Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
TL;DR: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
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Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Yogesh K. Dwivedi,Laurie Hughes,Elvira Ismagilova,Gert Aarts,Crispin Coombs,Tom Crick,Yanqing Duan,Rohita Dwivedi,John S. Edwards,Aled Eirug,Vassilis Galanos,P. Vigneswara Ilavarasan,Marijn Janssen,Paul Jones,Arpan Kumar Kar,Hatice Kizgin,Bianca Kronemann,Banita Lal,Biagio Lucini,Rony Medaglia,Kenneth Le Meunier-FitzHugh,Leslie Caroline Le Meunier-FitzHugh,Santosh K. Misra,Emmanuel Mogaji,Sujeet Kumar Sharma,Jang Bahadur Singh,Vishnupriya Raghavan,Ramakrishnan Raman,Nripendra P. Rana,Spyridon Samothrakis,Jak Spencer,Kuttimani Tamilmani,Annie Tubadji,Paul Walton,Michael D. Williams +34 more
TL;DR: This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Posted Content
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.
Sam Corbett-Davies,Sharad Goel +1 more
TL;DR: It is argued that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce, rather than requiring that algorithms satisfy popular mathematical formalizations of fairness.
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Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
Journal ArticleDOI
Human Decisions and Machine Predictions
TL;DR: While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
References
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Non-Choice Evaluations Predict Behavioral Responses to Changes in Economic Conditions
TL;DR: In this paper, the authors explore an alternative approach that generates predictions based on relationships across decision problems between actual choice frequencies and non-choice subjective evaluations of the available options. And they find that this method yields accurate estimates of price sensitivities for a collection of products under conditions that render standard methods either inapplicable or highly inaccurate.
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Machine learning portfolios with equal risk contributions: evidence from the Brazilian market
TL;DR: In this paper, the authors investigated the use of machine learning (ML) to forecast stock returns in the Brazilian market using a rich proprietary dataset and showed that an Equal Risk Contribution (ERC) approach significantly improves risk-adjusted returns.
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Can we predict firms' innovativeness? The identification of innovation performers in an Italian region through a supervised learning approach.
TL;DR: The study shows the feasibility of predicting firms’ expenditures in innovation, as reported in the Community Innovation Survey, applying a supervised machine-learning approach on a sample of Italian firms, using an integrated dataset of administrative records and balance sheet data.
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Capturing deep tail risk via sequential learning of quantile dynamics
TL;DR: This paper developed a conditional quantile model that can learn long-term and short-term memories of sequential data and applied the model to asset return time series across eleven asset classes using historical data from the 1960s to 2018.
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Sovereign risk zones in Europe during and after the debt crisis
TL;DR: The authors employ a machine learning approach to build a European sovereign risk stratification using macroeconomic fundamentals and contagion measures, proxied by copula-based credit default swap (CDS) de...