Coronation Day of Financial Market, Investor Attention, and Stock Return: A Perspective of Local and Global Media

2021 ◽  
pp. 1-14
Author(s):  
Yin-Siang Huang ◽  
You-Xun Lu
Keyword(s):  
Financial Market ◽  
Stock Return ◽  
Global Media ◽  
Investor Attention

Lead-Lag Relationship between Investor Sentiment in Social Media9 Investor Attention in Google, and Stock Return

2018 ◽  
Author(s):  
Aldila Rizkiana ◽  
Sari Hasrini ◽  
Pameodji Hardjomidjojo ◽  
Budhi Prihartono ◽  
Indryati Sunaryo ◽  
...  
Keyword(s):  
Stock Return ◽  
Investor Sentiment ◽  
Investor Attention

Noisy information and stock market returns

2016 ◽  
Vol 33 (3) ◽  
pp. 338-358 ◽  
Author(s):  
Gang Li
Keyword(s):  
Growth Rate ◽  
Financial Market ◽  
Stock Return ◽  
Equity Premium ◽  
Average Risk ◽  
Return Volatility ◽  
Content Type ◽  
Stock Return Volatility ◽  
Noisy Information ◽  
The Us

Purpose This paper aims to study whether noisy public information that investors receive about the expected aggregate dividend growth rate can help better understand the large average equity premium and stock return volatility in the US financial market. Design/methodology/approach This paper considers a dynamic asset pricing model with a representative agent, who cannot observe the expected growth rate of dividends and must learn its value by using noisy information. In addition, this paper presents a simple model for noisy information calibration. Findings With a coefficient of relative risk aversion below 10 and the time impatience parameter between 0 and 1, the calibrated model is able to yield an average risk-free interest rate, equity premium and stock return volatility that are close to the stylized facts in the US financial market. Originality/value First, this paper presents a different equilibrium model with a simple “catching up with the Joneses” preference and noisy information. Second, this paper develops a simple calibration procedure to calibrate the information process to study whether the calibrated model can help explain the large average equity premium and stock return volatility in the US financial market data.

scholarly journals News Co-Occurrences, Stock Return Correlations, and Portfolio Construction Implications

2019 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
Yi Tang ◽  
Yilu Zhou ◽  
Marshall Hong
Keyword(s):  
Stock Return ◽  
Global Minimum ◽  
Predictive Power ◽  
Minimum Variance ◽  
Superior Performance ◽  
Portfolio Construction ◽  
Investor Attention ◽  
Retail Investor ◽  
Big Data Technologies ◽  
Future Return

In this paper, we construct a sample of news co-occurrences using big data technologies. We show that stocks that co-occur in news articles are less risky, bigger, and more covered by financial analysts, and economically-connected stocks are mentioned more often in the same news articles. We decompose a news co-occurrence into an expected component and a shock component. We find that it is the shock component that arouses abnormal retail investor attention. The expected and shock components significantly predict return correlations 12 months into the future. Finally, a global minimum variance (GMV) portfolio with the covariance matrix augmented by the predictive power of news co-occurrences for future return correlations produces relatively superior performance compared to the benchmark GMV portfolio.

scholarly journals Pemodelan Volatilitas Return Saham: Studi Kasus Pasar Saham Asia

2017 ◽  
Vol 18 (1) ◽  
pp. 35-52
Author(s):  
Linda Karlina Sari ◽  
Noer Azam Achsani ◽  
Bagus Sartono
Keyword(s):  
Financial Market ◽  
Stock Return ◽  
Stock Index ◽  
Return Volatility ◽  
Interesting Phenomenon ◽  
Stock Return Volatility ◽  
Asymmetric Model ◽  
Stock Model ◽  
The Right ◽  
Fitting Model

