Academic Experience

  • Present 2016-08

    Assistant Research Fellow

    Research Center of Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan

  • Present 2016-08

    Adjunct Associate Professor

    Department of Computer Science, University of Taipei, Taipei, Taiwan

  • 2016-07 2016-02

    Associate Professor

    Department of Computer Science, University of Taipei, Taipei, Taiwan

  • 2016-01 2011-08

    Assistant Professor

    Department of Computer Science, University of Taipei, Taipei, Taiwan

  • 2014-07 2012-02

    Adjunct Assistant Professor

    Department of Computer Science, National Chengchi University, Taipei, Taiwan

  • 2011-3 2010-9

    Visiting Predoctoral Student

    Theory Group, Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA
    Advisor: Prof. Ming-Yang Kao

Education & Training

  • Ph.D.2011-04

    Ph.D. in Computer Science and Information Engineering

    National Taiwan University

  • B.S.2006-06

    Bachelor in Computer Science

    National Chiao Tung University

Honors & Awards

  • 2017
    Our paper, "Discovering Finance Keywords via Continuous Space Language Models," is selected by ACM Computing Reviews as Notable Article of the 21st Annual Best of Computing.
  • 2016
    第十屆證券暨期貨金椽獎-研究發展論文學術組優等獎
  • 2015
    Best Paper Award in derivatives sponsored by Chicago Trading Company, the 2015 Annual Meeting of the Financial Management Association (FMA'15), Orlando, USA
  • 2013 - 2015
    獎勵特殊優秀人才審定通過
    科技部補助大專校院獎勵特殊優秀人才審定通過, 2014/08-2015/07.
    科技部補助大專校院獎勵特殊優秀人才審定通過, 2013/08-2014/07.
  • 2012
    Best Paper Award, IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr'12), New York
  • 2011
    Best Doctoral Dissertation Award (2nd Place)
    Best Doctoral Dissertation Award (the Second Place) of Operations Research Society of Taiwan (ORSTW) and Chinese Institute of Decision Sciences (CID)
    台灣作業研究學會暨中華決策科學學會碩博士論文競賽博士組第二名
  • 2010
    NSC Predoctoral Research Abroad Award
    中華民國行政院國家科學委員會補助博士生赴國外從事研究獎學金
  • 2010
    Best Paper Award in the Complexity Session, International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC'10), Orlando, USA
  • 2009, 2010
    Student Grant of Foundation for the Advancement of Outstanding Scholarship
    財團法人傑出人才發展基金會優秀學生出國開會補助
  • 2010
    Southwestern Finance Association Student Travel Award
  • 2009
    ACM SIGAPP Student Travel Award for SAC 2009
  • 2003 - 2006
    Academic Achievement Award (NCTU)
    Academic Achievement Award, National Chiao Tung University
    國立交通大學資訊工程學系書卷獎

