The role of information uncertainty in moving-average technical analysis: A study of individual stock-option issuance in Taiwan

Chen, Chien‐Hua; Su, Xuan‐Qi; Lin, Jun-Biao

doi:10.1016/j.frl.2016.04.026

Cited by 12 publications

(8 citation statements)

References 42 publications

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“…Finally, when the risky and the nonrisky asset have approximately the same mean annual return (as in the study of Auer, 2016a), any trading strategy that switches between the two assets has a good chance of beating the buy-and-hold strategy, which is always long in the risky asset. Second, transaction costs were not taken into account or unrealistically low transaction costs were used to establish the profitability of a trading strategy (e.g., Chen et al, 2016;Levine & Pedersen, 2016). For example, it is unrealistic to assume that a highly liquid ETF cannot only be bought but also sold with very low transactions costs because the bid-ask spread of the inverse ETF is usually much wider than that of the original ETF (Stasinakis et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…However, spreads are usually much higher in the case of small stocks. Taking only fees and taxes into account and turning a blind eye to spreads (Levine & Pedersen, 2016), which is particularly problematic in the case of short positions (see Section 3.4), or ignoring transactions costs altogether (Chen et al, 2016) will therefore impair the validity of the results obtained under these conditions.…”

Section: Technical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of current research on stock return predictability

Reschenhofer

Mangat

Zwatz

et al. 2019

Journal of Forecasting

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The results of recent replication studies suggest that false positive findings are a big problem in empirical finance. We contribute to this debate by reviewing a sample of articles dealing with the short-term directional forecasting of the prices of stocks, commodities, and currencies. Screening all relevant articles published in 2016 by one of the 96 journals covered by the Social Sciences Citation Index in the category "Business, Finance," we select only those studies that use easily accessible data of daily or higher frequency. We examine each study in detail, from the selection of the dataset to the interpretation of the results. We also include empirical analyses to illustrate the shortcomings of certain approaches. There are three main findings from our review. First, the number of selected papers is very low, which is surprising even when the strict selection criteria are taken into account. Second, there are hardly any relevant studies that use high-frequency data-despite the hype about financial big data and machine learning. Third, the economic significance of the findings-for example, their usefulness for trading purposes-is questionable. In general, apparently good forecasting performance does not translate into profitability once realistic transaction costs and the effect of data snooping are taken into account. Other typical problems include unsuitable benchmarks, short evaluation periods, and nonoperational trading strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Technical Analysismentioning

confidence: 99%

Evaluation of current research on stock return predictability

Reschenhofer

Mangat

Zwatz

et al. 2019

Journal of Forecasting

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show abstract

“…La utilización de esta técnica supone que la serie de tiempo es estable, esto es, que los datos que la componen se generan sin variaciones importantes entre un dato y otro, aunque muestren un crecimiento o un decrecimiento lo hagan con una tendencia constante. Asimismo, un análisis técnico de promedio móviles es usado comúnmente como una de las técnicas de predicción para predecir futuros movimientos en los datos, a partir de datos pasados [12].…”

Section: Promedio Móvilunclassified

Implementación de las técnicas de predicción en la generación de energía eléctrica en el sector industrial

Peña

Ávila

Zúñiga

et al. 2020

RITI

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Implementación de las técnicas de predicción en la generación de energía eléctrica en el sector industrialResumen: Las técnicas de predicción actualmente cuentan con un gran potencial, mediante ellas se pueden obtener estimaciones o pronósticos de valores futuros de una manera confiable, las cuales pueden ser utilizadas para la toma de decisiones. Ahora bien, las industrias son unas de las mayores consumidoras de electricidad, por lo que, muchas de ellas utilizan generadores eléctricos propios para poder cumplir con la demanda de energía que requieren sus procesos. Pero también, dentro de este proceso existen problemas o situaciones no beneficiosas, uno de ellos, es que, si no hay monitoreo y control en la generación de energía eléctrica de una manera adecuada, provocará gran pérdida de recursos monetarios. Por tal motivo, se propone implementar las técnicas de predicción dentro del proceso de generación de energía para comparar y seleccionar la técnica que muestre el mejor pronóstico de consumo y así evitar la pérdida de recursos en la industria.Palabras clave: Técnicas de Predicción, Energía, Toma de Decisiones, Industria.Abstract: Prediction techniques currently have great potential, through them you can obtain estimates or forecasts of future values in a reliable way, which can be used for decision making. However, industries are one of the largest consumers of electricity, so many of them use their own electric generators to be able to meet the energy demand that their processes require. But also, within this process there are problems or situations that are not beneficial, one of them, is that, if there is no monitoring and control in the generation of electrical energy in an adequate way, it will cause a great loss of monetary resources. For this reason, it is proposed to implement the prediction techniques within the power generation process to compare and select the technique that shows the best consumption forecast and thus avoid the loss of resources in the industry.

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“…Fang et al (2014); Fama and Blume (1966) and Jensen and Bennington (1970);Fama (1965Fama ( , 1970 However, a growing body of literature has been recognized recently that technical analysis could generate abnormal returns. For example (Zapranis & Tsinaslanidis 2012;Cervello-Royo;Guijarro & Michniuk 2015) for classical price patterns, (Marshall & Cahan 2005;Gustafsson 2012;Ülkü & Prodan 2013;Boboc & Dinică 2013;Baetje & Menkhoff 2016;Chen;Su & Lin 2016;El-Ansary & Atuea 2017) for technical indicators and (Brock;Lakonishok & LeBaron 1992;Bessembinder & Chan 1995) for both technical patterns and indicators. There was a common conclusion that returns achieved using technical rules significantly exceeded the returns generated through buy and hold strategy and that technical rules can help in predicting future returns.…”

Section: The Predicting Ability Of Technical Analysismentioning

confidence: 99%

Testing the Predicting Ability of Technical Analysis Classical Patterns in the Egyptian Stock Market

El-Ansary

Mohssen

2017

AFR

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As an emerging market, Egyptian stock market is characterized by inefficiency which is confirmed empirically in this research. This provoked us to test the ability of technical analysis classical patterns in predicting the future returns through calculating the expected price target consequently the expected future return and compare it with the actual return.Statistical techniques and models including Box Pierce (Ljung-Box), Variance ratio test, Runs test, and t-test bootstrapping technique have been applied to test the research proposed hypotheses. The empirical results revealed that the Egyptian stock market is inefficient as returns don't follow random walk and are dependent, it is found also that the actual returns have significantly exceeded the expected returns of the detected patterns indicating that classical patterns can perfectly predict the direction of the price movements rather than the exact price targets.

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The role of information uncertainty in moving-average technical analysis: A study of individual stock-option issuance in Taiwan

Cited by 12 publications

References 42 publications

Evaluation of current research on stock return predictability

Evaluation of current research on stock return predictability

Implementación de las técnicas de predicción en la generación de energía eléctrica en el sector industrial

Testing the Predicting Ability of Technical Analysis Classical Patterns in the Egyptian Stock Market

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