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Although some research has been carried out on feedback trading in different asset classes, there have been few empirical investigations that consider both major and emerging stock markets (Koutmos, 1997; Antoniou et al., 2005; Kim, 2009) stock index futures (Salm and Schuppli, 2010). In this study, the author examines positive/negative feedback trading in both developed-emerging-frontier-standalone (51) stock markets for 2010–2020 and sub-periods including COVID-19 period.

The hypothesis “feedback trading behaviour led the price boom/bust in the stock markets during the first quarter of COVID-19 pandemic” is tested by employing the Sentana and Wadhwani (1992) framework and using asymmetrical GARCH models (GJRGARCH, EGARCH) in accordance with the empirical literature.

The following conclusions can be drawn from the present study; (1) There is no evidence to support a significant distinction between developed, emerging, frontier or standalone markets or high/upper middle, lower middle income economies in the case of feedback trading. It is more likely to be a general phenomenon reflecting the outcomes of general human psychology (2) in the long term (2010–2020) based on the feedback trading results Asian stock markets appear to be far from efficiency.

Stock markets are selected based on data availability.

Several inferences can be drawn about overall results. First, investors and portfolio managers should beware of their investment decisions during bearish market conditions where volatility is on the rise and also when there is a strong reaction to bad news/negative shocks in the market. Moreover, investing in Asia stock markets may require more attention since those markets are reputed to be more “idiosyncratic”, less reliant on economic and corporate fundamentals in their pricing. Moreover, the impact of foreign investors on stock market volatility and returns and weaker implementation of regulations also affect the efficiency of the markets (Lipinsky and Ong, 2014).

To the best of the author’s knowledge, most studies in the field of feedback trading in stock markets have only focused on a small sample of countries and second, the effect of COVID-19 uncertainty on the stock markets have not been addressed in the literature with respect to feedback trading. This paper fills these literature gaps. This study is expected to provide useful insights for understanding the instabilities in stock markets particularly under conditions of high uncertainty and to fill the gap in the literature by comparing the results for a large sample of countries both in the long term and in the pandemic.

Highlights for review

1. This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

2. Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

3. Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

4. In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”