2020
DOI: 10.1007/s00521-020-05377-6
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Convert index trading to option strategies via LSTM architecture

Abstract: In the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company’s financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, m… Show more

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Cited by 7 publications
(3 citation statements)
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“…Fed into CNN for pair trading strategy, this helps to improve accuracy and profitability. It is also common to observe LSTM-based strategies, either for converting futures into options (Wu et al 2020), in combination with Autoencoders for training market data (Koshiyama et al 2020), or in more general trade strategy applications (Sun et al 2019;Silva et al 2020;Wang et al 2020;Chalvatzis and Hristu-Varsakelis 2020).…”
Section: Findings: Trade Strategymentioning
confidence: 99%
“…Fed into CNN for pair trading strategy, this helps to improve accuracy and profitability. It is also common to observe LSTM-based strategies, either for converting futures into options (Wu et al 2020), in combination with Autoencoders for training market data (Koshiyama et al 2020), or in more general trade strategy applications (Sun et al 2019;Silva et al 2020;Wang et al 2020;Chalvatzis and Hristu-Varsakelis 2020).…”
Section: Findings: Trade Strategymentioning
confidence: 99%
“…The electricity consumption prediction using conventional methods is a challenging task. Because most of the tradi-tional LSTM [25][26][27][28][29][30] only optimize hyper-parameters, whereas the proposed E-LSTM optimize hyper-parameters of the model and adjust the hidden layers which increase the performance of the model. Also, the traditional LSTM has low accuracy, higher time complexity, low error rates such as MSE, MAE, RMSE, high training, and validation loss.…”
Section: Electricity Consumption Predictionmentioning
confidence: 99%
“…e LSTM [15] algorithm performs well in time-series data mining and has had many applications in the financial field [16][17][18][19], such as customer service marketing, risk control, and trading strategy. In the field of antifraud, for example, deep learning technology automatically recognizes fraudulent transactions from massive amounts of transaction data, realizes successful interception, and blocks fraudulent transactions, thereby improving system effectiveness, reducing the rate of false alarms, and reducing compliance risks [20].…”
Section: Introductionmentioning
confidence: 99%