Research & Industry Projects

  • present 2017/07
    [Industry Project]
    Product Recommendation and Customer Status Prediction Using Customer Profile Data, Cathay Financial Holdings
    基於客戶歷程資料之產品推薦及客戶未來狀態預測,國泰金控
    計畫主持人:陳宏銘楊奕軒蔡銘峰、王釧茹
  • present 2017/07
    Path-Dependent Financial Instruments Evaluation and Analysis on the Nonlinearity/Approximation Errors (II), MOST
    路徑相關型金融商品評價與其引進之非線性及近似誤差分析 (II),科技部 (MOST 106-2221-E-001-003-)
    計畫主持人:王釧茹
  • present 2017/07
    Heterogeneous Big Data Embedding for Recommender Systems, MOST
    異質性巨量資料表示法學習於推薦系統之應用,科技部 (MOST 106-2221-E-004-009-MY2)
    計畫主持人:蔡銘峰
    共同主持人:楊奕軒、王釧茹
  • present 2017/06
    A Unified Framework for Processing and Understanding Heterogeneous Data for Intelligent Recommendation (1/3), MOST
    整合異質性資料之智能推薦架構 (1/3),科技部 (MOST 106-3114-E-002-007-)
    總計畫主持人:陳宏銘
    共同主持人:楊奕軒蔡銘峰、王釧茹
  • present 2017/06
    Intelligent OTC Trading and Robo-advising System using Multimodal Big-data Analytics and Distributed Blockchain Computation (1/3), MOST
    以多模式大數據分析與分散式區塊鏈運算開發智慧型店頭市場交易與機器人理財系統 (1/3),科技部 (MOST 106-3114-E-009-011-)
    總計畫主持人:戴天時
    共同主持人:黃俊龍黃思皓、王釧茹
  • 2017/07 2016/10
    [Industry Project]
    Valuation of zero coupon callable bonds, Taipei Exchange
    建立美元零息可贖回國際債券公平價格評價模型,財團法人中華民國證券櫃檯買賣中心
    計畫主持人:戴天時
    協同主持人:王釧茹
  • 2017/07 2016/08
    Path-Dependent Financial Instruments Evaluation and Analysis on the Nonlinearity/Approximation Errors, MOST
    路徑相關型金融商品評價與其引進之非線性及近似誤差分析,科技部 (MOST 105-2221-E-001-035-)
    計畫主持人:王釧茹
  • 2017/01 2016/08
    [Industry Project]
    User Topic Modeling Based on Web Browsing Logs, Appier
    基於使用者瀏覽網頁資料之文字探勘主題模型,沛星互動科技股份有限公司
    計畫主持人:蔡銘峰、王釧茹
  • 2016/07 2015/08
    Word Sentiment Analysis and Lexicon Expansion via Deep Learning for Financial Risk Predictions, MOST
    基於『文字情緒分析』與『深度學習之情緒詞彙擴增』於財務風險預測之應用 (MOST-104-2221-E-004-010)
    計畫主持人:蔡銘峰
    共同主持人:王釧茹
  • 2016/07 2013/08
    Optimal Search for Parameters in Monte Carlo Simulation with Applications in Finance, MOST
    蒙地卡羅法參數搜尋之最佳化及其在財務上之應用,科技部 (MOST-102-2221-E-845-002-MY3)
    計畫主持人:王釧茹
  • 2013/07 2011/09
    Multivariate Lattice Construction and Its Applications, MOST
    多變量樹狀模型之建構分析及其應用,科技部 (NSC-100-2218-E-133-001-MY2)
    計畫主持人:王釧茹

Recent Research Projects

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    ICE: Item Concept Embedding via Textual Information

    Chuan-Ju Wang, Ting-Hsiang Wang*, Hsiu-Wei Yang*, Bo-Sin Chang, and Ming-Feng Tsai
    The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17), 2017.

    This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information. Specifically, in the proposed framework there are two stages: graph construction and embedding learning. In the first stage, we propose a generalized network construction method to build a network involving heterogeneous nodes and a mixture of both homogeneous and heterogeneous relations. The second stage leverages the concept of neighborhood proximity to learn the embeddings of both items and words. With the proposed carefully designed ICE networks, the resulting embedding facilitates both homogeneous and heterogeneous retrieval, including item-to-item and word-to-item retrieval. Moreover, as a distributed embedding approach, the proposed ICE approach not only generates related retrieval results but also delivers more diverse results than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, ICE encodes useful textual information and thus outperforms traditional methods in various item classification and retrieval tasks.

  • image

    On the Risk Prediction and Analysis of Soft Information in Finance Reports

    Ming-Feng Tsai and Chuan-Ju Wang
    European Journal of Operational Research, 257(1):243-250, 2017.

    We attempt in this paper to utilize soft information in financial reports to analyze financial risk among companies. Specifically, on the basis of the text information in financial reports, which is the so-called soft information, we apply analytical techniques to study relations between texts and financial risk. Furthermore, we conduct a study on financial sentiment analysis by using a finance-specific sentiment lexicon to examine the relations between financial sentiment words and financial risk. A large collection of financial reports published annually by publicly-traded companies is employed to conduct our experiments; moreover, two analytical techniques – regression and ranking methods – are applied to conduct these analyses. The experimental results show that, based on a bag-of-words model, using only financial sentiment words results in performance comparable to using the whole texts; this confirms the importance of financial sentiment words with respect to risk prediction. In addition to this performance comparison, via the learned models, we draw attention to some strong and interesting correlations between texts and financial risk. These valuable findings yield greater insight and understanding into the usefulness of soft information in financial reports and can be applied to a broad range of financial and accounting applications.

