Algorithmic Trading Trends in Stock Markets
Algorithmic Trading Trends in Stock MarketsAlgorithms have sparked a fundamental change in everything an exciting era of opportunity for those who innovate. It is difficult to explain precisely all the concepts of algorithmic landscape. But some broad trends are referred in this post. In the coming years, the evolution of the algorithmic landscape will result in firms re-evaluating and evolving their views, trading strategy, asset-class mix, the relationship between buy-side and sell-side, the very composition and skills of the people they employ and information technology.
Customized AlgorithmsThe buy-side until now predominantly access algorithms pre-built by sell-side brokers. Buy-side players are gradually moving away from “commoditized” algorithms in order to capture their own intellectual property in customized algorithms.
Algorithms Migrating to CurrenciesThe use of algorithms in multiple asset classes will continue to increase. There are strong indications to date that algorithms also have a place in the trillions of global foreign exchange market, at a time when investors are incorporating foreign exchange (FX) into multi-asset-class strategies. Market participants have long recognized that established equity trading techniques such as baskets and order slicing apply to FX. They are quickly finding out that in the fast-moving FX markets, algorithmic trading is even more effective. It is a fact that algorithms in FX markets are still at an early stage relative to the equities markets.Almost all the brokers of FX markets are providing the algorithmic trading terminals for their clients.
Fixed Income MarketThe introduction of algorithmic trading is being explored in the fixed-income market. It is happening slower than in foreign exchange. The reason for the slow uptake is due to a different market structure in terms of how it functions and operates and algorithmic trading takes off fastest where there is an order-driven environment and greater price transparency.
Algorithms Connect Dark Pools Creating More LiquidityTechnically Speaking, any off-exchange marketplace that executes shares anonymously (without quoting) could be considered “dark” in that it provides limited opportunity for information leakage. According to TABB Group, crossing networks handle five percent to eight percent of buy-side flow. Some of the broker-dealer dark books include Goldman Sachs’ Sigma X, Credit Suisse’s CrossFinder, and UBS’ Price Improvement Network (PIN), while crossing networks include ITG’s Posit, LiquidNet, Instinet Crossing, NYFIX Millennium and Pipeline. Algorithms are used extensively by broker-dealers to match buy and sell orders without publishing quotes. By controlling information leakage and taking both the bid and offer sides of a trade, broker algorithms are in a way enabling improved liquidity, pricing on shares for client, and higher commissions to brokers.
Cross-Asset Trading Adoption of Algorithmic TechniquesTraders are quick to find out cross-asset trading opportunities to generate Alpha (risk-adjusted “excess return” on an investment).Technology has enabled the traders to monitor and respond to multiple liquidity pools across various asset classes. A trader may, for example, buy equity, hedge with a derivative of the equity, and take out an FX position—all within the same strategy. We will see an uptake in innovative algorithms to capitalize on high frequency cross-asset opportunities. The sophistication of these new combinations requires detailed simulation and careful testing. Modern algorithmic trading platforms provide the tools to back-test, profile, and tune new strategies before deployment.
Algorithms for News AnalysisMarkets are moved by news. Buy-side firms and traders are increasingly interested in strategies that are able to analyze news events and its impact on a firm or industry. If the algorithm can analyze and react to the news faster before a human trader; advantages can be realized. An algorithm could, for example alert a trader if a news is released on a company X and if the company stock rises or falls by say one percent in the value of that stock within five minutes. For example, Reuters NewsScope Real-time product lets clients use live news content to drive automated trading and respond to market-moving events as they occur. Each news item is ‘meta tagged’ electronically to identify sectors, individual companies, stories or specific items of data to assist automated trading.
Algorithms for Managing Trading Risk and to Meet Regulatory Requirements
Given the criticality of risk management there is an increasing demand for algorithms that monitor and respond to risk conditions on real-time basis. Using real-time analytics, algorithms can continuously re-calculate metrics like Value-at-Risk (VaR) and automatically hedge a position if VaR is exceeded. Compliance with law is of utmost importance and it is becoming burdensome with ever increasing stringent regulations. Firms going forward will increasingly harness the latest in algorithmic trading technology to address regulatory compliance issues. In parallel, regulators will begin to automate surveillance to monitor trading operations for patterns of abuse.