Liquidity risks, transaction costs and online portfolio selection

The performance of online (sequential) portfolio selection (OPS), which rebalances a portfolio in every period (e.g. daily or weekly) in order to maximise the portfolio's expected terminal wealth in the long run, has been overestimated by the ideal assumption of unlimited market liquidity (i.e. no market impact costs). Therefore, a new transaction cost factor model that considers both market impact costs, estimated from limit order book data, and proportional transaction costs (e.g. brokerage commissions or transaction taxes in a fixed percentage) has been proposed in this paper to measure existing OPS strategies performance in a more practical way as well as to develop a more effective OPS method. Backtesting results from the historical limit order book (LOB) data of NASDAQ-traded stocks show both the performance deterioration of existing OPS methods by the market impact costs and the superiority of our proposed OPS method in the environment of limited market liquidity

Fast multi-output relevance vector regression

This paper has applied the matrix Gaussian distribution of the likelihood function of the complete data set to reduce time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3 +M^3), where V and M are the number of output dimensions and basis functions respectively and V < M. Our experimental results demonstrate that the proposed method is more competitive and faster than the existing methods like Thayananthan et al. (2008). Its computational efficiency and accuracy can be attributed to the different model specifications of the likelihood of the data, as the existing method expresses the likelihood of the training data as the product of Gaussian distributions whereas the proposed method expresses it as the matrix Gaussian distribution

Machine learning in quantitative finance

This thesis consists of the three chapters. Chapter 1 aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V<M. The experimental results demonstrate that the proposed method is more competitive than the existing method, with regard to computation time. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/49131. The performance of online (sequential) portfolio selection (OPS), which rebalances a portfolio in every period (e.g. daily or weekly) in order to maximise the portfolio's expected terminal wealth in the long run, has been overestimated by the ideal assumption of unlimited market liquidity (i.e. no market impact costs). Therefore, a new transaction cost factor model that considers market impact costs, estimated from limit order book data, as well as proportional transaction costs (e.g. brokerage commissions or transaction taxes in a fixed percentage) is proposed in Chapter 2 for both measuring OPS performance in a more practical way and developing a new OPS method. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show both the performance deterioration of OPS by the market impact costs and the superiority of the proposed OPS method in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/56496. Chapter 3 proposes an optimal intraday trading strategy to absorb the shock to the stock market when an online portfolio selection algorithm rebalances a portfolio. It considers real-time data of limit order books and splits a very large market order into a number of consecutive market orders to minimise overall transaction costs, consisting of market impact costs as well as proportional transaction costs. To be specific, it optimises both the number of intraday tradings and an intraday trading path for a multi-asset portfolio. Backtesting results from the historical limit order book data of NASDAQ-traded stocks show the superiority of the proposed trading algorithm in the environment of limited market liquidity. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/62503

Algorithmic Trading for Online Portfolio Selection under Limited Market Liquidity

We propose an optimal intraday trading algorithm to reduce overall transaction costs through absorbing price shocks when an online portfolio selection (OPS) rebalances its portfolio. Having considered the real-time data of limit order books (LOB), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimise the overall transaction costs, including both the market impact costs and the proportional transaction costs. The proposed trading algorithm, compatible to any OPS methods, optimises the number of intraday trades as well as finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs in an environment of limited market liquidity

Algorithmic trading for online portfolio selection under limited market liquidity

We propose an optimal intraday trading algorithm to reduce overall transaction costs by absorbing price shocks when an online portfolio selection (OPS) method rebalances its portfolio. Having considered the real-time data of limit order books (LOB), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimize the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimizes the number of intraday trades and finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs when market liquidity is limited

Fast and Accurate Cosimulation of MPSoC Using Trace-Driven Virtual Synchronization

Abstract—As MPSoC has become an effective solution to everincreasing design complexity of modern embedded systems, fast and accurate cosimulation of such systems is becoming a tough challenge. Cosimulation performance is in inverse proportion to the number of processor simulators in conventional cosimulation frameworks with lock-step synchronization schemes. To overcome this problem, we propose a novel time synchronization technique called trace-driven virtual synchronization. Having separate phases of event generation and event alignment in the cosimulation, time synchronization overhead is reduced to almost zero, boosting cosimulation speed while accuracy is almost preserved. In addition, this technique enables (1) a fast mixed level cosimulation where different abstraction level simulators are easily integrated communicating with traces and (2) a distributed parallel cosimulation where each simulator can run at its full speed without synchronizing with other simulator too frequently. We compared the performance and the accuracy with MaxSim, a well-known commercial SystemC simulation framework, and the proposed framework showed 11 times faster performance for H.263 decoder example, while the error was below 5%. Index Terms—HW/SW cosimulation, multiprocessor systemon-chip (MPSoC), parallel simulation, SystemC, system simulation, virtual synchronization