scholarly journals Hybrid Deep Learning Based Stock Market Prediction with both Sentiment and Historic Trend Data

2020 ◽  
Vol 9 (4) ◽  
pp. 1166-1171
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Accurate Prediction ◽  
Short Term ◽  
Stock Market Prediction ◽  
Trend Forecasting ◽  
Trend Data

Stock market is highly volatile and it is necessary for investors to have an accurate prediction of stock prices for a better profitability. Towards this need many methods have been proposed for stock market prediction with aim to provide a higher prediction accuracy. Current methods for stock market prediction are in two categories of machine learning and statistics based. Considering the need for accurate prediction in short term and long term, the merits of both methods must be combined for accurate prediction. This work proposes a hybrid deep learning approach for stock market prediction which combines the historic price-based trend forecasting along with stock market sentiments expressed in twitter to predict the stock price trend.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

scholarly journals The Effect of Macroeconomic Variables On the Saudi Stock Market

2019 ◽  
Vol 7 (12) ◽  
pp. 126-152
Author(s):  
Amani Mohammed Aldukhail
Keyword(s):  
Stock Market ◽  
Performance Indicators ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Market Value ◽  
Short Term ◽  
Macroeconomic Variables ◽  
Time Deposits

This study aimed at exploring the effect of macroeconomic variables on the activity of the Saudi stock market for the period 1997-2017. Macroeconomic variables were: GDP, interest rate on time deposits, inflation rate. The variables of the Saudi stock market activity were: stock price index, market value of shares, value of traded shares. To achieve this objective, the researcher used the ARDL model for the self-regression of the lagged distributed time gaps. The most important results of the research are: The effect of macroeconomic variables on the performance indicators in the Saudi stock market is not important in the short term and is statistically significant in the long term according to the proposed models, so investors in this market can rely on macroeconomic variables in Predict the movement of the stock market and predict long-term profits and losses.

scholarly journals ANALISIS HARGA SAHAM INDEX KOMPAS 100 DENGAN PENDEKATAN ERROR CORRECTION MODEL

2021 ◽  
Vol 4 (1) ◽  
pp. 406-414
Author(s):  
Amir Hamzah
Keyword(s):  
Error Correction ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Financial Statements ◽  
Error Correction Model ◽  
Short Term ◽  
Correction Model ◽  
Positive Effect

The purpose of this research is to analyze the short term and long term relationship between ROI, EPS, PER ,inflation, SBI, exchange rate,and GDP on Stock Price. The data in this research is company financial statements which included Compas 100 Index on the Indonesia Stock Exchange. statistical analysis in this research used stasionarity test, The Classical Assumptions Test, Cointegration Test, Error Correction Model Test. This research found that partially ROI, EPS, PER variables a positive effect on stock prices in the short term and long term, KURS and SBI a positive effect on stock prices in the short term, but there is no effect in the long term, inflation and GDP do not affect the stock price both in the short term and long term. Simultaneously affected the stock prices significantly affect on stock price both in the short term and long term.

scholarly journals The Economics of the Name Change: Long-term Adjustments towards EU/NATO or Short-term Resolution of Political Uncertainty?

2021 ◽  
Vol 12 (1) ◽  
pp. 86-105
Author(s):  
Bojan Srbinoski ◽  
Klime Poposki ◽  
Ksenija Dencic-Mihajlov ◽  
Milica Pavlovic
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Eu Enlargement ◽  
Political Uncertainty ◽  
Short Term ◽  
Market Reactions ◽  
Name Change ◽  
Stock Market Reactions ◽  
The Eu

North Macedonia and Greece resolved the 27-year country name dispute and removed the main hurdle for North Macedonia to start the accession processes towards the EU and NATO. The paper analyzes the stock market movements around several events related to the name issue resolution to uncover whether Macedonian companies experienced stock price adjustments according to the long-term benefits/costs of joining the EU/NATO. The dynamics of the market reactions suggest that the investors reacted systematically to the short-term political uncertainty created around the referendum rather than to the long-term perspectives of the EU/NATO integration. We integrate the knowledge from the literature which explores stock market reactions to EU enlargement/exit and political elections and provide contributions for researchers and policymakers.

