|
on Big Data |
| By: | Gabriel Loumeau; Christian Stettler |
| Abstract: | This paper studies the equilibrium effects of local fiscal autonomy accounting for benefits from self-determination. It proposes a quantifiable structural equilibrium framework in which imperfectly mobile heterogeneous households sort themselves across jurisdictions under endogenous public good provision. We calibrate the framework to fit the economic and geographic characteristics of the Canton of Bern using household-level data. In particular, we exploit quasi-natural policy variation in voting rights to quantify benefits from self-determination, and employ machine learning methods to accurately represent the local political process. We find that restricting local fiscal autonomy decreases welfare for (almost) all households. |
| Keywords: | fiscal autonomy, self-determination, decentralization, household, equilibrium, quasi-natural variation |
| JEL: | H71 H77 R51 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9445&r= |
| By: | Dafei Yin; Jing Li; Gaosheng Wu |
| Abstract: | Predicting the success of startup companies is of great importance for both startup companies and investors. It is difficult due to the lack of available data and appropriate general methods. With data platforms like Crunchbase aggregating the information of startup companies, it is possible to predict with machine learning algorithms. Existing research suffers from the data sparsity problem as most early-stage startup companies do not have much data available to the public. We try to leverage the recent algorithms to solve this problem. We investigate several machine learning algorithms with a large dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform best and achieve 53.03% and 52.96% F1 scores. We interpret the predictions from the perspective of feature contribution. We construct portfolios based on the models and achieve high success rates. These findings have substantial implications on how machine learning methods can help startup companies and investors. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.07985&r= |
| By: | Benjamin Gardner; Chiara Scotti; Clara Vega |
| Abstract: | While the literature has already widely documented the effects of macroeconomic news announcements on asset prices, as well as their asymmetric impact during good and bad times, we focus on the reaction to news based on the description of the state of the economy as painted by the Federal Open Market Committee (FOMC) statements. We develop a novel FOMC sentiment index using textual analysis techniques, and find that news has a bigger (smaller) effect on equity prices during bad (good) times as described by the FOMC sentiment index. Our analysis suggests that the FOMC sentiment index offers a reading on current and future macroeconomic conditions that will affect the probability of a change in interest rates, and the reaction of equity prices to news depends on the FOMC sentiment index which is one of the best predictors of this probability. |
| Keywords: | Monetary policy; Public information; Probability of a recession; Price discovery |
| JEL: | C53 D83 E27 E37 E44 E47 E50 G10 |
| Date: | 2021–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-74&r= |
| By: | Qinkai Chen; Christian-Yann Robert |
| Abstract: | The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.09015&r= |
| By: | Carlo Perroni; Kimberley Ann Scharf; Oleksandr Talavera; Linh Vi |
| Abstract: | We explore the link between online salience and charitable donations. Using a unique dataset on phone text donations that includes detailed information on the timing of cash gifts to charities, we link donations to time variation in online searches for words that appear in those charities’ mission statements. The results suggest that an increase in the online salience of the activities pursued by different charities affects the number and volume of donations made to those charities and to charities that pursue different goals. We uncover evidence of positive “own-salience” effects and negative “cross-salience” effects on donations. |
| Keywords: | charitable donations, online search, news shocks |
| JEL: | H41 D12 D64 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9436&r= |
| By: | Jean-Sébastien Lacam (ESSCA - Groupe ESSCA, CleRMa - Clermont Recherche Management - ESC Clermont-Ferrand - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne); David Salvetat (ESSCA - Groupe ESSCA) |
