• Corpus ID: 12787815

Using AI to Make Predictions on Stock Market

  title={Using AI to Make Predictions on Stock Market},
  author={Alice Zheng},
In the world of finance, stock trading is one of the most important activities. Professional traders have developed a variety of analysis methods such as fundamental analysis, technical analysis, quantitative analysis, and so on. Such analytically methods make use of different sources ranging from news to price data, but they all aim at predicting the company’s future stock prices so they can make educated decisions on their trading. 

Figures and Tables from this paper

Ensemble Learning in Stock Market Prediction
The purpose of this thesis is to leverage the aggregation of technical, fundamental, and sentiment analysis with stacked machine learning models capable of predicting profitable actions to be executed.
The main topic of study here will be the comparative analysis of the SVM and LTSM algorithms.
Stock Market Prediction Using Machine Learning
Being the exchange where the issuing and trading of equities or stocks of publicly held companies take place, the stock market is one of the most vital components of a market’s economy. Stock market
Towards High Performance Stock Market Prediction Methods
In an effort to devise an ensemble learning predictive system that will utilize an array of big data sources, research into the use of long-term short-term recurrent neural networks in stock prediction and planned experiments around the optimization of the machine learning model's timeliness for it to be an effective implementation into the proposed predictive system.
Recommending System for Penny Stock Trading
Penny stocks at times makes the investors wealthy by turning to be a multi-bagger stocks or erode the wealth of the investors with poor performance in volatile conditions. While there are many
Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction
This paper intends to discuss the 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.
Stock Market Strengthens Economy And Strengthened by AI to Minimize Risk
Inflation is not a friend particularly when one tries to avoid wasting money for a few major outlays, such as buying a house or financing a comfortable retirement. In the world of finance, stock
Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models
A novel approach using graph theory leverages Spatio-temporal relationship information between different stocks by modeling the stock market as a complex network and concludes that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.
Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective
In recent times there has been an increasing level of debate whether patterns do exist in equity market movements and whether they can be predicted. In order to overcome the shortcomings of
Comparison of Predictive Algorithms: Backpropagation, SVM, LSTM and Kalman Filter for Stock Market
A comparative analysis between backpropagation and Long Short-Term Memory algorithms on the basis of accuracy, variation and time required for different number of epochs is provided.


A Machine Learning Model for Stock Market Prediction
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.
Stock Market Forecasting Using Machine Learning Algorithms
A new prediction algorithm is proposed 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.
Equity forecast: Predicting long term stock price movement using machine learning
A machine learning aided approach to evaluate the equity's future price over the long time is presented and is able to correctly predict whether some company's value will be 10% higher or not over the period of one year in 76.5% of cases.
Extracting the best features for predicting stock prices using machine learning
Predicting stock price is always a challenging task. In this paper we are trying to predict the next day’s highest price for eight different companies individually. For this we are using different
Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction
This thesis explores predictability in the market and then designs a decision support framework that can be used by traders to provide suggested indications of future stock price direction along with
A LSTM-based method for stock returns prediction: A case study of China stock market
The presented paper modeled and predicted China stock returns using LSTM and improved the accuracy of stock returns prediction from 14.3% to 27.2% compared with random prediction method.
A machine learning approach for stock price prediction
This paper applies structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure and uses an SSVM to predict positive or negative movement in their stock prices.
Alpha Vantage - Free Apis For Realtime And Historical Financial Data, Technical Analysis, Charting, And More! Alphavantage.co
  • N.p., 2017. Web
  • 2017
Soliman , and Mustafa Abdul Salam . ” A Machine Learning Model for Stock Market Prediction
  • 2014