Intraday Market Return Predictability Culled from the Factor Zoo
References
- (2022) How and when are high-frequency stock returns predictable? Working paper, Princeton University, Princeton, NJ.Google Scholar
- (2014) High-Frequency Financial Econometrics (Princeton University Press, Princeton, NJ).Google Scholar
- (2022) Overnight returns, daytime reversals, and future stock returns. J. Financial Econom. 145(3):850–875.Crossref, Google Scholar
- (2023) The high-frequency factor zoo. Working paper, Duke University, Durham, NC.Google Scholar
- (2024) News and asset pricing: A high-frequency anatomy of the SDF. Rev. Financial Stud. Forthcoming.Crossref, Google Scholar
- (2003) Arbitrage risk and the book-to-market anomaly. J. Financial Econom. 69(2):355–373.Crossref, Google Scholar
- (1997) Intraday periodicity and volatility persistence in financial markets. J. Empir. Finance 4(2):115–158.Crossref, Google Scholar
- (2003) Micro effects of macro announcements: Real-time price discovery in foreign exchange. Amer. Econom. Rev. 93(1):38–62.Crossref, Google Scholar
- (2014) Vpin and the flash crash. J. Financial Marketing 17:1–46.Crossref, Google Scholar
- (2023) Volatility measurement with pockets of extreme return persistence. J. Econometrics 237(2):105048.Crossref, Google Scholar
- (2023) Machine learning vs. economic restrictions: Evidence from stock return predictability. Management Sci. 69(5):2587–2619.Link, Google Scholar
- (2023) Discontinuous trading in continuous-time econometrics. Working paper, John Hopkins University, Baltimore.Google Scholar
- (2004) Power and bipower variation with stochastic volatility and jumps. J. Financial Econom. 2(1):1–37.Crossref, Google Scholar
- (2007) Asymmetric information and stock return cross-autocorrelations. Econom. Lett. 96(1):14–22.Crossref, Google Scholar
- (2021) Bond risk premiums with machine learning. Rev. Financial Stud. 34(2):1046–1089.Crossref, Google Scholar
- (2021) The cross-section of intraday and overnight returns. J. Financial Econom. 141(1):172–194.Crossref, Google Scholar
- (2022) Realized semibetas: Disentangling ‘good’ and ‘bad’ downside risks. J. Financial Econom. 144(1):227–246.Crossref, Google Scholar
- (2009) Expected stock returns and variance risk premia. Rev. Financial Stud. 22(11):4463–4492.Crossref, Google Scholar
- (2011) Tails, fears, and risk premia. J. Finance 66(6):2165–2211.Crossref, Google Scholar
- (2023) Market return around the clock: A puzzle. J. Financial Quant. Anal. 58(3):939–967.Crossref, Google Scholar
- (2022) The anatomy of out-of-sample forecasting accuracy. Working paper, Aarhus University, Aarhus, Denmark.Google Scholar
- (2024) Forest through the trees: Building cross-section of stock returns. J. Finance. Forthcoming.Google Scholar
- (2008) Predicting excess stock returns out of sample: Can anything beat the historical average? Rev. Financial Stud. 21(4):1509–1531.Crossref, Google Scholar
- (2017) Systemic co-jumps. J. Financial Econom. 126(3):563–591.Crossref, Google Scholar
- (2017) Improving polynomial estimation of the shapley value by stratified random sampling with optimum allocation. Comput. Oper. Res. 82:180–188.Crossref, Google Scholar
- (1993) Imperfect information and cross-autocorrelation among stock prices. J. Finance 48(4):1211–1230.Google Scholar
- (2022) Open source cross-sectional asset pricing. Crit. Finance Rev. 27(2):207–264.Crossref, Google Scholar
- (2023) Deep learning in asset pricing. Management Sci. 70(2):714–750.Google Scholar
- (2019) Sparse signals in the cross-section of returns. J. Finance 74(1):449–492.Crossref, Google Scholar
- (2005) Evidence on the speed of convergence to market efficiency. J. Financial Econom. 76(1):271–292.Crossref, Google Scholar
- (2007) The microstructure of cross-autocorrelations. Working paper, Federal Reserve Bank of New York, New York.Google Scholar
- (2001) Trading activity and expected stock returns. J. Financial Econom. 59(1):3–32.Crossref, Google Scholar
- (2002) Trading volume and cross-autocorrelations in stock returns. J. Finance 55(2):913–935.Crossref, Google Scholar
- (2022) The drift burst hypothesis. J. Econometrics 227(2):461–497.Crossref, Google Scholar
- (2008) Return differences between trading and non-trading hours: Like night and day. Working paper, Virginia Tech University, Blacksburg.Google Scholar
- (2009) A simple approximate long-memory model of realized volatility. J. Financial Econom. 7(2):174–196.Crossref, Google Scholar
- (2023) The anatomy of machine learning-based portfolio performance. Working paper, University of Montreal, Montreal.Google Scholar
- (2024) Nonparametric estimation for high-frequency data incorporating trading information. J. Econometrics 240:105690.Crossref, Google Scholar
- (1995) Comparing predictive accuracy. J. Bus. Econom. Statist. 13(3):253–265.Crossref, Google Scholar
- (2019) Machine learning for regularized survey forecast combination: Partially-egalitarian lasso and its derivatives. Internat. J. Forecast. 35(4):1679–1691.Crossref, Google Scholar
- (2022) Anomalies and the expected market return. J. Finance 77(1):639–681.Crossref, Google Scholar
- (2023) Do cross-sectional predictors contain systematic information? J. Financ. Quant. Anal. 58(3):1172–1201.Crossref, Google Scholar
