scholarly journals Do Blockchain Stocks Exhibit More Herding Behaviour than Traditional Stocks?

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Prakhar Goel ◽  
Abhishek Dev
Keyword(s):  
Social Media ◽  
Stock Returns ◽  
Automotive Industry ◽  
Stock Prices ◽  
Stock Price ◽  
Absolute Deviation ◽  
Cross Sectional ◽  
Price Movements ◽  
The Impact ◽  
Stock Price Movements

While the volatile behaviour of cryptocurrency is extensively studied, the stock market’s blockchain sector, which has not been given much attention in the academic world, operates very differently from traditional stock industries. The paper hypothesizes that blockchain stocks exhibit more herding behaviour than traditional stocks and uses quantitative data analysis techniques to study it. The automotive industry is taken as a representative of traditional stocks. Cross-Sectional Absolute Deviation, the academic standard for herding behaviour, is used as the primary comparative measure between blockchain and automotive stocks. It reveals that blockchain industry has significant herding, while rational pricing mechanisms prevail in the automotive industry. Supporting this conclusion, a correlation matrix of stock prices of small market capitalisation firms in each industry is constructed, analysing how closely stock price movements in an industry are related. The correlation coefficient for blockchain stocks is 20% higher than the coefficient for automotive stocks. This indicates that blockchain stocks likely exhibit higher levels of herding. The impact of social media on stock price movements in the two industries is analysed by conducting a correlation study between Google Trends data for industry-related keywords and individual stock returns. The blockchain industry saw a significantly higher correlation, likely suggesting that social media has a stronger influence on blockchain stock price movements. Finally, the paper provides possible explanations for why herding behaviour is more prominent in the blockchain stocks compared to traditional stocks. These include absence of traditional stock valuation metrics, lack of financial knowledge and role of social media.

Social media posts and stock returns: The Trump factor

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carl Ajjoub ◽  
Thomas Walker ◽  
Yunfei Zhao
Keyword(s):  
Social Media ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
The United States ◽  
Science Field ◽  
Content Type ◽  
Social Media Platforms ◽  
Media Firms ◽  
The Impact

PurposeThis paper explores the effects of US President Donald Trump's Twitter messages (tweets) on the stock prices of media and non-media companies.Design/methodology/approachThe authors’ empirical analysis considers all Twitter messages posted by Donald Trump from May 26, 2016 (the date he passed the threshold of 1,237 delegates required to guarantee his presidential nomination) to August 30, 2018. The authors accessed President Trump's tweets through http://www.trumptwitterarchive.com, which provides links to all Twitter messages the President has ever posted. Of the 6,983 presidential tweets during our sample period, the authors select 513 messages that mention companies that are publicly traded in the United States for this study. The selected messages are then classified as having a positive, neutral or negative sentiment. The authors employ a series of univariate and multivariate tests as well as Heckman two-step regressions and partial least squares regressions to examine the effect of the President's tweets on the stock prices of the firms he tweets about.FindingsFor media firms, the authors find that positive tweets have a pronounced positive stock price impact, whereas negative and neutral tweets have little or no effect. For non-media firms, the authors observe the opposite: negative tweets tend to be associated with significant stock price declines, whereas neutral and positive tweets incur weakly positive stock price reactions. To a large extent, these stock price declines reverse on the following day. The authors further find that the President's reiteration of information that is already known by the market incurs an additional stock price reaction. The President's attitude towards the news appears to play a major role in this context.Originality/valueThe authors contribute to the literature by offering various new insights regarding the effect social media has on the stock markets. In addition, this paper expands the emerging strand of literature that explores how President Trump affects the stock prices of firms he tweets about. This paper differs from prior studies in this area by considering a broader range of tweets, by controlling for potential selection biases, by differentiating between Trump's tweets about media and non-media firms and by exploring the impact of “old” vs “new” news based on whether the President repeats information that is already known to the market. If social media posts by single influential people are found to affect markets, they may create trading opportunities for investors and financial managers and risk arbitrage opportunities for arbitrageurs. In the political science field, the findings of this research provide valuable insights into how politicians can employ social media platforms to affect the public, and the differential influence of nominees and politicians in office. Finally, our study gives corporations that wish to back a certain campaign or a candidate in an election a better idea of the possible risks and benefits of their actions, considering that candidates or politicians could post negative messages on social media platforms targeting companies that backed their opponents.

