Investor Pessimism and the German Stock Market: Exploring Google Search Queries

2019 ◽  
Vol 20 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Thomas Dimpfl ◽  
Vladislav Kleiman

Abstract We analyze the relationship of retail investor sentiment and the German stock market by introducing four distinct investor pessimism indices (IPIs) based on selected aggregate Google search queries. We assess the predictive power of weekly changes in sentiment captured by the IPIs for contemporaneous and future DAX returns, volatility and trading volume. The indices are found to have individually varying, but overall remarkably high explanatory power. An increase in retail investor pessimism is accompanied by decreasing contemporaneous market returns and an increase in volatility and trading volume. Future returns tend to increase while future volatility and trading volume decrease. The outcome is in line with the conjecture of correction effects. Overall, the results are well in line with modern investor sentiment theory.

Author(s):  
Philipp Finter ◽  
Alexandra Niessen-Ruenzi ◽  
Stefan Ruenzi

2012 ◽  
Vol 82 (2) ◽  
pp. 133-163 ◽  
Author(s):  
Philipp Finter ◽  
Alexandra Niessen-Ruenzi ◽  
Stefan Ruenzi

2020 ◽  
Vol 4 (1) ◽  
pp. 61-76
Author(s):  
Yousra Trichilli ◽  
Mouna Boujelbène Abbes ◽  
Sabrine Zouari

PurposeThis paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.Design/methodology/approachFirst, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.FindingsUsing the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.Research limitations/implicationsThis study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.Originality/valueThe important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.


Author(s):  
Nils Muhlack ◽  
Christian Soost ◽  
Christian Johannes Henrich

AbstractThis paper examines the impact of weather phenomena on the German stock market, evaluating cloud cover, humidity, air pressure, precipitation, temperature, and wind speed as weather variables. We use stock market data (returns, trading volume, and volatility) from the DAX, MDAX, SDAX, and TecDAX for the period from 2003 to 2017 and show, with modern time-series (GARCH) models that air pressure is the only weather variable that exerts a potentially consistent effect on the stock market. Air pressure reduces the trading volume on the SDAX and TecDAX, and changes in air pressure lead to increases in returns on the DAX, MDAX and SDAX. The effects of the other weather variables show no clear pattern and are critically discussed. In addition, this article contains an overview of the historical research results on the effects of weather on stock markets.


Author(s):  
Eero J. Pätäri ◽  
Timo H. Leivo ◽  
Sheraz Ahmed

AbstractThis paper examines the added value of using financial statement information, particularly that of Piotroski’s (J Account Res 38:1, 2000. https://doi.org/10.2307/2672906) FSCORE, for equity portfolio selection in the German stock market in a realistic research setting in which the critique against the implementability of FSCORE-based trading strategies is taken into account. We show that the performance of annually rebalanced long-only portfolios formed on any of the examined 12 accounting-based primary criteria improves by including the FSCORE as a supplementary criterion. Our study is the first to show that although the FSCORE boost is strongest for the 1-year holding period length, it also holds, on average, for the 3-year holding period. The use of a 3-year updating frequency is particularly beneficial for the low-accrual portfolio that—when supplemented with the high-FSCORE threshold—generates the best overall performance among all 75 portfolios examined. Moreover, we show that a high FSCORE is also an efficient stand-alone criterion for long-only portfolio formation.


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