Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume

This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.

The Impact of Serious Safety Incidents on Airline Stocks

The impact of commercial airplane crashes on the shareholder wealth of US-listed airline stocks has been the focus of many prior studies, but none have explored the concomitant impact on trading volume. We expand the scope of prior studies to include near crashes. We examine 262 ‘incidents’ from 1962 to 2018 (220 with return evidence) and document a significant (negative) wealth impact for crashes with fatalities and casualties, and an insignificant impact for incidents with no casualties. We find that log-transformed trading volume spikes upward in the three-day crash-period window and that trading volume remains abnormally high in the three plus weeks that follow the crash when casualties occur. We interpret the high level of post-event trading to be consistent with a noise trader hypothesis: naïve trading hoping to take advantage of airline stock over-reaction – which we do not detect.

Can the market divide and multiply? A case of 807 percent mispricing

PurposeThis paper documents a strong violation of the law of one price surrounding a large rights issue.Design/methodology/approachIf prices are right, the relation between the prices of shares and rights follows the outcome of a simple calculation.FindingsIn the case of Royal Imtech N.V. in 2014, prices deviated sharply and persistently from the theoretical prediction. Throughout the term of the rights, investors were buying shares at prices that were many times what they should have been given the price of the rights. Short-selling constraints in the form of high recall risk and lacking stock lending supply are the most likely explanation for the failure of arbitrage as a safeguard of market efficiency. Still, it remains remarkable that investors were buying large volumes of shares at highly inflated prices in the presence of a cheap, perfect substitute.Originality/valueThe mispricing was special not just because of its severity but also because unlike previously documented cases there was no fundamental risk and no material noise trader risk.

GARCH in spinor field

We extend generalized autoregressive conditional heteroscedastic (GARCH) errors in the Euclidean plane of the scalar field to the tensor field and to the spinor field [Formula: see text], the so-called spinor garch, S-GARCH. We use the model of S-GARCH to explain the stylized fact in financial time series, the so-called volatility cluster, by using hyperbolic coordinate with induced complex lag of delay time scale in mirror symmetry concept. As the result of this theory, we obtain an equivalent form of Yang–Mills equation for financial time series as the interaction between the behavior of traders, the so-called, fundamentalist, chatlist and noise trader, by using volatility in spinor field with invariant of the gauge group [Formula: see text], the so-called modeling of the financial market in icosahedral supersymmetry gauge group.

NOISE TRADER RISK: EVIDENCE FROM VIETNAM STOCK MARKET

<p><strong>Abstract: </strong>This paper investigates the existence of noise trader risk in Vietnam’s stock market and its effect on the daily returns of stock prices. The methodologies contain the estimation of GARCH (1,1) model to filter the residuals using the moving average method to calculate the impact of information traders. Noise trader risk or the risk that is caused by noise traders is derived by subtracting the residuals by the rational traders’ impact. We find that the noise trader risk does exist in Vietnam’s stock market and its impact on daily returns of stocks is unpredictable. Meanwhile, we find a positive impact of information traders on the stock returns. It increases the daily stock returns, and in turn, helps the market to correct itself because the stock prices move back to its fundamental value.</p><p><strong>Keywords</strong>: noise trader risk, GARCH (1,1), Vietnam’s stock market</p>

Noise Trader

This chapter briefly reviews the literature on the topics of noise traders in the financial market. The authors cover the no‐trade theorem under complete and competitive markets in the 1980s, the noise trader approach to finance in the 1990s, and recent studies from several approaches related to noise traders, such as heterogeneous agent models, investor sentiment, retail investors, experimental analysis, and extrapolation. Understanding and tackling the issues resulted from noise traders would be essential for us to realize how financial and economic markets work.

Noise Trader

This paper briefly reviews the literature on the topics of noise traders in the financial market. We cover the no-trade theorem under complete and competitive markets in the 1980s, the noise trader approach to finance in the 1990s, and recent studies from several approaches related to noise traders, such as heterogeneous agent models, investor sentiment, retail investors, experimental analysis, and extrapolation. Understanding and tackling the issues resulted from noise traders would be essential for us to realize how financial and economic markets work.