An Empirical Study on Efficient Market Hypothesis with reference to FMCG Sector

GIS Business ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 109-126
Author(s):  
Nitin Tanted ◽  
Prashant Mistry
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Controversial Issues ◽  
Efficient Market ◽  
Price Movements ◽  
Run Test ◽  
Stock Price Movements

One of the highly controversial issues in the area of finance is “Efficient Market Hypothesis”. Efficient Market Hypothesis states that, “In an efficient market, all available price information is reflected in the stock prices and it is not possible to generate abnormal returns compared to other investors.” A lot of studies conducted previouslyto test the Efficient Market Hypothesis, confirmed the theory until recent years, when some academicians found it to be non-applicable in financial markets. According to them, it is possible to forecast the stock price movements using Technical Analysis. The results of various studies have been inconclusive and indefinite about the issue. This study attempted to test the efficiency of FMCG Sector stocks in India in its weak form. For the study, closing prices of top 10 stocks from Nifty FMCG index has been taken for the 5-year period ranging from 1st October 2014 to 30th September 2019. Wald-Wolfowitz Run test has been used to test the haphazard movements in the stock price movements. The results indicated that FMCG sector stocks does support the Efficient Market Hypothesis and exhibit efficiency in its weak form. Hence, it is not possible to accurately predict the price movements of these stocks.

scholarly journals The Effect of The Match Results on Bali United’s Stock Price Movements

2020 ◽  
Vol 3 (1) ◽  
pp. 26
Author(s):  
Agung Novianto Margarena ◽  
Arian Agung Prasetiyawan
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
European Countries ◽  
Football Club ◽  
Study Results ◽  
Price Movements ◽  
Positive Effect ◽  
Stock Price Movements

This study was conducted due to differences in the study results inseveral countries related to the effect of the match results on stockmovements. Dimic et. al (2019) stated the match results effect themovement of stock prices, while Mishra & Smyth (2010) stated thevice versa. Then, Floros (2014) put forward different results throughthe study of four clubs in four European countries. Thus, this studyreexamines the effect of the match results on the stock pricemovement of Bali United. Moreover, Bali United is the first SoutheastAsian football club to be listed on the stock market. This study uses aquantitative method with a sample of 31 Bali United’s matches afterlisted on the stock market. The data were analyzed using simple linearregression with SPSS 21 with either won, drawn or lost match resultsrepresented by goal margins. The stock price movements arerepresented by stock prices after the results of the match. It was foundthat the results of the match had a positive effect on the stockmovement of Bali United

scholarly journals DETERMINANT ANALYSIS OF FINANCIAL RATIO ON STOCK RETURNS IN CONSTRUCTION COMPANIES REGISTERED AT INDONESIA STOCK EXCHANGE 2015-2019

2020 ◽  
Vol 2 (1) ◽  
pp. 67-80
Author(s):  
Muhammad Reza Alfianto Siregar ◽  
Pardomuan Sihombing
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Financial Ratio ◽  
Construction Companies ◽  
Price Performance ◽  
Price Movements ◽  
Stock Price Movements

The growth of the construction sector in Indonesia has indirectly contributed to the growth in the performance of construction companies. This construction performance growth has an impact on stock price movements, apart from the influence of demand and supply of shares. The condition of fluctuating stock price movements requires investors to analyze financial statements before making investment decisions. To find out how the stock price performance can be done by measuring stock returns. In connection with these conditions, the purpose of this study is to analyze the effect of ROE, DER, CR, PBV and TATO on stock returns in construction companies listed on the IDX in 2015 - 2019. This research is included in the category of comparative causal research. The number of samples used in this study were 13 sample companies, with the sampling technique using purposive sampling. The type of data in this study is secondary data taken by the documentation method at Yahoo Finance. The data analysis method uses panel data regression analysis assisted by the Eviews 9.0 software. The results of the study partially show that ROE; DER, CR, PBV, and TATO have a positive and significant effect on stock returns. In addition, ROE, DER, CR, PBV, and TATO simultaneously have a significant effect on stock returns.

scholarly journals Macroeconomic impact on stock returns in the Croatian hospitality industry

2019 ◽  
Vol 7 (1) ◽  
pp. 53-68
Author(s):  
Siniša Bogdan
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Hospitality Industry ◽  
Price Index ◽  
Explanatory Power ◽  
Consumer Price Index ◽  
Stock Index ◽  
Macro Variables ◽  
The Impact

Tourism is one of the most important sectors in the Republic of Croatia. It plays a significant role in its economic development. This research investigates whether the macro-variables have an impact on the stock returns in the hospitality industry. The focus of the work consists in causality relationship between four macro variables (consumer price index, industrial production, exchange rate and number of tourist arrivals) and a stock index composed of Croatian hospitality companies. After applying Granger-causality tests based on the VAR methodology, results suggest that only consumer price index Granger-cause stock returns in the hospitality industry in the observed period from July 2008 to July 2018. Further analysis through impulse response function indicates that the impulse responses of inflation meet expectations in terms of the direction of impact. In the second month, stock prices react negatively to shock, implying that higher inflation causes negative stock price returns. After applying the variance decomposition method, a very low explanatory power of consumer price index on stock returns in the hospitality industry was revealed. This paper contributes to the existing literature on the topic of the impact of macro-economic variables on hospitality stock returns by extending the scope to Croatia and by testing a different set of variables compared to those from previous studies.

