Psychology and society: Singles look for love in a bear market

This paper analyzes the response of sustainable indexes to the pandemic lockdown orders in Europe and the USA, contributing to both the research on the effects of the global pandemic outbreak and the resiliency of sustainable investments under market distress. Our results demonstrate that sustainable indexes were negatively impacted by lockdown orders; however, they did not show statistically significant different abnormal returns compared to traditional indexes. Similarly, our empirical results confirm that sustainable screening strategies (negative, positive, best in class) did not have an influence during such announcements. These results are robust across several model specifications and robustness tests, including nonparametric tests, generalized autoregressive conditionally heteroskedastic (GARCH) estimation of abnormal returns, and alternative events. The findings suggest that investors do not have to pay the price for the investments in sustainable assets when a bear market occurs; consequently, ceteris paribus, these investments appear suitable for financial-first investors. Such results have relevant practical consequences in terms of sustainable investment attractiveness and market growth.

Download Full-text

Equity valuation under Bull and Bear market regimes in South East Asia firms: a switching regression approach

Applied Economics ◽

10.1080/00036840802599966 ◽

2011 ◽

Vol 43 (7) ◽

pp. 837-844 ◽

Cited By ~ 3

Author(s):

James P. Gander

Keyword(s):

East Asia ◽

Equity Valuation ◽

South East Asia ◽

Switching Regression ◽

Bear Market ◽

Regression Approach ◽

Market Regimes

Download Full-text

Conditional stock liquidity premium: is Warsaw stock exchange different?

Studies in Economics and Finance ◽

10.1108/sef-03-2020-0075 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Szymon Stereńczak

Keyword(s):

Emerging Markets ◽

Stock Returns ◽

Stock Exchange ◽

Emerging Market ◽

Market Liquidity ◽

Investment Horizon ◽

Liquidity Premium ◽

Stock Liquidity ◽

Content Type ◽

Bear Market

Purpose This paper aims to empirically indicate the factors influencing stock liquidity premium (i.e. the relationship between liquidity and stock returns) in one of the leading European emerging markets, namely, the Polish one. Design/methodology/approach Various firms’ characteristics and market states are analysed as potentially affecting liquidity premiums in the Polish stock market. Stock returns are regressed on liquidity measures and panel models are used. Liquidity premium has been estimated in various subsamples. Findings The findings vividly contradict the common sense that liquidity premium raises during the periods of stress. Liquidity premium does not increase during bear markets, as investors lengthen the investment horizon when market liquidity decreases. Liquidity premium varies with the firm’s size, book-to-market value and stock risk, but these patterns seem to vanish during a bear market. Originality/value This is one of the first empirical papers considering conditional stock liquidity premium in an emerging market. Using a unique methodological design it is presented that liquidity premium in emerging markets behaves differently than in developed markets.

Download Full-text

Dynamic Evolution of Securities Market Network Structure under Acute Fluctuation Circumstances

Complexity ◽

10.1155/2017/4370203 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11

Author(s):

Haifei Liu ◽

Tingqiang Chen ◽

Zuhan Hu

Keyword(s):

Stock Market ◽

Network Structure ◽

Topological Structure ◽

Securities Market ◽

Structure Stability ◽

Dynamic Evolution ◽

Bear Market ◽

Bull Market ◽

Market Transaction ◽

Measuring Network

This empirical research applies cointegration in the traditional measurement method first to build directed weighted networks in the context of stock market. Then, this method is used to design the indicators and the value simulation for measuring network fluctuation and studying the dynamic evolution mechanism of stock market transaction networks as affected by price fluctuations. Finally, the topological structure and robustness of the network are evaluated. The results show that network structure stability is strong in the bull market stage and weak in the bear market stage. And the convergence rate of the dynamic evolution of network fluctuation is higher in the bull market stage than in the bear market stage.

