Technical Analysis Profitability Without Data Snooping Bias: Evidence from Chinese Stock Market

Jiang, Fuwei; Tong, Guoshi; Song, Guokai

doi:10.1111/irfi.12161

Cited by 13 publications

(6 citation statements)

References 38 publications

(46 reference statements)

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“…Similar empirical evidence was also provided by Marshall et al (2017). Next, Jiang et al (2017) found robust and substantial evidence of the profitability of TRs for the Chinese stock market. M'ng (2018) devised a novel adjustable moving average indicator for the Far East equity markets that resulted in excess returns over the buy-and-hold strategy.…”

Section: Introductionsupporting

confidence: 72%

The Profitability of Technical Analysis during the COVID-19 Market Meltdown

Lento

Gradojević

2022

JRFM

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This article explores the profitability of technical trading rules around the COVID-19 pandemic market meltdown for the S&P 500 index, Bitcoin, Comex gold spot, crude oil WTI, and the VIX. Trading rule profits are estimated from January to May 2020, including three sub-periods, on a high-frequency data set. The results reveal that the trading rules can beat the buy-and-hold trading strategy. However, only the Bollinger Bands and trading range break-out rules become profitable after transaction costs during the market crash. Moreover, it is found that composite trading signals effectively improve the profitability of technical analysis around the COVID-19 market crash.

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Section: Introductionsupporting

confidence: 72%

The Profitability of Technical Analysis during the COVID-19 Market Meltdown

Lento

Gradojević

2022

JRFM

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“…Empirical evidence shows that according to the technical analysis, the proposed pattern-based approach can get excess returns and beat buy-and-hold strategies. In addition, recent studies have shown that technical analysis indicators can obtain excess returns compared to the broader market in investment trading strategies [38], [39], [40], [41], [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

Lee

Chang

Hung

et al. 2021

ComSIS

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The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

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“…Authors reported evidence of profitability for rules based on classical moving average as well as rules based on Bollinger bands and relative strength index. Jiang, Tong & Song (2019) investigated the profitability of trading rules in Chinese stock market. The authors used 19 years daily data from Chinese aggregate market return and confirmed the profitability of trading rules even in the presence of transaction costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Using algorithmic trading to analyze short term profitability of Bitcoin

Ahmad

Alqarni

et al. 2021

PeerJ Computer Science

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Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.

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Technical Analysis Profitability Without Data Snooping Bias: Evidence from Chinese Stock Market

Cited by 13 publications

References 38 publications

The Profitability of Technical Analysis during the COVID-19 Market Meltdown

The Profitability of Technical Analysis during the COVID-19 Market Meltdown

Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

Using algorithmic trading to analyze short term profitability of Bitcoin

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