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The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market.

The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated.

The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton.

To the best of the authors’ knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA.

The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.

This chapter examines how foreign banks respond to domestic monetary policy in host countries during crisis periods, in particular, the response shown toward the Asian financial crisis of 1997–1999 and the global financial crisis of 2008–2009. By observing 283 domestic and foreign banks in seven emerging Asian economies, we find that foreign banks are slower than domestic banks in adjusting the growth of their loans to changes in host monetary policy. This inertia by foreign banks is found to be more pronounced in the recent 2008–2009 global crisis than in the 1997–1999 Asian regional crisis, suggesting that the buffering/hampering effects of foreign banks on the effectiveness of the domestic monetary policy transmission mechanism become stronger in a recent global crisis originating from outside Asia than a regional crisis imploded within Asia a decade earlier. We also find that foreign banks’ lower sensitivity than domestic banks to host monetary policy during the crisis periods is heterogeneous, depending on factors such as the extent of the adverse impact of crises on parent banks, the scope of business operation by parent banks, and foreign banks’ mode of entry into host banking markets.

This paper examines the predictive content of financial variables above and beyond past GDP growth in a small open economy in the Eurozone. We aim to clarify potential differences in forecasting economic activity during periods of steady growth and economic turbulence.

The out-of-sample forecasting analysis is conducted recursively for two different time periods: the steady growth period from 2004:1 to 2007:4 and the financial crisis period from 2008:1 to 2011:2.

Our results from Finland suggest that the proper choice of forecasting variables relates to general economic conditions. During steady economic growth, the preferable financial indicator is the short-term interest rate combined with past growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth.

The results highlight the importance of long-term interest rates in determining the level of the term spread when the central bank implements a zero interest rate policy. Moreover, during economic turbulence, stock markets are able to signal the expected effects of unconventional monetary policy on GDP growth.

Finance continuously evolves as the technological innovation progresses in the society. Numerous prior studies have discussed emerging financial services due to this innovation. However, limited scholarly work has evaluated the trends and state of the art of financial innovation. Therefore, this study aims to review recent literature on financial innovation by using a bibliometric and content-analysis approach.

Documents for this study are sampled from financial innovation, a journal focusing on recent innovations in finance. A total of 354 peer-reviewed articles published in eight years (2015–2022) are first examined and mapped using the bibliometrix package in RStudio software. Furthermore, content analysis was performed to investigate the adopted research methods and types, and produce directions for future studies.

The trend of financial innovation research kept increasing, with China as the leader in publication quantity, affiliation productivity and paper citation acquisition. Topics related to “FinTech,” “Bitcoin” and “Covid-19” have been the most discussed topics by financial innovation researchers. FinTech and Bitcoin studies are expected to grow in emerging countries like China, India and Pakistan. The study also indicates that most financial innovation studies use quantitative research methods and are categorized as empirical papers.

This study contributes to the finance literature by comprehensively evaluating current research on financial innovation using one specific journal in the field. Also, this study examines financial innovation literature using different approaches from previous bibliometric financial innovation studies.

It has often been alleged that the financial markets, with all their speculative excesses, wastefully absorb resources that could be better employed in the real economy. Fritz Machlup, originally a student of Ludwig von Mises, dealt with that charge in the aftermath of the 1929 crash. His defense of the stock market remains germane to our time. In it, he argues that the stock exchange offers an important alternative mechanism of allocating savings to investment, while generally being a way station through which money travels on its way to the real economy either to finance capital projects or to be spent on consumer goods. To the extent the stock market ever absorbs capital, it is only during stock market booms. Yet these are generated by the uncertain course of central bank monetary expansion. Bull and bear markets cycles are, at bottom, politically driven events.

The aim of this research is to re-examine the dynamic linkages between macroeconomic variables and the stock market indices in Malaysia following some transformational changes in the policies and the exchange rate regime.

Using monthly data points for all the economic variables and the stock market index (KLCI Index), the authors applied vector autoregression (VAR) model to examine the relationship. The authors also used impulse response function (IRF) in order to explore the effect of one-unit shock in “X” on “Y” under the VAR environment.

The authors' study finds a significant relationship between all the macroeconomic variables and the stock market index of Malaysia. The cointegration results indicate a long-term relationship, whereas the vector autoregressive-based impulse response analysis suggests that the Malaysian stock index (KLCI) responds negatively to the money supply, inflation and producer price index (PPI). However, the authors' results indicate a positive response from the stock index to the exchange rate.

The authors' study's results are based on selected macroeconomic variables and the VAR model. Researchers may find other variables and methods more useful and may provide findings accordingly.

Since the results are quite asymmetric, it would be interesting for the market players, policymakers and regulators to consider the findings and explore appropriate opportunities.

While the relationship between macroeconomic variables and stock market indices has been widely examined, a significant gap in the literature remains concerning the role of exchange rate variable on the stock market in an emerging economy context.

This study investigates the impact of financial development, measured by the ratio of broad money to gross domestic products, on de jure central bank (CB) independence (CBI) in 17 countries in the Asia–Pacific region from 1995 to 2014.

This study uses the feasible generalized least squares (FGLS) approach, which is suitable since the CBI equation suffers from contemporaneous correlation, serial correlation and heteroscedasticity.

The FGLS results suggest a positive association between CBI and financial market development (FMD). This relationship is confirmed when estimating different indicators of de jure CBI and adopting the panel-corrected standard error estimate. However, the statistical significance of FMD is not supported when the ratio of domestic credit to the private sector to GDP is measured.

It is significant to have a developed financial system to foster a better CBI. Moreover, it is important to measure the influence of financial market players on the operations of a CB.

The financial market in the Asia–Pacific has improved over the years. Hence, the results show the determinants of CBI in the Asia–Pacific, especially the role of FMD.

This study aims to explore the nexus between insurance penetration and economic development in Vietnam, one of the fastest-growing economies over the past two decades.

This study uses an updated data set of the insurance sector in Vietnam from 1996 to 2020. The autoregressive lagging distribution and cointegrating non-linear autoregressive lagging distribution (NARDL) models are used to explore the nexus between the insurance market development and economic growth.

This study confirms the unidirectional causality and positive impacts of insurance market development on economic growth both in the short and long term, supporting the “supply-leading” hypothesis. Nonlife insurance has more significant but slower impacts on contributing to economic development in the long run. From the NARDL approach, this study also discloses the asymmetric relationship between the insurance industry and economic growth. Aggregate and life insurance display short- and long-term asymmetric impacts, whereas nonlife insurance shows long-term asymmetry.

To the best of the authors’ knowledge, this is the first study to examine the hidden asymmetries of the insurance-growth nexus in Vietnam from non-linear models. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for insurance businesses, policymakers and investors.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.