scholarly journals Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 297
Author(s):  
Mariano González-Sánchez ◽  
M. Encina Morales de Vega
Keyword(s):  
European Union ◽  
Financial Services ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Daily Basis ◽  
Investor Behavior ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Type Process ◽  
Standardized Residuals

A part of the financial literature has attempted to explain idiosyncratic asset shocks through investor behavior in response to company news and events. As a result, there has been an increase in the development of different investor sentiment measurements. This paper analyses whether the Bloomberg investor sentiment index has a causal relationship with the abnormal returns and volume shocks of major European Union (EU) financial companies through a sample of 85 financial institutions over 4 years (2014–2018) on a daily basis. The i.i.d. shocks are obtained from a factorial asset pricing model and ARMA-GARCH-type process; then we checked whether there is both individual and joint causality between the standardized residuals. The results show that the explanatory capacity of the shocks of the firm Bloomberg sentiment index is low, although there is empirical evidence that the effects correspond more to the situation of the financial subsector (banks, real estate, financial services and insurance) than to the company itself, with which we conclude that the sentiment index analyzed reflects a sectorial effect more than individual one.

Revisiting Fama–French’s asset pricing model with an MCB volatility risk factor

2020 ◽  
Vol 21 (3) ◽  
pp. 233-251
Author(s):  
Xiaoying Chen ◽  
Nicholas Ray-Wang Gao
Keyword(s):  
Risk Factor ◽  
Asset Pricing ◽  
Explanatory Power ◽  
Investor Sentiment ◽  
Size Factor ◽  
Pricing Model ◽  
Volatility Risk ◽  
Content Type ◽  
Asset Pricing Model ◽  
Market Factor

Purpose Since the introduction of VIX to measure the spot volatility in the stock market, VIX and its futures have been widely considered to be the standard of underlying investor sentiment. This study aims to examine how the magnitude of contango or backwardation (MCB volatility risk factor) derived from VIX and VIX3M may affect the pricing of assets. Design/methodology/approach This paper focuses on the statistical inference of three defined MCB risk factors when cross-examined with Fama–French’s five factors: the market factor Rm–Rf, the size factor SMB (small minus big), the value factor HML (high minus low B/M), the profitability factor RMW (robust minus weak) and the investing factor CMA (conservative minus aggressive). Robustness checks are performed with the revised HML-Dev factor, as well as with daily data sets. Findings The inclusions of the MCB volatility risk factor, either defined as a spread of monthly VIX3M/VIX and its monthly MA(20), or as a monthly net return of VIX3M/VIX, generally enhance the explanatory power of all factors in the Fama and French’s model, in particular the market factor Rm–Rf and the value factor HML, and the investing factor CMA also displays a significant and positive correlation with the MCB risk factor. When the more in-time adjusted HML-Dev factor, suggested by Asness (2014), replaces the original HML factor, results are generally better and more intuitive, with a higher R2 for the market factor and more explanatory power with HML-Dev. Originality/value This paper introduces the term structure of VIX to Fama–French’s asset pricing model. The MCB risk factor identifies underlying configurations of investor sentiment. The sensitivities to this timing indicator will significantly relate to returns across individual stocks or portfolios.

scholarly journals How to gauge investor behavior? A comparison of online investor sentiment measures

2021 ◽  
Author(s):  
Daniele Ballinari ◽  
Simon Behrendt
Keyword(s):  
Machine Learning ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Investor Behavior ◽  
Learning Approaches ◽  
Financial Applications ◽  
Model Free ◽  
Retail Investors ◽  
Regression Framework ◽  
Social Media Platforms

AbstractGiven the increasing interest in and the growing number of publicly available methods to estimate investor sentiment from social media platforms, researchers and practitioners alike are facing one crucial question – which is best to gauge investor sentiment? We compare the performance of daily investor sentiment measures estimated from Twitter and StockTwits short messages by publicly available dictionary and machine learning based methods for a large sample of stocks. To determine their relevance for financial applications, these investor sentiment measures are compared by their effects on the cross-section of stocks (i) within a Fama and MacBeth (J Polit Econ 81:607–636, 1973) regression framework applied to a measure of retail investors’ order imbalances and (ii) by their ability to forecast abnormal returns in a model-free portfolio sorting exercise. Interestingly, we find that investor sentiment measures based on finance-specific dictionaries do not only have a greater impact on retail investors’ order imbalances than measures based on machine learning approaches, but also perform very well compared to the latter in our asset pricing application.

scholarly journals EFFECTS OF THE PRE-REPURCHASE SYSTEMATIC RISK ON THE RELATIONSHIP BETWEEN INVESTOR BEHAVIOR, MARKET FACTORS AND THE STOCK PRICE RESPONSES

2018 ◽  
Vol 19 (4) ◽  
pp. 673-705
Author(s):  
Ying-Sing Liu
Keyword(s):  
Stock Price ◽  
Abnormal Returns ◽  
Investor Sentiment ◽  
Systematic Risk ◽  
Investor Behavior ◽  
Open Market ◽  
Short Term ◽  
Price Response ◽  
Market Factors ◽  
The Relationship

