Day Trading and Addiction: Related Concepts?

BAWe: Entwurf einer Day-Trading-Richtlinie

Zeitschrift für Bankrecht und Bankwirtschaft ◽

10.15375/zbb-2001-0207 ◽

2001 ◽

Vol 13 (2) ◽

Keyword(s):

Das Bundesaufsichtsamt für den Wertpapierhandel hat mit Datum vom 1. März 2001 den Entwurf einer Richtlinie zum Day-Trading-Geschäft der Wertpapierdienstleistungsunternehmen vorgelegt. Es handelt sich um die überarbeitete Fassung eines ersten so genannten Diskussionsentwurfs vom 27. Juli 2000. Der aktuelle Richtlinienentwurf wurde der Kreditwirtschaft mit einer Fristsetzung bis zum 31. März 2001 zur Stellungnahme zugeleitet. Nachfolgend ist der Entwurf vom März 2001 mit allen Änderungen gegenüber dem Diskussionsentwurf vom Juli 2000 abgedruckt. Die Kursivsetzungen heben die Neuerungen hervor. Die zunächst vorgeschlagenen oder weggefallenen Formulierungen sind in eckige Klammern gesetzt.

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The Liquidity and Volatility Impacts of Day Trading by Individuals in the Taiwan Index Futures Market

SSRN Electronic Journal ◽

10.2139/ssrn.2126526 ◽

2012 ◽

Author(s):

Robin K. Chou ◽

George H. K. Wang ◽

Yun-Yi Wang

Keyword(s):

Futures Market ◽

Index Futures ◽

Day Trading

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Specialized Day Trading - A New View of an Old Game

SSRN Electronic Journal ◽

10.2139/ssrn.957172 ◽

2006 ◽

Author(s):

Vladimir Simovic ◽

Vladimir Simovic

Keyword(s):

Day Trading ◽

New View

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Profitable day trading Bitcoin futures following continuous bullish (bearish) candlesticks

Applied Economics Letters ◽

10.1080/13504851.2021.1899115 ◽

2021 ◽

pp. 1-8

Author(s):

Min-Yuh Day ◽

Paoyu Huang ◽

Yirung Chen ◽

Yin-Tzu Lin ◽

Yensen Ni

Keyword(s):

Day Trading

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Stock Market Index Data and indicators for Day Trading as a Binary Classification problem

Data in Brief ◽

10.1016/j.dib.2016.12.044 ◽

2017 ◽

Vol 10 ◽

pp. 569-575 ◽

Cited By ~ 5

Author(s):

Renato Bruni

Keyword(s):

Stock Market ◽

Binary Classification ◽

Classification Problem ◽

Stock Market Index ◽

Day Trading ◽

Index Data ◽

Market Index ◽

Binary Classification Problem

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

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/637 ◽

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.

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