  • image

    Evaluating Corporate Bonds and Analyzing Claim Holders' Decisions with Complex Debt Structure

    Liang-Chih Liu, Tian-Shyr Dai, and Chuan-Ju Wang
    Journal of Banking and Finance, 72: 151-174, 2016.

    Although many different aspects of debt structures such as bond covenants and repayment schedules are empirically found to significantly influence values of bonds and equity, many theoretical structural models still oversimplify debt structures and fail to capture phenomena found in financial markets. This paper proposes a carefully designed structural model that faithfully models typical complex debt structures containing multiple bonds with various covenants. For example, the ability for an issuing firm to meet an obligation is modeled to rely on its ability to meet previous repayments, and the default trigger is shaped according to the characteristics of its debt structure such as the amount and schedule of bond repayments. Thus our framework reliably provides theoretical insight and concrete quantitative measurements consistent with extant empirical research such as the shapes of yield spread curves under various firm's financial statuses, and the impact of payment blockage covenants on newly-issued and other outstanding bonds. We also develop the forest, a novel quantitative method to handle contingent changes in the debt structure due to premature bond redemptions. A forest consists of several trees %arranged in layers, that capture different debt structures, for instance those before or after a bond redemption. This method can be used to analyze how poison put covenants in the target firm's bonds influence the bidder's costs of debt financing for a leveraged buyout, or investigate how the presence of wealth transfer among the remaining claim holders due to a bond redemption influences the firm's call policy, or further reconcile conflicts among previous empirical studies on call delay phenomena.

  • image

    Discovering Finance Keywords via Continuous Space Language Models

    Visualization Demonstration   Dataset
    Ming-Feng Tsai and Chuan-Ju Wang
    ACM Transactions on Management Information Systems, 7(3), Article No. 7, 2016.

    The growing amount of public financial data makes it more and more important to learn how to discover valuable information for financial decision-making. This paper proposes an approach to discovering financial keywords from a large number of financial reports. In particular, we apply the continuous bag-of-words (CBOW) model, a well-known continuous space language model, to the textual information in 10-K financial reports to discover new finance keywords. In order to capture word meanings to better locate financial terms, we also present a novel technique to incorporate syntactic information into the CBOW model. Experimental results on four prediction tasks using the discovered keywords demonstrate that our approach is effective for discovering predictability keywords for postevent volatility, stock volatility, abnormal trading volume, and excess return predictions. Furthermore, we also analyze the discovered keywords which attest the ability of the proposed method to capture both syntactic and contextual information between words; this shows the success of this method when applied to the field of Finance.

  • image

    Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing

    Chuan-Ju Wang and Ming-Yang Kao
    European Journal of Operational Research, 249(2):683-690, 2016.  

    This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

Filtered by type:

Exploring the Persistent Behavior of Financial Markets

Yi-Cheng Tsai, Chin-Laung Lei, William Cheung, Chung-Shu Wu, Jan-Ming Ho, Chuan-Ju Wang
Journal PaperTo appear Finance Research Letters.

Abstract

This paper presents the persistent behavior hypothesis for financial markets, which is tested statistically on five stock indices from 2001 to 2014. We find significant results in all five stock markets for the full sample period as well as subperiods. A persistent behavior strategy (PBS) on index futures is also presented, the net annual returns of which are significantly higher than 15% in all futures markets including transaction costs. The best performance, about 27%, occurs in the E-mini NASDAQ 100 and TAIEX futures. We also present studies on the impact of investor behavior over market price of TAIEX futures.

Text Embedding for Sub-Entity Ranking from User Reviews

Chih-Yu Chao*, Yi-Fan Chu*, Hsiu-Wei Yang, Chuan-Ju Wang, and Ming-Feng Tsai
Conference ProceedingsIn Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM'17), Singapore, 2017. To appear. (short paper, acceptance rate: 30%, * indicates equal contributions)

ICE: Item Concept Embedding via Textual Information

Chuan-Ju Wang, Ting-Hsiang Wang*, Hsiu-Wei Yang*, Bo-Sin Chang, and Ming-Feng Tsai
Conference ProceedingsIn Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17), Tokyo, 2017, pp. 85-94. (full paper, acceptance rate: 22%, * indicates equal contributions)

On the Risk Prediction and Analysis of Soft Information in Finance Reports

Ming-Feng Tsai and Chuan-Ju Wang
Journal PaperEuropean Journal of Operational Research, 257(1):243-250, 2017.

