Pairs selection and outranking: An application to the S&P 100 index

By Nicolas Huck. 2009

145 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Algorithms & Complexity, Economics & Operations Research

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Citations in the corpus

Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “Huck (2009, 2010); Krauss et al. (2017); Moritz and Zimmermann (2014); Takeuchi and Lee (2013), who have developed similar methods for U.S. cash equities, but on much lower frequencies (days to months).”
Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, methodology): “Here, tanh (x) = exp (x) − exp (−x) exp (x) + exp (−x) In the last step, the output ht is denoted in the following two equations: ot = sigmoid [ (ht−1, xt)Wo + bo ] (12)”
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “2001; Huck, 2009; Xiong, 2015] and measuring the impact of incorporating news for selected companies [Siah and Myers, Joshi et al. 2013; Ding et al. 2015].”
The profitability of pairs trading strategies: distance, cointegration and copula methods (2016, unknown): “Several other studies also focus on deriving automated trading strategies from technical analysis or creating profitable algorithms based on different cross-disciplinary concepts (Dempster and Jones 2001, Huck 2009, 2010, Creamer and Freund 2010).”
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017, methodology): “Third, we train with 400 epochs, i.e., we pass 400 times over the training set, as in Huck (2009).”
Statistical arbitrage pairs trading strategies: Review and outlook (2017, unknown): “Huck is the main author who has developed and published on this pairs trading methodology in Huck (2009) and Huck (2010), respectively.”
Pairs trading and outranking: The multi-step-ahead forecasting case (2010, methodology): “The negative link observed in Huck (2009) will appear partly in the result.”
A Comparison of ARIMA and LSTM in Forecasting Time Series (2018, methodology): “In particular, LSTM has been used in time-series prediction [4], [8], [9], [18] and in economics and ﬁnance data such as predicting the volatility of the S&P 500 [10].”
Deep learning with long short-term memory networks for financial market predictions (2017, background): “As such, we go long the top k and short the ﬂop k stocks of each ranking, for a long-short portfolio consisting of 2 k stocks - see Huck (2009, 2010).”
ELECTRE: A comprehensive literature review on methodologies and applications (2016, methodology): “In order to select stocks for pairs trading, Huck (2009) used an integrated approach combining forecasting and MCDA methods to identify potentially under-and overvalued stocks for a selection of 90 stocks from the S&P 100 index.”