emerging 37 US ALGORITHMIC TRADING SPEND (DATABOOK) 39 Introduction 39 Definitions 39 Sell-side front office trading and market data technology spend 40 Buy-side front office trading and market data technology spend 41 Sell-side algorithmic trading spend, split by product 42 Buy-side algorithmic trading spend, split by product 44 EUROPEAN ALGORITHMIC TRADING SPEND (DATABOOK) 46 Introduction 46 Definitions 46 Sell-side front office trading and market data technology spend 47 Buy-side front office trading and market data technology spend 48 Sell-side algorithmic trading spend, split by product 49 Buy-side algorithmic trading spend, split by product 50 ASIA-PACIFIC ALGORITHMIC TRADING SPEND (DATABOOK) 52 Introduction 52 Definitions 52 Sell-side front office trading and market data technology spend 53 Buy-side front office trading and market data technology spend 54 Sell-side algorithmic trading spend, split by product 55 Buy-side algorithmic trading spend, split by product 56 GLOBAL ALGORITHMIC TRADING SPEND (DATABOOK) 58 Introduction 58 Definitions 58 Sell-side front office trading and market data technology spend 59 Buy-side front office trading and market data technology spend 60 Sell-side algorithmic trading spend, split by product 61 Buy-side algorithmic trading spend, split by product 62 TOTAL ALGORITHMIC TRADING SPEND (DATABOOK) 64 Introduction 64 Definitions 64 European front office trading and market data technology spend 65 US office trading and market data technology spend 66 Asia-Pacific trading and market data technology spend 67 Global trading and market data technology spend 68 European algorithmic trading spend, split by product 69 US algorithmic trading spend, split by product 70 Asia-Pacific algorithmic trading spend, split by product 71 Global algorithmic trading spend 72 APPENDIX 74 Definitions 74 Extended Methodology 74 Further reading 74 Ask the analyst 75 List of Tables Table 1: Algorithmic trading definitions 39 Table 2: Sell-side front office trading and market data technology spend 40 Table 3: Buy-side front office trading and market data technology spend 41 Table 4: Sell-side algorithmic trading spend, split by product 43 Table 5: Buy-side algorithmic trading spend, split by product 45 Table 6: Algorithmic trading definitions 46 Table 7: Sell-side front office trading and market data technology spend 47 Table 8: Buy-side front office trading and market data technology spend 48 Table 9: Sell-side algorithmic trading spend, split by product 50 Table 10: Buy-side algorithmic trading spend, split by product 51 Table 11: Algorithmic trading definitions 52 Table 12: Sell-side front office trading and market data technology spend 53 Table 13: Buy-side front office trading and market data technology spend 54 Table 14: Sell-side algorithmic trading spend, split by product 56 Table 15: Buy-side algorithmic trading spend, split by product 57 Table 16: Algorithmic trading definitions 58 Table 17: Sell-side front office trading and market data technology spend 59 Table 18: Buy-side front office trading and market data technology spend 60 Table 19: Sell-side algorithmic trading spend, split by product 62 Table 20: Buy-side algorithmic trading spend, split by product 63 Table 21: Algorithmic trading definitions 64 Table 22: European front office trading and market data technology spend 65 Table 23: US front office trading and market data technology spend 66 Table 24: Asia-Pacific front office trading and market data technology spend 67 Table 25: Global front office trading and market data technology spend 68 Table 26: European algorithmic trading spend, split by product 69 Table 27: US algorithmic trading spend, split by product 70 Table 28: Asia-Pacific algorithmic trading spend, split by product 71 Table 29: Global algorithmic trading spend 73 List of Figures Figure 1: Trading process 12 Figure 2: Electronic trading growth 2000-2005 13 Figure 3: Algorithmic trading strategy examples 15 Figure 4: Algorithmic trading strategy examples 16 Figure 5: Asian market trading details 18 Figure 6: Investment currently focused on trading tools in the front office 22 Figure 7: The path to algorithmic adoption for equities may not be standard for other products 25 Figure 8: 2006 next-generation broker algorithms 38 Figure 9: Sell-side front office trading and market data technology spend 40 Figure 10: Buy-side front office trading and market data technology spend 41 Figure 11: Sell-side algorithmic trading spend, split by product 42 Figure 12: Sell-side algorithmic trading spend, split by product 43 Figure 13: Buy-side algorithmic trading spend, split by product 44 Figure 14: Buy-side algorithmic trading spend, split by product 44 Figure 15: Sell-side front office trading and market data technology spend 47 Figure 16: Buy-side front office trading and market data technology spend 48 Figure 17: Sell-side algorithmic trading spend, split by product 49 Figure 18: Sell-side algorithmic trading spend, split by product 49 Figure 19: Buy-side algorithmic trading spend, split by product 50 Figure 20: Buy-side algorithmic trading spend, split by product 51 Figure 21: Sell-side front office trading and market data technology spend 53 Figure 22: Buy-side front office trading and market data technology spend 54 Figure 23: Sell-side algorithmic trading spend, split by product 55 Figure 24: Sell-side algorithmic trading spend, split by product 55 Figure 25: Buy-side algorithmic trading spend, split by product 56 Figure 26: Buy-side algorithmic trading spend, split by product 57 Figure 27: Sell-side front office trading and market data technology spend 59 Figure 28: Buy-side front office trading and market data technology spend 60 Figure 29: Sell-side algorithmic trading spend, split by product 61 Figure 30: Sell-side algorithmic trading spend, split by product 61 Figure 31: Buy-side algorithmic trading spend, split by product 62 Figure 32: Buy-side algorithmic trading spend, split by product 63 Figure 33: European front office trading and market data technology spend 65 Figure 34: US front office trading and market data technology spend 66 Figure 35: Asia-Pacific front office trading and market data technology spend 67 Figure 36: Global front office trading and market data technology spend 68 Figure 37: European algorithmic trading spend, split by product 69 Figure 38: US algorithmic trading spend, split by product 70 Figure 39: Asia-Pacific algorithmic trading spend, split by product 71 Figure 40: Global algorithmic trading spend 72 Figure 41: Global algorithmic trading spend, split by product 72 Figure 42: Global algorithmic trading spend, split by product 73 AbstractIntroductionThis report combines all eight briefs from the trading system transformations theme for Q3 2006. Areas covered include an overview of drivers behind automation in the front office, an explanation of various algorithmic trading strategies in use as well as an examination of technology considerations behind their implementation. Scope *Covers US, Europe and Asia *Forecasts IT spend, split by region and institution type Highlights Sophisticated trading tools are rising in demand for lowering transaction costs, discovering hidden liquidity located amongst abundant data volumes and generating superior returns, whilst maintaining a view of risk management. Products such as futures, options and FX have been the first instruments to move into the algorithmic trading space, building on their electronic capabilities. Market leaders in this sector are thought of as being more technologically savvy and having experienced algorithms in equities may find the switch to more structured products easier. Another area of focus for institutions has been event stream processing (ESP), a technology fuelled by the desire to use greater computing power to analyze streaming data. This technology aims to detect patterns in vast amounts of incoming market data in order for algorithms to efficiently act. Reasons to Purchase *Understand the driving forces behind demand for automated trading tools *Gain visibility on the latest developments in algorithmic trading *Use Datamonitor's timely and concise analysis of key business and technology issues to develop a compelling go-to-market strategy Get Full Details About This Report >> |
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