Attention to the Fads and Fashions in the Indian Stock Markets During COVID-19

Do people show fads and fashions in their attention searches? With the Google online search data during COVID-19, particularly from January to May 2020 for the socio-economic keywords, this study examines if online searches show short-run and long-run attention dynamics leading to fads and fashions in attention to the NSE Nifty and BSE Sensex indices. This study employs the methodology of cointegrating relationship with autoregressive distributed lag (ARDL) model and explains investors' attention search dynamics at the 'NSE Nifty Index' and 'BSE Sensex Index' caused by socio-economic attention searches. It also examines if the dynamics of attention coordination are parsimonious in nature and it explores the same with the generalized autoregressive conditional heteroskedastic (GARCH-X) model. With the ARDL models, this study finds robust and unbiased cointegrating impacts of socio-economic attention searches on the attention search for the NSE Nifty index but these are not the best linear unbiased and efficient (BLUE) ones, while the same on the BSE Sensex Index are BLUE. For the NSE Nifty index, the attention dynamics at the GARCH-X specification are BLUE while for the BSE Sensex index, the GARCH-X specification also has some additional information in terms of the ARCH effect only.

Effects of market liquidity on price dynamics in a heterogeneous belief model

To understand the effect of liquidity on asset pricing, this study constructs a boundedly rational asset pricing model, introducing market liquidity and heterogeneous beliefs. Based on our model, we conduct empirical tests using the S&P 500 index from 1991 to 2021 and the CSI 500 index from 2007 to 2021. We find that market liquidity significantly influences investors' expectations and belief switching. When market liquidity is scarce, fundamentalists in both markets expect the price to converge more quickly to its fundamental value, whereas chartists perceive that the price deviates from its fundamental value less rapidly. Lack of liquidity mitigates the investors' original switching strategy, resulting in positive feedback as a net effect. Moreover, the S&P 500 index is efficient, whereas the CSI 500 index is slightly undervalued in the long run. Both markets exhibit large fluctuations and inefficiency during short periods such as the 2008 financial crisis and COVID-19 pandemic. As such, safeguards should be implemented against sudden shocks and the resulting price deviation and market inefficiency.

Effects of market liquidity on price dynamics in a heterogeneous belief model

To understand the effect of liquidity on asset pricing, this study constructs a boundedly rational asset pricing model, introducing market liquidity and heterogeneous beliefs. Based on our model, we conduct empirical tests using the S&P 500 index from 1991 to 2021 and the CSI 500 index from 2007 to 2021. We find that market liquidity significantly influences investors’ expectations and belief switching. When market liquidity is scarce, fundamentalists in both markets expect the price to converge more quickly to its fundamental value, whereas chartists perceive that the price deviates from its fundamental value less rapidly. Lack of liquidity mitigates the investors’ original switching strategy, resulting in positive feedback as a net effect. Moreover, the S&P 500 index is efficient, whereas the CSI 500 index is slightly undervalued in the long run. Both markets exhibit large fluctuations and inefficiency during short periods such as the 2008 financial crisis and COVID-19 pandemic. As such, safeguards should be implemented against sudden shocks and the resulting price deviation and market inefficiency. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Risk premia, asset price bubbles, and monetary policy

We develop a dynamic general equilibrium asset pricing model with heterogeneous beliefs to study the effects of monetary policy on prices, risk premia, asset price bubbles, and financial stability. We propose a new framework for monetary policy with respect to bubbles. Because bubble risk premia arise from an interaction between disagreements among investors and dynamic trading constraints, under a non-accommodative monetary policy, liquidity adjusted risk and bubble risk premia increase. What matters for policy is the trading constrained fraction/mass of agents that disagree about fundamentals (i.e. optimists/pessimists). Accommodative policy can lead to a larger fraction of trading constrained agents that disagree, larger bubbles, and increased systemic risk. An implication of our results is that accommodative monetary policy in response to the Covid-19 crisis does not increase systemic risk due to asset price bubbles, as long as the policy keeps inflation under control.