Abstract

We attempt in this paper to utilize soft information in financial reports to analyze financial risk among companies. Specifically, on the basis of the text information in financial reports, which is the so-called soft information, we apply analytical techniques to study relations between texts and financial risk. Furthermore, we conduct a study on financial sentiment analysis by using a finance-specific sentiment lexicon to examine the relations between financial sentiment words and financial risk. A large collection of financial reports published annually by publicly-traded companies is employed to conduct our experiments; moreover, two analytical techniques – regression and ranking methods – are applied to conduct these analyses. The experimental results show that, based on a bag-of-words model, using only financial sentiment words results in performance comparable to using the whole texts; this confirms the importance of financial sentiment words with respect to risk prediction. In addition to this performance comparison, via the learned models, we draw attention to some strong and interesting correlations between texts and financial risk. These valuable findings yield greater insight and understanding into the usefulness of soft information in financial reports and can be applied to a broad range of financial and accounting applications.

Discovering Finance Keywords via Continuous Space Language Models

Ming-Feng Tsai, Chuan-Ju Wang, and Po-Chuan Chien
Journal PaperACM Transactions on Management Information Systems, 7(3), Article No. 7, 2016. (Selected by ACM Computing Reviews as Notable Article of the 21st Annual Best of Computing)

Abstract

The growing amount of public financial data makes it more and more important to learn how to discover valuable information for financial decision-making. This paper proposes an approach to discovering financial keywords from a large number of financial reports. In particular, we apply the continuous bag-of-words (CBOW) model, a well-known continuous space language model, to the textual information in 10-K financial reports to discover new finance keywords. In order to capture word meanings to better locate financial terms, we also present a novel technique to incorporate syntactic information into the CBOW model. Experimental results on four prediction tasks using the discovered keywords demonstrate that our approach is effective for discovering predictability keywords for postevent volatility, stock volatility, abnormal trading volume, and excess return predictions. Furthermore, we also analyze the discovered keywords which attest the ability of the proposed method to capture both syntactic and contextual information between words; this shows the success of this method when applied to the field of Finance.

Evaluating Corporate Bonds and Analyzing Claim Holders’ Decisions with Complex Debt Structure

Liang-Chih Liu, Tian-Shyr Dai, and Chuan-Ju Wang
Journal PaperJournal of Banking and Finance, 72:151-174, 2016.

Abstract

Although many different aspects of debt structures such as bond covenants and repayment schedules are empirically found to significantly influence values of bonds and equity, many theoretical structural models still oversimplify debt structures and fail to capture phenomena found in financial markets. This paper proposes a carefully designed structural model that faithfully models typical complex debt structures containing multiple bonds with various covenants. For example, the ability for an issuing firm to meet an obligation is modeled to rely on its ability to meet previous repayments, and the default trigger is shaped according to the characteristics of its debt structure such as the amount and schedule of bond repayments. Thus our framework reliably provides theoretical insight and concrete quantitative measurements consistent with extant empirical research such as the shapes of yield spread curves under various firm's financial statuses, and the impact of payment blockage covenants on newly-issued and other outstanding bonds. We also develop the forest, a novel quantitative method to handle contingent changes in the debt structure due to premature bond redemptions. A forest consists of several trees %arranged in layers, that capture different debt structures, for instance those before or after a bond redemption. This method can be used to analyze how poison put covenants in the target firm's bonds influence the bidder's costs of debt financing for a leveraged buyout, or investigate how the presence of wealth transfer among the remaining claim holders due to a bond redemption influences the firm's call policy, or further reconcile conflicts among previous empirical studies on call delay phenomena.

Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing

Chuan-Ju Wang, and Ming-Yang Kao
Journal PaperEuropean Journal of Operational Research, 249(2):683-690, 2016.

