Corpus ID: 16643114

Stock Market Forecasting Using Machine Learning Algorithms

  title={Stock Market Forecasting Using Machine Learning Algorithms},
  author={Shunrong Shen and Haomiao Jiang and Tong Zhang},
Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend with the aid of SVM… Expand
Forecasting Stock Market Future Movement Direction : Supervised Machine Learning Algorithm
Time series forecasting has been widely used to determine the future trends of stock and the analysis and modeling of finance time series importantly guide investor’s decisions and trades. In aExpand
Empirical Study on Stock Market Prediction Using Machine Learning
A survey of various algorithms and its parameters for stock market prediction is presented and will assist the readers & researchers in selecting algorithms that can be useful for a predicting the stock market. Expand
Review on the applications of machine learning models for stock market predictions: A literature survey
Financial stocks values are non-linear, volatile, and chaotic, making them one of the most challenging financial time series to predict. The incentive of financial gain has led many researchers andExpand
Evaluating machine learning algorithms for stock market prediction
Stock market prediction and developing profitable trading strategies have always attracted businesses and academia, and many studies have been conducted in the field to solve the puzzle of stockExpand
Stock market prediction using machine learning techniques
The results suggest that performance of KSE-100 index can be predicted with machine learning techniques. Expand
Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression
An intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices of Taiwan construction companies one step ahead is proposed. Expand
Investigating Algorithmic Stock Market Trading using Ensemble Machine Learning Methods
Simulation results show significant returns relative to the benchmark and large values of alpha are produced from all methods, which strengthen the role of ensemble method based machine learning in automated stock market trading. Expand
Stock Price Prediction Using Machine Learning Techniques
An approach combining two distinct fields for analysis of stock exchange, which combines price prediction based on historical and real-time data along with news analysis and gives a recommendation for future increases. Expand
US financial market forecasting using data classification with features from global markets
This work applies support vector machines (SVM) and gradient boosting (GB) to the daily direction of the stock market, focusing on the NASDAQ index, and indicates that, during an up market, the SVM model is the superior predictor. Expand
Performance Analysis of Deep Learning and Statistical Models on Enhancing Stock Market Portfolio
Time series data is considered very useful in the domains of business, finance and economics. Stock market data specifically is generated at high volumes and excessively used for forecasting purposesExpand


Forecasting stock market movement direction with support vector machine
This paper investigates the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index and proposes a combining model by integrating SVM with the other classification methods. Expand
On Developing a Financial Prediction System : Pitfalls and Possibilities
A successful financial prediction system presents many challenges. Some are encountered over again, and though an individual solution might be system-specific, general principles still apply. UsingExpand
Learning to trade via direct reinforcement
It is demonstrated how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. Expand