Corpus ID: 235125787

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures

  title={Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures},
  author={Daniel Poh and Bryan Lim and S. Zohren and Stephen J. Roberts},
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction. While this ranking step is traditionally performed using heuristics, or by sorting outputs produced by pointwise regression or classification models, Learning to Rank algorithms have recently presented themselves as competitive and viable alternatives. Despite improving ranking accuracy on average however, these techniques do not account for the possibility… Expand

Figures and Tables from this paper


Building Cross-Sectional Systematic Strategies by Learning to Rank
The authors propose a framework to enhance cross-sectional portfolios by incorporating learning-to-rank algorithms, which lead to improvements in ranking accuracy by learning pairwise and listwise structures across instruments. Expand
Stock portfolio selection using learning-to-rank algorithms with news sentiment
It is shown that learning-to-rank algorithms are effective in producing reliable rankings of the best and the worst performing stocks based on investors’ sentiment, and these strategies produce risk-adjusted returns superior to the S&P 500 index return, the hedge fund industry average performance - HFRIEMN, and some sentiment-based approaches without learning- to-rank algorithm. Expand
Learning a Deep Listwise Context Model for Ranking Refinement
This work proposes to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps to fine tune the initial ranked list and can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora. Expand
Do Momentum-Based Strategies Still Work in Foreign Currency Markets?
Abstract This paper examines the performance of momentum trading strategies in foreign exchange markets. We find the well-documented profitability of momentum strategies during the 1970s and theExpand
Empirical Asset Pricing Via Machine Learning
Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation. Expand
The Cross-Section of Stock Returns in Frontier Emerging Markets
textabstractWe are the first to investigate the cross-section of stock returns in the new emerging equity markets, the so-called frontier emerging markets. Our unique survivorship-bias free data setExpand
Enhancing the momentum strategy through deep regression
This paper investigates some variations of the existing momentum strategies to increase profit and gain other desirable properties such as low kurtosis, small negative skewness and small maximum drawdown by using regression that is based on the latest techniques from deep learning such as stacked autoen coders and denoising autoencoders. Expand
Autoencoder Asset Pricing Models
This model retrofits the workhorse unsupervised dimension reduction device from the machine learning literature – autoencoder neural networks – to incorporate information from covariates along with returns themselves, and delivers estimates of nonlinear conditional exposures and the associated latent factors. Expand
Learning to rank for information retrieval
Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated. Expand
Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
This work proposes a global optimization framework for mutual influence aware ranking in e-commerce search that directly optimizes the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two tasks. Expand