Forecasting GDP Growth Considering Crisis Shocks Based on Business Survey Results

The article analyzes the short-term effects of aggregate economic sentiment on the expected GDP growth in Russia based on the results of regular large-scale surveys of business activity of the Federal State Statistics Service (Rosstat) for the period 1998–2021. The main purpose of the study is to substantiate the predictive value of the opinions of economic agents in expanding macroeconomic information, especially during crisis periods. The authors aggregate quarterly information for the analyzed period on 18 indicators of surveys with a sample of about 24,000 organizations in basic kinds of economic activity and 5,000 consumers in all Russian regions in a composite economic sentiment indicator (ESI). Then, a statistical analysis of the time series of ESI and GDP growth is carried out, including the identifcation of the integrability order with testing for stationarity and the presence of causality between indicators. The authors prove the possibility of using a vector autoregression (VAR) model with dummy variables to measure the investigated relationship.The forecasting results reflect the interconnection of two time series with the response in the dynamics of the estimated variable (GDP growth) to the reaction of the business environment and the simulation of fluctuations in the ESI dynamics, which are set by the authors and correspond to the expected economic sentiments amid possible crisis changes. Probabilistic estimates of GDP growth until mid-2022 are based on scenario impulses in the ESI dynamics at the 3rd quarter of 2021, which differ in the amplitude and duration of their impact on economic growth, primarily due to coronavirus shocks. According to the results, under all scenarios for the development of business trends introduced by the authors, national economic growth can exceed by the middle of 2022 the pre-pandemic level of the 4th quarter of 2019 (102,9%).

Did Politicians Use Non-Public Macroeconomic Information in Their Stock Trades? Evidence from the STOCK Act of 2012

Existing research shows that members of Congress made informed trades prior to the passage of the STOCK Act of 2012. There is also evidence in the literature to suggest that the STOCK Act was able to deter politicians from trading based on non-public information. However, the question of whether politicians made informed trades at the market level (using non-public macroeconomic information, not just firm-specific information) in the first place and whether they continued to do so even after the passage of the STOCK Act remains unexamined. We analyze 101,191 individual stock transactions covering the 2004–2014 period and find that the STOCK Act adversely affected the ability of politicians’ aggregated stock trades to predict the stock market returns. Our results imply that politicians used non-public macroeconomic information prior to the STOCK Act, and this legislation was influential in deterring politicians from using non-public macroeconomic information in their stock trades. Our findings also provide input on the current debate on the need for the STOCK Act 2.0.

Aggregate congressional trading and stock market returns

Purpose What information do members of Congress (politicians) use when they trade stocks? The purpose of this paper is to attempt to answer this question by investigating the relationship between an aggregate measure of trading by members of Congress (aggregate congressional trading) and future stock market returns. Design/methodology/approach The authors follow the empirical framework used in academic work on corporate insiders. In particular, they aggregate 61,998 common stock transactions by politicians over the 2004–2010 period and estimate time series regressions at a monthly frequency with heteroskedasticity and autocorrelation robust t-statistics. Findings The authors find that aggregate congressional trading predicts future stock market returns, suggesting that politicians use economy-wide (i.e. macroeconomic) information in their stock trades. The authors also present evidence that aggregate congressional trading is related to the growth rate of industrial production, suggesting that industrial production serves as a potential channel through which aggregate congressional trading predicts future stock market returns. Originality/value To the best of the authors’ knowledge, this study is the first to document a relationship between aggregate congressional trading and stock market returns. The media and scholarly attention on politicians’ trades have mostly focused on the question of whether politicians have superior information on individual firms. The results from this study suggest that politicians’ informational advantage may go beyond individual firms such that they potentially have superior information on the overall trajectory of the economy as well.

Modeling Recovery Rates of Small- and Medium-Sized Entities in the US

A sound statistical model for recovery rates is required for various applications in quantitative risk management, with the computation of capital requirements for loan portfolios as one important example. We compare different models for predicting the recovery rate on borrower level including linear and quantile regressions, decision trees, neural networks, and mixture regression models. We fit and apply these models on the worldwide largest loss and recovery data set for commercial loans provided by GCD, where we focus on small- and medium-sized entities in the US. Additionally, we include macroeconomic information via a predictive Crisis Indicator or Crisis Probability indicating whether economic downturn scenarios are expected within the time of resolution. The horserace is won by the mixture regression model which regresses the densities as well as the probabilities that an observation belongs to a certain component.

Stock prices and macroeconomic information in Ghana: speed of adjustment and bi-causality analysis

PurposeThe purpose of the paper is to investigate the causal relationships and speed of adjustment of stock prices to changes in macroeconomic information (MEI) in Ghana from 1996 to 2018 using monthly data. The paper seeks to conduct the investigation at individual MEI level rather than the composite MEI.Design/methodology/approachQuantitative approach was used in this paper. Monthly data span of 1996–2018 was used. The delay and half-life technique was used to determine the speed with which the information resulting from the changes in the macroeconomic are evident in the stock price. Thereafter, Toda–Yamamoto Granger no-causality approach was used to examine the causal relationship amongst variables.FindingsThe paper revealed that although the market adjustment to MEI has improved, the speed is till slow. The exchange rate exhibited the slowest speed in respect of the market reaction while the market reaction to money supply was the fastest. Toda–Yamamoto Granger no-causality estimation also revealed a bi-directional causality between MEI (gross domestic product, interest rate and money supply) and stock price and uni-directional relationship flowing from MEI (the exchange rate and foreign direct investment) to stock price. The paper also found no causality between inflation and stock price.Research limitations/implicationsThe findings although revealed improved level of market efficiency in comparison with the earlier data, the speed of adjustment is still undesirable. Rigorous approach should be adopted for the implementation of major reforms such as alternative market so as to increase the number of share listing and to increase the scope of investors' participation to enhancing trading volume and marketability and ultimately speed up information diffusion.Practical implicationsThe practical implication of the low level of information processing rate of Ghana Stock Exchange (averagely more than a month) is that astute investors and market analysts could employ MEI to outperform the market prior to their infusion onto the stock market.Originality/valueThis study is one of the few studies in the Ghanaian literature that has extended the investigation of the speed of adjustment beyond composite or aggregate macroeconomic level estimation to estimation at individual variable level. This contribution is very relevant since each macroeconomic variable has unique characteristics and require specific policy framework, it is important to consider the speed of adjustment from the perspective of each of the individual variables.

Another Look at the Macroeconomic Information Content of Aggregate Earnings: Evidence from the Labor Market

We examine the macroeconomic information content of aggregate earnings from the labor market's perspective. We use insights from the labor economics literature to characterize the information contained in aggregate GAAP earnings and its components that is relevant for predicting aggregate job creation and destruction. Our results suggest that not only does aggregate earnings news convey information about future labor market aggregates, but its information content is incremental to other macroeconomic variables at near-term horizons. Further, the source of this information stems primarily from two earnings components: aggregate core earnings and special items. Shocks to core earnings signal persistent changes in economy-wide profitability that predict aggregate job creation up to four quarters ahead, while shocks to special items predict job destruction up to one quarter. Taken together, our results suggest that aggregate earnings contain useful information about future labor market conditions, with the nature of such information varying across earnings components.