Short selling constraints and stock returns volatility: Empirical evidence from the German stock market

Bohl, Martin T.; Reher, Gerrit; Wilfling, Bernd

doi:10.1016/j.econmod.2016.05.025

Cited by 20 publications

(6 citation statements)

References 17 publications

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“…Additionally, [2] reviews many stock market trading techniques, variables, and statistical tests. There have been several studies concerning the German stock markets, e.g., relations between beta and realized returns [23], liquidity [31], volatility and short selling constraints [6], or behavioural finance [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

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Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

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show abstract

“…For example, Beber and Pagano (2013) document that constraints on short sellling during the 2007–2009 financial crisis decreased liquidity and impeded price discovery. Similarly, Bohl et al (2016) find that short-selling bans in Germany between 2008 and 2010 increased stock return volatility.…”

Section: Introductionmentioning

confidence: 91%

Firm-level short selling and the local COVID-19 pandemic: Evidence from China

2022

Economic Modelling

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“…In the first stage of the stock market disaster in China, there was a common phenomenon of the limit drop in the market. A large number of selling orders piled up on the limit drop price, and the transaction volume was small [12] . At this time, the precipitous drop in price was characterized by liquidity adjustment.…”

Section: Theoretical Analysismentioning

confidence: 99%