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A stock price process model reflecting dynamics of traders' behaviors

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dc.contributor.advisor최형인-
dc.contributor.author김원세-
dc.date.accessioned2018-05-28T17:11:27Z-
dc.date.available2018-05-28T17:11:27Z-
dc.date.issued2018-02-
dc.identifier.other000000149721-
dc.identifier.urihttps://hdl.handle.net/10371/141139-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 수리과학부, 2018. 2. 최형인.-
dc.description.abstractIn this paper, we propose a new stock price process model that reflects the dynamics of traders behaviors. Our model has two implications: First, in the both seller group and the buyer group, the stock price moves in favor of the minority group, not the majority group, and the smaller the minority group is, the larger the change in the price. Second, in both the seller group and the buyer group, traders follow (herd to) the behavior of the minority, and the smaller the minority group is, the larger the herding. Then, exploiting our proprietary data set, we show that our model explains the market well. We also use our model to show that we can predict stock prices via a machine-learning technique that we develop.-
dc.description.tableofcontents1 Introduction 1
2 The stock price process model reflecting the dynamics of traders behavior 5
2.1 Motivations 5
2.1.1 Motivation based on Information theory 5
2.1.2 Motivation based on empirical studies of traders' trading skills 11
2.2 The model description 12
2.2.1 SDE form 12
2.2.2 Closed-form solution of the SDE (linear assumption) 14
3 Empirical Results 17
3.1 Data description 17
3.2 Empirical results 20
3.2.1 The dynamics of stock price processes 20
3.2.1.1 Cardinal property of SBR 20
3.2.1.2 Ordinal property of SBR 21
3.2.2 The dynamics of SBR 26
3.2.2.1 Cardinal analysis of the dynamics of traders 26
3.2.2.2 Ordinal analysis of the dynamics of traders 27
3.3 Robustness check:subperiod test 31
4 Return prediction via a machine learning technique 34
4.1 Test data set description 35
4.2 Data filteration 35
4.3 Key predictors 36
4.3.1 Interaction between types 36
4.3.2 LSV herding measure of each types 40
4.4 Other predictors 45
4.4.1 Intraday volatility 45
4.4.2 Predictors related to returns 45
4.4.3 Predictors related to prices 46
4.5 predictor model 47
4.5.1 Model description 48
4.5.1.1 Random forest 48
4.5.1.2 Elastic Net 52
4.5.1.3 Our new model: two step learning (residual fitting) 57
4.5.2 Empirical Result 57
4.5.2.1 SBR prediction 58
4.5.2.2 Return prediction 59
5 Conclusion 62
Abstract (in Korean)
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dc.formatapplication/pdf-
dc.format.extent4650011 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectStock price process-
dc.subjectInformed investor-
dc.subjectTrading skills-
dc.subjectMachine learning-
dc.subjectReturn prediction-
dc.subject.ddc510-
dc.titleA stock price process model reflecting dynamics of traders' behaviors-
dc.typeThesis-
dc.description.degreeDoctor-
dc.contributor.affiliation자연과학대학 수리과학부-
dc.date.awarded2018-02-
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