Diversification philosophy and boosting technique for trade execution strategy

University of Technology, Sydney. Faculty of Information Technology.This thesis explores the rationale and effectiveness of diversification across time and strategies, which is an important philosophy for risk management in practice, in the framework of developing trade execution strategies. In this thesis, the strategies are defined as making a series of decisions based on real-time state variables over a fixed period to achieve high reward and low risk with given resources. Trade execution strategies are to make a series of decisions on how to place an order in markets based on real-time market information over a fixed period to fill the order with low cost and risk in the end. In the 1st part, this thesis explores diversification across time. The research of trade execution has shown that although limit order strategy achieves lower cost than market order strategy does, it may incur nonexecution risk and miss trading opportunities. This thesis proposes a strategy that reflects the idea of diversification across time to improve the limit order strategy. In the 2nd part, this thesis explores diversification across strategies. Techniques for implementing this idea have been proposed to acquire strategies from a candidate strategy set and determine their weights. For those techniques, the candidate strategy set normally only contains finite strategies and the risk that they reduce is only measured by one specific standard. This thesis proposes a technique that overcomes those drawbacks. In the 3rd part, the proposed technique is applied to improve trade execution strategies. The strategy proposed in the 1st part is called DF (dynamic focus) strategy, which incorporates a series of small market orders with different volume into the limit order strategy and dynamically adjusts each market order volume based on two real-time state variables: inventory and order book imbalance. The sigmoid function is adopted to map the variables to the market order volume. Experiments show that the DF strategy achieves lower cost and risk than the limit order strategy does. The technique proposed in the 2nd part extends the key idea of the AdaBoost (adaptive boosting) technique, which is discussed mostly in the supervised learning field. It is named DAB (diversification based on AdaBoost) in this thesis. The DAB technique adaptively updates the probability distribution on training examples in the learning process, acquires strategies from a candidate strategy set and determines their weights. Resources (e.g. money or an order) are allocated to each acquired strategy in proportion with its weight and all acquired strategies are then executed in parallel with their allocated resources. The DAB technique allows the candidate strategy set to contain infinite strategies. Analysis shows that as the learning steps increase, the DAB technique lowers the candidate strategy set's risk, which can be measured by different standards, and limits the decrease in its reward. The DAB technique is applied in the 3rd part to acquire DF strategies from a candidate DF strategy set and determine their weights. The entire order is allocated to each acquired DF strategy in proportion with its weight and all acquired DF strategies are then executed in parallel to fill their allocated order. In this thesis, this parallel execution is called BONUS (boosted dynamic focus) strategy. Experiments support theoretical analysis and show that the BONUS strategy achieves lower risk and cost than the optimal DF strategy and two simple diversification techniques do. This thesis is contributed to both finance and computer science fields from the theoretical and empirical perspectives. First, the proposed DF strategy verifies the effectiveness of diversification across time through improving the existing trade execution strategies. Second, the proposed DAB technique provides a flexible way for implementing diversification across strategies to complement the existing diversification techniques and enrich the research of the AdaBoost technique. Third, the proposed DAB technique and BONUS strategy provide a flexible way to improve trade execution strategies

Dynamic focus strategies for electronic trade execution in limit order markets

Trade execution has attracted lots of attention from academia and financial industry due to its significant impact on investment return. Recently, limit order strategies for trade execution were backtested on historical order/trade data and dynamic price adjustment was proposed to respond state variables in execution. This paper emphasizes the effect of dynamic volume adjustment on limit order strategies and proposes dynamic focus (DF) strategies, which incorporate a series of market orders of different volume into the limit order strategy and dynamically adjusts their volume by monitoring state variables such as inventory and order book imbalance in real-time. The sigmoid function is suggested as the quantitative model to represent the relationship between the state variables and the volume to be adjusted. The empirical results on historical order/trade data of the Australian Stock Exchange show that the DF strategy can outperform the limit order strategy, which does not adopt dynamic volume adjustment. © 2006 IEEE

Algorithmic Trading: Model of Execution Probability and Order Placement Strategy

Most equity and derivative exchanges around the world are nowadays organised as order-driven markets where market participants trade against each other without the help of market makers or other intermediaries as in quote-driven markets. In these markets, traders have a choice either to execute their trade immediately at the best available price by submitting market orders or to trade patiently by submitting limit orders to execute a trade at a more favourable price. Consequently, determining an appropriate order type and price for a particular trade is a fundamental problem faced everyday by all traders in such markets. On one hand, traders would prefer to place their orders far away from the current best price to increase their pay-offs. On the other hand, the farther away from the current best price the lower the chance that their orders will be executed. As a result, traders need to find a right trade-off between these two opposite choices to execute their trades effectively. Undoubtedly, one of the most important factors in valuing such trade-off is a model of execution probability as the expected profit of traders who decide to trade via limit orders is an increasing function of the execution probability. Although a model of execution probability is a crucial component for making this decision, the research into how to model this probability is still limited and requires further investigation. The objective of this research is, hence, to extend this literature by investigating various ways in which the execution probability can be modelled with the aim to find a suitable model for modelling this probability as well as a way to utilise these models to make order placement decisions in algorithmic trading systems. To achieve this, this thesis is separated into four main experiments: 1. The first experiment analyses the behaviour of previously proposed execution probability models in a controlled environment by using data generated from simulation models of order-driven markets with the aim to identify the advantage, disadvantage and limitation of each method. 2. The second experiment analyses the relationship between execution probabilities and price fluctuations as well as a method for predicting execution probabilities based on previous price fluctuations and other related variables. 3. The third experiment investigates a way to estimate the execution probability in the simulation model utilised in the first experiment without resorting to computer simulation by deriving a model for describing the dynamic of asset price in this simulation model and utilising the derived model to estimate the execution probability. 4. The final experiment assesses the performance of utilising the developed execution probability models when applying them to make order placement decisions for liquidity traders who must fill his order before some specific deadline. The experiments with previous models indicate that survival analysis is the most appropriate method for modelling the execution probability because of its ability to handle censored observations caused by unexecuted and cancelled orders. However, standard survival analysis models (i.e. the proportional hazards model and accelerated failure time model) are not flexible enough to model the effect of explanatory variables such as limit order price and bid-ask spread. Moreover, the amount of the data required to fit these models at several price levels simultaneously grows linearly with the number of price levels. This might cause a problem when we want to model the execution probability at all possible price levels. To amend this problem, the second experiment purposes to model the execution probability during a specified time horizon from the maximum price fluctuations during the specified period. This model not only reduces the amount of the data required to fit the model in such situation, but it also provides a natural way to apply traditional time series analysis techniques to model the execution probability. Additionally, it also enables us to empirically illustrate that future execution probabilities are strongly correlated to past execution probabilities. In the third experiment, we propose a framework to model the dynamic of asset price from the stochastic properties of order arrival and cancellation processes. This establishes the relationship between microscopic dynamic of the limit order book and a long-term dynamic of the asset price process. Unlike traditional methods that model asset price dynamic using one-dimensional stochastic process, the proposed framework models this dynamic using a two dimensional stochastic process where the additional dimension represents information about the last price change. Finally, the results from the last experiment indicate that the proposed framework for making order placement decision based on the developed execution probability model outperform naive order placement strategy and the best static strategy in most situations