Download Full-text

Asymmetric Reactions of China’s Stock Market to Short-Term Interest Rates

International Journal of Economics and Finance ◽

10.5539/ijef.v8n5p260 ◽

2016 ◽

Vol 8 (5) ◽

pp. 260 ◽

Cited By ~ 2

Author(s):

Fang Fang ◽

Weijia Dong ◽

Xin Lv

Keyword(s):

Stock Market ◽

Interest Rates ◽

Stock Returns ◽

Regime Switching ◽

Developed Countries ◽

Short Term ◽

Bear Market ◽

Bull Market ◽

Negative Effect ◽

Markov Regime Switching Model

This paper investigates how China’s stock market reacts to short-term interest rates, as represented by the Shanghai Interbank Offered Rate (Shibor). We adopt the Markov Regime Switching model to divide China’s stock market into Medium, Bull and Bear market; and then examine how Shibor influences market returns and risk in different market regimes. We find that short-term interest rates have a significant negative effect on stock returns in Medium and Bull market, but could not affect stock returns in Bear market. In addition, different maturities of Shibor have different effects on stock returns. Furthermore, we find that the short-term interest rates have a negative effect on market risk in Bull market, but a positive effect in Bear market. Our findings show that China’s market is quite peculiar and distinctive from the U.S. market or other developed countries’ markets in many ways.

Download Full-text

Comparative Research on Influencing Factors of LSTM Deep Neural Network in Stock Market Time Series Prediction

Research in Economics and Management ◽

10.22158/rem.v4n1p84 ◽

2019 ◽

Vol 4 (1) ◽

pp. 84

Author(s):

TANG Yin ◽

YANG Jin Yu ◽

CHEN Jian

Keyword(s):

Time Series ◽

Stock Market ◽

Influencing Factors ◽

Network Structure ◽

Time Series Prediction ◽

Structure Optimization ◽

Optimization Techniques ◽

Training Process ◽

Bear Market ◽

Network Structure Optimization

<p><em>During training process of LSTM, the prediction accuracy is affected by a variation of factors, including the selection of training samples, the network structure, the optimization algorithm, and the stock market status. This paper tries to conduct a systematic research on several influencing factors of LSTM training in context of time series prediction. The experiment uses Shanghai and Shenzhen 300 constituent stocks from 2006 to 2017 as samples. The influencing factors of the study include indicator sampling, sample length, network structure, optimization method, and data of the bull and bear market, and this experiment compared the effects of PCA, dropout, and L2 regularization on predict accuracy and efficiency. Indice sampling, number of samples, network structure, optimization techniques, and PCA are found to be have their scope of application. Further, dropout and L2 regularization are found positive to improve the accuracy. The experiments cover most of the factors, however have to be compared by data overseas. This paper is of significance for feature and parameter selection in LSTM training process.</em></p>

Download Full-text

The correlation strength of the most important cryptocurrencies in the bull and bear market

Investment Management and Financial Innovations ◽

10.21511/imfi.17(3).2020.06 ◽

2020 ◽

Vol 17 (3) ◽

pp. 67-81

Author(s):

Sebastian Lahajnar ◽

Alenka Rožanec

Keyword(s):

Correlation Coefficient ◽

Strong Correlation ◽

Pearson Correlation ◽

Pearson Correlation Coefficient ◽

Strongly Correlated ◽

Moderate Correlation ◽

Bear Market ◽

Research Findings ◽

Correlation Strength ◽

Over Time

The article explores the correlation strength of the ten most important cryptocurrencies, emphasizing the examination of differences during the periods of rising and falling prices. The daily and weekly returns of selected cryptocurrencies are taken as the basis for calculating and determining the correlation strength using the Pearson correlation coefficient. The survey covers the period from the beginning of 2017 to Bitcoin’s last local bottom in mid-March 2020. Research findings are as follows: 1) the most important cryptocurrencies are mostly moderately positively correlated with each other over time; 2) correlation strength decreases slightly during the bull period, but mostly remain in the range of moderate correlation; 3) correlation strength increases significantly during the bear period, with most cryptocurrencies strongly correlated with each other. The results do not change significantly if the daily or weekly cryptocurrency returns are used as the basis. A strong correlation in the period of falling prices prevents the effective diversification of the cryptocurrency portfolio, which must be considered when investing funds in the cryptocurrency market.

Download Full-text