Eurasian Economic Review最新文献

Forecasting volatility of certain stocks plays an important role for investors as it allows to quantify associated trading risk and thus make right decisions. This work explores econometric alternatives for time series forecasting, such as the ARIMA and GARCH models, which have been widely used in the financial industry. These techniques have the advantage that training the models does not require high computational cost. To improve predictions obtained from ARIMA, the discrete Fourier transform is used as ARIMA pre-processing, resulting in the wavelet ARIMA strategy. Due to the linear nature of ARIMA, non-linear patterns in the volatility time series cannot be captured. To solve this problem, two hybridisation techniques are proposed, combining wavelet ARIMA and GARCH. The advantage of applying this methodology is associated with the ability of each to capture linear and non-linear patterns present in a time series. These two hybridisation techniques are evaluated to verify which provides better prediction. The volatility time series is associated with Tesla stock, which has a highly volatile nature and it is of major interest to many investors today.

We perform a neural network analysis of the impact of Russian retail investors´ sentiment on the stock price behavior of well-known American companies. We study American stocks in a situation of a time-segmentation of the stock market. A special feature of our analysis is the separate time trading mode, when trading is active at the SPB (formerly St. Petersburg) exchange and inactive at the US stock exchanges. Building on the unique local exchange data and original technique for constructing a neural network to identify the sentiment of messages from several Internet forums, we uncover the existence of behavioral anomalies in a non-English-speaking emerging market and analyze sentiment and attention metrics in social networks. We construct several sentiment metrics based on AI text analysis and use panel regression to identify their statistical significance under the selected hypotheses. The impact of sentiment is examined across the entire sample of US companies available to investors on the SPB exchange and a separate zooming is made at the top 10, 25, 50, and 100 stocks that are under special interest manifested by volume of discussions and trading volume. We also analyze the impact of sentiment on price reaction for individual popular stocks and by industry. We find that retail investors’ sentiment exercises a statistically significant influence on price spikes. The stocks, most sensitive to sentiment, are healthcare and high tech.

The purpose of this paper is to evaluate the efficiency of Active Labor Market Policies (ALMP) expenditures of OECD countries taking into account the institutional quality of governance. The emphasis is on the quality of governance that affects the supply and demand of employment and the ability of governments to master public spending. Using the general method of moments (GMM) for 31 OECD countries over the 2000/2020 period, we show that measures of institutional quality such as Control of Corruption, the Effectiveness of Governance and the Regulatory Quality affects significantly the efficiency of ALMP on unemployment rate. This result is found consistent using Institutional Quality Index ‘IQI’. The influence of institutional quality on the link between ALMP expenditure and unemployment rate suggest the importance for policy makers to enhance institutional quality. They consider it as a possible instrument for improvement results in labor market of OECD countries.

The paper examines the effectiveness of macroprudential policy in Indonesia and policy reactions to economic developments. Using the structural vector autoregression and data on the regulatory LTV ratio, we investigate the policy effectiveness in controlling credit growth and real property prices along with the effects on economic activity. We find that the LTV-based policy in Indonesia is effective in taming credit growth in the medium run. It, however, is not the case with real property prices whose response to policy changes is counterintuitive and resembles the price puzzle found in the studies on monetary policy. Moreover, our results lend moderate support to the effect of LTV policy on economic activity, especially in the non-COVID-19 sample. We also show that the LTV policy in Indonesia is conducted in an active and circumspective way. In a series of robustness checks, we demonstrate that the results hold when the ordering of variables is changed, alternative proxies for macroprudential policy, output gap, and financial conditions are employed, or the sample is limited to the non-COVID-19 period.

While several studies have explored the impact of economic shocks on output volatility, little attention has been given to the role of a country’ productive knowledge in moderating such relationship. In this paper, we raise two unsettled questions: Does productive knowledge reduce the volatility of output growth? and Does it magnify or alleviate the impacts of economic shocks on output volatility? These two questions are answered by applying the system-generalized method of moments to a dataset of 122 countries from 1996 to 2019. The paper shows that high economic complexity is significantly associated with reduced output growth volatility. However, such beneficial effect only occurs in high- and upper middle-income countries. Economic complexity is also found to strongly moderate the relationship between terms of trade shock, inflation shock, and output growth volatility. Economic complexity significantly exacerbates the unfavorable effects of these two shocks on output growth volatility, especially in highly complex economies. The discovered inter-related relationship between economic complexity, shocks, and economic volatility suggests the policymakers to be cautious in the process of transforming their economies towards higher knowledge-intensive ones.

The debate on optimal bank capital and size and their association with bank performance is still ongoing and high on policy makers’ agenda. This study analyzes the impact of capital and size on bank performance for 1053 banks operating in the EU and MENA regions over the period 2009–2020. The results show that capital is an important determinant of bank performance as it is highly significant in both regions; however, banks’ performance in MENA seems to be more responsive to changes in the capital ratios. Further, while bank size has a positive non-linear relationship with profitability in the EU region, it does not seem to have a significant impact in the MENA region. This study contributes to the literature by providing a strong empirical evidence that more capital is associated with higher profitability in regions characterized by different levels of development, contrary to the bankers’ belief that capital is costly. Regulators may find this conclusion to be useful and in line with their objective of boosting bank resilience and performance.