Stock Market Optimism and Portfolio Allocation of American Households

The main aim of this article is to examine the inter-relationships among the top cryptocurrencies on the crypto stock market in the presence and absence of the COVID-19 pandemic. The nine chosen cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Eos, BitcoinCash, Binance, Stellar, and Tron and their daily closing price data are captured from coinmarketcap over the period from 13 September 2017 to 21 September 2020. All of the cryptocurrencies are integrated of order 1 i.e., I(1). There is strong evidence of a long-run relationship between Bitcoin and altcoins irrespective of whether it is pre-pandemic or pandemic period. It has also been found that these cryptocurrencies’ prices and their inter-relationship are resilient to the pandemic. It is recommended that when the investors create investment plans and strategies they may highly consider Bitcoin and altcoins jointly as they give sustainability and resilience in the long run against the geopolitical risks and even in the tough time of the COVID-19 pandemic.

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■ Cyclicality in Stock Market Volatility and Optimal Portfolio Allocation

Stock Market Volatility ◽

10.1201/9781420099553-16 ◽

2009 ◽

pp. 225-238

Keyword(s):

Stock Market ◽

Portfolio Allocation ◽

Optimal Portfolio ◽

Market Volatility ◽

Stock Market Volatility

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Bitcoin and Altcoins Price Dependency: Resilience and Portfolio Allocation in COVID-19 Outbreak

10.20944/preprints202103.0440.v1 ◽

2021 ◽

Author(s):

Ahmet Faruk Aysan ◽

Asad-ul-Islam Khan ◽

Humeyra Topuz

Keyword(s):

Stock Market ◽

Strong Evidence ◽

Unit Root ◽

Portfolio Allocation ◽

Unit Root Tests ◽

Price Data ◽

Long Run ◽

Closing Price ◽

Augmented Dickey Fuller ◽

Presence And Absence

The main aim of this article is to examine the inter-relationships among the top cryptocurrencies on the crypto stock market in the presence and absence of the COVID-19 pandemic. The nine chosen cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Eos, BitcoinCash, Binance, Stellar, and Tron and their daily closing price data are captured from coinmarketcap over the period from 9/13/2017 to 9/21/2020. To determine the order of integration, the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) unit root tests were carried out, and all of the cryptocurrencies were found to be integrated of order 1 i.e. I(1). It has been found that there is strong evidence of a long-run relationship between Bitcoin and altcoins irrespective of whether it is pre-pandemic or pandemic period. Hence these cryptocurrencies' prices and their inter-relationship are resilient to the pandemic.

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Predicting Stock Market Performance

Journal of Individual Differences ◽

10.1027/1614-0001/a000330 ◽

2020 ◽

pp. 1-10

Author(s):

Thomas Plieger ◽

Thomas Grünhage ◽

Éilish Duke ◽

Martin Reuter

Keyword(s):

Stock Market ◽

Risk Taking ◽

Life Stress ◽

Price Volatility ◽

Individual Characteristics ◽

Self Report ◽

Financial Behavior ◽

Market Simulation ◽

Stock Market Performance ◽

Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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