scholarly journals Do Higher-Rated or Enhancing ESG of Firms Enhance Their Long–Term Sustainability? Evidence from Market Returns in Korea

2020 ◽  
Vol 12 (7) ◽  
pp. 2664 ◽  
Author(s):  
Yeonwoo Do ◽  
Sunghwan Kim
Keyword(s):  
Stock Market ◽  
Firm Value ◽  
Stock Price ◽  
Abnormal Returns ◽  
Market Returns ◽  
Short Term ◽  
Stock Market Returns ◽  
Listed Firms ◽  
The Third

In this study, we investigate the effects of the level and changes in environmental, social and corporate governance (ESG) rating, an index developed to represent a firm’s long-term sustainability, on the stock market returns of Korea Composite Stock Price Index (KOSPI) listed firms over the period 2011–2018. We find that the changes in ESG ratings have statistically significant short-term effects on their abnormal returns. However, their impacts on short-term abnormal returns decrease some days after the disclosure and become negative in the third year. The results imply that investors in the Korean stock market do not view corporate social responsibility activities as a means of supporting their long-term sustainability, judging from the firm value for a long period after their rating. Rather, based on the effects of the changes on coefficient signs over the period—positive in the year and the year after, no effects in the following year, and negative in the third year and later—we can infer that the short-term oriented market sentiments of investors might worsen their long-term stock performances, thus deteriorating their sustainability and growth opportunities.

scholarly journals Stock Price Prediction Using Machine Learning

2021 ◽  
pp. 386-389
Author(s):  
Warade Kalyani Gopal ◽  
Jawale Mamta Pandurang ◽  
Tayade Pratiksha Devaram ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Mathematical Functions ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Future Value ◽  
A Company ◽  
Stock Values

In Stock Market Prediction, the aim is to predict for future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market by training on their previous values. Machine learning itself employs different models to make prediction easier. The paper focuses on Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume. In order to predict market movement, the stock prices and stock indicators in addition to the news related to these stocks. Most of the previous work in this industry focused on either classifying the released market news and demonstrating their effect on the stock price or focused on the historical price movement and predicted their future movement. In this work, we propose an automated trading system that integrates mathematical functions, machine learning, and other external factors such as news’ sentiments for the purpose of a better stock prediction accuracy and issuing profitable trades. The aim to determine the price of a certain stock for the coming end-of-day considering the first several trading hours of the day.

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 Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

2021 ◽  
Author(s):  
Yigit Alparslan ◽  
Edward Kim
Keyword(s):  
Computer Vision ◽  
Stock Market ◽  
Current Literature ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Market Prediction ◽  
Convolution Operation ◽  
Price Data ◽  
Dollar Amount

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

Dynamic Relationship Between Stock Market Sector Indices and Macroeconomic Variables in India

2019 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Neeru Gupta ◽  
Ashish Kumar
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Positive Relationship ◽  
Short Term ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Market Sector ◽  
Long Run ◽  
Integration Test

This study investigates the long-term and short-term relationships between selected macroeconomic variables and the selected Indian stock market sector indices over the period of 2010 to 2017. The Johansen Co-integration Test, the Vector error correction model (VECM), is applied to calculate the long-term and short-term relationship between sector indices and macroeconomic variables. It is found that stock prices are exposed to macroeconomic factors, but the level of sensitivity is different in different sectors. Out of five sectors taken in the study, it is found that only the realty sector has long run relationship with macroeconomic variables. Other sectors have no long run relationship with macroeconomic variables. Along with this, it is also found that the Auto index has a significant short-term positive relationship with gold prices and the FMCG sector index has a significant short-term positive relationship with industrial production. The consumer price index and exchange rate have significant short run relationship with realty sector index.

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