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ref.bib
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@article{feng_taming_2020,
title = {Taming the Factor Zoo: A Test of New Factors},
volume = {75},
issn = {1540-6261},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.12883},
doi = {10.1111/jofi.12883},
shorttitle = {Taming the Factor Zoo},
abstract = {We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.},
pages = {1327--1370},
number = {3},
journaltitle = {The Journal of Finance},
author = {Feng, Guanhao and Giglio, Stefano and Xiu, Dacheng},
urldate = {2022-01-26},
date = {2020},
langid = {english},
}
@article{fama_five-factor_2015,
title = {A five-factor asset pricing model},
volume = {116},
issn = {0304405X},
doi = {10.1016/j.jfineco.2014.10.010},
pages = {1--22},
number = {1},
journaltitle = {Journal of Financial Economics},
shortjournal = {Journal of Financial Economics},
author = {Fama, Eugene F. and French, Kenneth R.},
urldate = {2022-01-26},
date = {2015},
langid = {english},
}
@article{sharpe_mutual_1966,
title = {Mutual Fund Performance},
volume = {39},
issn = {00219398, 15375374},
url = {http://www.jstor.org/stable/2351741},
pages = {119--138},
number = {1},
journaltitle = {The Journal of Business},
author = {Sharpe, William F.},
urldate = {2022-02-17},
date = {1966},
note = {Publisher: University of Chicago Press},
}
@inproceedings{goodfellow_generative_2014,
title = {Generative Adversarial Nets},
doi = {10.48550/arXiv.1406.2661},
booktitle = {{NIPS}},
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron C. and Bengio, Yoshua},
date = {2014},
}
@article{hochreiter_long_1997,
title = {Long Short-Term Memory},
volume = {9},
doi = {10.1162/neco.1997.9.8.1735},
pages = {1735--1780},
number = {8},
journaltitle = {Neural Computation},
author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
date = {1997},
}
@article{fama_choosing_2017,
title = {Choosing Factors},
doi = {10.2139/ssrn.2668236},
journaltitle = {Capital Markets: Market Efficiency {eJournal}},
author = {Fama, Eugene F. and French, Kenneth R.},
date = {2017},
}
@article{houdt_review_2020,
title = {A review on the long short-term memory model},
volume = {53},
doi = {10.1007/s10462-020-09838-1},
pages = {1--27},
number = {1},
journaltitle = {Artificial Intelligence Review},
author = {Houdt, Greg Van and Mosquera, Carlos and Nápoles, Gonzalo},
date = {2020},
}
@article{tobek_does_2018,
title = {Does It Pay to Follow Anomalies Research? Machine Learning Approach with International Evidence},
journaltitle = {Microeconomics: General Equilibrium \& Disequilibrium Models of Financial Markets {eJournal}},
author = {Tobek, Ondrej and Hronec, Martin},
date = {2018},
}
@article{srivastava_dropout_2014,
title = {Dropout: a simple way to prevent neural networks from overfitting},
volume = {15},
pages = {1929--1958},
number = {56},
journaltitle = {J. Mach. Learn. Res.},
author = {Srivastava, Nitish and Hinton, Geoffrey E. and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
date = {2014},
}
@article{yao_early_2007,
title = {On Early Stopping in Gradient Descent Learning},
volume = {26},
doi = {10.1007/s00365-006-0663-2},
pages = {289--315},
journaltitle = {Constructive Approximation},
author = {Yao, Y. and Rosasco, Lorenzo and Caponnetto, Andrea},
date = {2007},
}
@article{kingma_adam_2015,
title = {Adam: A Method for Stochastic Optimization},
volume = {abs/1412.6980},
journaltitle = {{CoRR}},
author = {Kingma, Diederik P. and Ba, Jimmy},
date = {2015},
}
@inproceedings{geron_hands-machine_2017,
title = {Hands-On Machine Learning with Scikit-Learn and {TensorFlow}: Concepts, Tools, and Techniques to Build Intelligent Systems},
author = {Géron, Aurélien},
date = {2017},
}
@inproceedings{nair_rectified_2010,
title = {Rectified Linear Units Improve Restricted Boltzmann Machines},
author = {Nair, Vinod and Hinton, Geoffrey E.},
