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A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data

https://doi.org/10.2139/ssrn.5351772
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Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?
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Deep Learning for Finance: Deep Portfolios
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Asset pricing via deep graph learning to incorporate heterogeneous predictors
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Identifying factors via automatic debiased machine learning
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The three-factor model and artificial neural networks: predicting stock price movement in China
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Intraday Anomalies and Market Efficiency: A Trading Robot Analysis
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Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy
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Cryptocurrencies asset pricing via machine learning
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Common risk factors in the returns on stocks and bonds
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Stock performance modeling using neural networks: A comparative study with regression models
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Rate distortion theory and the volatility of asset prices
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Research on Amazon's stock price forecasting based on arbitrage pricing model based on big data
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A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market
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A factor approach to realized volatility forecasting in the presence of finite jumps and cross-sectional correlation in pricing errors
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A machine learning based asset pricing factor model comparison on anomaly portfolios
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Factor-based portfolio optimization
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Machine learning and oil price point and density forecasting
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A hierarchical deep model integrating economic facts for stock movement prediction
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Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM
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An unsupervised learning framework for marketneutral portfolio
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Mispricing, returns and the quest for parsimony
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Potential pricing factors in the Korean market
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Attention based dynamic graph neural network for asset pricing
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Mining Big Data Using Parsimonious Factor, Machine Learning, Variable Selection and Shrinkage Methods
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30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial
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Factor models for large and incomplete data sets with unknown group structure
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Factor-Augmented Forecasting in Big Data
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Big data analytics for financial Market volatility forecast based on support vector machine
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A factor pricing model based on machine learning algorithm
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A latent factor model for the Chinese stock market
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Nonlinear asset pricing in Chinese stock market: A deep learning approach
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Latent factor model for asset pricing
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Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis
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How to conduct a bibliometric analysis: An overview and guidelines
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The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models
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The continuous time random walk formalism in financial markets
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Jumps in equilibrium prices and market microstructure noise
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Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading
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Sufficient forecasting using factor models
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Testing against constant factor loading matrix with large panel high-frequency data
inferredcites →https://doi.org/10.1016/j.jeconom.2018.09.005openalex-snapshot
Knowing Factors or Factor Loadings, or Neither? Evaluating Estimators of Large Covariance Matrices with Noisy and Asynchronous Data
inferredcites →https://doi.org/10.1016/j.jeconom.2018.10.003openalex-snapshot
Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction
inferredcites →https://doi.org/10.1016/j.jeconom.2019.03.004openalex-snapshot
A rank test for the number of factors with high-frequency data
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Rank regularized estimation of approximate factor models
inferredcites →https://doi.org/10.1016/j.jeconom.2020.07.009openalex-snapshot
Autoencoder Asset Pricing Models
inferredcites →https://doi.org/10.1016/j.jeconom.2022.04.006openalex-snapshot
Identifying latent factors based on high-frequency data
inferredcites →https://doi.org/10.1016/j.jeconom.2022.07.004openalex-snapshot
News-Implied Linkages and Local Dependency in the Equity Market
inferredcites →https://doi.org/10.1016/j.jeconom.2022.11.001openalex-snapshot
Intraday cross-sectional distributions of systematic risk
inferredcites →https://doi.org/10.1016/j.jeconom.2022.11.004openalex-snapshot
A flexible predictive density combination for large financial data sets in regular and crisis periods
inferredcites →https://doi.org/10.1016/j.jeconom.2022.11.007openalex-snapshot
Uniform Predictive Inference for Factor Models with Instrumental and Idiosyncratic Betas
inferredcites →https://doi.org/10.1016/j.jeconom.2023.02.015openalex-snapshot
Realized regression with asynchronous and noisy high frequency and high dimensional data
inferredcites →https://doi.org/10.1016/j.jedc.2013.06.015openalex-snapshot
Asset price dynamics with heterogeneous beliefs and local network interactions
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Machine Learning Goes Global: Cross-Sectional Return Predictability in International Stock Markets
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A five-factor asset pricing model
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Macroeconomic forecasting using factor models and machine learning: an application to Japan
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Robust monitoring of CAPM portfolio betas II
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Using least square support vector regression with genetic algorithm to forecast beta systematic risk
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The numerical solution of one-dimensional discrete asset pricing model based on the improved trigonometric extreme learning machine
inferredcites →https://doi.org/10.1016/j.pacfin.2022.101886openalex-snapshot
Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market
inferredcites →https://doi.org/10.1016/j.resourpol.2023.104060openalex-snapshot
Conditional autoencoder pricing model for energy commodities
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Asset returns in deep learning methods: An empirical analysis on SSE 50 and CSI 300
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Artificial intelligence and machine learning in finance: A bibliometric review
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Big data analytics and investment
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Arbitrage pricing theory-based Gaussian temporal factor analysis for adaptive portfolio management
inferredcites →https://doi.org/10.1016/s0927-5398(97)00003-0openalex-snapshot
High frequency data in financial markets: Issues and applications
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Our Knowledge of the Past
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Deep Learning in Characteristics-Sorted Factor Models
inferredcites →https://doi.org/10.1038/s41467-024-51661-7openalex-snapshot
Artificial intelligence investments reduce risks to critical mineral supply
inferredcites →https://doi.org/10.1038/s41598-024-70342-5openalex-snapshot
Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values
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A citation and co-citation analysis of 10 years of KM theory and practices
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Stock Market Anomalies and Machine Learning Across the Globe
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The cross-section of Indian stock returns: evidence using machine learning
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Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data
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Large-Dimensional Factor Analysis Without Moment Constraints
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A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data
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Asset Pricing via the Conditional Quantile Variational Autoencoder
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Realised volatility prediction of high-frequency data with jumps based on machine learning
inferredcites →https://doi.org/10.1080/10293523.2016.1255469openalex-snapshot
The time traveller’s CAPM
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The five-factor model analysed by machine learning classification techniques
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Interpretable image-based deep learning for price trend prediction in ETF markets
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Conditional risk–return relationship in a time-varying beta model
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Deep-learning models for forecasting financial risk premia and their interpretations
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Data Factor and Financial Market Equilibrium
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The Behavior of Stock-Market Prices
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Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
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Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model
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New Methods for the Cross-Section of Returns
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Thousands of Alpha Tests
inferredcites →https://doi.org/10.1093/rfs/hhab038openalex-snapshot
Big Data in Finance
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Does Algorithmic Trading Reduce Information Acquisition?