| Abstract: | This research examines for the first time the relationship between Big data and Smart data among French automotive distributors. Many low-tech firms engage in these data policies to improve their decisions and performance through the predictive capacities of their data. A discussion emerges in the literature according to which an effective policy lies in the conversion of a mass of raw data into so-called intelligent data. In order to understand better this digital transition, we question the transformation of data policies practiced in low-tech firms through the founding model of 3Vs (Volume, Variety and Velocity of data). First of all, this empirical study of 112 French automotive distributors develops the existing literature by proposing an original and detailed typology of the data policies practiced (Low data, Big data and Smart data). Secondly, after specifying the elements of the differences between the quantitative nature of Big data and the qualitative nature of Smart data, our results reveal and analyse for the first time the existence of their synergistic relationship. Companies transform their Big data approach into Smart data when they move from massive exploitation to intelligent exploitation of their data. The phenomenon is part of a high-end loop data exploitation. Initially, the exploitation of intelligent data can only be done by extracting a sample from a large raw data pool previously made by a Big data policy. Secondly, the organization's raw data pool is in turn enriched by the repayment of contributions made by the Smart data approach. Thus, this study develops three important ways. First off, we identify, detail and compare the current data policies of a traditional industry. Secondly, we reveal and explain the evolution of digital practices within organizations that now combine both quantitative and qualitative data exploitation. Finally, our results guide decision-makers towards the synergistic and the legitimate association of different forms of data management for better performance. |
| Keywords: | Big data,Smart data,volume,velocity,variety,automotive distribution |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03434863&r= |
| By: | Draca, Mirko (Department of Economics, University of Warwick and Centre for Economic Performance, LSE); Schwarz, Carlo (Department of Economics, University of Warwick and Centre for Competitive Advantage in the Global Economy (CAGE)) |
| Abstract: | Strong evidence has been emerging that major democracies have become more politically polarized, at least according to measures based on the ideological positions of political elites. We ask: have the general public (`citizens') followed the same pattern? Our approach is based on unsupervised machine learning models as applied to issue position survey data. This approach firstly indicates that coherent, latent ideologies are strongly apparent in the data, with a number of major, stable types that we label as: Liberal Centrist, Conservative Centrist, Left Anarchist and Right Anarchist. Using this framework, and a resulting measure of `citizen slant', we are then able to decompose the shift in ideological positions across the population over time. Specifically, we find evidence of a `disappearing center' in a range of countries with citizens shifting away from centrist ideologies into anti-establishment `anarchist' ideologies over time. This trend is especially pronounced for the US. |
| Keywords: | Polarization ; Ideology ; Unsupervised Learning JEL Classification: D72 ; C81 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wqapec:07&r= |
| By: | Lai Xinglin |
| Abstract: | To further develop the statistical inference problem for heterogeneous treatment effects, this paper builds on Breiman's (2001) random forest tree (RFT)and Wager et al.'s (2018) causal tree to parameterize the nonparametric problem using the excellent statistical properties of classical OLS and the division of local linear intervals based on covariate quantile points, while preserving the random forest trees with the advantages of constructible confidence intervals and asymptotic normality properties [Athey and Imbens (2016),Efron (2014),Wager et al.(2014)\citep{wager2014asymptotic}], we propose a decision tree using quantile classification according to fixed rules combined with polynomial estimation of local samples, which we call the quantile local linear causal tree (QLPRT) and forest (QLPRF). |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.15320&r= |
| By: | Jiafeng Chen (Department of Economics, Harvard University); Xiaohong Chen (Cowles Foundation, Yale University); Elie Tamer (Harvard University) |
| Abstract: | Artiï¬ cial Neural Networks (ANNs) can be viewed as {nonlinear sieves} that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the computational performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an orthogonalized plug-in with optimally-weighted sieve minimum distance (OP-OSMD) procedure and a sieve efficient score (ES) procedure. Both estimators for WAD use ANN sieves to approximate the unknown NPIV function and are root-n asymptotically normal and first-order equivalent. We provide a detailed practitioner’s recipe for implementing both efficient procedures. This involves the choice of tuning parameters for the unknown NPIV, the conditional expectations and the optimal weighting function that are present in both procedures but also the choice of tuning parameters for the unknown Riesz representer in the ES procedure. We compare their finite-sample performances in various simulation designs that involve smooth NPIV function of up to 13 continuous covariates, different nonlinearities and covariate correlations. Some Monte Carlo ï¬ ndings include: 1) tuning and optimization are more delicate in ANN estimation; 2) given proper tuning, both ANN estimators with various architectures can perform well; 3) easier to tune ANN OP-OSMD estimators than ANN ES estimators; 4) stable inferences are more difficult to achieve with ANN (than spline) estimators; 5) there are gaps between current implementations and approximation theories. Finally, we apply ANN NPIV to estimate average partial derivatives in two empirical demand examples with multivariate covariates. |
