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on International Finance |
| By: | Isha Agarwal; Wentong Chen; Eswar S. Prasad |
| Abstract: | We provide the first empirical evidence on how media-driven narratives influence cross-border institutional investment flows. Applying natural language processing techniques to one-and-a-half million newspaper articles, we document substantial cross-country variation in sentiment and risk indices constructed from domestic media narratives about China in 15 countries. These narratives significantly affect portfolio flows, even after controlling for macroeconomic and financial fundamentals. This impact is smaller for investors with greater familiarity or private information about China and larger during periods of heightened uncertainty. Political and environmental narratives are as influential as economic narratives. Investors react more sharply to negative narratives than positive ones. |
| JEL: | F30 G11 G15 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33159 |
| By: | Joshua Aizenman; Jamel Saadaoui |
| Abstract: | This paper is a case study of the exchange rate adjustments during the first week following the swapping US election results. We compute three measures of exchange rate depreciation: the maximum depreciation during the 1st trading day after November 6 UTC 0:00 to capture the reaction on the FOREX immediately after the news for our sample of 73 currencies against the USD, practically all currencies depreciated sharply at the news. Second, the depreciation after 4 days to capture the reaction of monetary authorities and the global markets to the news; third, the depreciation 1 week after the shock to observe whether some countries have experienced a further depreciation or a return to the pre-shock exchange rate level. In 26 countries out of a sample of 73 bilateral exchange rates against the US Dollar, the depreciation after 1 week was even more pronounced than just after the election. We also find that the correlation between the depreciation rate after a week from the initial news and the ICRG institutional score is positive and significant at the 1 percent level. A multivariate regression for exchange rate movements indicates that after a week, the bilateral trade surplus with the US, and better institutional scores are associated with stronger depreciations. Exchange rate interventions have helped to stabilize the currencies at all time horizons. The exposure to policy changes, measured by EIU’s Trump Risk Index seems to be at play after 4 days. |
| JEL: | F01 F31 F36 F4 F40 F42 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33193 |
| By: | Marcos Cardozo; Yaroslav Rosokha; Cathy Zhang |
| Abstract: | We integrate theory and experimental evidence to study the emergence of different international monetary arrangements based on the circulation of two intrinsically worthless fiat currencies as media of exchange. Our framework is based on a two-country, two-currency search model where the value of each currency is jointly determined by private agents’ decisions and monetary policy formalized as changes in a country’s money growth rate. Results from the experiments indicate subjects coordinate on a regime where both currencies are accepted even when other regimes are theoretically possible. At the same time, we find the acceptance of foreign currency depends on relative inflation rates where sellers tend to reject payment with a more inflationary foreign currency. We also document the presence of learning in shaping acceptance patterns over time. |
| Keywords: | international currency, monetary policy, inflation, experimental macroeconomics |
| JEL: | C92 D83 E40 |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:pur:prukra:1351 |
| By: | Zhuohuan Hu; Fu Lei; Yuxin Fan; Zong Ke; Ge Shi; Zichao Li |
| Abstract: | In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi-asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing image processing techniques, financial time series data are converted into two-dimensional images to extract high-order features and enhance the accuracy of risk prediction. Through empirical analysis of data from multiple asset classes such as stocks, bonds, commodities, and foreign exchange, the results show that the proposed CNN model significantly outperforms traditional models in terms of prediction accuracy and robustness, especially under extreme market conditions. This research provides a new method for financial risk management, with important theoretical significance and practical value. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.03618 |