주 제 : Machine Learning Based Stock Classification

연 사 : Prof. 한철우 (Durham University)

내 용 : In this paper, we develop a neural network model for multi-class stock classification using input features derived from widely known momentum factors and apply it to long-short portfolio construction. A naive approach to construct a long-short portfolio, i.e., buying the stocks in the highest return class and selling those in the lowest return class poses two problems in the financial context: the two classes can be significantly imbalanced; stocks predicted to be in the highest (lowest) return class do not necessarily carry the highest (lowest) expected return. We address these problems by reclassifying stocks utilizing the probabilities of the stocks to be assigned in each class. Empirical findings suggest that our model can create a long-short portfolio generating a significant profit and high Sharpe ratio. We also find that economic performance of a model is not always consistent with its statistical performance.

일 시 : 2018년 9월 7일 (금) 오후 3시

장 소 : 39동 325호

문 의 : 장우진교수 (changw@snu.ac.kr)