nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒02‒13
23 papers chosen by
Stan Miles
Thompson Rivers University

  1. An overview of machine learning, deep learning, and artificial intelligence By Gebreel, Alia Youssef
  2. Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence By Jian Guo; Saizhuo Wang; Lionel M. Ni; Heung-Yeung Shum
  3. Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects By Tohid Atashbar; Rui Aruhan Shi
  4. What makes a satisfying life? Prediction and interpretation with machine-learning algorithms By Clark, Andrew E.; D'Ambrosio, Conchita; Gentile, Niccoló; Tkatchenko, Alexandre
  5. Calibrating Agent-based Models to Microdata with Graph Neural Networks By Farmer, J. Doyne; Dyer, Joel; Cannon, Patrick; Schmon, Sebastian
  6. Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets By Peer Nagy; Jan-Peter Calliess; Stefan Zohren
  7. Artificial Intelligence & Machine Learning in Finance: A literature review By Wassima Lakhchini; Rachid Wahabi; Mounime El Kabbouri; Casa Bp; Settat Hassan
  8. Human wellbeing and machine learning By Oparina, Ekaterina; Kaiser, Caspar; Gentile, Niccoló; Tkatchenko, Alexandre; Clark, Andrew E.; De Neve, Jan-Emmanuel; D'Ambrosio, Conchita
  9. Deep Reinforcement Learning for Gas Trading By Yuanrong Wang; Yinsen Miao; Alexander CY Wong; Nikita P Granger; Christian Michler
  10. Inequality-Constrained Monetary Policy in a Financialized Economy By Luca Eduardo Fierro; Federico Giri; Alberto Russo
  11. Deep Reinforcement Learning for Power Trading By Yuanrong Wang; Vignesh Raja Swaminathan; Nikita P. Granger; Carlos Ros Perez; Christian Michler
  12. Measuring the digitalisation of firms: A novel text mining approach By Axenbeck, Janna; Breithaupt, Patrick
  13. Empirical Asset Pricing via Ensemble Gaussian Process Regression By Damir Filipović; Puneet Pasricha
  14. Stochastic Langevin Monte Carlo for (weakly) log-concave posterior distributions By Crespo, Marelys; Gadat, Sébastien; Gendre, Xavier
  15. Similarity and Consistency in Algorithm-Guided Exploration By Yongping Bao; Ludwig Danwitz; Fabian Dvorak; Sebastian Fehrler; Lars Hornuf; Hsuan Yu Lin; Bettina von Helversen
  16. The Role of Government Effectiveness in the Light of ESG Data at Global Level By Laureti, Lucio; Costantiello, Alberto; Leogrande, Angelo
  17. Insurance analytics with clustering techniques By Jamotton, Charlotte; Hainaut, Donatien; Hames, Thomas
  18. Simulation schemes for the Heston model with Poisson conditioning By Jaehyuk Choi; Yue Kuen Kwok
  19. Optimal randomized multilevel Monte Carlo for repeatedly nested expectations By Yasa Syed; Guanyang Wang
  20. Truncated Poisson-Dirichlet approximation for Dirichlet process hierarchical models By Zhang, Junyi; Dassios, Angelos
  21. What do we Learn from a Machine Understanding: News Content? Stock Market Reaction to News By Brière, Marie; Huynh, Karen; Laudy, Olav; Pouget, Sébastien
  22. Artificial intelligence and labour market matching By OECD
  23. Digital finance research and developments around the World: a literature review By Ozili, Peterson K;

  1. By: Gebreel, Alia Youssef
    Abstract: An overview of machine learning, deep learning, and artificial intelligence
    Date: 2023–01–11
  2. By: Jian Guo; Saizhuo Wang; Lionel M. Ni; Heung-Yeung Shum
    Abstract: Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.
    Date: 2022–12
  3. By: Tohid Atashbar; Rui Aruhan Shi
    Abstract: The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
    Keywords: Reinforcement learning; Deep reinforcement learning; Artificial intelligence, RL; DRL; Learning algorithms; Macro modeling; RL algorithm overview; trust region policy optimization; DRL algorithm; decision process; RL algorithm; Machine learning; Artificial intelligence; Debt relief; General equilibrium models; Global
    Date: 2022–12–16
  4. By: Clark, Andrew E.; D'Ambrosio, Conchita; Gentile, Niccoló; Tkatchenko, Alexandre
    Abstract: Machine Learning (ML) methods are increasingly being used across a variety of fields and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non-linear method, Random Forests. We present two key model-agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non-penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non-negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non-linear analysis.
