scholarly journals Google Search and Stock Market Performance: Evidence from Malaysia

2019 ◽  
Vol 2 (1) ◽  
pp. 49-55
Author(s):  
Kelvin Yong Ming Lee
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Trading Volume ◽  
Market Performance ◽  
Volume Index ◽  
Investment Strategies ◽  
Search Volume ◽  
Stock Market Performance ◽  
Google Search ◽  
The Relationship

Nowadays, the internet changes the way for information searching and processing. Along with that, Google search had become the most popular search engine on the web since it allows users access to the information at a minimal cost. This study intends to investigate the relationship between Google search volume and the Malaysian stock market performance in the aspects of returns, volatility, and trading volume. The sample of this study consisted of 29 listed companies from the Malaysian stock market. The sample period of this study covered the period from 2016 to 2018. The data related to the stock market were downloaded from Investing.com, whereas the data related to Google search were downloaded from the database of Google Trend. The results indicated that the Google search volume index (GSVI) of the previous week tends to have significant positive impacts on the stock price changes. Thus, a higher search volume of the specific company name tends to increase the stock price of the particular company in the following week. Besides that, this study also revealed that the stock market performance tends to be affected by stock market performance in the previous week. Lastly, this study suggested that signals of GSVI need to be included in the investment strategies.  

scholarly journals Macroeconomic factors and LQ45 stock price index: evidence from Indonesia

2019 ◽  
Vol 16 (3) ◽  
pp. 251-259 ◽  
Author(s):  
Sugeng Hadi Utomo ◽  
Dwi Wulandari ◽  
Bagus Shandy Narmaditya ◽  
Puji Handayati ◽  
Suryati Ishak
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Market Performance ◽  
Macroeconomic Variables ◽  
Stock Market Performance ◽  
Stock Price Index ◽  
The Relationship

This paper provides the relationship between macroeconomic variables, including exchange rate, BI rate and inflation, and stocks performance, particulary bluechip stocks listed in LQ45 index in Indonesia Stock Exchange. The study particularly gives insights on bluechip stocks listed in LQ45 stock price index in Indonesia Stock Exchange between 2015 and 2017. The data were obtained from various sources during the period, including the Indonesia Stock Exchange (IDX), the Central Bank of Indonesia (BI), and the Ministry of Trade. This study followed a Vector Error Correction Model (VECM) attempting to estimate the relationship between variables both in the short term and in the long term. The findings of the study showed that in the long run, exchange rate, BI rate and inflation have a negative impact on stock market performance, particularly on LQ45 index in Indonesia Stock Exchange. It implies that an increase in macroeconomic variables results in the decline of stock market performance. Meanwhile, in the short run, two variables, namely the exchange rate and inflation, positively affect stock market performance in Indonesia. On the contrary, the relationship between BI rate and stock market performance showed a negative correlation. These findings have significant implication for the understanding of how macroeconomic variables affect the stock market performance, particularly LQ45 price index in Indonesia Stock Exchange.

scholarly journals PENGARUH KEPEMILIKAN INSTITUSIONAL TERHADAP KARAKTERISTIK PASAR SAHAM DI BURSA EFEK INDONESIA

2017 ◽  
pp. 1-12
Author(s):  
Donalson Silalahi
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Adverse Selection ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Exchange ◽  
Control Variable ◽  
Institutional Ownership ◽  
Adverse Selection Costs ◽  
The Relationship

The role of institutional ownership in the financial markets became very important. However, until today there is no consensus among researchers about the influence of institutional ownership on the characteristic of stock market. Therefore, researchers are motivated to conduct further research the influence of institutional ownership on the characteristic of stock market. The research conducted at the Indonesian Stock Exchange with traded spread and adverse selection costs as dependent variable and institutional ownership as independent variable. In addition to institutional ownership, also used standard deviation of common stock price and trading volume as a control variable to clarify the relationship of institutional ownership on the characteristic of stock market. The study was conducted on 120 firms with observations in the period 2010-2011. All the required data obtained from the Indonesian Capital Market Directory. The results showed that: First, institutional ownership has a negative and significant effect on traded spread. Second, the variability of traded spread is able to be explained by the variability of institutional ownership, standard deviation of the stock price, and trading volume 24.8 percent. Third, institutional ownership has a negative and significant effect on adverse selection costs. Fourth, the variability of adverse selection costs is able to be explained by the variability of institutional ownership, standard deviation of the stock price, and trading volume 26.2 percent. Fifth, the relationship between institutional ownership to traded spread and adverse selection cost before and after entering the control variables remain negative and significant.

scholarly journals Weibo Attention and Stock Market Performance: Some Empirical Evidence

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Minghua Dong ◽  
Xiong Xiong ◽  
Xiao Li ◽  
Dehua Shen
Keyword(s):  
Regression Analysis ◽  
Stock Market ◽  
Quantile Regression ◽  
Empirical Evidence ◽  
Trading Volume ◽  
Market Performance ◽  
Correlation Coefficients ◽  
Investor Attention ◽  
Stock Market Performance ◽  
Quantile Regression Analysis

In this paper, we employ Weibo Index as the proxy for investor attention and analyze the relationships between investor attention and stock market performance, i.e., trading volume, return, and volatility. The empirical results firstly show that Weibo attention is positively related to trading volume, intraday volatility, and return. Secondly, there exist bidirectional causal relationships between Weibo attention and stock market performance. Thirdly, we generally find that higher Weibo attention indicates higher correlation coefficients with the quantile regression analysis.

