Deep Learning Statistical Arbitrage

References

• Araujo A, Norris W, Sim J (2019) Computing receptive fields of convolutional neural networks. Distill, published online November 4.Crossref, Google Scholar
• Avellaneda M, Lee J-H (2010) Statistical arbitrage in the US equities market. Quant. Finance 10(7):761–782.Crossref, Google Scholar
• Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. Preprint, submitted July 21, https://arxiv.org/abs/1607.06450.Google Scholar
• Bali TG, Goyal A, Huang D, Jiang F, Wen Q (2020) Predicting corporate bond returns: Merton meets machine learning. Preprint, submitted July 24, https://doi.org/10.2139/ssrn.3686164.Google Scholar
• Barr A (2007) Quant quake shakes hedge-fund giants. MarketWatch (August 13), https://www.marketwatch.com/story/quant-quake-shakes-hedge-fund-giants?gaa_at=eafs&gaa_n=AWEtsqebNel6ugfDoe4NAamnggkQR9XrUt0mv8GuWQOk1PRIsbEdgMiG7mz61pMips0%3D&gaa_ts=69026dec&gaa_sig=l8u6DbDEfh7NH2EjLdinuluQ2LnYY1XKaRW4-RFHY3o7QUBwsyOCgytrEgiemzY3qAfAHZpKBHurXlAiQqgoSQ%3D%3D.Google Scholar
• Bianchi D, Büchner M, Tamoni A (2020) Bond risk premiums with machine learning. Rev. Financial Stud. 34(2):1046–1089.Crossref, Google Scholar
• Boyd S, Busseti E, Diamond S, Kahn RN, Koh K, Nystrup P, Speth J (2017) Multi-period trading via convex optimization. Foundations Trends Optim. 3(1):1–76.Crossref, Google Scholar
• Bryzgalova S, Pelger M, Zhu J (2025) Forest through the trees: Building cross-sections of stock returns. J. Finance 80(5):2447–2506.Crossref, Google Scholar
• Carhart MM (1997) On persistence in mutual fund performance. J. Finance 52(1):57–82.Crossref, Google Scholar
• Cartea A, Jaimungal S (2016) Algorithmic trading of co-integrated assets. Internat. J. Theoret. Appl. Finance 19(6):165038.Crossref, Google Scholar
• Chen X, White H (1999) Improved rates and asymptotic normality for nonparametric neural network estimators. IEEE Trans. Inform. Theory 45(2):682–691.Crossref, Google Scholar
• Chen Y, Chen W, Huang S (2018) Developing arbitrage strategy in high-frequency pairs trading with Filterbank CNN algorithm. 2018 IEEE Internat. Conf. Agents (ICA) (IEEE, New York), 113–116.Google Scholar
• Chen L, Pelger M, Zhu J (2023) Deep learning in asset pricing. Management Sci. 70(2):714–750.Link, Google Scholar
• Cong L, Tang K, Wang J, Zhang Y (2021) AlphaPortfolio: Direct construction through deep reinforcement learning and interpretable AI. Preprint, submitted August 1, https://doi.org/10.2139/ssrn.3554486.Google Scholar
• D’Aspremont A (2011) Identifying small mean-reverting portfolios. Quant. Finance 11(3):351–364.Crossref, Google Scholar
• DeMiguel V, Gil-Bazo J, Nogales F, Santos A (2023) Machine learning and fund characteristics help to select mutual funds with positive alpha. J. Financial Econom. 150(3):103737.Crossref, Google Scholar
• Dunis C, Laws J, Evans B (2009) Modelling and trading the soybean-oil crush spread with recurrent and higher order networks: A comparative analysis. Artificial Higher Order Neural Networks for Economics and Business (IGI Global, Hershey, PA), 348–366.Google Scholar
• Ebrahimi H (2013) Hedge fund quants lose money in 2012. The Telegraph (January 8), https://www.telegraph.co.uk/finance/newsbysector/banksandfinance/9789082/Hedge-fund-quants-lose-money-in-2012.html.Google Scholar
• Elliott RJ, Van Der Hoek J, Malcolm WP (2005) Pairs trading. Quant. Finance 5(3):271–276.Crossref, Google Scholar
• Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J. Financial Econom. 33(1):3–56.Crossref, Google Scholar
• Fama EF, French KR (2015) A five-factor asset pricing model. J. Financial Econom. 116(1):1–22.Crossref, Google Scholar
• Fischer TG, Krauss C, Deinert A (2019) Statistical arbitrage in cryptocurrency markets. J. Risk Financial Management 12(1):31.Crossref, Google Scholar
• French KR (2008) Presidential address: The cost of active investing. J. Finance 63(4):1537–1573.Crossref, Google Scholar
• Freyberger J, Neuhierl A, Weber M (2020) Dissecting characteristics nonparametrically. Rev. Financial Stud. 33(5):2326–2377.Crossref, Google Scholar
• Gatev E, Goetzmann WN, Rouwenhorst KG (2006) Pairs trading: Performance of a relative-value arbitrage rule. Rev. Financial Stud. 19(3):797–827.Crossref, Google Scholar
• Giglio S, Xiu D (2021) Asset pricing with omitted factors. J. Political Econom. 129(7):1947–1990.Crossref, Google Scholar
• Goldstein MA, Irvine P, Kandel E, Wiener Z (2009) Brokerage commissions and institutional trading patterns. Rev. Financial Stud. 22(12):5175–5212.Crossref, Google Scholar
• Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
