Browsing by Author "Chou, Chin"

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A Market Making Quotation Strategy Based on Dual Deep Learning Agents for Option Pricing and Bid-Ask Spread Estimation
(2018-01-01)
Hsu, Pei-Ying
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Chou, Chin
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Huang, Szu-Hao
;
Chen, An-Pin
;
資訊管理與財務金融系註：原資管所+財金所
Traditional professional traders and institutional investors utilized complex statistical models to price various derivative contracts and make trading decisions in the option and future markets. In recent years, with the rapid growth of algorithmic trading and program trading, the advanced information and communication technology has become an indispensable element for high-frequency traders, especially for the market makers. In addition, artificial intelligence and deep learning also plays an important role in novel financial technology (FinTech) research field. In this paper, we proposed a market making quotation strategy based on deep learning structure and practical finance domain knowledge. The proposed dual agents will simultaneously model the option prices and bid-ask spreads. The experiments demonstrate that our system can precisely estimate the value of options than famous financial engineering models. It also can be extended to develop proper market making quotation strategies to trade the options of Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX).
Affinity Propagation Clustering for Intelligent Portfolio Diversification and Investment Risk Reduction
(2016-01-01)
Chang, Chu-Chun
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Koc, Wai-Wan
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Chou, Chin
;
Lin, Zhi-Ting
;
Huang, Szu-Hao
In this paper, an intelligent portfolio selection method based on Affinity Propagation clustering algorithm is proposed to solve the stable investment problem. The goal of this work is to minimize the volatility of the selected portfolio from the component stocks of S&P 500 index. Each independent stock can be viewed as a node in graph, and the similarity measurements of stock price variations between companies are calculated as the edge weights. Affinity Propagation clustering algorithm solve the graph theory problem by repeatedly update responsibility and availability message passing matrices. This research tried to find most representative and discriminant features to model the stock similarity. The testing features are divided into two major categories, including time-series covariance, and technical indicators. The historical price and trading volume data is used to simulate the portfolio selection and volatility measurement. After grouping these investment targets into a small set of clusters, the selection process will choose fixed number of stocks from different clusters to form the portfolio. The experimental results show that the proposed system can effectively generate more stable portfolio by Affinity Propagation clustering algorithm with proper similarity features than average cases with similar settings.
Deep Learning in Model Risk Neutral Distribution for Option Pricing
(2019-01-01)
Chou, Chin
;
Liu, Jhih-Chen
;
Chen, Chiao-Ting
;
Huang, Szu-Hao
;
資訊管理與財務金融系註：原資管所+財金所
Option pricing has been studied extensively in recent years. An important issue in option pricing is the estimation of the risk neutral distribution of an underlying asset. Better estimation of this distribution can lead to a more rational investment, enabling one to earn an equal return with lower risk. To price options precisely and correctly, traditional financial engineering methods make some assumptions for the risk neutral distribution. However, some assumptions of traditional methods have proved inappropriate and insufficient in empirical option pricing analysis. To address these problems in option pricing, this study adopts a data-driven approach. Owing to advances in hardware and software, studies have been using deep learning methods to price options; however, these have not adequately considered the risk neutral distribution. This may cause an uncontrollable risk, thereby preventing the real-world application of the model. To overcome these problems, this study proposes a deep learning method with a mixture distribution model. Further, it generates a rational risk neutral distribution with accurate empirical pricing analysis.