Abstract

This paper provides a novel and general framework for the problem of searching parameter space in Monte Carlo simulations. We propose a deterministic online algorithm and a randomized online algorithm to search for suitable parameter values for derivative pricing which are needed to achieve desired precisions. We also give the competitive ratios of the two algorithms and prove the optimality of the algorithms. Experimental results on the performance of the algorithms are presented and analyzed as well.

FIN10K: A Web-based Information System for Financial Report Analysis and Visualization

Yu-Wen Liu, Liang-Chih Liu, Chuan-Ju Wang, and Ming-Feng Tsai
Conference ProceedingsIn Proceedings of the 25th ACM Conference on Information and Knowledge Management (CIKM'16), Indianapolis, 2016, pp. 2441-2444. (demo paper, acceptance rate: 34.5%)

Dish Discovery via Word Embeddings on Restaurant Reviews

Chih-Yu Chao, Yi-Fan Chu, Yi Ho, Chuan-Ju Wang, and Ming-Feng Tsai
Conference ProceedingsIn Proceedings of the 10th ACM Conference on Recommender Systems (RecSys'16), Boston, 2016. (poster paper)

Pricing Convertible Bonds under the First-Passage Credit Risk Model

Chuan-Ju Wang, Tian-Shyr Dai, and Jr-Yan Wang
Conference TalksThe 2016 Annual Meeting of the Financial Management Association (FMA'16), Las Vegas, 2016.

Measuring Social Influence on Online Collaborative Communities

Zhe-Li Lin, Yu-Ming Lu, Ming-Feng Tsai, and Chuan-Ju Wang
Conference TalksThe 7th Asian Conference on Social Sciences (ACSS '16), Kobe, 2016. (poster paper)

Evaluating Corporate Bonds and Analyzing Market Participants Behaviors with Complex Debt Structure

Tian-Shyr Dai, Chuan-Ju Wang, and Liang-Chih Liu
Conference TalksThe 2015 Annual Meeting of the Financial Management Association (FMA'15), Orlando, 2015. (Best paper award in derivatives sponsored by Chicago Trading Company)

Deep Belief Networks for Predicting Corporate Defaults

Shu-Hao Yeh, Chuan-Ju Wang, and Ming-Feng Tsai
Conference ProceedingsIn Proceedings of the 24th IEEE Wireless and Optical Communication Conference (WOCC'15), Taipei, 2015, pp. 159-163.

Social Influencer Analysis with Factorization Machines

Ming-Feng Tsai, Chuan-Ju Wang, and Zhe-Li Lin
Conference ProceedingsIn Proceedings of the 2015 ACM Web Science Conference (WebSci'15), Oxford, 2015, Article No. 50. (poster paper)

On the Construction and Analysis of Financial Time-Series-Oriented Lexicons

Chen-Yi Lai, Chuan-Ju Wang, and Ming-Feng Tsai
Conference TalksThe 35th International Symposium on Forecasting (ISF'15), Riverside, 2015.

Evaluating Corporate Bonds with Complicated Liability Structures and Bond Provisions

Chuan-Ju Wang, Tian-Shyr Dai, and Yuh-Dauh Lyuu
Journal PaperEuropean Journal of Operational Research, 237(2): 749-757, 2014.

Abstract

This paper presents a general and numerically accurate lattice methodology to price risky corporate bonds. It can handle complex default boundaries, discrete payments, various asset sales assumptions, and early redemption provisions for which closed-form solutions are unavailable. Furthermore, it can price a portfolio of bonds that accounts for their complex interaction, whereas traditional approaches can only price each bond individually or a small portfolio of highly simplistic bonds. Because of the generality and accuracy of our method, it is used to investigate how credit spreads are influenced by the bond provisions and the change in a firm’s liability structure due to bond repayments.

Evaluating Corporate Bonds with Complex Debt Structure

Tian-Shyr Dai, Chuan-Ju Wang, and Liang-Chih Liu
Conference TalksConference on Recent Developments in Financial Econometrics and Applications, Melbourne, 2014.

On the Design of Trading Schemes of Equity Funds Based on Random Traders

Ta-Wei Hung, Mu-En Wu, Chuan-Ju Wang, William W.Y. Hsu, and Jan-Ming Ho
Conference ProceedingsIn Proceedings of the 2014 IEEE International Conference on Granular Computing (GrC'14), Noboribetsu, 2014, pp. 106-111.

