臺灣博碩士論文加值系統

本篇論文搜集傳統文獻中對於獨特性波動率疑題的解釋外，加入了近年研究中最新有關資產定價因素及可能原因如公司對於比特幣的敏感度、企業社會責任等，並使用近年發表的資產定價模型（Fama-French五因子模型）來計算獨特性波動率。本文透過特殊的分解式分析法能直覺、量化的提供各項潛在因素解釋獨特性波動率疑團的程度。
我們發現其中諸多因素解釋不到百分之十的謎團，其中以市場摩擦（Market Friction）相關之變數解釋力最好。雖然企業社會責任及比特幣相關變數並無明顯解釋力，但我們仍發現其中存在獨特性波動率有價值資訊。具有企業社會責任資料及低比特幣敏感度的公司中，其獨特性波動對於報酬的影響較弱，甚至於不顯著，且在這些樣本內各項潛在因素的解釋力也隨之降低。此外，在體質良好、資訊透明的公司中，獨特性波動率更為市場整體因素所影響。兩者分別歸咎於資訊不對稱以及新科技、政策對交易摩擦的影響。
在近年投資人對於企業外資訊的關注日益漸增及金融創新應用的普及，本研究發現兩者確實對資產定價及風險有所影響，透過不同的衡量方式進行全面性的探討，並認為企業社會責任及比特幣敏感度有納入資產定價模型的價值。

In this paper, we evaluate comprehensive potential explanations to date for the negative relation between idiosyncratic volatility (IVOL) and stocks return. Except for traditional variable (Market Friction and Lottery preference) and model, we also include recently found explanations such as Bitcoin Sensitivity and Corporate Social Responsibility. The methodology we apply provide quantified fraction allow us to make a direct comparison. Many candidate variables individually explain less than 10% of the puzzle.
Although CSR and Bitcoin Sensitivity fail to explain IVOL-return relation, we still find something linked to IVOL. For those firms without CSR data, the IVOL effect and explanatory power of candidates is higher due to low transparency. Besides, IVOL effect will be weakened once the company is incorporated in CSR watch list. Firms with low Bitcoin Sensitivity tends to have low IVOL effect for its speculative nature. In addition, we also find that IVOL-return relation is weaker in recent year.
In sum, Market Friction, Lottery Preference, Analyst and CSR explained 17.75%, 16.75%, 9.83% and 12.86% of the puzzle respectively. Transactional friction contributes significantly to the puzzle. Besides, we find that CSR and Bitcoin Sensitivity are worthy of consideration in asset pricing model.

Table of Contents
Chinese Abstract iii
English Abstract iv
Table of Contents v
List of Tables vii
1.Introduction 1
2.Literature Review 5
2.1 Idiosyncratic Volatility (IVOL) 5
2.2 Potential Explanation of Idiosyncratic Volatility 5
2.3 Asset Pricing Model 5
3.Data and methodology 9
3.1 Idiosyncratic Volatility (IVOL) 9
3.2 Lottery Preference Variable 9
3.3 Market Friction variable 9
3.4 CSR and other variable 10
3.5 Decomposition Methodology 11
3.6 Interaction Effect analysis in Fama-Macbeth regression 14
4.Empirical Result 15
4.1 Descriptive statistic (IVOL) 15
4.2 Idiosyncratic volatility effect 16
4.3 Puzzle solved by lottery preference variables 17
4.4Puzzle solved by Market Friction variables 18
4.5 Puzzle solved by CSR and Other variables 19
4.6 Subsample Analysis 20
4.6.1 CSR 20
4.6.2 Bitcoin Sensitivity 22
4.7 Interaction Analysis 24
4.8 Principal Component Analysis 25
4.9 Robustness test 26
5. Conclusion 28
Reference 29

List of Tables
Table 1 Sample descriptive statistics 35
Panel A Frim characteristics, Lottery preference and Market friction variables 35
Panel B CSR and Other variables 36
Table 2 Correlation coefficient matrix 37
Table 3 Relation between idiosyncratic volatility and return 38
Table 4 Decomposing the idiosyncratic volatility puzzle 39
Panel A Lottery Preference variable 39
Panel B Market Friction variables 40
Panel C Corporate Social Responsibility variables 41
Panel D Other variables 42
Table 5 Subsample Analysis: CSR 43
Panel A Lottery Preference and Analyst related variables 43
Panel B Market Friction and Other variables 44
Table 6 Subsample Analysis: Bitcoin Sensitivity 45
Panel A Lottery Preference and Analyst related variables 45
Panel B Market Friction and Other variables 46
Table 7 Interaction Analysis 47
Panel A Lottery Preference and Market Friction variables 47
Panel B Market Friction, Analyst and Other variables 48
Table 8 Principal Component Analysis 49
Table 9 Robustness test 50

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