Analyzing Stock Market Movements Using News, Tweets, Stock Prices and Transactions Volume Data for APPLE (AAPL), GOOGLE (GOOG) and SONY (SNE)

2018 ◽  
Author(s):  
Brijen Rai ◽  
Mangala Kasturi ◽  
Ching-yu Huang
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Volume Data

scholarly journals Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

2021 ◽  
Vol 13 (3) ◽  
pp. 1011
Author(s):  
Seung Hwan Jeong ◽  
Hee Soo Lee ◽  
Hyun Nam ◽  
Kyong Joo Oh
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Weighted Average ◽  
Market Liquidity ◽  
Average Price ◽  
Volume Data ◽  
The Impact

Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

Applying Clustering Algorithms to Construct a Stock Trend Decision Model

2013 ◽  
Vol 311 ◽  
pp. 81-86
Author(s):  
Chung Min Wu ◽  
Sheng Chun Chou ◽  
Horng Twu Liaw
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Clustering Algorithms ◽  
Investment Strategy ◽  
Investment Strategies ◽  
Volume Data ◽  
Accurate Identification ◽  
Research Results ◽  
Clustering Model

The stock market is a well-developed and mature market. Nevertheless, it is not immune to international financial market changes, where volatility has reigned in recent years. Investors who misgauge stock trends can suffer dramatic losses. Accurate identification of market trends can still achieve outstanding performance and has become a major investor concern. This paper proposes a new stock price trend clustering model using a genetic algorithm to search for optimal investment strategies. Daily stock prices and trading volume data from the Taiwan stock exchange weighted index (TAIEX) was used to examine the proposed trend clustering model’s performance. The model was also compared to other popular stock market investment strategies to verify its validity. Research results confirmed that the trend clustering model correctly identified three different trends in the stock market. Furthermore, the trend investment strategy model using genetic algorithms performed better than other investment strategies, i.e. Granville’s rules for buy and hold strategies, in both bull and bear markets. Research results confirmed trend investing outperformed the other two investment strategies in return and capital distribution, both during the training period and the testing period.

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.

Prior Return Effect in Indian Stock Market: An Intra-day Analysis

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vanita Tripathi ◽  
Shalini Aggarwal
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
High Frequency ◽  
Good News ◽  
High Frequency Data ◽  
Short Term ◽  
Fund Managers ◽  
News Events ◽  
Return Effect ◽  
Indian Stock Market

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

Big data investments in knowledge and non-knowledge intensive firms: what the market tells us

2017 ◽  
Vol 21 (3) ◽  
pp. 623-639 ◽  
Author(s):  
Tingting Zhang ◽  
William Yu Chung Wang ◽  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Stock Market ◽  
Value Chain ◽  
Stock Prices ◽  
Economic Value ◽  
Information Value ◽  
Publicly Traded ◽  
Content Type ◽  
Knowledge Intensive

Purpose This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not. Design/methodology/approach This study is based on an event study using data from two stock markets in China. Findings The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along. Research limitations/implications This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets. Originality/value Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

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.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Sign in / Sign up

Export Citation Format

Share Document