Deep Learning Factor Alpha

Feng, Guanhao; Polson, Nicholas G.; Xu, Jianeng

doi:10.2139/ssrn.3243683

Cited by 31 publications

(16 citation statements)

References 52 publications

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“…In the study, they proposed Weighted Multichannel Time-series Regression (WMTR), Multilinear Discriminant Analysis (MDA). The authors of [113] used 57 characteristic features such as Market equity, Market Beta, Industry momentum, Asset growth, etc. as inputs to a Fama-French n-factor model DL for predicting monthly US equity returns in New York Stock Exchange (NYSE), American Stock Exchange (AMEX), or NASDAQ.…”

Section: Stock Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

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Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.

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Section: Stock Price Forecastingmentioning

confidence: 99%

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

View full text Add to dashboard Cite

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“…Such models are found in return prediction, market microstructure, portfolio choice, game strategy, and investors' behavior modeling. Feng et al (2018) apply deep learning (long-shortterm-memory (LSTM)) to predict asset returns, and then Feng et al (2021) develop a characteristics-sorted factor model based on deep learning (non-reducedform neural network) for asset pricing. Iwasaki and Chen (2018) propose a deep neural network model to conduct sentiment analysis and incorporate this knowledge into asset pricing and portfolio construction.…”

Section: Asset Pricing Modelsmentioning

confidence: 99%

FinTech as a Game Changer: Overview of Research Frontiers

Hendershott

Zhang

Zhao

et al. 2021

Information Systems Research

102

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Technologies have spawned finance innovations since the early days of computer applications in businesses, most recently reaching the stage of disruptive innovations, such as mobile payments, cryptocurrencies, and digitization of business assets. This has led to the emerging field called financial technology or simply FinTech. In this editorial review, we first provide an overview on relevant technological, pedagogical, and managerial issues pertaining to FinTech teaching and research, with a focus on market trading, artificial intelligence, and blockchain in finance. And then we introduce the articles appearing in this special section. We hope that our discussions of potential research directions and topics in FinTech will stimulate future research in the fields of information systems and finance toward making their unique marks in the FinTech evolution and the associated business and societal innovations.

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“…To provide the model with more information, quantitative traders or researchers often create hundreds or even thousands of features (aka factors) [13,14,15,16]. However, training a prediction model with all the available features may lead to poor performance.…”

Section: Introductionmentioning

confidence: 99%