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The Limits of the Limits of Arbitrage
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Implementing corporate sustainability information in socially responsible investing: a systematic review of empirical research
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Predictability of HK-REITs returns using artificial neural network
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Cluster-Specific Latent Factor Estimation in High-Dimensional Financial Time Series
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Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism
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Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
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Emerging new themes in environmental, social and governance investing: a systematic literature review
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Timing the Factor Zoo via Deep Learning: Evidence from China
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Diagnostics for asset pricing models
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Mapping women's involvement in family firms: A review based on bibliographic coupling analysis
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Is aggregate volatility a priced risk factor?
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CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*
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The Cross-Section of Expected Stock Returns
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Multifactor Explanations of Asset Pricing Anomalies
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On Persistence in Mutual Fund Performance
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Asset Pricing and Machine Learning: A critical review
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
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Short Communication: Deep Fundamental Factor Models
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The Arbitrage Theory of Capital Asset Pricing
inferredcites →https://doi.org/10.1142/s0217590822480022openalex-snapshot
DIGESTING THREE-FACTOR MODEL
inferredcites →https://doi.org/10.1146/annurev-financial-101521-104735openalex-snapshot
Factor Models, Machine Learning, and Asset Pricing
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Some Studies in Machine Learning Using the Game of Checkers
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Some studies in machine learning using the game of checkers
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Long Short‐Term Memory Recurrent Neural Network for Predicting the Return of Rate Underframe the Fama‐French 5 Factor
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Conducting systematic literature reviews and bibliometric analyses
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Stock return prediction with multiple measures using neural network models
inferredcites →https://doi.org/10.1191/1471082x06st112oaopenalex-snapshot
Estimation and filtering by reversible jump MCMC for a doubly stochastic Poisson model for ultra-high-frequency financial data
inferredcites →https://doi.org/10.1191/1478088706qp063oaopenalex-snapshot
Using thematic analysis in psychology
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The Capital Asset Pricing Model: Theory and Evidence
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The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas
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The Conceptual Flaws of Decentralized Automated Market Making
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A Regularized High-Dimensional Positive Definite Covariance Estimator with High-Frequency Data
inferredcites →https://doi.org/10.1287/mnsc.2023.4695openalex-snapshot
Deep Learning in Asset Pricing
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The Relationship between Default Risk and Asset Pricing: Empirical Evidence from Pakistan
inferredcites →https://doi.org/10.1371/journal.pone.0281783openalex-snapshot
Conditional autoencoder asset pricing models for the Korean stock market
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Is Liquidity Risk Priced in Artificial Intelligence ETFs?
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Asset Pricing and Portfolio Investment Management Using Machine Learning: Research Trend Analysis Using Scientometrics
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A comparison of CAPM and Fama-French three-factor model under Machine Learning approaching
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Jumps in Equilibrium Prices and Market Microstructure Noise
inferredcites →https://doi.org/10.2139/ssrn.3692805openalex-snapshot
Artificial Intelligence in Asset Management
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Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model
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Efficient Capital Markets: A Review of Theory and Empirical Work
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Capital Ideas: The Improbable Origins of Modern Wall Street.
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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
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Thematic Analysis Approach: A Step by Step Guide for ELT Research Practitioners
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Research on influencing factors of stock returns based on multiple regression and artificial intelligence model
inferredcites →https://doi.org/10.3233/jifs-222328openalex-snapshot
Application of DeepForest-CQP multi-factor model in quantitative stock selection
inferredcites →https://doi.org/10.3390/e24101487openalex-snapshot
Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
inferredcites →https://doi.org/10.3390/econometrics11030019openalex-snapshot
Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum
inferredcites →https://doi.org/10.3390/ijfs10040099openalex-snapshot
Improving Returns on Strategy Decisions through Integration of Neural Networks for the Valuation of Asset Pricing: The Case of Taiwanese Stock
inferredcites →https://doi.org/10.3390/jrfm13120314openalex-snapshot
Models for Expected Returns with Statistical Factors
inferredcites →https://doi.org/10.3390/jrfm14070302openalex-snapshot
Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature
inferredcites →https://doi.org/10.3390/jrfm15110535openalex-snapshot
A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing
inferredcites →https://doi.org/10.3390/risks12090148openalex-snapshot
Automated Machine Learning and Asset Pricing
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DO THE FAMA AND FRENCH FIVE-FACTOR MODEL FORECAST WELL USING ANN?
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Instantaneous and Averaged Volatility in Two-Side Filtration Model of Financial Asset Pricing
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Jump factor models in large cross‐sections
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Summing Up
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Large-dimensional Factor Analysis without Moment Constraints
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Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model
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Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.