Search Results
Showing results 1 to 6 of approximately 6.
(refine search)
Newsletter
How to keep markets safe in the era of high-speed trading
A number of recent technology-related snafus have focused attention on high-speed trading and affected investor confidence in the markets. These incidents and the resulting losses highlight the need for risk controls at every step of the trading process.
Newsletter
Market Structure, Incentives, and Fragility
The factors that have contributed to the adoption of high-speed trading and affected market structure in recent years include competition, technology, and regulation. The unexpected ways in which these dynamic forces are coming together raise a number of important policy issues.
Journal Article
Shopping without cash: the emergence of the e-purse
This article finds that successful e-purse programs tend to have a captive audience that drives critical mass, such as those found in the transportation industry or government sector; an affordable cost structure relative to other payment instruments; compelling incentives for consumers and merchants; and technology that is well tested and addresses standards issues before rollout.
Newsletter
Controlling risk in a lightning-speed trading environment
A handful of high-frequency trading firms accounted for an estimated 70 percent of overall trading volume on U.S. equities markets in 2009. One firm with such a computerized system traded over 2 billion shares in a single day in October 2008, amounting to over 10 percent of U.S. equities trading volume for the day. What are the advantages and disadvantages of this technology-dependent trading environment, and how are its risks controlled?
Discussion Paper
Controlling risk in a lightning-speed trading environment
A small group of high-frequency algorithmic trading firms have invested heavily in technology to leverage the nexus of high-speed communications, mathematical advances, trading and high-speed computing. By doing so, they are able to complete trades at lightning speeds. High-frequency algorithmic trading strategies rely on computerized quantitative models that identify which type of financial instruments to buy or sell (e.g., stocks, options or futures), as well as the quantity, price, timing and location of the trades. These so-called black boxes are capable of reading market data, ...