Computerized Trading

Background

Computerized trading, also known as algorithmic trading or automated trading, refers to the use of computer programs to monitor market information and execute trades automatically when pre-determined conditions are met. This form of trading takes advantage of the speed, efficiency, and data-processing capabilities of computers to potentially outperform traditional, manually-executed trading methods.

Historical Context

The origins of computerized trading trace back to the development of program trading systems in the 1970s and 1980s, mainly used by hedge funds and institutional investors. Markets saw a significant influx of computerized trading starting in the late 1980s, following advances in technology, increased electronic access to financial markets, and innovation in software and algorithms.

Definitions and Concepts

Computerized trading involves complex algorithms and software systems that can analyze and interpret real-time data, such as stock prices, volume, and market trends. Once the algorithm recognizes conditions that match its predefined criteria, it automatically generates buying or selling orders.

Major Analytical Frameworks

Classical Economics

Classical economics primarily focuses on long-term market growth and macroeconomic fundamentals, which are less the focus of rapid, dynamic computerized trading systems.

Neoclassical Economics

Neoclassical economics, with its focus on efficiency and optimization, aligns more closely with computerized trading, which aims to streamline trades to optimize timing and pricing automatically.

Keynesian Economics

While Keynesian theories emphasize market intervention and economic cycles, computerized trading operates seamlessly in both up and down markets, often enhancing liquidity despite economic conditions.

Marxian Economics

Marxian analysis might critique computerized trading as a furthering of capital’s dominance and efficiency over labor, with the software systems representing a peak of non-human intervention in markets dominating decision-making processes.

Institutional Economics

Institutional economics would examine how computerized trading practices are embedded within regulatory, organizational, and sectoral norms and the systemic implications of their widespread adoption.

Behavioral Economics

Behavioral economics might study the effects of computerized trading on market psychology and individual traders’ behavior, particularly around market volatility and reactionary trading.

Post-Keynesian Economics

Post-Keynesian perspectives would address the impact of computerized trading on financial stability and its role in liquidity creation, market microstructure, and potential speculative bubbles.

Austrian Economics

Austrian economists might approach computerized trading with skepticism, favoring human judgment and insight over reliance on algorithmic precision and computational capacity.

Development Economics

Development economists would be interested in the barriers to technical adoption, the spread and effects of computerized trading in emerging markets, and possibly its impacts on resource allocation within these environments.

Monetarism

Monetarists might focus on how computerized trading interacts with monetary policy and interest rates, examining potential impacts on inflation and money supply control efforts.

Comparative Analysis

In practice, computerized trading represents a spectrum ranging from fully automated high-frequency trading (HFT) strategies to more discretionary algorithmic approaches that assist in executing pre-planned trades. The debate persists on the benefits it brings in terms of market liquidity and efficiency versus the challenges it poses, such as market manipulation or the instigation of flash crashes.

Case Studies

Notable case studies include:

1. Flash Crash of 2010 - How high-frequency trading and algorithmic failures led to a market meltdown and subsequent recovery within a very short period.
2. 1987 Black Monday - Early implications for automated trading operations during unprecedented market conditions.
3. Renaissance Technologies - A pioneer in the field of computer-based quantitative trading techniques achieving consistent market outperformances.

Suggested Books for Further Studies

• Flash Boys: A Wall Street Revolt by Michael Lewis
• The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It by Scott Patterson
• Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson

Related Terms with Definitions

• Algorithmic Trading: A subset of computerized trading specifically involving complex algorithms to automate trading decisions.
• High-Frequency Trading (HFT): A form of algorithmic trading characterized by extremely high speeds, significant turnover rates, and order-to-trade ratio.
• Quantitative Trading: This approach relies heavily on mathematical models and statistical techniques to identify trading opportunities.
• Market Microstructure: The study of how various market participants’ interactions and trading processes influence the price discovery and transaction process.

This format should give users comprehensive knowledge about computerized trading alongside relevant financial concepts and their implications in various economic contexts.