paper-with-me

홈 › Papers

On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules

2020-05-19 · Can Yang, Junjie Zhai, Helong Li

Although there is a wide use of technical trading rules in stock markets, the profitability of them still remains controversial. This paper first presents and proves the upper bound of cumulative return, and then introduces many of conventional technical trading rules. Furthermore, with the help of bootstrap methodology, we investigate the profitability of technical trading rules on different international stock markets, including developed markets and emerging markets. At last, the results show that the technical trading rules are hard to beat the market, and even less profitable than the random trading strategy.

📄 PDF Abstract BibTeX arXiv:2005.13974

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Deep reinforcement learning on a multi-asset environment for trading

2021-06-15 · Ali Hirsa, Joerg Osterrieder, Branka Hadji-Misheva, Jan-Alexander Posth

Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorate…

Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

Exploring the Advantages of Transformers for High-Frequency Trading

2023-02-20 · Fazl Barez, Paul Bilokon, Arthur Gervais, Nikita Lisitsyn

This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model…

DecoderPositionTime SeriesTime Series Analysis+2

A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization

2026-01-19 · Shuozhe Li, Du Cheng, Leqi Liu arxiv

Learning profitable intraday trading policies from financial time series is challenging due to heavy noise, non-stationarity, and strong cross-sectional dependence among related assets. We propose \emph{WaveLSFormer}, a …

Practical Deep Reinforcement Learning Approach for Stock Trading

2018-11-19 · Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang 외

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning …

Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)

Hawkes-based cryptocurrency forecasting via Limit Order Book data

2023-12-21 · Raffaele Giuseppe Cestari, Filippo Barchi, Riccardo Busetto, Daniele Marazzina 외

Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency retu…

Point ProcessesTime Series