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Purpose: Investors and portfolio managers can earn profitably when they correctly predict when stock prices will go up or down. For this reason, it is crucial to know the effect levels of the factors that affect stock prices. In addition to macroeconomic factors, the psychological behavior of investors also affects stock prices. Therefore, the study aims to reveal the different sensitivity levels of the stock index against macroeconomic and psychological factors.

Design/Methodology/Approach: In this study, dollar rate (USD), euro rate (EURO), time deposit interest rate (IR), gold price (GOLD), industrial production index (IPI), and consumer price index (CPI) (inflation (INF)) were used as macroeconomic factors, while Consumer Confidence Index (CCI) and VIX Fear Index (VIX) were used as psychological factors. In addition, the BIST-100 index, which is listed in Borsa Istanbul, was used as the stock index. The sensitivity of the stock index to macroeconomic and psychological factors was investigated using the Multivariate Adaptive Regression Spline (MARS) method using data from January 2012 to October 2020.

Findings: In the analyses performed using the MARS method, the coefficients of INF, USD, EURO, IR, CCI, and VIX Index were found to be statistically significant and effective on the stock index. Among these variables, INF has the highest effect on stocks. It is followed by USD, IR, EURO, CCI, and VIX. GOLD and IPI variables did not show statistical significance in the model. The most important difference of the MARS model from other regressions is that each factor’s effect on the stock index is analyzed by separating it according to the value of the factor. According to the results obtained from the MARS model: (1) it has been determined that USD, EURO, IR, and CPI have both positive and negative effects on the stock market index and (2) CCI and VIX have been found to have negative effects on stocks. These results provide essential information about how investors who plan to invest in the stock index should take into consideration different macroeconomic and psychological values.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the effects of factors affecting the stock index by decomposing it according to the values it takes. Also, this study provides additional information by listing the factors affecting the stock index in order of importance. These results will help investors, portfolio managers, company executives, and policy-makers understand the stock markets.

This study aimed to know the effect of cross-sectional risk, which comprises business-specific risk and stock market volatility, as a variable for estimating macroeconomic risk in Indonesia. This study observes public companies in Indonesia and Indonesian macroeconomic data from 2004 to 2020. In this study, the author uses term spread as the dependent variable that reflects macroeconomic risk. The cross-sectional risk comprises financial friction (FF), cash flow (CF), debt–service ratio, and stock market volatility as independent variables. By using the Autoregressive Distributed Lag (ARDL) Model method, this study shows that business-specific and stock market risk can estimate macroeconomic risk, so that it becomes an early signal of economic shock, such as recession or high inflation, in the future. The model in this study also examines the cross-sectional risk relationship with other macroeconomic indicators, such as the Consumer Confidence Index (CCI), money supply (M0), and Indonesia’s trade balance (TB).

This paper examines the profitability and performance measurement of U.S. regional banks during the period 1994–2011, using the GMM estimator technique. Our study extends prior research by including several factors not previously considered using U.S. data.

We use bank-specific, industry-specific, and macroeconomic determinants of profitability contemporaneous with our performance indicators. We follow the accounting fundamental analysis path in explaining the bank performance.

Among the performance measures, the efficiency ratio and provisions for credit losses are negatively and equity scaled by assets is positively related to profitability. However, these relationships either reverse (efficiency ratio and provisions for credit losses) or become insignificant (equity scaled by assets) when the target becomes change in profitability. The level of nonperforming assets is negatively related to profitability across all measures of profitability used. Macroeconomic variables are largely unrelated to profitability during the year they are measured. However, they have a significant relationship with earnings change measures, suggesting they have a lagged effect on profitability. The slope of the yield curve is especially strong in this regard.

We use our determinants to model changes in bank profitability one year ahead, in addition to including several factors not previously considered, using the predictive focus of the fundamental analysis research.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.