Modelling Volatility of Return Stock Index: Evidence from Asian CountriesVolatility is one of the interesting phenomenon in financial market; the reason is because of its eect to the existence of global financial market. The existence of volatility closely related to the risk in stock model. This research aims to determine the right model in modeling stock return volatility taken from four Asian countries with symmetric and various asymmetric model of GARCH. The result from fitting the right model for all of four stock markets showed that asymmetric model of GARCH showing a better estimation in portraying stock return volatility. Moreover, the model can reveal the existence of asymmetric eects on those four stock markets.Keywords: GARCH Asymmetric; Stock Market; Modelling; GARCH Symmetry; Volatility AbstrakVolatilitas pada pasar keuangan merupakan salah satu fenomena yang sangat menarik karena dampaknya terhadap eksistensi pasar finansial global. Keberadaan volatilitas berhubungan dengan risiko sebuah. Tulisan ini bertujuan menentukan model terbaik dalam memodelkan volatilitas return saham pada empat negara di Asia dengan menggunakan model simetris GARCH dan berbagai macam model asimetris GARCH. Hasil dari fitting model terbaik untuk keempat pasar saham menunjukkan bahwa model asimetris GARCH menunjukkan estimasi yang lebih baik dalam menggambarkan volatilitas return saham. Lebih jauh lagi, model tersebut mengungkapkan keberadaan efek asimetris pada keempat pasar saham. 

scholarly journals INVESTOR ATTENTION AND STOCK RETURNS UNDER NEGATIVE SHOCKS: AN EMPIRICAL ANALYSIS BASED ON “DRAGON AND TIGER” LIST IN CHINA

2020 ◽  
Vol 21 (3) ◽  
pp. 914-941
Author(s):  
Xiao-ying Zhai ◽  
Ying-ying Hou ◽  
Yuan-shun Li
Keyword(s):  
Stock Returns ◽  
Stock Return ◽  
Academic Research ◽  
State Ownership ◽  
Secondary Market ◽  
Regulatory Agencies ◽  
Investor Attention ◽  
Bear Market ◽  
Monthly Return ◽  
The Relationship

Using the “Dragon and Tiger” list, we construct a clean indicator that directly measures investor attention, empirically test the effect of investor attention on stock return under negative shocks and whether the effect is affected by the bull or bear market, the industry, firm size, age and state ownership, institutional shareholder holding percentage. The results show that i) an increase in investor attention negatively predicts stock returns when cumulative daily return of a stock listed on “Dragon and Tiger” list on listing day is negative; ii) Investor attention is negatively correlated with stock returns when the stock entered in “Dragon and Tiger” list experienced current cumulative monthly return is negative; iii) Investor attention is negatively correlated with stock returns when monthly cumulative net purchase amount of top 10 institution to the stock listed in “Dragon and Tiger” list is negative; iv) Investor attention is negatively correlated with stock returns when the stock listed in “Dragon and Tiger” list, the ratio of monthly cumulative trading amount of the top 10 institutional traders to total trading amount of the secondary market is in the bottom 30 percentile. These findings not only contribute to the academic research about the relationship between investor attention and stock return, but also provide some guidance to the financial regulatory agencies as to the capital market stability.

Robust Optimal Investment-Reinsurance Strategies for a Jump-Diffusion Risk Model with Mean-reverting Stock Return

2021 ◽  
Vol 7 (6) ◽  
pp. 6100-6114
Author(s):  
Wu Yungao
Keyword(s):  
Financial Market ◽  
Stock Return ◽  
Value Function ◽  
Risk Model ◽  
Optimal Investment ◽  
Jump Diffusion ◽  
Insurance Companies ◽  
Insurance Company ◽  
Terminal Time ◽  
The Value Function

Objectives: This paper proposes a strategy of robust optimal investment reinsurance for insurance companies. It was assumed that the surplus procedure of the insurance company satisfies the jump-diffusion procedure. Insurance companies could invest their surplus funds in the financial market consisted of both risk assets and one risk-free asset. The price procedure of risk assets satisfies the stochastic procedure with a mean reversion rate. Considering the uncertainty of the model, the ambiguity-averse insurance firm aims to enhance the exponential utility of insurance surplus at terminal time. This paper has investigated the problem of robust optimal investment reinsurance and obtained the differential equation supported by the value function.

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