Product market power,industry structure,and corporate earnings management

This is the first study to establish a link between product market power of firms and the degree of earnings management. We hypothesize and document a significant and robust association between (a) a firm’s product market pricing power and its degree of earnings management, and (b) industry competitiveness and the degree of earnings management in the industry. Our study reveals that firms with inferior product market pricing power engage in greater discretionary earnings accruals, adding a new dimension to our understanding of the transparency and informativeness of firms’ financial statements. These findings are mirrored at the industry level where we document that more competitive industries are associated with greater earnings manipulation. The empirical evidence has direct implication on the informativeness and earnings quality of firms based on their product market power and competitiveness.     

Cross‐Section of Expected Returns and Extreme Returns: The Role of Investor Attention and Risk Preferences

Previous work finds a negative and significant relation between the maximum daily return over the past one month and expected future stock returns. We determine that this effect is more pronounced for stocks that achieve their maximum daily returns toward the end of the month and stocks that are associated with capital losses show greater reversals. These results suggest the effect is related to investor attention and risk preferences.     

Political regimes and financial crises

This study investigates how democratic or autocratic regimes, their transitions, and durations, affect the probability of experiencing financial crises. The empirical strategy employs novel instruments and Lewbel's method to address potential endogeneity concerns. The results reveal that democratic transition reduces the probability of crises by around seven percent, whereas autocratic switchovers enhance the crises likelihood by twelve percent. The findings remain robust in presence of a number of heterogeneity checks, with alternative measures of crises indicators, and employing different proxy for democratic transition. Thus, institutions in transition countries could play important role in managing such financial upheavals.     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.

     

The role of investor attention in idiosyncratic volatility puzzle and new results

Review of Quantitative Finance and Accounting - We find that stocks with low investor attention show a more substantial return-idiosyncratic volatility puzzle than stocks with high investor...     

Can short sellers constrain aggressive non-GAAP reporting?

Review of Accounting Studies - Prior research finds an association between short selling volume and aggressive non-GAAP earnings disclosures but does not explore whether increased short selling...