scholarly journals Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital ‘Haves’ and ‘Have-mores’

2021 ◽  
Author(s):  
Chong Guan ◽  
Wenting Liu ◽  
Jack Yu-Chao Cheng
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Stock Market Crash

Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash

2020 ◽  
Vol 57 (4) ◽  
pp. 102218 ◽  
Author(s):  
Yidi Ge ◽  
Jiangnan Qiu ◽  
Zhiyong Liu ◽  
Wenjing Gu ◽  
Liwei Xu
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Stock Market Crash

How Social Media Impacts Mass Media News Viewership in the Stock Market

2019 ◽  
Author(s):  
Jie Ren ◽  
Hang Dong ◽  
Gaurav Sabnis ◽  
Jeffrey V. Nickerson
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Stock Market

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.

scholarly journals Layers of Trauma: the Yiddish-Hebrew Fiction of Yosl Birshteyn (1920-2003)

2007 ◽  
Vol 52 (3) ◽  
pp. 434-449
Author(s):  
Albert Waldinger
Keyword(s):  
Stock Market ◽  
Short Story ◽  
Stock Market Crash ◽  
The Future ◽  
Story Writer

Abstract This article evaluates the function of Yiddish-Hebrew creative diglossia in the work of Yosl Birshteyn, a prominent Israeli novelist and short-story writer, particularly in the “first Kibbutz novel” in Yiddish, Hebrew-Yiddish fiction based on the Israeli stock market crash, and the future of Yiddishism in Hebrew and Yiddish. In short, Yiddish acts as a layer of all texts as a fact of communal pain and uncertainty in past, present and future. Birshteyn’s Hebrew originals were translated back into Yiddish and his Yiddish work was translated into Hebrew by famous and representative hands with stylistic and linguistic consequences examined here.

Characteristics of 2020 stock market crash: The COVID-19 induced extreme event

2021 ◽  
Vol 31 (5) ◽  
pp. 053115
Author(s):  
Ajit Mahata ◽  
Anish Rai ◽  
Md. Nurujjaman ◽  
Om Prakash ◽  
Debi Prasad Bal
Keyword(s):  
Stock Market ◽  
Extreme Event ◽  
Stock Market Crash

What drives the venture capital investments in China?

2019 ◽  
Vol 13 (3) ◽  
pp. 574-602 ◽  
Author(s):  
Yixi Ning ◽  
Gubo Xu ◽  
Ziwu Long
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Global Financial Crisis ◽  
High Growth ◽  
Investment Strategies ◽  
Stock Market Crash ◽  
Content Type ◽  
Risk Levels ◽  
Aggregate Amount ◽  
A Share

Purpose This study aims to examine the venture capital (VC) industry in China. It has demonstrated a history of high growth with significant variations over time. The authors have examined the trends and determinants of VC investments in China over a 20-year period from 1995 to 2014. They find that the aggregate amount of VC investments, the total number of venture deals and the average amount of venture investments per deal in China are all significantly impacted by macroeconomic conditions (i.e. GDP, export, money supply), technology innovations and financial market indicators (i.e. initial public offerings (IPOs), interest rate, price-to-earnings ratio, etc.). They also find that the 2007 China A-Share stock market crash and the subsequent global financial crisis have motivated VCists in China to adjust their investment strategies and risk levels by allocating more capital to later-stage investments and securing more deals with later-round financings. However, after the 2008 global financial crisis, the China’s venture industry has recovered faster compared to the US counterpart response. Design/methodology/approach The authors first perform trend analysis of VC investments at an aggregate level, by stages of development, and across industry from 1995 to 2014.To test H1 and H2, the authors use multiple regression models with lagged explanatory variables. To test H3, the authors use univariate tests to compare the measures of VC investments at an aggregate level, stage funds ratios, stage deals ratios and financing series ratios during both a five-year and seven-year time windows around the 2007 A-Share stock market crash and the subsequent financial crisis. Findings The development of the VC industry in China has demonstrated a history of high growth with significant variation over time. The authors find that the aggregate amount of VC investments, the total number of venture deals and the average amount of venture investments per deal in China are all significantly impacted by macroeconomic conditions (i.e. GDP, export, money supply), technology innovations and financial market indicators (i.e. IPOs, interest rate, price-to-earnings ratio, etc.). The authors also find that the 2007 China A-Share stock market crash and the subsequent global financial crisis have motivated VCists in China to adjust their investment strategies and risk by allocating more capital to later-stage investments and securing more deals with later-round financings. However, the China VC industry has recovered faster compared to the USA just after the 2008 global financial crisis. Research limitations/implications There are also limitations in the study. The VC data in China in the earlier 1990s might not be very reliable due to the quality of statistics. Therefore, the trend analysis and discussions mainly focus on the time after 2000. Also, the authors cannot find VC financing sequence data for the analysis. Second, there is no doubt that the policy impact from Chinese transforming economic system and government policies on its VC industry is substantial (Su and Wang, 2013). However, they cannot find an appropriate variable to be included in the empirical models to consider this effect. Further study on this area would provide meaningful information. Third, although the authors have done comparison study between the VC industry in China in this study and the VC industry in the US documented in Ning et al. (2015) and discussed some interesting findings, more in-depth research in this area will be very useful. Practical implications The findings have meaningful implications for VCists and start-up companies seeking equity financings in China. VCists should closely monitor macroeconomic and market conditions to make appropriate adjustments to their risk and investment strategies. Entrepreneurs seeking equity financings for their business could also monitor the identified macroeconomic and market indicators, which can help them with their timing and to negotiate a better equity financing deal. VC financing is more likely to succeed when key macroeconomic and market indicators become favorable. Originality/value This paper contributes to the literature by testing the supply and demand theory on the VC market proposed by Poterba (1989) and Gompers and Lerner (1998) from the macroeconomic perspective using 20 years’ VC data from China. The authors also examine how the 2007 A-Share stock market crash and the subsequent financial crisis affected VCists to adjust their risk levels and investment strategies. It provides useful information for international academia and policymakers to understand the quick rise of China VC industry. The authors also find that the macroeconomic drivers of VC industry are somewhat different under different economic systems.

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