Introduction: The credit default swap (CDS) represents a country’s credit risk premium. CDS premium changes by being affected by several factors. These changes are followed by international investor for their investment decisions. CDS premium is important for country to determine the country default risk correctly. Purpose: In this study, the authors seek to examine the effects of macroeconomic indicators on the CDS premium, which is used as a measure of sovereign credit risk. Accordingly, in addition to the CDS premium, economic growth, the inflation rate, the interest rate, the real exchange rate, the net foreign debt rate, and the foreign trade deficit rate were employed to represent macroeconomic indicators. Methodology: The relationship between the given variables during the period spanning from 2009:I–2019:II in Turkey was analyzed with the help of the Dolado–Lütkepohl causality test and the autoregressive distributed lag method. Findings: The inflation rate, the real exchange rate, the interest rate, the net foreign debt rate, and the foreign trade deficit rate, which are among the macroeconomic variables (excluding economic growth), have a positive effect on the CDS premium in the short term as well as the long term. The effect of economic growth is negative. Additionally, from an economic standpoint, the coefficients of macroeconomic variables are in the expected direction. These findings verify the effects of macroeconomic indicators on the CDS premium.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

This chapter aims at examining financial distress issue by designing a comprehensive model to explain and predict financial distress in Egypt. This comprehensive model incorporates accounting ratios, market-based ratios and macroeconomic ratios. The sample of the existing research includes all the listed firms in two main sectors: basic resources and chemicals. Using logistic regression model, the results showed that adding market ratios and macroeconomic ratios enhances the predictability of the model and accounting information are not sufficient to explain financial distress.

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.

The main objective of this study is to identify the main determinants of the Islamic banks’ performance in Gulf Cooperation Council (GCC) regions.

The research uses both static model (fixed effects and random effects) and Generalized method of Moments (GMM). The data for this study are obtained from the annual reports of 29 Islamic banks from GCC countries using Bankscope database for the period from 2005 to 2013.

The empirical findings reveal that Islamic banks’ specific factors such as the equity financing and bank size are positive and statistically significant to the profitability of Islamic banks. The operating efficiency ratio is negatively and statistically significant to return on asset. It is also found that macroeconomic variables such as money supply and inflation are negatively and statistically significant to the performance of Islamic banks whereas oil price has been found positive and statistically significant to the performance of Islamic banks in the GCC region.

The present study seeks to fill a demanding gap in the literature by providing new empirical evidence on the factors that influence the profitability of the Islamic banking sector in GCC regions.

These findings have significant contribution to the literature by comprehensively clarifying and critically analyzing the current state of profitability among the Islamic banks in GCC regions.

Banking sectors in central, eastern and southeastern European (CESEE) countries have gone through a transformation from state-ownership and central planning to private ownership and market-oriented decision making during the first decade of the 21st century. However, financial markets in these countries are still developing and the private sector is highly exposed to changes in exchange rates, especially in terms of the balance sheet channel. The fact that these banking sectors are predominantly owned by eurozone banks makes them vulnerable to macroeconomic tensions in the European union. This analysis investigates macroeconomic determinants of the realisation of credit risk in the loan portfolio of banks in Serbia using a panel data set covering the period from 2008Q3 to 2012Q2. Three different panel methods were applied separately for loans to households and loans to enterprises. The results indicate that a deteriorating business cycle and exchange rate depreciation led to the worsening of the quality of banks’ loan portfolio in Serbia in the period under review. In addition, statistical evidence indicates that the CPI inflation additionally affected the quality of loans. Furthermore, we find that household loan portfolios are also sensitive to changes in the short-run interest rates. As for policy implications, the importance of international cooperation between regulators is rising. A very important topic for such cooperation should be the risk-taking channel between countries with significant differences in interest rates and degree of riskiness. The interrelationship between the exchange rate and credit risk should be a major focus of both domestic macro- and micro-prudential policy – banks should be motivated to pay more attention to the possible negative spillovers when making credit decisions. Also, further development of the domestic primary and secondary T-bills market would help reducing unhedged FX risks.