Trend definition or holding strategy: What determines the profitability of candlestick charting?

Lu, Tsung Hsun; Chen, Yi Chi; Hsu, Yu Chin

doi:10.1016/j.jbankfin.2015.09.009

Cited by 22 publications

(18 citation statements)

References 37 publications

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“…This article analyzes both MYR and CL exit strategies to evaluate the impacts of exit strategies on profitability of candlestick patterns. In fact, Lu et al (2015) apply candlestick trading strategies to the 30 component stocks of the DJIA index over the 1992-2012 period and find that candlestick patterns with the CL exit strategy, but not with the MYR exit strategy, are profitable. They argue that the profitability of the CL exit strategy comes from a risk sharing mechanism when positions are unwound over the holding periods.…”

Section: Candlestick Patternsmentioning

confidence: 99%

“…However, other studies find that applying certain candlestick patterns is profitable at least for short-term trading (Goo, Chen, & Chang, 2007;Lu, Shiu, & Liu, 2012;Shiu & Lu, 2011;Zhu, Atri, & Yegen, 2016), at least in the Taiwanese and Chinese stock markets. Interestingly, Lu, Chen, and Hsu (2015) even find significant positive returns in the U.S. stock markets if investors follow the Caginalp-Laurent (CL) exit strategies (Caginalp & Laurent, 1998) but not the Marshall-Young-Rose (MYR) exit strategies (Marshall et al, 2006). The MYR applies a prespecified date to exit the market, whereas the CL sets an exit price equal to an average holding period closing price.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the article investigates further the impact of different exit strategies. This important issue has not been investigated outside the U.S. market or after Lu et al (2015). In addition, the use of Thai stock data to test candlestick patterns, which were developed using Japanese rice data and formerly tested only in the U.S., Japanese, and Chinese stock markets, is clearly an ex ante evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand

2017

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This article investigates the profitability of candlestick patterns. The holding periods are 1, 3, 5, and 10 days. Two exit strategies are studied. One is the Marshall-Young-Rose (MYR) exit strategy and the other is the Caginalp-Laurent (CL) exit strategy. The MYR applies a prespecified date to exit the market. In contrast, the CL sets an exit price equal to an average holding period closing price, assuming that investors liquidate their positions evenly within this period. The daily data include open, high, low, and close prices of component stocks of the SET50 index (the 50 largest capitalization stocks in the Stock Exchange of Thailand [SET]) for a 10-year period from July 3, 2006, to June 30, 2016. This study tests the predictive power of bullish and bearish candlestick reversal patterns both without technical filtering and with technical filtering (Stochastics [%D], Relative Strength Index [RSI], Money Flow Index [MFI]) by applying the skewness adjusted t test and the binomial test. The statistical analysis finds little use of both bullish and bearish candlestick reversal patterns since the mean returns of most patterns are not statistically different from zero. Even the ones with statistically significant returns do have high risks in terms of standard deviations. The binomial test results also indicate that candlestick patterns cannot reliably predict market directions. In addition, this article finds that filtering by %D, RSI, or MFI generally does not increase profitability nor prediction accuracy of candlestick patterns.

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Section: Candlestick Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand

2017

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“…On the positive side, Xie et al (2012) [6] find that candlestick patterns have significant predictive power to forecast US equity returns. Lu et al (2015) [7] find predictive power in several patterns, but these are rare and the research in addition did not sufficiently address the distinction between candlestick patterns being able to yield profit and their being able to predict trends. One study, that of Lu (2014) [8], finds that traditional patterns have little value but that novel ones may do so; this finding is in line with observations made in the current work, though it should again be emphasised that our use of the mid-price as target creates a very different context.…”

Section: Literature Reviewmentioning

confidence: 98%

A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data

Mann

Gorse

2017

Lecture Notes in Computer Science

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Prediction of financial markets using neural networks and other techniques has predominately focused on the close price. Here, in contrast, the concept of a mid-price based on an Open, High, Low, Close (OHLC) data structure is proposed as a prediction target and shown to be a significantly easier target to forecast, suggesting previous works have attempted to extract predictive power from OHLC data in the wrong context. A prediction framework incorporating a factor discovery and mining process is developed using Randomised Decision Trees, with Long Short Term Memory Recurrent Neural Networks subsequently demonstrating remarkable predictive capabilities of up to 50.73% better than random (75.42% accuracy) on hourly data based on the FGBL German Bund futures contract, and 42.5% better than random (72.04% accuracy) on a comparison Bitcoin dataset.

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“…For example, Caginalp and Laurent performed a statistical test including eight kinds of three-day patterns and noted that the candlestick patterns have predictive power [10]. Then Lu et al examined these eight three-day patterns with three definitions of trend and four holding strategies in the DJIA component data, and found that regardless of which definition of the trend was used, eight three-day reversal patterns with a Caginalp-Laurent holding strategy were profitable [11]. Chen et al gave the quantitative definitions of four pairs of two-day candlestick patterns to study their predictive power in Chinese stock market.…”

Section: Introductionmentioning

confidence: 99%

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

et al. 2021

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Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results for the period of 2000 to 2017 of China's stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve the problem of data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. Transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially the momentum indicators, can improve forecasting accuracy in most cases.

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Trend definition or holding strategy: What determines the profitability of candlestick charting?

Cited by 22 publications

References 37 publications

Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand

Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand

A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

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