- (2021) Selecting directors using machine learning. Rev. Financial Stud. 34(7):3226–3264.Crossref, Google Scholar
- (1970) Efficient capital markets: A review of theory and empirical work. J. Finance 25(2):383–417.Crossref, Google Scholar
- (2015) A five-factor asset pricing model. J. Financial Econom. 116(1):1–22.Crossref, Google Scholar
- (2022) Structural deep learning in conditional asset pricing. Working paper, Princeton University, Princeton, NJ.Google Scholar
- (2023) Pockets of predictability. J. Finance 78(3):1279–1341.Crossref, Google Scholar
- (2019) On the existence of sure profits via flash strategies. J. Appl. Probab. 56(2):384–397.Crossref, Google Scholar
- (2018) Market intraday momentum. J. Financial Econom. 129(1):394–414.Crossref, Google Scholar
- (2013) Dynamic trading with predictably returns and transaction costs. J. Finance 68(6):2309–2340.Crossref, Google Scholar
- (2016) Dynamic portfolios choice with frictions. J. Econom. Theory 165:487–516.Crossref, Google Scholar
- (2021) Asset pricing with omitted factors. J. Political Econom. 129(7):1947–1990.Crossref, Google Scholar
- (2010) Limits of arbitrage. Ann. Rev. Finance 2:251–275.Crossref, Google Scholar
- (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
- (2022) Deep learning statistical arbitrage. Working paper, Stanford University, Stanford, CA.Google Scholar
- (2020) Asset pricing: A tale of night and day. J. Financial Econom. 138(3):635–662.Crossref, Google Scholar
- (2010) Intraday patterns in the cross-section of stock returns. J. Finance 65(4):1369–1407.Crossref, Google Scholar
- (2007) Industry information diffusion and the lead-lag effect in stock returns. Rev. Financial Stud. 20(4):1113–1138.Crossref, Google Scholar
- (2022) A frog in every pan: Information discreteness and the lead-lag returns puzzle. J. Financial Econom. 145(2):83–102.Crossref, Google Scholar
- (2023) Intraday market predictability: A machine learning approach. J. Financial Econom. 21(2):485–527.Crossref, Google Scholar
- (2023) Is there a replication crisis in finance? J. Finance 78(5):2465–2518.Crossref, Google Scholar
- (2023) (Re-)imag(in)ing price trends. J. Finance 78(6):3193–3249.Crossref, Google Scholar
- (2015) Measuring uncertainty. Amer. Econom. Rev. 105(3):1177–1216.Crossref, Google Scholar
- (2021) Predicting returns with text data. Working paper, Yale University, New Haven, CT.Google Scholar
- (2014) Tail risk and asset prices. Rev. Financial Stud. 27(10):2841–2871.Crossref, Google Scholar
- (2024) The virtue of complexity in return prediction. J. Finance 79(1):459–503.Crossref, Google Scholar
- (2023) Financial machine learning. Working paper, Yale University, New Haven, CT.Google Scholar
- (2019) Smart sdfs. Working paper, University of Geneva, Geneva.Google Scholar
- (2022) Machine-learning in the Chinese stock market. J. Financial Econom. 145(2):64–82.Crossref, Google Scholar
- (2020) Factors that fit the time series and cross-section of stock returns. Rev. Financial Stud. 33(5):2274–2325.Crossref, Google Scholar
- (2010) A skeptical appraisal of asset pricing tests. J. Financial Econom. 96(1):175–194.Crossref, Google Scholar
- (2020) Predicting intraday return patterns based on overnight returns for the US stock market. Working paper, University of Amsterdam, Amsterdam.Google Scholar
- (2006) A liquidity-augmented capital asset pricing model. J. Financial Econom. 82(3):631–671.Crossref, Google Scholar
- (1990) When are contrarian profits due to stock market overreaction? Rev. Financial Stud. 3(2):175–205.Crossref, Google Scholar
- (2019) Antithetic and Monte Carlo kernel estimators for partial rankings. Statist. Comput. 29:1127–1147.Crossref, Google Scholar
- (2019) A tug of war: Overnight vs. intraday expected returns. J. Financial Econom. 134(1):192–213.Crossref, Google Scholar
- (2017) A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30:1–10.Google Scholar
- (2001) Disentangling the jumps of the diffusion in a geometric jumping Brownian motion. Giornale dell’Istituto Italiano degli Attuari LXIV:19–47.Google Scholar
- (1993) Portfolio return autocorrelation. J. Financial Econom. 34(3):307–344.Crossref, Google Scholar
- (2022) Sampling permutations for shapley value estimation. J. Mach. Learn. Res. 23(43):1–46.Google Scholar
- (2021) Portfolio performance attribution via shapley value. Working paper, BlackRock, New York.Google Scholar
- (1995) Predictability of stock returns: Robustness and economic significance. J. Finance 50(4):1201–1228.Crossref, Google Scholar
- (1996) Costly arbitrage: Evidence from closed-end funds. Quart. J. Econom. 111(4):1135–1151.Crossref, Google Scholar
- (2010) Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Rev. Financ. Stud. 23(2):821–862.Crossref, Google Scholar
- (2004) Neoclassical Finance (Princeton University Press, Princeton, NJ).Google Scholar
- (2022) The shapley value in machine learning. Working paper, University of Edinburgh, Edinburgh, UK.Google Scholar
- (1997) The limits of arbitrage. J. Finance 52(1):35–55.Crossref, Google Scholar
- (2015) Arbitrage asymmetry and the idiosyncratic volatility puzzle. J. Finance 70(5):1903–1948.Crossref, Google Scholar
- (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev. Financial Stud. 21(4):1455–1508.Crossref, Google Scholar