date = {2010},
}
@article{gregory_constructing_2011,
title = {Constructing and Testing Alternative Versions of the Fama-French and Carhart Models in the {UK}},
journaltitle = {European Finance {eJournal}},
author = {Gregory, Alan and Tharyan, Rajesh and Christidis, Angela},
date = {2011},
}
@article{carhart_persistence_1997,
title = {On Persistence in Mutual Fund Performance},
volume = {52},
doi = {10.2307/2329556},
pages = {57--82},
journaltitle = {Journal of Finance},
author = {Carhart, Mark M.},
date = {1997},
}
@article{sharpe_capital_1964,
title = {Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk},
volume = {19},
pages = {425--442},
number = {3},
journaltitle = {Journal of Finance},
author = {Sharpe, William F.},
date = {1964},
}
@article{lintner_valuation_1965,
title = {The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets},
volume = {47},
doi = {10.2307/1924119},
pages = {13--37},
number = {1},
journaltitle = {The Review of Economics and Statistics},
author = {Lintner, John},
date = {1965},
}
@article{mossin_equilibrium_1966,
title = {Equilibrium in a Capital Asset Market},
volume = {34},
doi = {10.2307/1910098},
pages = {768--783},
journaltitle = {The Econometric Society},
author = {Mossin, Jan},
date = {1966},
}
@article{freyberger_dissecting_2017,
title = {Dissecting Characteristics Nonparametrically},
doi = {10.3386/w23227},
journaltitle = {{CESifo}: Macro},
author = {Freyberger, Joachim and Neuhierl, Andreas and Weber, Michael},
date = {2017},
}
@article{campbell_explaining_2000,
title = {Explaining the Poor Performance of Consumption-Based Asset Pricing Models},
volume = {55},
issn = {00221082, 15406261},
url = {http://www.jstor.org/stable/222404},
abstract = {We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain why the Capital Asset Pricing Model ({CAPM}) and its extensions are better approximate asset pricing models than is the standard consumption-based model. The model economy produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture. Therefore, though the consumption-based model and {CAPM} are both perfect conditional asset pricing models, the portfolio-based models are better approximate unconditional asset pricing models.},
pages = {2863--2878},
number = {6},
journaltitle = {The Journal of Finance},
author = {Campbell, John Y. and Cochrane, John H.},
date = {2000},
note = {Publisher: [American Finance Association, Wiley]},
}
@article{karras_training_2020,
title = {Training Generative Adversarial Networks with Limited Data},
doi = {10.48550/arXiv.2006.06676},
journaltitle = {{ArXiv}},
author = {Karras, Tero and Aittala, Miika and Hellsten, Janne and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo},
date = {2020},
}
@article{zhao_differentiable_2020,
title = {Differentiable Augmentation for Data-Efficient {GAN} Training},
volume = {abs/2006.10738},
journaltitle = {{ArXiv}},
author = {Zhao, Shengyu and Liu, Zhijian and Lin, Ji and Zhu, Jun-Yan and Han, Song},
date = {2020},
}
@online{noauthor_finage_2022,
title = {Finage {LTD}},
url = {https://finage.co.uk/},
titleaddon = {Finage {\textbar} Real-time Stock {APIs} and Websocket},
urldate = {2022-03-01},
date = {2022},
}
@online{noauthor_google_2019,
title = {Google {LLC}},
url = {https://developers.google.com/machine-learning/data-prep/construct/collect/data-size-quality},
titleaddon = {Data Preparation and Feature Engineering for Machine Learning},
urldate = {2022-03-27},
date = {2019-07-11},
}
@online{noauthor_nvidia_2022,
title = {Nvidia Corporation},
url = {https://www.nvidia.com/en-us/titan/titan-v/},
titleaddon = {{NVIDIA} {TITAN} V},
urldate = {2022-03-27},
date = {2022},
}
@article{bhatnagar_capital_2012,
title = {The capital asset pricing model versus the three factor model: A United Kingdom Perspective},
volume = {2},
pages = {51--65},
journaltitle = {International journal of business and social research},
