We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.

No abstract

No abstract

No abstract

Using panel regression estimates from the IMF's CPIS survey of foreign debt and equity portfolios across 174 originating and 50 destination countries from 2001 to 2007, we clarify the role of culture and extend the set of cultural variables that have been investigated in gravity models of foreign portfolio investment (FPI). Incorporating Hofstede's cultural dimensions of individualism, masculinity, power distance and uncertainty avoidance, we show how cultural traits in both originating and destination countries, as well as the cultural distances that separate them, interact with geographic distance and other gravity variables to determine global FPI patterns. We find hitherto unreported effects and show that while gravity always deters FPI, aspects of culture and cultural distance can offset this by supporting FPI.JEL classification: F21; F23 ; F37 ; G15

We construct a series of 3-, 4and 5-variable multivariate GARCH models of exchange rate volatility transmission across the important European Monetary System (EMS) currencies including the French franc, the German mark, the Italian lira, and the European Currency Unit. The models are estimated without imposing the common restriction of constant correlation on both daily and weekly data from April 1979-March 1997. Our results indicate the importance of checking for specification robustness in multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling, we find that increased temporal aggregation reduces observed volatility transmission, and that the mark plays a dominant position in terms of volatility transmission.

Some rights reserved. For more information, please see the item record link above.

The paper examines the extent to which the conditional volatility of stock market returns in a small, internationally integrated stock market are related to the conditional volatility of financial and business cycle variables. It employs a low frequency monthly dataset for Australia including stock market returns, interest rates, inflation, the money supply, industrial production and the current account deficit over the period from July 1972 to January 1994. A novel feature of the analysis is the estimation strategy employed to overcome the generated regressors problem which pervades some recent related research. Specifically, the procedure of employing a two-stage estimation process to first estimate conditional volatilities and then model their interrelationships yields inefficient estimates, introduces bias into a number of diagnostic test statistics and generates potentially invalid inferences. This problem is overcome in the current paper by jointly estimating the equation for the conditional volatility of stock market returns together with the equations determining the conditional volatilities of all variables included in the model using the generalized least squares (GLS) estimation procedure together with the Hendry general-to-specific modelling strategy. Among the most important determinants of the conditional volatility of the Australian stock market are found to be the conditional volatilities of inflation and interest rates which are directly associated with stock market volatility, and the conditional volatilities of industrial production, the current account deficit and the money supply which are indirectly associated with stock market conditional volatility. Among these variables, the strongest effect is found to be from the conditional volatility of the money supply to the conditional volatility of the stock market. By contrast, no evidence is found of volatility spillover from the foreign exchange market to the stock market in Australia.