The pricing of sentiment risk in European stock markets

Keiber, Karl Ludwig; Samyschew, Helene

doi:10.1080/1351847x.2018.1521340

Cited by 12 publications

(3 citation statements)

References 56 publications

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“…Elshendy and Fronzetti Colladon (2017) and Olokoyo et al (2020) used social networks and semantic analysis to improve macroeconomic indicator forecasting. Perez-Liston et al (2018) and Keiber and Samyschew (2019) established direct links between sentiment and stock performance in specific countries. Hussain et al (2023) analyzed the impact of economic policy uncertainty on investments.…”

Section: Literature Reviewmentioning

confidence: 99%

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Interplay of financial market performance, macroeconomic indicators and business sentiment: a comprehensive study of the Turkish economy

Tekin,

Badwan

2024

IMEFM

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Purpose The purpose of this study is to examine the long- and short-term relationships between the BIST100, RSC index, the EURO/TRY exchange rate, bank loans provided to the private sector, imports and exports, and nonperforming loans (NPLs) with the autoregressive distributed lag (ARDL) bound, Johansen co-integration and vector error correction model (VECM) causality tests. Political developments, pandemics, conflicts between countries, trade chains and general economic and financial problems that have frequently occurred worldwide in recent years have significantly affected the Turkish economy as well as all other countries. Türkiye's economy is intricately linked with global financial markets, and understanding the dynamics between domestic macroeconomic variables and external financial indicators can provide insights into the country's economic resilience and vulnerabilities to external shocks. Design/methodology/approach Two distinct models are used in the analysis, with the Borsa Istanbul 100 (BIST100) Index and the Real Sector Confidence (RSC) Index serving as the dependent variables. This study examines the long- and short-term relationships between the BIST100, RSC index, the EURO/TRY exchange rate, bank loans provided to the private sector, imports and exports, and nonperforming loans (NPLs) with the ARDL bound, Johansen cointegration and VECM causality tests. The study uses monthly data spanning from December 31, 2002, to July 29, 2022, offering a comprehensive perspective on the dynamics of the Turkish economy. Findings The findings reveal significant long-run relationships between the BIST100 and the exchange rate, imports and exports. Short-run dynamics indicate the importance of changes in these variables, as well as NPLs and RSC, in affecting the BIST 100. The model captures the impact of economic indicators such as imports, NPLs and exports on RSC. In addition, it underscores a long-run equilibrium relationship, suggesting a responsive RSC to deviations. There is a strong positive relationship between BIST100 and the RSC. Causality tests reveal temporal relationships and causal links, with evidence of bidirectional causality for some variables, providing comprehensive insights into the short-term dynamics and adjustment mechanisms influencing RSC in the Turkish economic context. Practical implications Amidst global economic uncertainties and fluctuations, particularly in emerging markets such as Türkiye, understanding the relationships between financial market indicators and macroeconomic variables may help policymakers formulate effective monetary and fiscal policies aimed at stabilizing the economy, promoting sustainable growth and mitigating financial risks. In addition, these insights have practical implications for investors, regulators and other financial market participants seeking to make informed decisions in an increasingly interconnected and dynamic global economy. Originality/value This study uniquely examines a wide range of macroeconomic variables and financial indicators specific to Türkiye, including both traditional and nontraditional factors. This study also offers unprecedented insights into the unique characteristics and dynamics of the Turkish economy and provides valuable insights for businesses, investors and policymakers to consider Türkiye’s economic environment more effectively.

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Section: Literature Reviewmentioning

confidence: 99%

“…Perez-Liston et al. (2018) and Keiber and Samyschew (2019) established direct links between sentiment and stock performance in specific countries. Hussain et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Interplay of financial market performance, macroeconomic indicators and business sentiment: a comprehensive study of the Turkish economy

Tekin,

Badwan

2024

IMEFM

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“…The index includes stocks from eight European countries (Belgium, Italy, Spain, France, Germany, Ireland, Finland, and the Netherlands) and is also called the 'Eurex'. It has been applied in studies as a proxy for individual investor sentiment Pinho 2020a, 2020b;Keiber and Samyschew 2019;Schadner 2020) and as a regressor for investor sentiment analysis (Concetto and Ravazzolo 2019). Similar fear-gauge indexes were built for the main stock indexes.…”

Section: The Variablesmentioning

confidence: 99%

Determinants of Qualified Investor Sentiment during the COVID-19 Pandemic in North America, Asia, and Europe

Reis

2022

Economies

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This work delineates the factors determining investor sentiment in specific regions during the pandemic and the influence of attitudes towards vaccination. The findings show that the reactions of knowledgeable investors in different regions to the economic effects of the pandemic were not uniform but depended on a variety of individual factors. Risk perception varied widely due to idiosyncrasies in specific countries and regions, the level of pandemic information, reaction to case reports and deaths, attitudes towards vaccination, lockdown compliance, and government measures to support businesses. These various elements combined to create different outlooks in the minds of investors that strongly influenced their investment strategies. For this investigation, we tested three estimation models: the classic robust standard error for time series regression, the new robust standard errors regression, and the Prais robust estimation. This study applied the lasso system of machine learning to select relevant explanatory variables. The novelty of our work resides in its analysis of the conduct of informed investors, using a reliable proxy, and the discussion of how government policies and different pandemic-related factors, specifically the vaccination status, affected investor sentiment in different regions. As for practical implications, an understanding of how the various economic factors related to the pandemic influenced the behavior of qualified investors in different regions can help regulators, government leaders, fund managers, and investors deal with a future virus outbreak.

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