This label covers statistical learning outside deep networks: random forests and gradient boosting for cross-sectional return prediction, LASSO and ridge for factor selection, kernel methods and Gaussian processes for nonlinear signals, clustering for regime and asset grouping, and the model-selection machinery around them. In asset pricing it is the empirical workhorse of the “machine learning in the cross-section” literature.
What to check when reading. Financial data breaks the i.i.d. assumption behind ordinary cross-validation. A credible paper uses purged, embargoed, or walk-forward splits; reports feature importance with some stability check; shows the economic result (long-short portfolio after costs, not R-squared alone); and tests whether gains survive the removal of micro-caps and the inclusion of transaction costs. Out-of-sample R-squared in the cross-section is small when honest; a paper reporting 10% should be read with care.
Artificial intelligence (AI) can undermine financial stability because of malicious use, misinformation, misalignment, and the AI analytics market structure. The low frequency and uniqueness of financial crises, coupled with mutable and unclear objectives, frustrate machine learning. Even if the aut
Nowadays, the global booming of FinTech can be seen everywhere. FinTech has created innovative disruptions to traditional, long-established financial institutions (e.g., banks and insurance companies) in financial services markets. Despite of its popularity, there are many different definitions of F
In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve compl
Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning
With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognit
Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are application-specific, and it is impossible to produce an exhaustiv
This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI’s evolution from early statistical methods to sophisticated machine learning, highlighting XAI’s role in popular financial applications. The paper undersco
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the eff
Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent information, is zero onl
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