scholarly journals Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xingyu Zhou ◽  
Zhisong Pan ◽  
Guyu Hu ◽  
Siqi Tang ◽  
Cheng Zhao
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Forecast Error ◽  
Effective Strategies ◽  
Stock Price Prediction ◽  
Adversarial Training ◽  
Theory Research

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.

LITERATURE SURVEY ON STOCK PRICE PREDICTION USING MACHINE LEARNING

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
Anusha J Adhikar ◽  
Apeksha K Jadhav ◽  
Charitha G ◽  
Karishma KH ◽  
Supriya HS
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Technologies ◽  
Research Knowledge ◽  
Market Values ◽  
Stock Price Prediction ◽  
Price Prediction

In today’s financial world stock exchange has become one of the most significant events. The world’s economy today is widely dependent on the stock market prices. The Stock Market has been very successful in attracting people from various backgrounds be it educational or business .The nonlinear nature of the Stock Market has made its research one of the most trending and crucial topics all around the world.. People decide to invest in the stock market on the basis of some prior research knowledge or some prediction. In terms of prediction people often look for tools or methods that would minimize their risks and maximize their profits and hence the stock price prediction takes on an influential role in the ever challenging stock market business. Adopting traditional methodologies such as fundamental and technical analysis doesn’t seem to ensure the consistency and accuracy in the prediction. As a result the machine learning technologies have become the recent trend in the stock market prediction whose prediction is based on the existing stock market values eventually as an outcome of training on their previous values. This paper focuses on RNN (Recurrent Neural Networks) and LSTM (Long Short term memory) technologies in predicting the ongoing trend of the stock market.

scholarly journals A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Norbert Herencsar ◽  
Bay Vo ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Financial Management ◽  
Stock Price ◽  
Short Term Memory ◽  
Prediction Algorithm ◽  
Leading Indicators ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Wealth Management

AbstractIn today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

scholarly journals APPLYING MACHINE LEARNING MODELS IN STOCK MARKET PREDICTION

2020 ◽  
pp. 395-398
Author(s):  
Vignesh CK
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Support Vector ◽  
Company Stock ◽  
The Future ◽  
Future Value ◽  
Past Data

This paper deals with the techniques of attempting to calculate the future value of a company stock or any other financial instrument which is being traded in a stock exchange. This prediction plays a great role in many financing and investing decisions. This calculation can be done by Machine learning by training a model to identify the trend from past data in order to predict the future. The main topic of study here will be the comparative analysis of the SVM and LTSM algorithms. KEYWORDS: Machine learning, Stock price, Stock market, Support vector machine, neural network, long short term memory.

scholarly journals Systematic optimization of Long Short-Term Memory model for predicting NYSE Arca Airline Index (XAL) during COVID-19

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah Dong ◽  
Amber Wang
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Airline Industry ◽  
Stock Price ◽  
Short Term Memory ◽  
The United States ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Long Short Term Memory

Predicting stock prices has been both challenging and controversial. Since it first spread through the United States, the COVID-19 pandemic has impacted the stock market in a multitude of ways. Thus, stock price prediction has become even more challenging. Recurrent neural networks (RNN) have been widely used in many fields to predict financial time series. In this study, Long Short-Term Memory (LSTM), a special form of RNN, is used to predict the stock market direction for the US airline industry by using NYSE Arca Airline Index (XAL). The LSTM model was optimized through changing different hyperparameters of the model architecture to find the best combination for increased accuracy and performance evaluated by several metrics, including raw RMSE (3.51) and MAPA (4.6%), and very high MAPA (95.4%) and R^2 (0.978).

scholarly journals Stock Price Prediction using Technical Indicators: A Predictive Model using Optimal Deep Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 2297-2305
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Technical Analysis ◽  
Learning Task ◽  
Correlation Tensor ◽  
Technical Indicators ◽  
Stock Price Prediction ◽  
Proposed Model

The stock market is highly volatile and complex in nature. Technical analysts often apply Technical Analysis (TA) on historical price data, which is an exhaustive task and might produce incorrect predictions. The machine learning coupled with fundamental and / or Technical Analysis also yields satisfactory results for stock market prediction. In this work an effort is made to predict the price and price trend of stocks by applying optimal Long Short Term Memory (O-LSTM) deep learning and adaptive Stock Technical Indicators (STIs). We also evaluated the model for taking buy-sell decision at the end of day. To optimize the deep learning task we utilized the concept of Correlation-Tensor built with appropriate STIs. The tensor with adaptive indicators is passed to the model for better and accurate prediction. The results are analyzed using popular metrics and compared with two benchmark ML classifiers and a recent classifier based on deep learning. The mean prediction accuracy achieved using proposed model is 59.25%, over number of stocks, which is much higher than benchmark approaches.

scholarly journals Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽  
2019 ◽  
Author(s):  
Achyut Ghosh ◽  
Soumik Bose ◽  
Giridhar Maji ◽  
Narayan Debnath ◽  
Soumya Sen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Investor Sentiment ◽  
Spot Price ◽  
Physical Factors ◽  
Stock Price Prediction ◽  
The Impact ◽  
A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

scholarly journals Stock Market Predictor using Long short-term Memory (LSTM) Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 393-396
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Machine Learning Model ◽  
Long Short Term Memory

In the stock market, it is important to have accurate prediction of future behavior of stock price..Because of the great chance of financial loss as well as scoring profits at the same time, it is mandatory to have a secure prediction of the values of the stocks. But when it comes to predicting the value of a stock in future we tend to follow stock market experts but as technology is progressing we may use these technologies rather than following human experts who may be biased many times. Stock price prediction has been interesting area for investors and researchers. This article proposes an approach towards prediction of stock price using machine learning model Long Short Term Memory. This is an ensemble learning method that has been an exceedingly successful model for predicting sequence of numbers and words. Long Short Term Memory is a machine learning model for prediction. This technique is used to forecast the future stock price of a specific stock by using historical data of the stock gathered from Yahoo! Finance.

scholarly journals Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongying Zheng ◽  
Hongyu Wang ◽  
Jianyong Chen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Optimal Solution ◽  
Short Term ◽  
Term Memory ◽  
Stock Price Prediction ◽  
Memory Network ◽  
Long Short Term Memory

As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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