Financial Keyword Expansion via Continuous Word Vector Representations

Ming-Feng Tsai and Chuan-Ju Wang
Conference ProceedingsIn Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP'14), Doha, 2014, pp. 1453-1458. (short paper, acceptance rate: 28%)

Pricing Convertible Bonds under the First-Passage Credit Risk Model

Tian-Shyr Dai, Jr-Yan Wang, and Chuan-Ju Wang
Conference TalksThe 22nd Annual Conference on Pacific Basin Finance, Economics, Accounting, and Management (PBFEAM'14), Nagoya, 2014

Corporate Default Prediction via Deep Learning

Shu-Hao Yeh, Chuan-Ju Wang, and Ming-Feng Tsai
Conference TalksThe 34th International Symposium on Forecasting (ISF'14), Rotterdam, 2014.

Evaluating Corporate Bonds with Complex Debt Structure

Tian-Shyr Dai, Chuan-Ju Wang, and Liang-Chih Liu
Conference TalksThe 23rd European Financial Management Association Conference (EFMA'14), Rome, 2014.

Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing

Chuan-Ju Wang and Ming-Yang Kao
Conference ProceedingsIn Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr'14), London, 2014, pp. 384-390.

Realized Tax Benefits and Capital Structure

Tian-Shyr Dai and Chuan-Ju Wang
Journal PaperInternational Journal of Bonds and Derivatives, 1(1): 88-109, 2013.

Abstract

We examine the change of levered firm's capital structures due to different investment decisions of realised tax benefits and various sources of fund to finance coupon and dividend payouts. The complexity is analytically intractable but numerical approaches provide insights. Retaining realised tax benefits and investing them in risk-free assets instead of risky ones result in higher debt capacity and optimal firm value. The impact of positive-net-worth bond covenants on shareholders' investment decisions of realised tax benefits and the related agency problem are analysed. The impact of selling firm's asset (to finance payout) on optimal levered firm value is also analysed.

A Multi-Phase, Flexible, and Accurate Lattice for Pricing Complex Derivatives with Multiple Market Variables

Tian-Shyr Dai, Chuan-Ju Wang, and Yuh-Dauh Lyuu
Journal PaperJournal of Futures Markets, 33(9):795-826, 2013.

Abstract

With the rapid growth and the deregulation of financial markets, many complex derivatives have been structured to meet specific financial goals. Unfortunately, most complex derivatives have no analytical formulas for their prices, particularly when there is more than one market variable. As a result, these derivatives must be priced by numerical methods such as lattice. However, the nonlinearity error of lattices due to the nonlinearity of the derivative's value function could lead to oscillating prices. To construct an accurate, multivariate lattice, this study proposes a multiphase method that alleviates the oscillating problem by making the lattice match the “critical locations,” locations where nonlinearity of the derivative's value function occurs. Moreover, our lattice has the ability to model the jumps in the market variables such as regular withdraws from an investment account, which is hard to deal with analytically. Numerical results for vulnerable options, insurance contracts guaranteed minimum withdrawal benefit (GMWB), and defaultable bonds show that our methodology can be applied to the pricing of a wide range of complex financial contracts.

A Trading Scheme of Equity Funds Based on Random Traders

Ta-Wei Hung, Mu-En Wu, Chuan-Ju Wang, and Jan-Ming Ho
Conference TalksMacao International Symposium on Accounting and Finance, Macau, 2013.

Financial Sentiment Analysis for Risk Prediction

Chuan-Ju Wang, Ming-Feng Tsai, Tse Liu, and Chin-Ting Chang
Conference ProceedingsIn Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP'13), Nagoya, 2013, pp. 802-808. (short paper, acceptance rate: 38%)

Risk Ranking from Financial Reports

Ming-Feng Tsai and Chuan-Ju Wang
Conference ProceedingsIn Proceedings of the 35th annual European Conference on Information Retrieval (ECIR'13), Moscow, 2013, pp. 804-807. (short paper, acceptance rate: 30%)

Visualization on Financial Terms via Risk Ranking from Financial Reports

Ming-Feng Tsai and Chuan-Ju Wang
Conference ProceedingsIn Proceedings of the 24th International Conference on Computational linguistics (Coling'12), Mumbai, 2012, pp. 447-452. (demo paper)

Post-Modern Portfolio Theory for Information Retrieval

Ming-Feng Tsai and Chuan-Ju Wang
Conference ProceedingsIn Proceedings of the International Neural Network Society Symposium on Data Analytics and Competitions (Procedia Computer Science) (SoDAC '12), Bangkok, 2012, Volume 13, pp. 80-85.