This study explores the pre-repurchase systematic risk will affect the abnormal returns in the open-market repurchase event period and also change the relationship between the investor sentiment, trading activity, market factors and stock price response during the event on Taiwan stock market. Based on threshold regression models, it is found that the pre-repurchase systematic risk will significantly change the relationship between investor behavior, market factors and stock price responses and the asymmetry of the relationship exists when pre-repurchase systematic risk is lower than a repartition, which supports that institutional investors and credit trading investors differ in these existing relationships. When the pre-repurchase beta is below repartition, it will be detrimental to the returns in event period. But on the contrary, the returns in the short-term shock of news exposure period present the favorable results, which may be related to the fact that there exists sentiment premium in short-term when credit trading investors’ repurchase news exposure occurs. Finally, the study is to confirm the effect of systematic risk for returns and investor sentiment, these results have not been further explored in the past, and can be used as the firm’s evalu-ation reference to the repurchase program in the future.

scholarly journals A Statistical Factor Asset Pricing Model Versus the 4-Factor Model

2019 ◽  
Vol 16 (4) ◽  
pp. 545
Author(s):  
Verônica De Fátima Santana ◽  
Alex Augusto Timm Rathke
Keyword(s):  
Time Series ◽  
Asset Pricing ◽  
Cross Section ◽  
Factor Model ◽  
Abnormal Returns ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Statistical Factor ◽  
The Cross ◽  
Section Dimension

This research aims to compare the performance of a statistical factor asset pricing model with the Fama-French-Carhart 4-factor model. We perform a Principal Component Analysis (PCA) to extract latent risk factors using data of stocks listed on B3 from 2001 to 2015. We test the abilities of the two models to explain assets' returns both in the time-series and in the cross-section dimension. We found that the statistical factor models generates statistically significant abnormal returns in the time-series analysis while the 4-factor model does not. In the cross section dimension, neither model generates significant abnormal returns but they also are not able to generate positive risk premia. Similar results are found if we consider different sets of time and assets. Therefore, although the 4-factor model performs slightly better in the set of tests, neither of the models can be considered fully adequate to explain expected returns of assets in the Brazilian stock market.

scholarly journals Analisis Pengembalian Keputusan Investasi Saham Dengan Pendekatan Capital Asset Pricing Model (CAPM) pada Perusahaan Perbankan Terdaftar di Bursa Efek Indonesia

2018 ◽  
Vol 2 (2) ◽  
pp. 215
Author(s):  
Nurmala Nurmala
Keyword(s):  
Asset Pricing ◽  
Financial Services ◽  
Investment Decision ◽  
Return Rate ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Stock Investment

ABSTRACT The investors chose the banking shares because the management of this banking is overseen and regulated by Financial Services Authority in a transparent manner. This banking world will always be professional and transparent in managing public funds. It certainly will provide trust and positive value in the eyes of the community. The problem of this research is how to make Stock Investment Decision in accordance with Capital Asset Pricing Model (CAPM) Method on Banking Companies registered in Indonesia Stock Exchange. The purpose of this research is to analyze the decision of stock investment in accordance with Capital Asset Pricing Model (CAPM) method in Bankingcompanies registered in Indonesia Stock Exchange.The method used in this research is descriptive quantitative method and data are collected by documentation technique. The data analysis technique is used to calculate Individual Shares Return Rate (Ri), Risk Free Return (Rf), Market Rate (Rm),  Premium Risk (Rp),  expected Return Rate {E (Ri)}, and to help the efficiency and the decision of Stock Investments.Based on the results of the research, it can be seen that the risk with the lowest expected stock return is 0.340 and the highest expected rate of return is equal to 0.00532. There are 25 companies stocks included in the category of efficient stocks and 13 companies stocks included in the category of inefficient stocks among 38 companies stocks taken as this research sample. These stocks have greater Ri value than E (Ri) or [Ri> E (Ri)]. The investment decision taken by the investor is to buy the stocks. 

scholarly journals The Premium/Discount Of Closed-End Funds As A Measure Of Investor Sentiment: Evidence From Greece

2011 ◽  
Vol 27 (4) ◽  
pp. 29 ◽  
Author(s):  
Dimitrios V. Kousenidis ◽  
Dimitrios I. Maditinos ◽  
Željko Šević Šević
Keyword(s):  
Regression Analysis ◽  
Asset Pricing ◽  
Explanatory Power ◽  
Investor Sentiment ◽  
Predictive Ability ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Portfolio Returns ◽  
Closed End Funds ◽  
Book To Market Ratio

<p>We examine the proposition that the premium/discount (PD) of Greek closed-end funds (CEFs) is an accurate proxy for the small-investor sentiment risk. We find that the average PD explains the returns of portfolios of large capitalization and low book-to-market ratio stocks. In this context, we are unable to confirm a link between the perceived PD anomaly and the small size effect. Moreover, we show that the explanatory power of the PD for portfolio returns depends on the form of the asset pricing model used in the regression analysis. Finally, in terms of predictive ability, we find evidence that the PD predicts the size and the book-to-market premiums but little evidence that the PD predicts individual portfolio returns.</p>

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