author = {Bhatnagar, Chandra Shekhar and Ramlogan, Riad},
date = {2012},
}
@article{chen_deep_2021,
title = {Deep Learning in Asset Pricing},
doi = {10.2139/ssrn.3350138},
journaltitle = {Research Methods \& Methodology in Accounting {eJournal}},
author = {Chen, Luyang and Pelger, Markus and Zhu, Jason},
date = {2021},
}
@article{coulombe_can_2021,
title = {Can Machine Learning Catch the {COVID}-19 Recession?},
doi = {10.2139/ssrn.3796421},
journaltitle = {{SSRN} Electronic Journal},
author = {Coulombe, Philippe Goulet and Marcellino, Massimiliano and Stevanović, Dalibor},
date = {2021},
}
@article{fama_common_1993,
title = {Common risk factors in the returns on stocks and bonds},
volume = {33},
doi = {10.1016/0304-405X(93)90023-5},
pages = {3--56},
journaltitle = {Journal of Financial Economics},
author = {Fama, Eugene F. and French, Kenneth R.},
date = {1993},
}
@article{gu_empirical_2020,
title = {Empirical Asset Pricing via Machine Learning},
volume = {33},
doi = {10.1093/rfs/hhaa009},
pages = {2223--2273},
number = {5},
journaltitle = {Review of Financial Studies},
author = {Gu, Shihao and Kelly, Bryan T. and Xiu, Dacheng},
date = {2020},
}
@article{karolyi_home_2012,
title = {Home Bias, an Academic Puzzle},
doi = {10.2139/ssrn.2153206},
journaltitle = {Emerging Markets: Finance {eJournal}},
author = {Karolyi, George},
date = {2012},
}
@article{korajczyk_empirical_1989,
title = {An Empirical Investigation of International Asset Pricing},
journaltitle = {Econometrics: Applied Econometric Modeling in Financial Economics {eJournal}},
author = {Korajczyk, Robert A. and Viallet, Claude J.},
date = {1989},
}
@article{pelger_interpretable_2020,
title = {Interpretable Sparse Proximate Factors for Large Dimensions},
doi = {10.2139/ssrn.3175006},
journaltitle = {{ERN}: Other Econometrics: Data Collection \& Data Estimation Methodology (Topic)},
author = {Pelger, Markus and Xiong, Ruoxuan},
date = {2020},
}
@article{s_generative_2021,
title = {Generative Adversarial Network ({GAN}): a general review on different variants of {GAN} and applications},
pages = {1--8},
journaltitle = {2021 6th International Conference on Communication and Electronics Systems ({ICCES})},
author = {S, Karthika and Durgadevi, M.},
date = {2021},
}
@article{strubell_energy_2019,
title = {Energy and Policy Considerations for Deep Learning in {NLP}},
volume = {abs/1906.02243},
journaltitle = {{ArXiv}},
author = {Strubell, Emma and Ganesh, Ananya and {McCallum}, Andrew},
date = {2019},
}
@article{ross_arbitrage_1976,
title = {The arbitrage theory of capital asset pricing},
volume = {13},
pages = {341--360},
journaltitle = {Journal of Economic Theory},
author = {Ross, Stephen A.},
date = {1976},
}
@article{gu_autoencoder_2021,
title = {Autoencoder asset pricing models},
volume = {222},
issn = {0304-4076},
url = {https://www.sciencedirect.com/science/article/pii/S0304407620301998},
doi = {https://doi.org/10.1016/j.jeconom.2020.07.009},
abstract = {We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su ({KPS}, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption of {KPS}, we model factor exposures as a flexible nonlinear function of covariates. Our 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. This delivers estimates of nonlinear conditional exposures and the associated latent factors. Furthermore, our machine learning framework imposes the economic restriction of no-arbitrage. Our autoencoder asset pricing model delivers out-of-sample pricing errors that are far smaller (and generally insignificant) compared to other leading factor models.},
pages = {429--450},
number = {1},
journaltitle = {Journal of Econometrics},
author = {Gu, Shihao and Kelly, Bryan and Xiu, Dacheng},
date = {2021},
keywords = {Autoencoder, Big data, Conditional asset pricing model, Machine learning, Neural networks, Nonlinear factor model, Stock returns},
}