A Multi-Phase, Flexible, and Accurate Lattice for Pricing Complex Derivatives on Multiple Market Variables

Chuan-Ju Wang, Tian-Shyr Dai, and Yuh-Dauh Lyuu
Conference ProceedingsIn Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr'12), New York, 2012, pp. 1-8. (Best paper award)

Evaluating Corporate Bonds with General Liability Structures and Bond Covenants under the Jump-Diffusion

Tian-Shyr Dai, Chuan-Ju Wang, and Yuh-Dauh Lyuu
Conference TalksThe 2011 Annual Meeting of Financial Management Association (FMA'11), Denver, 2011.

On the Construction and Complexity of Bivariate Lattice with Stochastic Interest Rate Models

Yuh-Dauh Lyuu and Chuan-Ju Wang (Authors are listed in alphabetical order.)
Journal PaperComputers and Mathematics with Applications, 61(4):1107-1121, 2011.

Abstract

Complex financial instruments with multiple state variables often have no analytical formulas and therefore must be priced by numerical methods, like lattice ones. For pricing convertible bonds and many other interest rate-sensitive products, research has focused on bivariate lattices for models with two state variables: stock price and interest rate. This paper shows that, unfortunately, when the interest rate component allows rates to grow in magnitude without bounds, those lattices generate invalid transition probabilities. As the overwhelming majority of stochastic interest rate models share this property, a solution to the problem becomes important. This paper presents the first bivariate lattice that guarantees valid probabilities. The proposed bivariate lattice grows (super)polynomially in size if the interest rate model allows rates to grow (super)polynomially. Furthermore, we show that any valid constant-degree bivariate lattice must grow superpolynomially in size with log-normal interest rate models, which form a very popular class of interest rate models. Therefore, our bivariate lattice can be said to be optimal.

A Closed-form Formula for an Option with Discrete and Continuous Barriers

Chun-Ying Chen, Pei-Ju Chou, Jeff Yu-Shun Hsu, Wisely Po-Hong Liu, Yuh-Dauh Lyuu, and Chuan-Ju Wang (Authors are listed in alphabetical order.)
Journal PaperCommunications in Statistics - Theory and Methods, 40(2):345-357, 2011.

Abstract

This article presents a methodology to derive analytical formulas for a class of complicated financial derivatives with a continuously monitored barrier and a few discretely monitored ones. Numerical results based on concrete numbers for the parameters are presented and analyzed.

An Efficient and Accurate Lattice for Pricing Derivatives under a Jump-Diffusion Process

Tian-Shyr Dai, Chuan-Ju Wang, Yuh-Dauh Lyuu, and Yen-Chun Liu
Journal Paper Applied Mathematics and Computation, 217(7):3174-3189, 2010.

Abstract

Derivatives are popular financial instruments whose values depend on other more fundamental financial assets (called the underlying assets). As they play essential roles in financial markets, evaluating them efficiently and accurately is critical. Most derivatives have no simple valuation formulas; as a result, they must be priced by numerical methods such as lattice methods. In a lattice, the prices of the derivatives converge to theoretical values when the number of time steps increases. Unfortunately, the nonlinearity error introduced by the nonlinearity of the option value function may cause the pricing results to converge slowly or even oscillate significantly. The lognormal diffusion process, which has been widely used to model the underlying asset’s price dynamics, does not capture the empirical findings satisfactorily. Therefore, many alternative processes have been proposed, and a very popular one is the jump-diffusion process. This paper proposes an accurate and efficient lattice for the jump-diffusion process. Our lattice is accurate because its structure can suit the derivatives’ specifications so that the pricing results converge smoothly. To our knowledge, no other lattices for the jump-diffusion process have successfully solved the oscillation problem. In addition, the time complexity of our lattice is lower than those of existing lattice methods by at least half an order. Numerous numerical calculations confirm the superior performance of our lattice to existing methods in terms of accuracy, speed, and generality.