    Keywords: life satisfaction; well-being; machine learning; British cohort study
    JEL: I31 C63
    Date: 2022–06–07
  5. By: Farmer, J. Doyne; Dyer, Joel; Cannon, Patrick; Schmon, Sebastian
    Abstract: Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inference methods have emerged as powerful tools for performing this task when the model likelihood function is intractable, as is often the case for ABMs. In some real-world use cases of ABMs, both the observed data and the ABM output consist of the agents' states and their interactions over time. In such cases, there is a tension between the desire to make full use of the rich information content of such granular data on the one hand, and the need to reduce the dimensionality of the data to prevent difficulties associated with high-dimensional learning tasks on the other. A possible resolution is to construct lower-dimensional time-series through the use of summary statistics describing the macrostate of the system at each time point. However, a poor choice of summary statistics can result in an unacceptable loss of information from the original dataset, dramatically reducing the quality of the resulting calibration. In this work, we instead propose to learn parameter posteriors associated with granular microdata directly using temporal graph neural networks. We will demonstrate that such an approach offers highly compelling inductive biases for Bayesian inference using the raw ABM microstates as output.
    Date: 2022–06
  6. By: Peer Nagy; Jan-Peter Calliess; Stefan Zohren
    Abstract: We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment and utilise it to simulate a realistic trading environment for NASDAQ equities based on historic order book messages. To train a trading agent that learns to maximise its trading return in this environment, we use Deep Duelling Double Q-learning with the APEX (asynchronous prioritised experience replay) architecture. The agent observes the current limit order book state, its recent history, and a short-term directional forecast. To investigate the performance of RL for adaptive trading independently from a concrete forecasting algorithm, we study the performance of our approach utilising synthetic alpha signals obtained by perturbing forward-looking returns with varying levels of noise. Here, we find that the RL agent learns an effective trading strategy for inventory management and order placing that outperforms a heuristic benchmark trading strategy having access to the same signal.
    Date: 2023–01
  7. By: Wassima Lakhchini (Université Hassan 1er [Settat], ENCGS - Ecole Nationale de Commerce et de Gestion de SETTAT); Rachid Wahabi (Université Hassan 1er [Settat]); Mounime El Kabbouri (Université Hassan 1er [Settat]); Casa Bp; Settat Hassan
    Abstract: In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers' innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.'s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
    Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market
    Date: 2022–12–18
  8. By: Oparina, Ekaterina; Kaiser, Caspar; Gentile, Niccoló; Tkatchenko, Alexandre; Clark, Andrew E.; De Neve, Jan-Emmanuel; D'Ambrosio, Conchita
    Abstract: There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches perform better than traditional models. Although the size of the improvement is small in absolute terms, it is substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - i.e. material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.
    Keywords: subjective wellbeing; prediction methods; machine learning
    JEL: C63 C53 I31
    Date: 2022–07–20
  9. By: Yuanrong Wang; Yinsen Miao; Alexander CY Wong; Nikita P Granger; Christian Michler
    Abstract: Deep Reinforcement Learning (Deep RL) has been explored for a number of applications in finance and stock trading. In this paper, we present a practical implementation of Deep RL for trading natural gas futures contracts. The Sharpe Ratio obtained exceeds benchmarks given by trend following and mean reversion strategies as well as results reported in literature. Moreover, we propose a simple but effective ensemble learning scheme for trading, which significantly improves performance through enhanced model stability and robustness as well as lower turnover and hence lower transaction cost. We discuss the resulting Deep RL strategy in terms of model explainability, trading frequency and risk measures.
    Date: 2023–01
  10. By: Luca Eduardo Fierro (Institute of Economics, Scuola Superiore Sant'Anna Pisa (SSSA),); Federico Giri (Department of Economics and Social Sciences, Universita' Politecnica delle Marche (UNIVPM)); Alberto Russo (Department of Economics, Universitat Jaume I (UJI) and Department of Economics and Social Sciences, Universita' Politecnica delle Marche (UNIVPM))
    Abstract: We study how income inequality affects monetary policy through the inequality household debt channel. We design a minimal macro Agent-Based model that replicates several stylized facts, including two novel ones: falling aggregate saving rate and decreasing bankruptcies during the household's debt boom phase. When inequality meets financial liberalization, a leaning against-the-wind strategy can preserve financial stability at the cost of high unemployment, whereas an accommodative strategy, i.e. lowering the policy rate, can dampen the fall of aggregate demand at the cost of larger leverage. We conclude that inequality may constrain the central bank, even when it is not explicitly targeted.