Investor attention using the Google search volume index – impact on stock returns

2019 ◽  
Vol 11 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Vighneswara Swamy ◽  
Munusamy Dharani
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Volume Index ◽  
Trading Behavior ◽  
Excess Returns ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Indian Stock Market ◽  
Google Search

Purpose The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market. Design/methodology/approach The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation. Findings The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies. Originality/value To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

scholarly journals Investor attention and the response of US stock market sectors to the COVID-19 crisis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lee A. Smales
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Information Discovery ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Global Stock Markets ◽  
Google Search ◽  
The Relationship ◽  
Global Stock

PurposeCOVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention towards COVID-19 surged as the virus spread, the number of cases grew and its consequences imposed on everyday life. We assess whether this increase in investor attention may explain stock returns across different sectors during this unusual period.Design/methodology/approachWe adopt the methodology of Da et al. (2015), using Google search volume (GSV) as a proxy for investor attention to examine the relationship between investor attention and stock returns across 11 sectors.FindingsOur results demonstrate that heightened attention towards COVID-19 negatively influences US stock returns. However, relatively speaking, some sectors appear to have gained from the increased attention. This outperformance is centred in the sectors most likely to benefit (or likely to lose least) from the crisis and associated spending by households and government (i.e. consumer staples, healthcare and IT). Such results may be explained by an information discovery hypothesis in the sense that investors are searching online for information to enable a greater understanding of COVID-19's impact on relative stock sector performance.Originality/valueWhile we do not claim that investor attention is the only driver of stock returns during this unique period, we do provide evidence that it contributes to the market impact and to the heterogeneity of returns across stock market sectors.

Extreme learning machine for stock price prediction

2021 ◽  
pp. 002072092098467
Author(s):  
Fangzhao Zhang
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Extreme Learning Machine ◽  
Stock Price ◽  
Market Performance ◽  
Market Analysis ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Market Performance ◽  
Learning Machine

Stock market performance prediction has always been a hit research topic and is attractive due to its strong potential to generate financial profit. Being able to predict future stock price in a relatively accurate way forms a significant task of stock market analysis. Different mechanisms from fundamental analysis to statistical modeling have been deployed to study stock market performance and various factors from fundamental factors, technical factors to market sentiments are also incorporated in the stock price prediction task. However, due to the chaotic stock market performance, which is close to random walk, and the difficulty in discerning influential factors, predicting stock price faces a lot of challenges. In recent years, fast development in fields such as machine learning has offered new ways to look at this task. In this paper, we employ Extreme Learning Machine (ELM) algorithm, a recent modification of traditional feedforward neural network with single hidden layer, whose learning speed is greatly improved based on solid mathematical background and capability to circumvent problems such as local minimum is also enhanced, to construct an ELM combination model to study stock market performance and predict stock price. A comparison between the predicted output and the real data is carried out to test the feasibility of applying ELM model to stock market analysis. The result indicates that ELM model is desirable for predicting stock price variation trend while some inaccuracy exists in the prediction of peak values, which may require further model modification. Overall, by applying the machine learning model ELM to predict stock price and generating desirable outcome, this paper both contributes to offering a new way to investigate stock market performance and enlarging the field deployment of ELM model as well.

scholarly journals Stock Price of Pandemic Covid-19 in Stock Market Performance

2021 ◽  
Vol 9 (2) ◽  
pp. 184-190
Author(s):  
Musdalifah Azis ◽  
Burhanuddin Burhanuddin ◽  
Heni Rahayu
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Market Performance ◽  
Stock Market Performance

scholarly journals Stock Market Reforms and Stock Market Performance

2021 ◽  
Vol 12 (2) ◽  
pp. 202
Author(s):  
Karthigai Prakasam Chellaswamy ◽  
Natchimuthu N ◽  
Muhammadriyaj Faniband
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Positive Impact ◽  
Market Performance ◽  
Market Reforms ◽  
Disclosure Requirements ◽  
Stock Market Performance ◽  
Event Study Method ◽  
Unfair Trade ◽  
The Impact

This paper analyses the impact of stock market reforms on the stock market performance in India using regression based event-study method. We consider nine stock market reforms introduced from 1998 to 2018. We find that the impact of stock market reforms on Nifty trading volume and Nifty return is different. This paper documents that the impact of the additional volatility measures, T+3 and T+2 settlement cycles, and margin provisions for intra-day crystallized losses reforms show a positive impact on trading volume post-reform. In contrast, internet trading, prohibition of fraudulent and unfair trade practices, delisting of equity shares, substantial acquisition of shares and takeovers listing obligations and disclosure requirements reforms decrease the trading volume post-reform. Our results of Nifty return reveal that the additional volatility measures, the T+2 settlement cycle, the prohibition of fraudulent and unfair trade practices, substantial acquisition of shares and takeovers, listing obligations and disclosure requirements have a significant and positive impact on return post-reform. It is evident that the impact of all nine stock market reforms is insignificant on Nifty return.

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