scholarly journals Exploring the Initial Impact of COVID-19 Sentiment on US Stock Market Using Big Data

2020 ◽  
Vol 12 (16) ◽  
pp. 6648
Author(s):  
Hee Soo Lee
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Factor Model ◽  
Time Lag ◽  
Google Trends ◽  
Investment Planning ◽  
Excess Returns ◽  
Time Series Regression ◽  
Strategic Investment ◽  
Daily News

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences.

Evidence on aggregate volatility risk premium for the French stock market

2019 ◽  
Vol 46 (1) ◽  
pp. 72-91
Author(s):  
Amal Zaghouani Chakroun ◽  
Dorra Mezzez Hmaied
Keyword(s):  
Risk Factor ◽  
Stock Market ◽  
Risk Premium ◽  
Factor Model ◽  
Excess Returns ◽  
Time Series Regression ◽  
Volatility Risk ◽  
Content Type ◽  
Volatility Risk Premium ◽  
Fama And French

Purpose The purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French’s (2018) aggregate volatility augmented model. Design/methodology/approach The models are tested using a time series regression and Fama and Macbeth’s (1973) methodology. Findings The authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French’s (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French’s (1993, 2015, 2018) and Carhart’s (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant αs across the different multifactor models and even after controlling for the exposure to all the other in Fama and French’s (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility. Originality/value The authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French’s (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors’ knowledge, to construct a volatility risk premium.

scholarly journals NOWCASTING TINGKAT PENGHUNIAN KAMAR HOTEL MENGGUNAKAN GOOGLE TRENDS

2021 ◽  
Vol 2020 (1) ◽  
pp. 338-343
Author(s):  
Ika Ayuningtyas ◽  
Ika Wirawati
Keyword(s):  
Time Series ◽  
Big Data ◽  
Google Trends ◽  
Time Series Regression ◽  
Google Search

Perkembangan penggunaan internet di Indonesia berkembang sangat pesat. Seiring dengan hal tersebut, semakin banyak orang yang memanfaatkan mesin pencari di internet. Salah satu yang paling banyak dimanfaatkan adalah Google Search dengan menggunakan kata kunci. Intensitas pencarian dengan berbagai kata kunci tersebut menghasilkan data yang berjumlah besar (big data) yang kemudian disajikan dalam bentuk Indeks Google Trends. Informasi pada indeks GT ini diprediksi memiliki korelasi dengan aktivitas saat ini sehingga dapat membantu dalam memprediksi rilis data berikutnya. Indeks GT ini sangat membantu dalam memprediksi masa kini yang sering disebut nowcasting. Penulis menggunakan data TPK hotel di Indonesia dan Indeks GT dengan kata kunci “hotel” selama periode Januari 2011- Juni 2020 yang kemudian diolah menggunakan metode analisis time series regression dan ARIMAX. Dari hasil pengolahan diperoleh ukuran keakuratan untuk masing-masing metode menggunakan RMSE dan MAPE. Metode ARIMAX memiliki nilai RMSE dan MAPE terkecil jika dibandingkan metode time series regression. Selanjutnya dilakukan prediksi masa kini (nowcasting) untuk TPK bulan Juli dan Agustus 2020 masing-masing sebesar 24,16 persen dan 24,49 persen.

scholarly journals Exploring the Dynamics between M&A Activities and Industry-Level Performance

2020 ◽  
Vol 12 (11) ◽  
pp. 4399 ◽  
Author(s):  
Jinho Choi ◽  
Nina Shin ◽  
Hee Soo Lee
Keyword(s):  
Financial Performance ◽  
Time Lag ◽  
Investment Planning ◽  
Firm Level ◽  
Strategic Investment ◽  
Emerging Industries ◽  
Industry Level ◽  
Industry Sectors ◽  
Transaction Value ◽  
Level Performance

This study investigates the correlation between mergers and acquisitions (M&As) activities and industry-level performance. While extensive research on M&As has focused on financial performance at the firm-level around the merger announcement, not much focus has been given to the relationship between M&A activities and financial performance at the industry level. Using global data from the S&P (Standard & Poor’s) Capital IQ platform database, this study examines the significance of relationships of 12 industry-level financial values with M&A frequency and transaction value across 11 industry sectors throughout 2009–2018. The results show that M&A activities play a key role in identifying industries with lots of potential and that strategic investment planning can be drawn from both industry and time lag perspectives. This study bridges the gap by exploring the complexity of M&A performance across various firms and industries, and supports forward-looking investment processes by delineating emerging industries with expected positive returns.

Pricing models of shares of Russian companies and their practical application

2019 ◽  
pp. 48-76 ◽  
Author(s):  
Alexander E. Abramov ◽  
Alexander D. Radygin ◽  
Maria I. Chernova
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Factor Model ◽  
Investment Strategies ◽  
Pricing Models ◽  
Foreign Portfolio Investment ◽  
Market Portfolio ◽  
Stock Market Performance ◽  
Long Time ◽  
Russian Stock Market

The article analyzes the problems of applying stock pricing models in the Russian stock market. The novelty of the study lies in the peculiarities of the methodology used and the substantive conclusions on the specifics of the influence of fundamental factors on the pricing of shares of Russian companies. The study was conducted using its own 5-factor basic pricing model based on a sample of the most complete number of issues of shares of Russian issuers and a long time horizon, from 1997 to 2017. The market portfolio was the widest for a set of issuers. We consider the factor model as a kind of universal indicator of the efficiency of the stock market performance of its functions. The article confirms the significance of factors of a broad market portfolio, size, liquidity and, in part, momentum (inertia). However, starting from 2011, the significance of factors began to decrease as the qualitative characteristics of the stock market deteriorated due to the outflow of foreign portfolio investment, combined with the low level of development of domestic institutional investors. Also identified is the cyclical nature of the actions of company size and liquidity factors. Their ability to generate additional income on shares rises mainly at the stage of the fall of the stock market. The results of the study suggest that as domestic institutional investors develop on the Russian stock market, factor investment strategies can be used as a tool to increase the return on investor portfolios.

Dividend Risk Premia

2019 ◽  
Vol 55 (4) ◽  
pp. 1199-1242
Author(s):  
Georg Cejnek ◽  
Otto Randl
Keyword(s):  
United States ◽  
Risk Factor ◽  
United Kingdom ◽  
Time Variation ◽  
Factor Model ◽  
International Markets ◽  
Risk Premia ◽  
Excess Returns ◽  
Stock Indices ◽  
Bond Risk

This article studies time variation in the expected excess returns of traded claims on dividends, bonds, and stock indices for international markets. We introduce a novel dividend risk factor that complements the bond risk factor of Cochrane and Piazzesi (2005). By aggregating over 4 regions (United States, United Kingdom, Eurozone, and Japan), we create global dividend and bond factors. Our global 2-factor model captures the excess returns of most Morgan Stanley Capital International (MSCI) country indices, as well as a variety of other test assets. Our findings highlight the value of the information contained in dividend and bond forward curves and suggest substantial comovement in international risk premia.

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.

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