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SciVee ◽  
2010 ◽  
Author(s):  
Jay Abbott
Keyword(s):  
Stock Market ◽  
Day Trading

scholarly journals Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection

2020 ◽  
Author(s):  
Chi Seng Pun ◽  
Lei Wang ◽  
Hoi Ying Wong
Keyword(s):  
Stock Market ◽  
Thought Experiment ◽  
Empirical Studies ◽  
Synthetic Data ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Generative Adversarial Network ◽  
Day Trading ◽  
Adversarial Network ◽  
Robust Portfolio

Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr's generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.

scholarly journals An indicator for month-trading of stocks

2021 ◽  
Vol 36 ◽  
pp. 02001
Author(s):  
You Beng Koh ◽  
Yew Seong Ng ◽  
Ah Hin Pooi
Keyword(s):  
Stock Market ◽  
Individual Investors ◽  
Monthly Basis ◽  
Share Prices ◽  
Day Trading ◽  
Selection Of

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.

scholarly journals Half-day trading and spillovers

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Yifan Chen ◽  
Limin Yu ◽  
Jianhua Gang
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
The United States ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Conditional Correlation ◽  
Day Trading ◽  
Market Impacts ◽  
Positive Spillover ◽  
The U.S

AbstractThis 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.

Predicting Stock Market Performance

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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