scholarly journals Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

2021 ◽  
Vol 14 (9) ◽  
pp. 394
Author(s):  
Francisco Guijarro ◽  
Ismael Moya-Clemente ◽  
Jawad Saleemi
Keyword(s):  
Financial Markets ◽  
Sentiment Analysis ◽  
Trading Volume ◽  
Price Dispersion ◽  
Daily Basis ◽  
Market Liquidity ◽  
Financial Information ◽  
Counterparty Risk ◽  
Trading Costs ◽  
Investor Sentiments

Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

scholarly journals Liquidity Risk and Investors’ Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index

2019 ◽  
Vol 11 (24) ◽  
pp. 7048 ◽  
Author(s):  
Francisco Guijarro ◽  
Ismael Moya-Clemente ◽  
Jawad Saleemi
Keyword(s):  
Financial Markets ◽  
Sentiment Analysis ◽  
Financial Market ◽  
Market Liquidity ◽  
Trading Costs ◽  
Financial Decisions ◽  
Area Of Interest ◽  
Liquidity Measures ◽  
The Impact ◽  
The Relationship

Microblogging services can enrich the information investors use to make financial decisions on the stock markets. As liquidity has immediate consequences for a trader’s movements, this risk is an attractive area of interest for both academics and those who participate in the financial markets. This paper focuses on market liquidity and studies the impact on liquidity and trading costs of the popular Twitter microblogging service. Sentiment analysis extracted from Twitter and different popular liquidity measures were gathered to analyze the relationship between liquidity and investors’ opinions. The results, based on the analysis of the S&P 500 Index, found that the investors’ mood had little influence on the spread of the index.

scholarly journals Investor Sentiments and Trading Volume’s Asymmetric Response: a Non-linear ARDL Approach Tested in PSX

2019 ◽  
Vol 5 (1) ◽  
pp. 47-56
Author(s):  
Irum Saba ◽  
Maria Shams Khakwani ◽  
Rehana Kouser ◽  
Abdul Wahab
Keyword(s):  
Trading Volume ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Market Liquidity ◽  
Short Term ◽  
Asymmetric Response ◽  
Ardl Approach ◽  
Investor Sentiments ◽  
Non Linear ◽  
Dynamic Linkages

The research paper entitled “Investor sentiments and trading volume’s asymmetric response: A non linear ARDL approach tested in PSX” is an attempt to investigate the dynamic linkages between trading volume and investor sentiments for Pakistan Stock Exchange (PSX) 100 index. Two sentiments indicators have been used to enlighten the linkages. These indicators are overconfidence and net optimism and pessimism. Trading volume has been used as a proxy for the measurement of market liquidity. Non-Linear Asymmetric Autoregressive Distributed Lag (NARDL) as well as Dynamic Conditional Correlation (DCC) GARCH have been used to explain the dynamic linkages between trading volume and investor sentiments. Empirical findings suggested an asymmetric long-term market liquidity reaction to investor sentiment as well as upcoming three-year correlation have been forecasted between the trading volume and investor sentiments. In the short term, stock market liquidity reacts rapidly and asymmetrically to changes in overconfidence sentiment while the net optimism and pessimism sentiment have insignificant short-term impact on trading volume.

Background Noise? TV Advertising Affects Real-Time Investor Behavior

2021 ◽  
Author(s):  
Jūra Liaukonytė ◽  
Alminas Žaldokas
Keyword(s):  
Real Time ◽  
Trading Volume ◽  
Data Gathering ◽  
Financial Information ◽  
Investor Behavior ◽  
Tv Advertising ◽  
Hourly Data ◽  
National Advertising ◽  
Retail Investor ◽  
Retail Trading

Using minute-by-minute TV advertising data covering some 300 firms, 327,000 ads, and $20 billion in ad spending, we study the real-time effects of TV advertising on investors’ searches for online financial information and subsequent trading activity. Our identification strategy exploits the fact that viewers in different U.S. time zones are exposed to the same programming and national advertising at different times, allowing us to control for contemporaneous confounding events. We find that an average TV ad leads to a 3% increase in EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system queries and an 8% increase in Google searches for financial information within 15 minutes of the airing of that ad. These searches translate into larger trading volume on the advertiser’s stock, driven primarily by retail investors. The findings on retail investor ad-induced trading are corroborated with hourly data from Robinhood, a popular retail trading platform. We also show that ads induce searches and trading of companies other than the advertiser, including of close rivals. Altogether, our findings suggest that advertising originally intended for consumers has a nonnegligible effect on financial markets. This paper was accepted by Karl Diether, finance.

