FPGA Speedup for Financial Network Models

We propose to use FPGA (Field Programmable Gate Arrays) to solve the nearly insurmountable computational challenges of Financial Network Models. Flow of funds models have been discussed for decades, but recently, the research activity has picked up due to international financial crises and the increased power of computers, mathematics, and economic models to address these crises. We survey many of these developments and discuss how FPGA can provide the critical technology to provide answers fast enough to be useable by managers in banks and regulatory agencies.

Technology Bundling

This chapter focuses on the theme of service innovation in the electronic brokerage sector. The discussion will cover the theories of “technology bundling” and how bundling together various technologies creates added value for the end-user. The proliferation of different e-trading systems raises the question of which systems provide better and more comprehensive bundled services to online stock traders. Many online brokers now provide low-cost transactions and financial research capabilities, so where is the next level of innovation? The objective of this chapter is to show that several innovations in broker e-services are critical in the following areas: a) how order processes are efficiently managed in financial e-markets; b) how responsive e-trading systems are in handling trading rules and regulations; c) how different systems address unique niches in financial e-markets; and d) improving systems stability and reliability. Combining different systems and technology features in these areas allow brokers to give much better services to their clients.

Trading Anytime Anywhere with Ubiquitous Financial Information Systems

Trading anytime anywhere ubiquitously is rapidly becoming a popular trading practice in the financial marketspace. When highly volatile financial global markets are becoming a normal phenomenon, trading stocks, options, and futures requires the flexibility to trade anywhere, anytime. Investors or traders who keep track of the financial market on a daily or hourly basis do so because they want to be able to make immediate buy and sell decisions wherever they are and whenever they see fit. Ubiquitous financial systems empower traders to limit risk or take advantage of trading opportunities by providing the capability to ‘time’ trading executions. Millions and billions of dollars are either lost or gained in a few seconds, and timing is key.

Virtual Reality Support for Trading

Virtual reality offers the promise that finally, most of the capabilities of the human mind and senses can be harnessed to improve global financial performance. Over millennia, humans have adapted to their environment by using 3D vision, 3D sound, touch, smell, taste, et cetera. The human brain is highly attuned to these sensory inputs. Survival has often been dependent on split-second perception and quick decisions while under stress. Trading is a similar setting, with 42% of all trading occurring within milliseconds (Business Week, 2009). Losing traders have gone bankrupt and become extinct without exploiting their full sensory capabilities to perceive and understand the trading environment. Most traders have relied on simplistic 2D graphs, text, tables of numbers, and vanishing sounds of the trading floor. More recently, vendors have tried to help traders use more of their senses. Modern computer technology allows traders to better assess the markets, make faster and better decisions, and to get an edge in trading.

Algorithmic Trading Strategy Making

Algorithmic trading strategy making is a very important research issue which attracts more and more people’s interests. This chapter will introduce several principal algorithms for algorithmic trading strategy making. How to design a trading strategy will also be discussed. Some latest research achievements on the algorithmic trading strategy making will be given with some examples and application results.

Linked Data Driven Information Systems as an Enabler for Integrating Financial Data

With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.

Using Genetic Programming Systems as Early Warning to Prevent Bank Failure

The main advantage of creating understandable rules is that users are able to interpret and identify the events that may trigger bankruptcy. By using the method that we propose in this work, it is possible to identify when certain financial indicators are getting close to specific thresholds, something that can turn into an undesirable situation. This is particularly relevant if the companies we are referring to are banks. The contribution of this chapter is to improve the prediction by means of a multi-population approach. The experimental results were evaluated using the Receiver Operating Characteristic (ROC) described in Fawcett and Provost (1997). We show that our approach could improve the Area Under the ROC Curve in 5% with respect to the same method proposed in Garcia et al. (2010). Additionally, a series of experiments were performed in order to find out the reasons of success of the EDR.

DSP Acceleration for Dynamic Financial Models

We present an extensive dynamic financial model that encompasses most models used today in finance and economics. We show that this model is a good match to the capabilities of DSP chips. Particularly, DSP is able to perform the high-speed Monte Carlo simulations that are required to solve many large-scale, intractable financial problems. By simulating a sufficiently large number of future scenarios, DSP chips can rapidly achieve a good approximation of the probable future joint probability distribution function of modeled variables. This probability distribution can be used for the valuation of financial derivatives, computing value at risk, studying macroeconomic policy decisions, and many other purposes. DSP enables such simulations to be faster, cooler, greener, and cheaper than ever before.

Information Technology and Financial Markets

Much has been written about information technology and its role in reinventing financial markets. Today’s markets are truly global, and the interconnectedness is the result of information and communication technologies (ICT) providing the necessary infrastructure. A financial crisis in any part of the world can cause widespread disruptions due to this interconnectedness. Clearly, the Asian crisis in the late 1990s, the sub-prime mortgage loan issues in 2006 and 2007, and the problems occurring in Greece and the U.S. “Flash Crash” in 2010 were exacerbated by the ability of technology to allow financial markets to instantaneously respond in a negative fashion.

The Persisting Human Element of the Electronic Trading Habit

The move towards electronic trading was believed by some to narrow the scope of information available to traders, due to the difference between the old paper-based and new IT-enhanced work environment. It was expected that a trader’s job performance would transform from a physical, social, embodied experience, into an individualized, rational, and detached operation. In this chapter, we discuss how the human element to trading has remained central to job performance, by illustrating how particular trading companies have excelled under the new job environment. Drawing on a data collected on electronic trading firms between mid-2007 to late 2010, we focus on a smaller set to illustrate our findings. We find that trading has remained a human endeavor; traders group together for learning and coordination benefits. Furthermore, firms now tap into a global talent pool, and have incorporated monitoring benefits made possible by electronic monitoring of positions for better risk management.