scholarly journals Stock Prediction Using Technical And Sentimental Analysis

2021 ◽  
pp. 584-588
Author(s):  
Prof. N.P. Kadale ◽  
Gavali Prajwal ◽  
Pratik Jadhav ◽  
Sachin Landge ◽  
Pratiksha Bhoite
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Multiple Kernel Learning ◽  
Technical Analysis ◽  
Regression Problem ◽  
Stock Market Prediction ◽  
The Public ◽  
Public Sentiment ◽  
Current Events

Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. The approach through sentimental analysis is to observe how well the changes in stock prices i.e. the rise and fall are correlated to the opinion of people that are expressed by them on Twitter. Sentimental analysis helps in analyzing the public sentiments on Twitter, this approach is our approach through using make of sentimental analysis. Another approach in the same topic of our project is using technical analysis. We model the stock price movement as a function of these input features and solve it as a regression problem in a multiple kernel learning regression framework. The machine learning coupled with fundamental and/ or technical analysis also yields satisfactory results for stock market prediction. We also evaluated the model for taking buy-sell decision at the end of day which is also known as intraday trading.

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 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.

scholarly journals Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
Dev Shah ◽  
Haruna Isah ◽  
Farhana Zulkernine
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Machine Learning Techniques ◽  
Great Promise ◽  
Stock Market Prediction ◽  
Stock Analysis ◽  
Prediction Techniques ◽  
Price Analysis

Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field.

scholarly journals IS STOCK PRICES REFLECTED IN MARKET RATIOS?

2020 ◽  
Vol 2 (2) ◽  
pp. 123
Author(s):  
Tan Kwang En
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Technical Analysis ◽  
Composite Indicator ◽  
Financial Accounting ◽  
Financial Ratios ◽  
Macroeconomic Factor ◽  
Price Trends ◽  
Almost All

<p>The most fascinating thing in stock market world is forecasting stock prices. Almost all players in stock market race to find the best method for forecast stock prices. After years of researching and practicing, we can divide all methods into two main methods, fundamental and technical analysis. Fundamental analysis based its forecasting method on macroeconomic factor, industry analysis, and company internal factors, while technical analysis based on studying financial accounting numbers and stock price trends in the past and present. This study will be focusing in the uses of technical analysing in forecasting stock prices.</p><p>There are many ways in technical analysis to forecast stock prices. Investors and analysts usually use stock price trends or financial ratios to do that. The latest is the most simple and powerful tools that almost everyone can use it, regardless to its limitations. When it comes to use financial ratios, there are a lot of contradicting results that make its users need to make a comparation between ratios and make a decision. </p><p>This paper try to use another solution to overcome those problem with using a composite indicators. The composite indicator will be compared with another market ratio to find out which method is the best on forecasting stock prices.</p><p>The result is composite indicator is the best method on forecasting stock prices compared with price to sales ratio, price to book value ratio, price to earnings per share ratio, and price to operating cash flow ratio.</p>

scholarly journals Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

2021 ◽  
Author(s):  
Yigit Alparslan ◽  
Ethan Moyer ◽  
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.

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.

scholarly journals Applications of neural network based methods on stock market prediction: survey

2018 ◽  
Vol 7 (2.6) ◽  
pp. 71 ◽  
Author(s):  
Avilasa Mohapatra ◽  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Optimization Techniques ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Statistical Measures ◽  
Financial Application

Financial forecasting is one of the domineering fields of research, where investor’s money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann’s development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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 Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

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