Realized Tax Benefits and Capital Structure

Tian-Shyr Dai, Wanye Lee, and Chuan-Ju Wang
Conference TalksThe 2010 Southern Finance Association Meetings (SFA'10), Asheville, 2010.

A Novel Lattice Model for Evaluating Corporate Debts with Complex Liability Structures and Debt Covenants

Tian-Shyr Dai, Chuan-Ju Wang, and Yuh-Dauh Lyuu.
Conference TalksAsian Finance Association 2010 International Conference (AsianFA'10), Hong Kong, 2010.

How To Build Formulas for Options with Both Continuous and Discrete Barriers from Few Basic Barrier-Type Options

Chun-Ying Chen, Yuh-Dauh Lyuu, and Chuan-Ju Wang (Authors are listed in alphabetical order.)
Conference TalksAsian Finance Association 2010 International Conference (AsianFA'10), Hong Kong, 2010.

On the Complexity of the Bivariate Lattice with Stochastic Interest Rate Models

Chuan-Ju Wang and Yuh-Dauh Lyuu
Conference ProceedingsIn Proceedings of the International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC'10), Orlando, 2010, pp. 144-149. (Best paper award in the session "Complexity")

A Closed-Form Formula for an Option with Discrete and Continuous Barriers

Chun-Ying Chen, Pei-Ju Chou, Jeff Yu-Shun Hsu, Wisely Po-Hong Liu, Yuh-Dauh Lyuu, and Chuan-Ju Wang (Authors are listed in alphabetical order.)
Conference TalksThe 49th Southwestern Finance Association Annual Meeting (SWFA'10), Dallas, 2010.

An Efficient and Accurate Lattice for Pricing Derivatives under a Jump-Diffusion Process

Chuan-Ju Wang, Tian-Shyr Dai, Yuh-Dauh Lyuu, and Yen-Chun Liu
Conference TalksThe 2009 Financial Management Association European Conference (FMAEuro'09), Turin, 2009.

A Novel Tree Model for Evaluating Credit Risk Based on Enhanced Structural Model

Chuan-Ju Wang, Tian-Shyr Dai, and Yuh-Dauh Lyuu
Conference ProceedingsIn Proceedings of the 44th EURO Working Group on Financial Modeling Meeting (EWGFM'09), San Jose, 2009.

An Efficient and Accurate Lattice for Pricing Derivatives under a Jump-Diffusion Process

Chuan-Ju Wang, Tian-Shyr Dai, Yuh-Dauh Lyuu, and Yen-Chun Liu
Conference ProceedingsIn Proceedings of the 24th annual ACM Symposium on Applied Computing (SAC'09), Hawaii, 2009, pp. 428-432. (full paper, acceptance rate: 29%)

On the Construction and Complexity of Bivariate Lattices

Chuan-Ju Wang
Doctoral DissertationNational Taiwan University, 2011. (Best Doctoral Dissertation Award (the Second Place) of Operations Research Society of Taiwan and Chinese Institute of Decision Sciences)

Current Teaching

  • Present 2016

    Programming (Python)

    Graduate Level
    TIGP Program on BIOinformatics (BP), Academia Sinica

Teaching History

  • 2017 2013

    Social Network and Applications

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2016 2013

    Probability

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2016 2012

    System Programming

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2015 2015

    Web Search and Mining

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2014 2012

    Computer Programming

    Undergraduate Level
    Department of Computer Science, University of Taipei
    Department of Mathematical Sciences, National Chengchi University

  • 2014 2014

    Java Programming (2)

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2013 2013

    Thesis Writing

    Graduate Level
    Department of Computer Science, University of Taipei

  • 2013 2011

    Advanced Educational Statistics

    Graduate Level
    Program of E-Learning, University of Taipei

  • 2012 2012

    Introduction to Financial Computing

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2011 2011

    Java Programming (1)

    Undergraduate Level
    Department of Computer Science, University of Taipei

  • 2011 2011

    Introduction to Software Engineering

    Undergraduate Level
    Department of Computer Science, University of Taipei