A LINEAR REGRESSION APPROACH TO PREDICTION OF STOCK MARKET TRADING VOLUME: A CASE STUDY

@inproceedings{Gharehchopogh2013ALR,
  title={A LINEAR REGRESSION APPROACH TO PREDICTION OF STOCK MARKET TRADING VOLUME: A CASE STUDY},
  author={Farhad Soleimanian Gharehchopogh and Tahmineh Haddadi Bonab and Seyyed Reza Khaze},
  year={2013}
}
Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting behavior of S&P 500 index, we prove that our proposed method has a similar and good performance in comparison to real volumes and the stockholders can invest confidentially based on that. 

Stock Market Prediction using Machine Learning Techniques

TLDR
The Study shows that logistic regression model compared to Linear Regression can be used by the investors, individual as well as fund managers to predict “good or poor” stock.

ARIMA Model for Stock Market Prediction

  • Reem M. Alotaibi
  • Computer Science
    2022 8th International Conference on Computer Technology Applications
  • 2022
TLDR
The popular ARIMA forecasting model is explored to predict returns on stock from stock market data to increase profits and minimize risks.

A comparative study of Different Machine Learning Regressors For Stock Market Prediction

TLDR
This article intensively studied the NASDAQ stock market and targeted to choose the portfolio of ten different companies belongs to different sectors to compute the opening price of next day stock using historical data.

Open Price Prediction of Stock Market using Regression Analysis

TLDR
The development and implementation of a stock price prediction is explained in this project and regression algorithm and object oriented approach of software development is used.

A quantitative intraday trading strategy based on regression algorithms

TLDR
It is found that prediction methods based on regression models in intraday trading can produce more efficient prediction systems than naive approaches by being successful for day ahead forecasting of daily stock price movements by using these techniques.

Prediction of Stock Market using Data Mining and Artificial Intelligence

TLDR
The system which will study the database of shares and will give predictions according to it will be designed and based on ARMA (autoregressive-movingaverage) algorithm, which will be able to give highest probability predictions for particular shares.

Stock Market Analysis and Prediction Applying Machine Learning

  • Computer Science
  • 2020
TLDR
A Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction of a stock applying Machine Learning.

Stochastic Signal Modeling Techniques for Stock Market Prediction

TLDR
The study employs this temporal correlation that exists between the various stock markets related variables to predict future trends and prices, using two stochastic signal modeling processes, and concludes that the two models should be used in consonance.

A Survey on Stock Prediction with Statistical and Social Media Analytics

TLDR
It is shown that there is a lot of improvement and research going on in the field of stock prediction and it will continue to improve the precision ofStock prediction in the future with more improvements.

A Survey on Stock Market Prediction

  • Mohit IyerRitika Mehra
  • Economics, Computer Science
    2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)
  • 2018
TLDR
The different strategies available and used for forecasting the stock markets are talked about and which method is the finest to use for predicting the stock market is reviewed.

References

SHOWING 1-10 OF 22 REFERENCES

Can Facebook Predict Stock Market Activity?

Using a novel and direct measure of investor sentiment, I find that Facebook’s Gross National Happiness (GNH) has the ability to predict changes both in daily returns and trading volume in the US

Forecasting stock returns an examination of stock market trading in the presence of transaction costs

The paper presents new evidence on the predictability of excess returns on common stocks for the Standard and Poor's 500 and the Dow Jones Industrial portfolios at the monthly, quarterly, and annual

Text mining approaches for stock market prediction

TLDR
The main components of such forecasting systems have been introduced and the way whereby the main components are implemented compared and the potential future research activities have been suggested.

Local Return Factors and Turnover in Emerging Stock Markets

The paper shows that the factors that drive cross-sectional differences in expected stock returns in emerging equity markets are qualitatively similar to those that have been found in developed

The use of data mining and neural networks for forecasting stock market returns

Daily stock market forecast from textual web data

TLDR
The aim is to predict stock markets using information contained in articles published on the Web, mostly textual articles appearing in the leading and influential financial newspapers, to produce periodically forecasts in stock markets.

Stock Market Fluctuations and Consumption Behaviour: Some Recent Evidence

This paper examines the likely influence of recent stock market fluctuations on major OECD economies, focusing on wealth effects and consumption. After reviewing the relevant theoretical framework

Stock market prediction of S&P 500 and DJIA using Bacterial Foraging Optimization Technique

TLDR
It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.

Discovering golden nuggets: data mining in financial application

TLDR
This paper describes data mining in the context of financial application from both technical and application perspectives and discusses important data mining issues involved in specific financial applications.