International Real Estate Review

2011 ◽  
Vol 14 (3) ◽  
pp. 311-329
Author(s):  
Charles Ka Yui Leung ◽  
◽  
Jun Zhang ◽  
Keyword(s):  
Real Estate ◽  
Trading Volume ◽  
Price Dispersion ◽  
Housing Prices ◽  
Housing Price ◽  
Short Paper ◽  
Future Research ◽  
Positive Correlation ◽  
Time On The Market ◽  
Empirical Regularities

Three striking empirical regularities have been repeatedly reported: the positive correlation between housing prices and trading volume, and between housing price and time-on-the-market (TOM), and the existence of price dispersion. This short paper provides perhaps the first unifying framework which mimics these phenomena in a simple competitive search framework. In the equilibrium, sellers with heterogeneous waiting costs and buyers are endogenously segregated into different submarkets, each with distinct market tightness and prices. With endogenous search efforts, our model also reproduces the well-documented price- volume correlation. Directions for future research are also discussed.

scholarly journals Textual Analysis and Sentiment Analysis in Accounting

2021 ◽  
Vol 24 (2) ◽  
pp. 168-183
Author(s):  
Juan L. Gandía ◽  
David Huguet
Keyword(s):  
Sentiment Analysis ◽  
Textual Analysis ◽  
Financial Information ◽  
Digital Tools ◽  
Future Research ◽  
Analysis Techniques ◽  
Additional Information ◽  
New Perspective ◽  
Medios De Comunicacion

A pesar del relativamente escaso uso de técnicas de análisis textual y de análisis del sentimiento en finanzas y contabilidad, éstas tienen un gran potencial en contabilidad, tanto por el elevado volumen de documentos utilizados para la comunicación de información financiera como por el crecimiento en el uso de herramientas digitales y medios de comunicación social. En este sentido, estas técnicas de análisis pueden ayudar a los investigadores a analizar pistas ocultas o buscar información adicional a la observada a través de los estados financieros, incrementando la cantidad y calidad de la información tradicionalmente utilizada, y proporcionando una nueva perspectiva de análisis. Por ello, el objetivo de este estudio es realizar una revisión del uso del análisis textual y del análisis del sentimiento en contabilidad. Tras presentar los conceptos de análisis textual y análisis del sentimiento y justificar teóricamente su papel en la investigación en contabilidad, llevamos a cabo una revisión de la literatura previa en el uso de estas técnicas en finanzas y contabilidad y describimos las principales técnicas de análisis del sentimiento, así como el procedimiento a seguir para el uso de esta metodología. Finalmente, sugerimos tres líneas de investigación futura que pueden beneficiarse del uso del análisis textual y del análisis del sentimiento. In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and social media. In that regard, these techniques of analysis may help researchers to analyse hidden clues or look for additional information to that one observed through financial information, increasing the quantity and quality of the information traditionally used, and providing a new perspective of analysis. The aim of this study is to review the use of textual analysis and sentiment analysis in accounting. After presenting the concepts of textual analysis and sentiment analysis and expose their interest in accounting, we perform a review of the previous literature on the use of these techniques in finance and accounting and describe the main techniques of sentiment analysis, as well as the procedure to be followed for the use of this methodology. Finally, we suggest three lines of future research that may benefit from the use of textual and sentiment analysis.

scholarly journals An Economic Examination of Collateralization in Different Financial Markets

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Risk Factors ◽  
Credit Risk ◽  
Financial Markets ◽  
Counterparty Risk ◽  
Financial Contracts ◽  
Theoretical Justification ◽  
Data Set ◽  
Unique Data ◽  
Economic Significance ◽  
The Difference

This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

Earnings Management and Stock Liquidity

2021 ◽  
pp. 66-84
Author(s):  
Hakan Özkaya
Keyword(s):  
Stock Market ◽  
Earnings Management ◽  
Trading Volume ◽  
Management Practices ◽  
Positive Association ◽  
Discretionary Accruals ◽  
Market Liquidity ◽  
Stock Liquidity ◽  
Turnover Ratio ◽  
Liquidity Measures

This chapter tests whether the earnings management practices in Turkey are considered informative or opportunistic by outside investors by examining its effect on stock liquidity. Earnings management is measured by discretionary accruals calculated by two different competing methods. Stock liquidity is also proxied by two different measures: the illiquidity measure of Amihud and the turnover ratio. Amihud's illiquidity measure indicates firms' daily price responses associated with the trading volume and the turnover ratio indicates how many times a stock changes its owner in a year. Relevant control variables are also included in the models. A positive association between earnings management and stock liquidity implies informative earnings management and vice versa. Earnings management is found to be positively associated with stock market liquidity. Results favor the informative earnings management view for Turkish firms and are robust to alternative specifications of earnings management and stock liquidity measures.

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