Anomalous Market Movements and the Rolling Settlement: Empirical Evidence from Indian Stock Markets

2007 ◽  
Vol 11 (4) ◽  
pp. 31-44 ◽  
Author(s):  
Ramesh Chander ◽  
Kiran Mehta
Keyword(s):  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Structural Changes ◽  
Abnormal Returns ◽  
Weekend Effect ◽  
Settlement Period ◽  
Price Movements ◽  
Anomalous Pattern ◽  
Stock Price Movements

Investors and analysts are unable to predict stock price movements consistently so as to beat the market in informationally efficient markets. Still, concerted efforts are being made to earn abnormal returns discerning some anomalous pattern in the stock price movements. Also, the study of some structural changes in the market leading to, or removing some anomalous pattern in the stock prices, are of interest to investors and analysts. The present study was conceptualised to scrutinise whether anomalous patterns yield abnormal return consistently for any specific day of the week even after introduction of the compulsory rolling settlement on Indian bourses. Three market series viz., BSE Sensex, S and P CNX Nifty and S and P CNX 500 were observed on daily basis for ten years viz., i) Pre-rolling settlement period, April 1997 - December, 2001 and ii) Post-rolling settlement period, January 2002 - March 2007 to discern evidences in this regard. Contrary to developed capital markets, the results reported in this study documented lowest (significant) Friday returns in the pre-rolling settlement period as credible evidence for the weekend effect. The findings recorded for post-rolling settlement period were in harmony with those obtained elsewhere in the sense that Friday returns were highest and those on Monday were the lowest. It implied that arbitrage opportunities existed (for different trade settlement cycle on two exchanges, BSE and NSE) have disappeared consequent to the rolling settlement. On the whole, the study noted stock markets moved more rationally and anomalous return pattern noticed earlier could not sustain, in the post rolling settlement period.

The influence of title sponsorships in sports events on stock price returns

2015 ◽  
Vol 16 (2) ◽  
pp. 37-56 ◽  
Author(s):  
Masaki Kudo ◽  
Yong Jae Ko ◽  
Matthew Walker ◽  
Daniel P Connaughton
Keyword(s):  
Stock Returns ◽  
Event Study ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Study Analysis ◽  
Sports Events ◽  
Price Increases ◽  
Event Date ◽  
The Impact

The purpose of this study was to examine stock price abnormal returns following title sponsorship announcement and event date of NASCAR, the PGA Tour, and the LPGA Tour. For this purpose, the authors used event study analysis where the analysis measures the impact that a specific event has on stock prices by comparing actual stock returns to estimated returns (Spais & Filis, 2008). An event study analysis demonstrated that title sponsors for the LPGA Tour and NASCAR garnered significant stock price increases on both the announcement date and the event date. The moderator tests suggested that high image congruence and high-technology related sponsorships assumed a key role in stock price increases.

scholarly journals Neural Network Backpropagation Identification of Jakarta Islamic Index (JII) Stock Price Patterns

2020 ◽  
Vol 4 (1) ◽  
pp. 90-94
Author(s):  
Musli Yanto ◽  
Liga Mayola ◽  
M. Hafizh
Keyword(s):  
Interest Rates ◽  
Stock Prices ◽  
Stock Price ◽  
Share Prices ◽  
Inflation Rates ◽  
Price Patterns ◽  
Price Movements ◽  
Pattern Determination ◽  
Network Patterns ◽  
Stock Price Movements

Jakarta Islamic Index (JII) is an organization engaged in the economy with the aim to pay attention to stock movements every day. With the JII, people who do not understand about shares and their movements, will be easy to know and understand the movement of shares that occur at certain times. The problem in this research is that many investors are unable to predict the rise and fall of stock prices. The prediction process can be done with a backpropagation algorithm. The algorithm is a concept of computer science which is widely used in the case of analysis, prediction and pattern determination. The process starts from the analysis of the variables used namely interest rates, exchange rates, inflation rates and stock prices that occurred in the previous period. The variables used are continued in the formation of network patterns and continued in the process of training and testing in order to produce the best network patterns so that they are used as a process of identifying JII stock price movements. The results obtained in the form of the value of stock price movements with an error rate based on the MSE value of 11.85% so that this study provides information in the form of knowledge for making a decision. The purpose of the research is used as input for investors in identifying share prices. In the end, the benefits felt from the results of this study, investors can make an initial estimate before investing in JII.

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