Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model

Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme. This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost. Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market

장기적 추세를 반영한 심층 임베딩 기반 금융 시계열 군집화에 관한 연구

학위논문(석사)--서울대학교 대학원 :공과대학 산업공학과,2019. 8. 이재욱.In the field of asset selection and portfolio, there are active researches on clustering for various reasons. In recent years, there have been increasing cases of applying machine learning and deep learning methodology to asset clustering studies. This is because it is difficult to reflect insights such as long-term trends and patterns reflected in high-dimensional image data by traditional correlation-based analysis. Therefore, this thesis investigated how to clustering financial time series through deep embedding network that is specialized for processing high-dimensional data efficiently. It is shown that the existing algorithm is not suitable for the financial time series data, and proposed algorithm can perform the clustering better than the existing algorithm. In addition, we have clustered KOSPI data with the proposed algorithm and determined the optimal number of clusters through various performance measures. We also examined whether the insights trends inherent in the actual high-dimensional images can be reflected in the clustering results. In addition, based on the results of this thesis, it can be shown that the actual effect of incorporating the results of this study to the portfolio management by comparing the performance measures of various portfolios with the benchmark results, in the future works.자산 선택 및 포트폴리오 분야에서 군집화에 대해 활발히 연구가 진행되고 있다. 특히 최근, 이러한 자산 군집화 연구에 기계학습 및 심층 학습 방법론을 적용하고자 하는 사례가 증가하고 있다. 기존의 상관관계 분석만으로는 고차원 이미지 데이터에 반영된 장기적 추세, 패턴 등의 직관적인 자산간의 관계를 반영하기 어렵기 때문이다. 따라서 본 연구는 고차원 이미지를 처리할 수 있는 심층 임베딩 기법을 통해 금융 시계열을 군집화할 수 있는 방법을 연구하였다. 기존 알고리즘이 금융 시계열 데이터에 적절하지 않음을 보이고, 본 연구진이 제안한 새로운 알고리즘이 기존 알고리즘보다 군집화를 더 적절히 수행할 수 있음을 보였다. 또한, 제안된 알고리즘을 통해 실제 KOSPI 데이터를 군집화하여 각종 성능 척도를 통해 최적 군집 수를 산출해 보았으며 실제 고차원 이미지 에 반영된 직관적인 자산간의 관계가 군집화 결과에도 반영될 수 있는지를 살펴보았다. 또한, 본 논문의 연구 결과를 바탕으로, 후속 연구를 통해 실제로 포트폴리오 구성에 이러한 연구 결과를 반영을 하게 됐을 때의 실제 효과에 대해 다양한 포트폴리오의 성능 측정 측도와 벤치마크 결과와의 비교를 통해 보여줄 수 있을 것이다.Abstract Contents List of Tables List of Figures Chapter 1 Introduction 1.1 Introduction 1.2 Research Motivation and Contribution 1.3 Organization of the Thesis Chapter 2 Related Work 2.1 Markowitzs mean-variance Portfolio Theory 2.2 Clustering 2.3 Deep Learning and Researches on Deep Embedding Clustering 2.3.1 Deep Learning 2.3.2 Batch Normalization 2.3.3 Deep Auto Encoder 2.3.4 Deep Embedding Clustering 2.4 Geometric Brownian Motion and Monte Carlo Simulation 2.4.1 Geometric Brownian Motion 2.4.2 Monte Carlo Simulation Chapter 3 Data Description and Proposed algorithm 3.1 Data Description 3.1.1 Toy Data: Simulated Financial Time Series Data from GBM 3.1.2 Real-Data: KOSPI data 3.1.3 Data Preprocessing for Three Types of Data Set 3.2 Proposed Algorithm 3.2.1 Problems of existing algorithm 3.2.2 Proposed Algorithm Chapter 4 Experimental Results 4.1 Performance Measure 4.2 Experiments for First and Second Data Set(the number of custer = 2) 4.2.1 First Experiment for Toy Data 4.2.2 Second Experiment for Real-Data 4.2.3 Performance Evaluation 4.3 Experiment for Third Data Set(the number of custer ≥ 2) 4.3.1 Experiment for Third Data and Performance Evaluation 4.3.2 Interpretation to Intuition in the Embedding Chapter 5 Conclusion 5.1 Conclusion 5.2 Future Direction Bibliography 국문초록Maste

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors

Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets

[EN] We consider a quasi-metric topological structure for the construction of a new reinforcement learning model in the framework of financial markets. It is based on a Lipschitz type extension of reward functions defined in metric spaces. Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states-which we call "dreams"-to enrich learning. 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