| Keywords: | Artiï¬ cial neural networks, Relu, Sigmoid, Nonparametric instrumental variables, Weighted average derivatives, Optimal sieve minimum distance, Efficient influence, Semiparametric efficiency, Endogenous demand |
| JEL: | C14 C22 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2319&r= |
| By: | Montobbio, Fabio (Università Cattolica del Sacro Cuore); Staccioli, Jacopo (Università Cattolica del Sacro Cuore); Virgillito, Maria Enrica (Università Cattolica del Sacro Cuore); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | This paper represents one of the first attempts at building a direct measure of occupational exposure to robotic labour-saving technologies. After identifying robotic and LS robotic patents retrieved by Montobbio et al. (2022), the underlying 4-digit CPC definitions are employed in order to detect functions and operations performed by technological artefacts which are more directed to substitute the labour input. This measure allows to obtain fine-grained information on tasks and occupations according to their similarity ranking. Occupational exposure by wage and employment dynamics in the United States is then studied, complemented by investigating industry and geographical penetration rates. |
| Keywords: | labour-saving technology, natural language processes, labour markets, technological unemployment |
| JEL: | O33 J24 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14879&r= |
| By: | Aparna Keshaviah; Cindy Hu; Robert P. Lennon; Robbie Fraleigh; Lauren J. Van Scoy; Bethany L. Snyder; Erin L. Miller; William A. Calo; Aleksandra E. Zgierska; Christopher Griffin |
| Abstract: | The authors developed an automated qualitative assistant (AQUA) using machine-learning methods to rapidly and accurately code large text datasets. The tool replaces Latent Semantic Indexing with a more transparent graph-theoretic topic extraction and clustering method. |
| Keywords: | qualitative research |
| URL: | http://d.repec.org/n?u=RePEc:mpr:mprres:27c5dbf3e4a04466902d3572443209ae&r= |
| By: | Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Kangying Zhou (Yale School of Management) |
| Abstract: | We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising \virtue of complexity:" Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions. |
| Keywords: | Portfolio choice, machine learning, random matrix theory, benign overfit, overparameterization |
| JEL: | C3 C58 C61 G11 G12 G14 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2190&r= |
| By: | Pietro Emilio Spini |
| Abstract: | This paper studies the robustness of estimated policy effects to changes in the distribution of covariates. Robustness to covariate shifts is important, for example, when evaluating the external validity of quasi-experimental results, which are often used as a benchmark for evidence-based policy-making. I propose a novel scalar robustness metric. This metric measures the magnitude of the smallest covariate shift needed to invalidate a claim on the policy effect (for example, $ATE \geq 0$) supported by the quasi-experimental evidence. My metric links the heterogeneity of policy effects and robustness in a flexible, nonparametric way and does not require functional form assumptions. I cast the estimation of the robustness metric as a de-biased GMM problem. This approach guarantees a parametric convergence rate for the robustness metric while allowing for machine learning-based estimators of policy effect heterogeneity (for example, lasso, random forest, boosting, neural nets). I apply my procedure to the Oregon Health Insurance experiment. I study the robustness of policy effects estimates of health-care utilization and financial strain outcomes, relative to a shift in the distribution of context-specific covariates. Such covariates are likely to differ across US states, making quantification of robustness an important exercise for adoption of the insurance policy in states other than Oregon. I find that the effect on outpatient visits is the most robust among the metrics of health-care utilization considered. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.09259&r= |
| By: | Wei Cao (HFUT - Hefei University of Technology); Yun He (HFUT - Hefei University of Technology); Wenjun Wang (HFUT - Hefei University of Technology); Weidong Zhu (HFUT - Hefei University of Technology); Yves Demazeau (LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes) |