• Gu S, Kelly B, Xiu D (2021) Autoencoder asset pricing models. J. Econom. 222(1):429–450.Crossref, Google Scholar
• He A, Zhou G (2023) Diagnostics for asset pricing models. Financial Management, ePub ahead of print September 13, https://doi.org/10.1111/fima.12436.Google Scholar
• He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conf. Comput. Vision Pattern Recognition (CVPR) (IEEE, New York), 770–778.Google Scholar
• Huck N (2009) Pairs selection and outranking: An application to the S&P 100 index. Eur. J. Oper. Res. 196(2):819–825.Crossref, Google Scholar
• Jiang J, Kelly B, Xiu D (2023) (Re-)imag(in)ing price trends. J. Finance 78(6):3193–3249.Crossref, Google Scholar
• Jurek JW, Yang H (2007) Dynamic portfolio selection in arbitrage. EFA 2006 Meetings Paper. Preprint, submitted April 2007, https://doi.org/10.2139/ssrn.8822536.Google Scholar
• Kaniel R, Lin Z, Pelger M, Van Nieuwerburgh S (2023) Machine-learning the skill of mutual fund managers. J. Financial Econom. 150(1):94–138.Crossref, Google Scholar
• Kelly B, Pruitt S, Su Y (2019) Characteristics are covariances: A unified model of risk and return. J. Financial Econom. 134(3):501–524.Crossref, Google Scholar
• Kim T, Kim HY (2019) Optimizing the pairs-trading strategy using deep reinforcement learning with trading and stop-loss boundaries. Complexity 2019(1):3582516.Crossref, Google Scholar
• Kim D, Lee B-J (2023) Shorting costs and profitability of long-short strategies. Accounting Finance 63(1):277–316.Crossref, Google Scholar
• Kozak S, Nagel S, Santosh S (2020) Shrinking the cross section. J. Financial Econom. 135(2):271–292.Crossref, Google Scholar
• Krauss C, Do XA, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur. J. Oper. Res. 259(2):689–702.Crossref, Google Scholar
• Lettau M, Pelger M (2020a) Estimating latent asset-pricing factors. J. Econometrics 218(1):1–31.Crossref, Google Scholar
• Lettau M, Pelger M (2020b) Factors that fit the time series and cross-section of stock returns. Rev. Financial Stud. 33(5):2274–2325.Crossref, Google Scholar
• Leung T, Li X (2015) Optimal mean reversion trading with transaction costs and stop-loss exit. Internat. J. Theoret. Appl. Finance 18(03):1550020.Crossref, Google Scholar
• Li B, Rossi AG (2020) Selecting mutual funds from the stocks they hold: A machine learning approach. Preprint, submitted December 3, http://dx.doi.org/10.2139/ssrn.3737667.Google Scholar
• Li S, Tang Y (2024) Automated volatility forecasting, Management Sci. 71(7):6248-6274.Google Scholar
• Lim B, Zohren S (2021) Time-series forecasting with deep learning: A survey. Philos. Trans. Roy. Soc. A 379(2194):20200209.Crossref, Google Scholar
• Lintilhac PS, Tourin A (2016) Model-based pairs trading in the bitcoin markets. Quant. Finance 17(5):703–716.Crossref, Google Scholar
• Mudchanatongsuk S, Primbs JA, Wong W (2008) Optimal pairs trading: A stochastic control approach. Proc. 2008 Amer. Control Conf. (American Control Conference, Seattle), 1035–1039.Google Scholar
• Mulvey JM, Sun Y, Wang M, Ye J (2020) Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network. Quant. Finance 20(8):1239–1261.Crossref, Google Scholar
• Murray S, Xiao H, Xia Y (2024) Charting by machines. J. Financial Econom. 153:103791.Crossref, Google Scholar
• Pelger M (2015) Understanding systematic risk: A high-frequency approach. Preprint, submitted August 24, http://dx.doi.org/10.2139/ssrn.2647040.Google Scholar
• Pelger M, Xiong R (2020) State-varying factor models of large dimensions. Preprint, submitted February 1, http://dx.doi.org/10.2139/ssrn.3109314.Google Scholar
• Rad H, Low RKY, Faff R (2016) The profitability of pairs trading strategies: Distance, cointegration and copula methods. Quant. Finance 16(10):1541–1558.Crossref, Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser L, Polosukhin I (2017) Attention is all you need. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 (NIPS 2017) (Curran Associates, Red Hook, NY), 6000–6010.Google Scholar
• Vidyamurthy G (2004) Pairs Trading: Quantitative Methods and Analysis (Wiley, Hoboken, NJ).Google Scholar
• Xu J, Sun X, Zhang Z, Zhao G, Lin J (2019) Understanding and improving layer normalization. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems 32 (NeurIPS 2019) (Curran Associates, Red Hook, NY), 4381–4391.Google Scholar
• Yeo J, Papanicolaou G (2017) Risk control of mean-reversion time in statistical arbitrage. Risk Decision Anal. 6(4):263–290.Crossref, Google Scholar
• Yun C, Bhojanapalli S, Rawat AS, Reddi SJ, Kumar S (2019) Are Transformers universal approximators of sequence-to-sequence functions? Preprint, submitted December 20, https://arxiv.org/abs/1912.10077.Google Scholar