Trading Automated
What is the most important thing in trading - instinct of a trader or algorithm of a trading system? Afterward, intermediaries only provide automated pre-trade risk checks that are mostly implemented within the exchange software and administered by the broker, for example, by setting a maximum order value or the maximum number of orders in a predefined time period.
Another major benefit is that using these robots actually gives you a great deal more free time, as one might imagine if there is far less work to be done by monitoring the markets and making frequent decisions on when is the precise best time to trade.
Nowadays, the securities trading landscape is characterized by a high level of automation, for example, enabling complex basket portfolios to be traded and executed on a single click or finding best execution via smart order-routing algorithms on international markets.
For example, the volatility triggered by the mysterious rogue trade on 6 May was exacerbated by the fact that some venues, like the New York Stock Exchange, had so-called ‘circuit breakers' that automatically suspend trading when a certain level of volatility is reached, while others did not.
Algorithmic trading not only has altered the traditional relation between investors and their market-access intermediaries but also has caused a change in the traders' focus as the invention of the telephone did in 1876 for communication between people.
The consequence of these developments has been an escalation in market volatility, an increase in unhedgeable catastrophic risk, and thus a decrease in risk-adjusted returns (if one truly accounted for catastrophic risk), all resulting in what I would argue is an unsustainable overweight in the overall market's portfolio away from fundamental investing into short-term trading.
In addition to these models, there are a number of other decision making models which can be used in the context of algorithmic trading (and markets in general) to make predictions regarding the direction of security prices or, for quantitative readers, to make predictions regarding the probability of any given move in a securities price.
They continuously gather real-time data from the respective venues concerning the available order book situations ( Ende et al. 2009 ). Foucault and Menkveld (2008) analyze executions among two trading venues for Dutch equities and argue that suboptimal trade executions result from a lack of automation of routing decisions.
As they focus on the lifetimes of the so-called no-fill deletion orders, that is, orders that are inserted and subsequently cancelled without being executed, they find algorithm-specific characteristics concerning the insertion limit of an order compared to ordinary trading by humans.
Additionally, Groth (2011) confirms this relation between volatility and algorithmic trading by analyzing data containing a specific quant algo trading flag provided by the respective market operator that allows one to distinguish between algorithmic and human traders.
In addition, with the help of new market access models, the buy side has gained more control over the actual trading and order allocation processes and is able to develop and implement its own trading algorithms or use standard software solutions from independent vendors.