    Keywords: Inequality, Financial Fragility, Monetary Policy, Agent-Based Model
    JEL: D31 E21 E25 E31 E52 G51
    Date: 2023–01
  11. By: Yuanrong Wang; Vignesh Raja Swaminathan; Nikita P. Granger; Carlos Ros Perez; Christian Michler
    Abstract: The Dutch power market includes a day-ahead market and an auction-like intraday balancing market. The varying supply and demand of power and its uncertainty induces an imbalance, which causes differing power prices in these two markets and creates an opportunity for arbitrage. In this paper, we present collaborative dual-agent reinforcement learning (RL) for bi-level simulation and optimization of European power arbitrage trading. Moreover, we propose two novel practical implementations specifically addressing the electricity power market. Leveraging the concept of imitation learning, the RL agent's reward is reformed by taking into account prior domain knowledge results in better convergence during training and, moreover, improves and generalizes performance. In addition, tranching of orders improves the bidding success rate and significantly raises the P&L. We show that each method contributes significantly to the overall performance uplifting, and the integrated methodology achieves about three-fold improvement in cumulative P&L over the original agent, as well as outperforms the highest benchmark policy by around 50% while exhibits efficient computational performance.
    Date: 2023–01
  12. By: Axenbeck, Janna; Breithaupt, Patrick
    Abstract: Due to the omnipresence of digital technologies in the economy, measuring firm digitalisation is of high importance. However, current indicators show several shortcomings, e.g., they lack timeliness and regional granularity. In this study, we show that advances in text mining and comprehensive firm website content can be leveraged to generate real-time and large-scale estimates of firm digitalisation. We use a transfer learning approach to capture the latent definition of digitalisation. For this purpose, we train a random forest regression model on labeled German newspaper articles and apply it on firm's website content. The predictions are used as a continuous indicator for firm digitalisation. Plausibility checks confirm the link to established digitalisation indicators at the firm and sectoral level as well as for firm size classes and regions. Lastly, we illustrate the indicator's potential for giving timely answers to pressing economic issues by analysing the link between digitalisation and firm resilience during the Covid-19 shock.
    Keywords: web-mining, text as data, machine learning, digitalisation
    JEL: C53 C81 O30
    Date: 2022
  13. By: Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Puneet Pasricha (École Polytechnique Fédérale de Lausanne (EPFL))
    Abstract: We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general online learning tasks. We conduct an empirical analysis on a large cross-section of US stocks from 1962 to 2016. We find that our method dominates existing machine learning models statistically and economically in terms of out-of-sample R-squared and Sharpe ratio of prediction-sorted portfolios. Exploiting the Bayesian nature of GPR, we introduce the mean-variance optimal portfolio with respect to the predictive uncertainty distribution of the expected stock returns. It appeals to an uncertainty averse investor and significantly dominates the equal- and value-weighted prediction-sorted portfolios, which outperform the S&P 500.
    Keywords: empirical asset pricing, Gaussian process regression, portfolio selection, ensemble learning, machine learning, firm characteristics
    JEL: C11 C14 C52 C55 G11 G12
    Date: 2022–12
  14. By: Crespo, Marelys; Gadat, Sébastien; Gendre, Xavier
    Abstract: In this paper, we investigate a continuous time version of the Stochastic Langevin Monte Carlo method, introduced in [39], that incorporates a stochastic sampling step inside the traditional overdamped Langevin diffusion. This method is popular in machine learning for sampling posterior distribution. We will pay specific attention in our work to the computational cost in terms of n (the number of observations that produces the posterior distribution), and d (the dimension of the ambient space where the parameter of interest is living). We derive our analysis in the weakly convex framework, which is parameterized with the help of the Kurdyka- Lojasiewicz (KL) inequality, that permits to handle a vanishing curvature settings, which is far less restrictive when compared to the simple strongly convex case. We establish that the final horizon of simulation to obtain an ε approximation (in terms of entropy) is of the order (d log(n)²)(1+r)² [log²(ε−1) + n²d²(1+r) log4(1+r)(n)] with a Poissonian subsampling of parameter n(d log²(n))1+r)−1, where the parameter r is involved in the KL inequality and varies between 0 (strongly convex case) and 1 (limiting Laplace situation).
