Trading signal, functional data analysis and time series momentum

Boubaker, Sabri; Liu, Zhenya; Lu, Shanglin; Zhang, Yifan

doi:10.1016/j.frl.2021.101933

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…For a long time, investors generally used classic financial methods to manage their funds [8][9][10]. The buy and hold method is an ancient method that assumes that each stock fluctuates around its true value.…”

Section: Related Literaturementioning

confidence: 99%

The Composite Multi-Level Prediction Model for Stock Market Time Series

Chang,

Zhang

2024

Artificial Intelligence and Human-Computer Interaction

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For a long time, investors have always been influenced by their own experience and investment expert advice. Machine Learning method in Quantitative Investment is an advanced method that replaces subjective judgments with artificial intelligence models to improve transaction accuracy. The authors have implemented a composite stock price prediction model based on multi-layer training networks, which is more suitable for predicting future stock prices compared to traditional methods. This model starts from the time series of stock data and deeply integrates the characteristics of stocks with artificial intelligence. The deep training sub-model is a combination of machine learning models and traditional statistical methods. This unique design cleverly solves the one-sidedness of machine learning methods and the petrification of classical financial methods. Through comparative experiments on multiple prediction models within the same time period, the model proposed in this paper has been proven to be the most effective model.

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Section: Related Literaturementioning

confidence: 99%

The Composite Multi-Level Prediction Model for Stock Market Time Series

Chang,

Zhang

2024

Artificial Intelligence and Human-Computer Interaction

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“…Tokyo platinum contracts also feature in recent published research. Boubaker et al (2021) evaluate the performance of momentum based on functional data analysis. Iwatsubo and Watkins (2020) present evidence on which traders influence the efficient prices of commodity futures traded in Tokyo.…”

Section: Introductionmentioning

confidence: 99%

Causality between Arbitrage and Liquidity in Platinum Futures

Iwatsubo

Watkins

2022

JRFM

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Arbitrage and liquidity are interrelated. Liquidity facilitates arbitrageurs’ trading on deviations from the law of one price. However, whether arbitrage opportunity leads to an increase or decrease in liquidity depends on the cause of the deviation. A demand shock leads to greater liquidity, while asymmetric information is toxic to liquidity. We examine how arbitrage and liquidity influence each other in the world’s largest platinum futures markets on exchanges in New York and Tokyo. The markets provide an interesting institutional setting because the futures are based on an identical underlying commodity but exhibit different liquidity characteristics both intraday and over their lifespans. Using intraday data, we find that deviation in currency-adjusted futures prices leads, on average, to an immediate increase in liquidity, suggesting that demand shocks are the dominant driver of arbitrage opportunities. Less actively traded futures experience a greater liquidity effect. Arbitrageurs improve liquidity in both New York and Tokyo by acting as discretionary liquidity traders and cross-sectional market-makers.

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