Equity Risk and Return across Hidden Market Regimes

The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations.

The prohibition of speculation in Islamic finance: fairness and framing

Purpose This paper aims to present a simple behavioural explanation of the prohibition of speculation in Islamic finance. Design/methodology/approach This paper proposes a theoretical model that describes how investors from low income strata of the society may be prone to make sub-optimal decisions when they compare their outcome from a speculative trading activity to that of the counterparty to the trade and perceive inequity to exist. Findings When individuals from low income strata of the society compare their current situation with the average income of the society, they perceive themselves to be in a loss. This creates a loss frame within which they then evaluate all future outcomes. When such individuals invest in speculative trading activities and incur a loss, they compare their outcome from the trade to that of the counterparty to the trade. As speculative trades are a zero sum game, the counterparty makes an equivalent gain from the trade. Thus, the comparison leads to a perception of inequity. This perception of inequity is aggravated by the loss frame within which the investor is operating. The aggravated inequity aversion may then motivate the investor to make further sub-optimal decisions like repeated speculative trading activities. The Islamic prohibition on speculative trading activities may serve to protect low income investors from entering into such cycles of sub-optimal decisions. Originality/value This paper offers a unique explanation of why day trading and short selling may be prohibited in Islamic capital markets.

Gambling-Like Day Trading During the COVID-19 Pandemic – Need for Research on a Pandemic-Related Risk of Indebtedness and Mental Health Impact

Stock exchange trading increasingly has been highlighted as a possible cause of gambling disorder, typically in rapid and excessive “day trading” which may cause over-indebtedness and mental health problems. The COVID-19 pandemic has been suspected to increase online gambling and gambling problems. In a number of recent media reports, day trading has been reported to increase during COVID-19, possibly in relation to changes in everyday life, financial problems and job insecurity during the pandemic. Increasing day trading has thereby been suspected to cause addictive behavior, financial difficulties, and poor mental health. However, there is hitherto a lack of research in the area. The present paper addresses the potential for day trading to cause problem gambling, debts and mental health problems, and calls for research and clinical guidelines in problem gambling related to stock market behavior as a problematic gambling behavior. Screening tools, awareness among clinicians, and longitudinal research studies may be warranted, both during the COVID-19 pandemic and beyond.

Are the shareholding and trading behaviors of diverse investors affected by the relaxation of day trading?

The day trading in Taiwanese stock market expands considerably at the beginning of 2016, which increases the transactions of stocks consequently and sparks our interest in exploring the issue of day trading. In this study, we use the data of Taiwan Stock Exchange listed firms to investigate whether the day trading volume over total trading volume (hereinafter referred to as the day trading ratio) and the turnover ratio enhanced by the increase of day trading volume would affect the shareholding and trading behaviors of diverse institutional and individual investors. Unquestionably, we bring out several impressive findings. First, foreign institutional investors would not prefer holding or trading the stocks with high day trading ratios, whereas individual investors would prefer holding these kinds of stocks. We infer that this finding might result from the fundamental and the speculative concerns of these various investors. Second, domestic institutional investors and security dealers would prefer trading the stocks with high turnover ratios, but foreign institutional investors still lack of interest in trading these stocks, implying that the investment strategies would be dissimilar among various institutional investors. Since foreign institutional investors are regarded as the successful institutional investors in Taiwan, we argue that our revealed results may help market participants trace the behaviors of diverse investors, especially the foreign institutional investors, after day trading relaxation in Taiwan.

Half-day trading and spillovers

This paper investigates the linkage of returns and volatilities between the United States and Chinese stock markets from January 2010 to March 2020. We use the dynamic conditional correlation (DCC) and asymmetric Baba–Engle–Kraft–Kroner (BEKK) GARCH models to calculate the time-varying correlations of these two markets and examine the return and volatility spillover effects between these two markets. The empirical results show that there are only unidirectional return spillovers from the U.S. stock market to the Chinese stock market. The U.S. stock market has a consistently positive spillover to China’s next day’s morning trading, but its impact on China’s next day’s afternoon trading appears to be insignificant. This finding implies that information in the U.S. stock market impacts the performance of the Chinese stock market differently in distinct semi-day trading. Moreover, with respect to the volatility, there are significant bidirectional spillover effects between these two markets.

An indicator for month-trading of stocks

Some individual investors who find day-trading of stocks incompatible with their lifestyle might want to adopt month–trading instead. In this paper, we use the historical monthly share prices to construct an indicator to guide us in trading a portfolio of two stocks in the Malaysian stock market on a monthly basis. Apart from helping us in the selection of the two stocks, the indicator also provides guidance on the choice of the weight of each stock in the portfolio and the determination of the time to invest in the portfolio.

Algorithmic Trading Bot

Algorithmic trading uses algorithms that follow a trend and defined set of instructions to perform a trade. The trade can generate revenue at an inhuman and enhanced speed and frequency. The characterized sets of trading guidelines that are passed on to the program are reliant upon timing, value, amount, or any mathematical model. Aside from profitable openings for the trader, algo-trading renders the market more liquid and trading more precise by precluding the effect of human feelings on trading. Our project aims to further this revolution in the markets of tomorrow by providing an effective and efficient solution to overcome the drawbacks faced due to manual trading by building an Algorithmic Trading Bot which will automatically trade user strategies alongside its own algorithms for day-to-day trading based on different market conditions and user approach ,and throughout the course of the day invest and trade with continuous modifications to ensure the best trade turnover for the day while reducing the transaction cost, hence enabling huge profits for concerned users be it Organizations or individuals.