Browsing by Author "Sun, Edward W."

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Coherent quality management for big data systems: a dynamic approach for stochastic time consistency
(2019-06-01)
Chen, Yi-Ting
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Sun, Edward W.
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Lin, Yi-Bing
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資訊工程學系
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Department of Computer Science
Big data systems for reinforcement learning have often exhibited problems (e.g., failures or errors) when their components involve stochastic nature with the continuous control actions of reliability and quality. The complexity of big data systems and their stochastic features raise the challenge of uncertainty. This article proposes a dynamic coherent quality measure focusing on an axiomatic framework by characterizing the probability of critical errors that can be used to evaluate if the conveyed information of big data interacts efficiently with the integrated system (i.e., system of systems) to achieve desired performance. Herein, we consider two new measures that compute the higher-than-expected error,that is, the tail error and its conditional expectation of the excessive error (conditional tail error)as a quality measure of a big data system. We illustrate several properties (that suffice stochastic time-invariance) of the proposed dynamic coherent quality measure for a big data system. We apply the proposed measures in an empirical study with three wavelet-based big data systems in monitoring and forecasting electricity demand to conduct the reliability and quality management in terms of minimizing decision-making errors. Performance of using our approach in the assessment illustrates its superiority and confirms the efficiency and robustness of the proposed method.
Financial Transaction Tax: Policy Analytics Based on Optimal Trading
(2015-06-01)
Sun, Edward W.
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Kruse, Timm
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Yu, Min-Teh
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Introducing a financial transaction tax (FTT) has recently attracted tremendous global attention, with both proponents and opponents disputing its dampening effects on financial markets. In this paper we present a model to show under some circumstances that there exists a win-win situation via optimal trading when the tax burden can be dispersed. The way to absorb FTT in our model is to adjust the bid-ask spread. In our optimal trading model that considers FTT, the representative traders depend on the liquidity (market depth) they supply to weight their associated transaction cost by adjusting the spread ex post. We illustrate the analytical properties and computational solutions of our model when finding the optimal trading strategy under different market situations in order to offset FTT. We also conduct a simulation study to show the superior performance of our proposed optimal trading strategy in comparison to the alternative strategies that do not consider absorbing FTT. The results demonstrate that there is indeed a win-win situation, because financial institutions will not be worse off if such an optimal trading strategy is applied to offset FTT and reduce their transaction cost.
Generalized optimal wavelet decomposing algorithm for big financial data
(2015-07-01)
Sun, Edward W.
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Chen, Yi-Ting
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Yu, Min-Teh
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資訊工程學系
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Department of Computer Science
Using big financial data for the price dynamics of U.S. equities, we investigate the impact that market microstructure noise has on modeling volatility of the returns. Based on wavelet transforms (DWT and MODWT) for decomposing the systematic pattern and noise, we propose a new wavelet-based methodology (named GOWDA, i.e., the generalized optimal wavelet decomposition algorithm) that allows us to deconstruct price series into the true efficient price and microstructure noise, particularly for the noise that induces the phase transition behaviors. This approach optimally determines the wavelet function, level of decomposition, and threshold rule by using a multivariate score function that minimizes the overall approximation error in data reconstruction. The data decomposition method enables us to estimate and forecast the volatility in a more efficient way than the traditional methods proposed in the literature. Through the proposed method we illustrate our simulation and empirical results of improving the estimation and forecasting performance. (C) 2015 Elsevier B.V. All rights reserved.
High frequency trading, liquidity, and execution cost
(2014-12-01)
Sun, Edward W.
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Kruse, Timm
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Yu, Min-Teh
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
We build a model under the framework of discrete optimization to explain how high frequency trading (HFT) can be applied to supply liquidity and reduce execution cost. We derive the analytical properties of our model in finding the optimal solution to minimize the overall execution cost of HFT. We show that the execution cost can be reduced after increasing trading frequency (i.e., the higher the trading frequency, the lower the execution cost) with a simulation study. In addition, we conduct an empirical investigation with tick level data from US equity market through January 2008 to October 2010 to verify our conclusion drawn from the simulation study. Based on the simulation and empirical results we collected, we show that the HFT can reduce the execution cost when supplying liquidity.
Improving model performance with the integrated wavelet denoising method
(2015-09-01)
Chen, Yi-Ting
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Sun, Edward W.
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Yu, Min-Teh
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交大名義發表
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National Chiao Tung University
Intelligent pattern recognition imposes new challenges in high-frequency financial data mining due to its irregularities and roughness. Based on the wavelet transform for decomposing systematic patterns and noise, in this paper we propose a new integrated wavelet denoising method, named smoothness-oriented wavelet denoising algorithm (SOWDA), that optimally determines the wavelet function, maximal level of decomposition, and the threshold rule by using a smoothness score function that simultaneously detects the global and local extrema. We discuss the properties of our method and propose a new evaluation procedure to show its robustness. In addition, we apply this method both in simulation and empirical investigation. Both the simulation results based on three typical stylized features of financial data and the empirical results in analyzing high-frequency financial data from Frankfurt Stock Exchange confirm that SOWDA significantly (based on the RMSE comparison) improves the performance of classical econometric models after denoising the data with the discrete wavelet transform (DWT) and maximal overlap discrete wavelet transform (MODWT) methods.
Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles
(2018-08-01)
Sun, Edward W.
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Wang, Yu-Jen
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Yu, Min-Teh
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管理學院
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College of Management
Portfolio management and integrated risk management are more commonly applied toward enterprise risk management, requiring multivariate risk measures that capture the dependence among many risk factors. In this paper we propose the non-parametric estimator for multivariate value at risk (MVaR) and multivariate average value at risk (MAVaR) based on the multivariate geometric quantile approach and derive the symptotic properties of the proposed estimators for MVaR. We also present their performances under both simulated data and high-frequency financial data from the New York Stock Exchange. In addition, we compare our method with the delta normal approach and order statistics approach. The overall empirical results confirm that the multivariate geometric quantile approach significantly improves the risk management performance of MVaR and MAVaR.
Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data
(2019-08-01)
Chen, Yi-Ting
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Lai, Wan-Ni
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Sun, Edward W.
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資訊學院
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College of Computer Science
High-frequency data is a big data in finance in which a large amount of intra-day transactions arriving irregularly in financial markets are recorded. Given the high frequency and irregularity, such data require efficient tools to filter out the noise (i.e. jumps) arising from the anomaly, irregularity, and heterogeneity of financial markets. In this article, we use a recurrently adaptive separation algorithm, which is based on the maximal overlap discrete wavelet transform (MODWT) and that can effectively: (1) identify the time-variant jumps, (2) extract the time-consistent patterns from the noise (jumps), and (3) denoise the marginal perturbations. In addition, the proposed algorithm enables reinforcement learning to optimize a multiple-criteria decision or convex programming when reconstructing the wavelet-denoised data. Using simulated data, we show the proposed approach can perform efficiently in comparison with other conventional methods documented in the literature. We also apply our method in an empirical study by using high-frequency data from the US stock market and confirm that the proposed method can significantly improve the accuracy of predictive analytics models for financial market returns.
Machine learning with parallel neural networks for analyzing and forecasting electricity demand
(2020-08-01)
Chen, Yi-Ting
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Sun, Edward W.
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Lin, Yi-Bing
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資訊工程學系
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Department of Computer Science
Traditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods.
Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability
(2020-03-16)
Chen, Yi-Ting
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Sun, Edward W.
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Lin, Yi-Bing
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資訊工程學系
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Department of Computer Science
Mobile internet usage has exploded with the mass popularity of smartphones that offer more convenient and efficient ways of doing anything from watching movies, playing games, and streaming music. Understanding the patterns of data usage is thus essential for strategy-focused data-driven business analytics. However, data usage has several unique stylized facts (such as high dimensionality, heteroscedasticity, and sparsity) due to a great variety of user behaviour. To manage these facts, we propose a novel density-based subspace clustering approach (i.e., a three-stage iterative optimization procedure) for intelligent segmentation of consumer data usage/demand. We discuss the characteristics of the proposed method and illustrate its performance in both simulation with synthetic data and business analytics with real data. In a field experiment of wireless mobile telecommunications for data-driven strategic design and managerial implementation, we show that our method is adequate for business analytics and plausible for sustainability in search of business value. (C) 2019 Published by Elsevier B.V.
Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading
(2018-08-01)
Chen, Yi-Ting
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Sun, Edward W.
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Yu, Min-Teh
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資訊學院
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College of Computer Science
Dynamic risk management requires the risk measures to adapt to information at different times, such that this dynamic framework takes into account the time consistency of risk measures interrelated at different times. Therefore, dynamic risk measures for processes can be identified as risk measures for random variables on an appropriate product space. This paper proposes a wavelet feature decomposing algorithm based on the discrete wavelet transform that optimally decomposes the time-consistent features from the product space. This approach allows us to generalize the multiple-stage risk measures of value at risk and conditional value at risk for the feature-decomposed processes, and implement them into portfolio selection using high-frequency data of U.S. DJIA stocks. The overall empirical results confirm that our proposed method significantly improves the performance of dynamic risk assessment and portfolio selection.
Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity
(2019-10-01)
Sun, Edward W.
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Kruse, Timm
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Chen, Yi-Ting
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資訊工程學系
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Department of Computer Science
Regulatory reform enacted (e.g., the Dodd-Frank Act enforced in the U.S.) requires the financial service industry to consider the "reasonably expected near term demand" (i.e., RENTD) in trading. To manage the price impact and transaction cost associated with orders submitted to an order driven market, market makers or specialists must determine their trading styles (aggressive, neutral, or passive) based on the market liquidity in response to RENTD, particularly for trading a large quantity of some financial instrument. In this article we introduce a model considering different trading styles to satisfy the predictive near-term customer demand of market liquidity in order to find an optimal order submission strategy based on different market situations. We show some analytical properties and numerical performances of our model in search of optimal solutions. We evaluate the performances of our model with simulations run over a set of experiments in comparison with two alternative strategies. Our results suggest that the proposed model illustrates superiority in performance.
Systemic risk, financial markets, and performance of financial institutions
(2018-03-01)
Lin, Edward M. H.
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Sun, Edward W.
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Yu, Min-Teh
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交大名義發表
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National Chiao Tung University
This paper studies the exposure and contribution of financial institutions to systemic risks in financial markets. We employ three popular indicators of a financial institution's exposure to systemic risks: the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (Volatility, correlation and tails for systemic risk measurement, Social Science Research Network, Rochester, NY, 2012) and the conditional Value-at-Risk (CoVaR) of Adrian and Brunnermeier (2011). We use a primary database of Taiwan financial institutions for our empirical study. A panel contains data of stock market returns and balance sheets of 31 Taiwan financial institutions for 2005-2014. We focus on systemic risk analysis so as to understand the dynamics of volatility, interdependency, and risk during the recent financial crisis. We then report the time series dynamics and cross sectional rankings of these systemic risk measures. The main results indicate that although these three measures differ in their definition of the contributions to systemic risk, all are quite similar in identifying systemically important financial institutions (SIFIs). Moreover, we find empirical evidence that systemic risk contributions are closely related to certain institution characteristic factors. The results of the Granger causality tests prove that a systemic risk measure is a great alternative tool for monitoring early warning signals of distress in the real economy.