Sparse Signals in the Cross‐Section of Returns

Chinco, Alexander; Clark-Joseph, Adam D.; Ye, Mao

doi:10.1111/jofi.12733

Cited by 221 publications

(43 citation statements)

References 83 publications

(109 reference statements)

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“…Yang, Qiao, Beling, Scherer & Kirilenko (2015) use Gaussian process inverse reinforcement learning to identify different types of traders. Chinco, Clark-Joseph & Ye (2015) use LASSO to study cross-stock information diffusion.…”

Section: Related Literaturementioning

confidence: 99%

Deep learning for limit order books

Sirignano

2018

Quantitative Finance

118

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This paper develops a new neural network architecture for modeling spatial distributions (i.e., distributions on R d ) which is computationally efficient and specifically designed to take advantage of the spatial structure of limit order books. The new architecture yields a low-dimensional model of price movements deep into the limit order book, allowing more effective use of information from deep in the limit order book (i.e., many levels beyond the best bid and best ask). This "spatial neural network" models the joint distribution of the state of the limit order book at a future time conditional on the current state of the limit order book. The spatial neural network outperforms status quo models such as the naive empirical model, logistic regression (with nonlinear features), and a standard neural network architecture.Both neural networks strongly outperform the logistic regression model. Due to its more effective use of information deep in the limit order book, the spatial neural network especially outperforms the standard neural network in the tail of the distribution, which is important for risk management applications. The models are trained and tested on nearly 500 U.S. stocks. Techniques from deep learning such as dropout are employed to improve performance. Due to the significant computational challenges associated with the large amount of data, models are trained with a cluster of 50 GPUs. Our data-driven approach offers

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Section: Related Literaturementioning

confidence: 99%

Deep learning for limit order books

Sirignano

2018

Quantitative Finance

118

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“…R and a subset S µ [p] with s := |S | being significantly smaller than p. Here, and in what follows, we use the notation x S = (x (i) ) i2S . In many applications it is not unusual that p = 100 (or even lager) while, probably, s º 5, see, e.g., the discussions in Rapach andZhou (2013) andChinco et al (2019). We will refer to variables in S as being strong.…”

Section: Analysis Of the Effect Of Targeting Strong Predictorsmentioning

confidence: 99%

The Effects of Targeting Predictors in a Random Forest Regression Model

et al. 2020

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“…Some of the other papers employ more statistics-oriented methods for sentiment analysis. Chinco et al (2019) apply Least Absolute Shrinkage and Selection Operator (LASSO) in high frequency stock return prediction using pre-processed financial news text sentiment as an explanatory variable. They emphasize the success of LASSO in the out of sample predictions.…”

Section: (Ii) Economicsmentioning

confidence: 99%

How Do Member Countries Receive IMF Policy Advice

et al. 2020

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This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries’ reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.

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Sparse Signals in the Cross‐Section of Returns

Cited by 221 publications

References 83 publications

Deep learning for limit order books

Deep learning for limit order books

The Effects of Targeting Predictors in a Random Forest Regression Model

How Do Member Countries Receive IMF Policy Advice

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