    Date: 2023–01–16
  15. By: Yongping Bao; Ludwig Danwitz; Fabian Dvorak; Sebastian Fehrler; Lars Hornuf; Hsuan Yu Lin; Bettina von Helversen
    Abstract: Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly dependent on individual preferences. In an online experiment, we study whether the willingness of participants to follow the advice of a reinforcement learning algorithm depends on the fit between their own exploration preferences and the algorithm’s advice. We vary the weight that the algorithm places on exploration rather than exploitation, and model the participants’ decision-making processes using a learning model comparable to the algorithm’s. This allows us to measure the degree to which one’s willingness to accept the algorithm’s advice depends on the weight it places on exploration and on the similarity between the exploration tendencies of the algorithm and the participant. We find that the algorithm’s advice affects and improves participants’ choices in all treatments. However, the degree to which participants are willing to follow the advice depends heavily on the algorithm’s exploration tendency. Participants are more likely to follow an algorithm that is more exploitative than they are, possibly interpreting the algorithm’s relative consistency over time as a signal of expertise. Similarity between human choices and the algorithm’s recommendations does not increase humans’ willingness to follow the recommendations. Hence, our results suggest that the consistency of an algorithm’s recommendations over time is key to inducing people to follow algorithmic advice in exploration tasks.
    Keywords: algorithms, decision support systems, recommender systems, advice-taking, multi-armed bandit, search, exploration-exploitation, cognitive modeling
    JEL: C91 D83
    Date: 2022
  16. By: Laureti, Lucio; Costantiello, Alberto; Leogrande, Angelo
    Abstract: In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.
    Keywords: Analysis of Collective Decision-Making, General, Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior, Bureaucracy, Administrative Processes in Public Organizations, Corruption, Positive Analysis of Policy Formulation, and Implementation.
    JEL: D7 D70 D72 D73 D78
    Date: 2023–01–16
  17. By: Jamotton, Charlotte (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hames, Thomas (Detralytics)
    Abstract: The k-means algorithm and its variants are popular clustering techniques. Their purpose is to uncover group structures in a dataset. In actuarial applications, these partitioning methods detect clusters of policies with similar features and allow one to draw up a map of dominant risks. The main challenge lies in de􏰂ning a distance between two observations exclusively characterised by categorical variables. This research paper starts with a review of the k-means algorithm and develops an extension based on Burt's framework to manage categorical rating factors. We then focus on a mini-batch version that keeps computation time under control when analysing a large-scale dataset. We next broaden the scope of application of the fuzzy k-means to fully categorised datasets. Lastly, we conclude with a thorough introduction to spectral clustering and work around the dimensionality issue by reducing the size of the initial dataset with k-means.
    Keywords: Clustering analysis ; unsupervised learning ; k-means ; spectral clustering
    Date: 2023–01–12
  18. By: Jaehyuk Choi; Yue Kuen Kwok
    Abstract: Exact simulation schemes under the Heston stochastic volatility model (e.g., Broadie-Kaya and Glasserman-Kim) suffer from computationally expensive Bessel function evaluations. We propose a new exact simulation scheme without the Bessel function, based on the observation that the conditional integrated variance can be simplified when conditioned by the Poisson variate used for simulating the terminal variance. Our approach also enhances low-bias and time discretization schemes, which are suitable for derivatives with frequent monitoring. Extensive numerical tests reveal the good performance of the new simulation schemes in terms of accuracy, efficiency, and reliability when compared with existing methods.
    Date: 2023–01
  19. By: Yasa Syed; Guanyang Wang
    Abstract: The estimation of repeatedly nested expectations is a challenging problem that arises in many real-world systems. However, existing methods generally suffer from high computational costs when the number of nestings becomes large. Fix any non-negative integer $D$ for the total number of nestings. Standard Monte Carlo methods typically cost at least $\mathcal{O}(\varepsilon^{-(2+D)})$ and sometimes $\mathcal{O}(\varepsilon^{-2(1+D)})$ to obtain an estimator up to $\varepsilon$-error. More advanced methods, such as multilevel Monte Carlo, currently only exist for $D = 1$. In this paper, we propose a novel Monte Carlo estimator called $\mathsf{READ}$, which stands for "Recursive Estimator for Arbitrary Depth.'' Our estimator has an optimal computational cost of $\mathcal{O}(\varepsilon^{-2})$ for every fixed $D$ under suitable assumptions, and a nearly optimal computational cost of $\mathcal{O}(\varepsilon^{-2(1 + \delta)})$ for any $0
    Date: 2023–01
  20. By: Zhang, Junyi; Dassios, Angelos
    Abstract: The Dirichlet process was introduced by Ferguson in 1973 to use with Bayesian nonparametric inference problems. A lot of work has been done based on the Dirichlet process, making it the most fundamental prior in Bayesian nonparametric statistics. Since the construction of Dirichlet process involves an infinite number of random variables, simulation-based methods are hard to implement, and various finite approximations for the Dirichlet process have been proposed to solve this problem. In this paper, we construct a new random probability measure called the truncated Poisson–Dirichlet process. It sorts the components of a Dirichlet process in descending order according to their random weights, then makes a truncation to obtain a finite approximation for the distribution of the Dirichlet process. Since the approximation is based on a decreasing sequence of random weights, it has a lower truncation error comparing to the existing methods using stick-breaking process. Then we develop a blocked Gibbs sampler based on Hamiltonian Monte Carlo method to explore the posterior of the truncated Poisson–Dirichlet process. This method is illustrated by the normal mean mixture model and Caron–Fox network model. Numerical implementations are provided to demonstrate the effectiveness and performance of our algorithm.
