Diversification philosophy and boosting technique for trade execution strategy
- Publication Type:
- Thesis
- Issue Date:
- 2006
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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.
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