Order Aggressiveness of Institutional and Individual Investors

2011

IJFMD

Abstract:Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument. Investors wishing to execute large orders face a tradeoff between market impact and opportunity cost. Trade execution strategies are designed to balance out these costs, thereby minimising total trading cost. Despite the importance of optimising the trade execution process, this is difficult to do in practice due to the dynamic nature of markets and due to our imperfect understanding of them. In this paper, we adopt a novel approach, combining an evolutionary methodology whereby we evolve high-quality trade execution strategies, with an agent-based artificial stock market, wherein the evolved strategies are tested. The evolved strategies are found to outperform a series of benchmark strategies and several avenues are suggested for future work.

Section: Order Book Information Contentmentioning

confidence: 99%

Dynamic trade execution: a grammatical evolution approach

2011

IJFMD

“…Traders are more willing to place market orders when the market depth on the same side of the order book is large. If the market depth on the opposite side is larger, traders prefer to submit limit orders [3,6,18,23]. The incoming limit orders will have lower execution probability, suffering higher non-execution risk.…”

Section: Information Indicatorsmentioning

confidence: 99%

“…When the bid-ask spread widens, traders prefer to submit limit orders in order to avoid large bid-ask spread cost [3,6,17,18,22,23]. Prior research is inconclusive on the effect of market volatility.…”

Section: Information Indicatorsmentioning

confidence: 99%

Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution

Applications of Evolutionary Computation

2010

Abstract. Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.

“…Traders are willing to place limit orders more aggressively if the depth away from the best price on the same side of the market is high, because higher depth away from the best bid (ask) price reduces the execution probability of incoming limit buy (sell) order [3], [8], [21], [26]. If the market depth on the opposite side is larger, traders prefer to submit limit orders conservatively.…”

Section: A Information Indicatorsmentioning

confidence: 99%

“…If the market depth on the opposite side is larger, traders prefer to submit limit orders conservatively. As the bid-ask spread narrows, the benefits of the better price available to limit order traders decrease, causing them to place more aggressive limit orders [8], [20], [21], [25], [26]. Oppositely, when the bid-ask spread widens, passive limit orders are preferable [3], [8], [21].…”

Section: A Information Indicatorsmentioning

confidence: 99%

Evolving efficient limit order strategy using Grammatical Evolution

IEEE Congress on Evolutionary Computation

2010

Abstract-Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. A practical problem in trade execution is how to trade a large order as efficiently as possible. A trade execution strategy is designed for this task to minimize total trade cost. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. It has been proved successfully to be able to evolve quality trade execution strategies in our previous work. In this paper, the previous work is extended by adopting two different limit order lifetimes and three benchmark limit order strategies. GE is used to evolve efficient limit order strategies which can determine the aggressiveness levels of limit orders. We found that GE evolved limit order strategies were highly competitive against three benchmark strategies and the limit order strategies with long-term lifetime performed better than those with short-term lifetime.