    JEL: C1
    Date: 2023–01–04
  21. By: Brière, Marie; Huynh, Karen; Laudy, Olav; Pouget, Sébastien
    Abstract: Using textual data extracted by Causality Link platform from a large variety of news sources (news stories, call transcripts, broker re-search, etc.), we build aggregate news signals that take into account the tone, the tense and the prominence of various news statements about a given firm. We test the informational content of these signals and examine how news is incorporated into stock prices. Our sample covers 1, 701, 789 news-based signals that were built on 4, 460 US stocks over the period January 2014 to December 2021. We document large and significant market reactions around the publication of news, with some evidence of return predictability at short horizons. News about the future drives much larger reactions than news about the present or the past. Stock returns also react more to high-coverage news, fresh news and purely financial news. Finally, firms’ size matters: stocks that are not components of the Russell 1000 index experience larger reactions to news compared to those that are Russell 1000 components. Implications of our results for financial analysts and investors are of-fered and related to the links between news, firms’ market value and investment strategies.
    Keywords: Natural Language Processing; Textual Analysis; Efficient Market Hypothesis; ESG
    Date: 2023–01–19
  22. By: OECD
    Abstract: While still in its infancy, Artificial Intelligence (AI) is increasingly used in labour market matching, whether by private recruiters, public and private employment services, or online jobs boards and platforms. Applications range from writing job descriptions, applicant sourcing, analysing CVs, chat bots, interview schedulers, shortlisting tools, all the way to facial and voice analysis during interviews. While many tools promise to bring efficiencies and cost savings, they could also improve the quality of matching and jobseeker experience, and even identify and mitigate human bias. There are nonetheless some barriers to a greater adoption of these tools. Some barriers relate to organisation and people readiness, while others reflect concerns about the technology and how it is used, including: robustness, bias, privacy, transparency and explainability. The present paper reviews the literature and some recent policy developments in this field, while bringing new evidence from interviews held with key stakeholders.
    Keywords: Artificial Intelligence, Employment Services, Human Resources, Matching, Recruitment
    JEL: J01 J20 J60 J70
    Date: 2023–01–30
  23. By: Ozili, Peterson K;
    Abstract: This paper presents a concise review of the existing digital finance research in the literature, and highlight some of the developments in digital finance around the world. The paper reached several conclusions. Firstly, it showed that digital finance has become an important part of modern finance and the major application of digital finance can be found in Fintech, embedded finance, open banking and decentralized finance, central bank digital currencies, among others. Secondly, it identified some international determinants of digital finance which includes the need for efficiency in financial services delivery, the need to achieve the United Nations sustainable development goals using existing digital technologies, the need to increase financial inclusion through digital financial inclusion and the need for efficient payments and payment settlement finality. The paper also finds that digital finance research is growing fast, and recent studies have investigated contemporary issues in digital finance that are relevant for policy and practice. Regarding the digital finance developments around the world, the paper shows that the Fintech and mobile money industries are the largest beneficiary of investments in digital finance with the total number of users of mobile money services surpassing 1 billion globally. Also, the paper predicts that the future of digital finance is to create a digital environment that permits the offering of all kinds of financial product and services that can be customized and personalized to meet the unique needs of all users on a single digital platform and without requiring any form of human assistance or intermediary. The paper then suggest some areas for future research which include the need for more research on how regulators can keep pace with emerging digital finance transformation, the need for more research on user information security and compliance, the need for more research on how to deal with bias caused by bad data, the need for more research on how to deal with algorithmic bias, and the need for more research on how to combine a risk-conscious culture with a higher risk appetite for digital finance transformation.
    Keywords: digital finance, artificial intelligence, machine learning, financial inclusion, fintech, access to finance, financial stability, economic growth, blockchain, central bank digital currency, robotics, cryptocurrency.
    JEL: G21 O32 O33
    Date: 2023

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