Multi-frequency analysis for high frequency trading

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Hongguang_Liu:thesis,
  author =       "Hongguang Liu",
  title =        "Multi-frequency analysis for high frequency trading",
  school =       "Dept. of Industrial and Systems Engineering, Hong Kong
                 Polytechnic University",
  year =         "2017",
  address =      "Hong Kong",
  keywords =     "genetic algorithms, genetic programming, Investment
                 analysis, Mathematical models, Portfolio management,
                 Mathematical models, Stocks, Prices, Mathematical
                 models",
  URL =          "http://theses.lib.polyu.edu.hk/handle/200/8985",
  URL =          "http://library.polyu.edu.hk/record=99102195",
  URL =          "http://theses.lib.polyu.edu.hk/bitstream/handle/200/8985/991021952843203411.pdf",
  size =         "xiv, 186 pages",
  abstract =     "High-Frequency Trading (HFT) in financial markets has
                 been making media headlines. The 2010 Flash Crash in
                 the US and the 2013 Everbright Securities incident in
                 China showed its dramatic impacts on the markets.
                 However, as a relatively new phenomenon, most of the
                 discussion on HFT is not backed by solid academic
                 research. At the same time, current academic research
                 on high-frequency trading focuses on its afterward
                 influences, the motivation and the trading logic behind
                 the HFT is rarely explored. Basically, there are two
                 kinds of HFT, the first kind of HFT takes advantage of
                 {"}time{"}, the most advanced computers are placed
                 right next to the exchanges to reduce the time delay of
                 the receiving of market data and the execution of
                 trading orders that aiming to capture a very small
                 fraction of the profit on every trade. The second kind
                 of HFT is conducted based on the analysis of the
                 historical data of the related financial time series.
                 This thesis focuses on the study of the second kind of
                 HFT. Multiple methods can be used in the design of the
                 second kind of HFT. In this research, multi-frequency
                 analysis and wavelet are combined with technical
                 indicators and modern machine learning tools.
                 Forecasting of the directions of the financial time
                 series is crucial in the design of such kind of HFT
                 systems, many economic and technical models and
                 indicators have been built in the past, however, most
                 of the past research merely analyse the data in time
                 domain, the frequency domain of the HFT is rarely
                 explored. This research focuses on the multi-frequency
                 predictions of the short-term movements of the
                 financial time series and the design of the trading
                 systems based on the forecast.HFT systems based on
                 moving averages and a simple trend following system are
                 developed to set benchmarks for the multi-frequency
                 related systems. An experiment on the performance of
                 two-frequency ARIMA model is also conducted to show the
                 prediction power of the multi-frequency analysis, as
                 time series in different resolutions may convey
                 different information on its characteristics, the
                 empirical results indicated that multi-frequency could
                 improve the forecast performance. After that, an
                 intra-day trading system is designed based on the
                 Genetic Programming (GP) and technical analysis,
                 wavelet de-noise is introduced to improve the
                 performance of the GP based system, the system with
                 wavelet de-noise showed best performance in the
                 empirical test. To explore the nonlinear relationship,
                 artificial neural network (ANN) is applied in the
                 prediction of the financial time series. Both Nonlinear
                 Auto-regressive with eXogenous (NARX) and wavelet based
                 Multi-layer perceptron models are used in the
                 forecasting of the intra-day high-frequency time
                 series, based on which, HFT systems are developed. To
                 test the performance of the HFT systems, the China
                 index futures is selected as the experiment asset.
                 Based on the experiments in this thesis, the HMA
                 trading system shows the best performance among the
                 tested moving averages trading systems; the
                 two-frequency ARIMA beats the traditional single
                 frequency models; the GP systems trained using the
                 wavelet de-noised data outperforms the GP systems
                 trained using the original data, and the hard-threshold
                 denoise method provides the best out-of-sample trading
                 performance; the WMLP based trading model outperforms
                 the NARX model in the out-of-sample trading test.",
}

Genetic Programming entries for Hongguang Liu

Citations