| Abstract: | Risk control is a central issue for Chinese peer-to-peer (P2P) lending services. Although credit scoring has drawn much research interest and the superiority of ensemble models over single machine learning models has been proven, the question of which ensemble model is the best discrimination method for Chinese P2P lending services has received little attention. This study aims to conduct credit scoring by focusing on a Chinese P2P lending platform and selecting the optimal subset of features in order to find the best overall ensemble model. We propose a hybrid system to achieve these goals. Three feature selection algorithms are employed and combined to obtain the top 10 features. Six ensemble models with five base classifiers are then used to conduct comparisons after synthetic minority oversampling technique (SMOTE) treatment of the imbalanced data set. A real-world data set of 33 966 loans from the largest lending platform in China (ie, the Renren lending platform) is used to evaluate performance. The results show that the top 10 selected features can greatly improve performance compared with all features, particularly in terms of discriminating "bad" loans from "good" loans. Moreover, comparing the standard |
| Keywords: | credit scoring,ensemble learning,feature selection,synthetic minority oversampling technique (SMOTE) treatment,Chinese peer-to-peer (P2P) lending |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03434348&r= |
| By: | Schmidt, Lorenz; Odening, Martin; Ritter, Matthias |
| Abstract: | Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers, but suffer from considerable basis risk. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in forecasting precision by using machine learning techniques compared with traditional estimation approaches is substantial and that the use of regionalized models and disaggregated high-resolution weather data improve the performance of artificial neural networks. |
| Keywords: | Agricultural Finance, Crop Production/Industries, Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2021–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:ags:haaewp:316598&r= |
| By: | Hu, Junjie; Härdle, Wolfgang |
| Abstract: | We uncover networks from news articles to study cross-sectional stock returns. By analyzing a huge dataset of more than 1 million news articles collected from the internet, we construct time-varying directed networks of the S&P500 stocks. The well-defined directed news networks are formed based on a modest assumption about firm-specific news structure, and we propose an algorithm to tackle type-I errors in identifying the stock tickers. We find strong evidence for the comovement effect between the news-linked stocks returns and reversal effect from the lead stock return on the 1-day ahead follower stock return, after controlling for many known effects. Furthermore, a series of portfolio tests reveal that the news network attention proxy, network degree, provides a robust and significant cross-sectional predictability of the monthly stock returns. Among different types of news linkages, the linkages of within-sector stocks, large size lead firms, and lead firms with lower stock liquidity are crucial for cross-sectional predictability. |
| Keywords: | Networks,Textual News,Cross-Sectional Returns,Comovement,Network Degree |
| JEL: | G11 G41 C21 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021023&r= |
| By: | Domonkos Vamossy; Rolf Skog |
| Abstract: | We develop a tool that extracts emotions from social media text data. Our methodology has three main advantages. First, it is tailored for financial context; second, it incorporates key aspects of social media data, such as non-standard phrases, emojis and emoticons; and third, it operates by sequentially learning a latent representation that includes features such as word order, word usage, and local context. This tool, along with a user guide is available at: https://github.com/dvamossy/EmTract. Using EmTract, we explore the relationship between investor emotions expressed on social media and asset prices. We document a number of interesting insights. First, we confirm some of the findings of controlled laboratory experiments relating investor emotions to asset price movements. Second, we show that investor emotions are predictive of daily price movements. These impacts are larger when volatility or short interest are higher, and when institutional ownership or liquidity are lower. Third, increased investor enthusiasm prior to the IPO contributes to the large first-day return and long-run underperformance of IPO stocks. To corroborate our results, we provide a number of robustness checks, including using an alternative emotion model. Our findings reinforce the intuition that emotions and market dynamics are closely related, and highlight the importance of considering investor emotions when assessing a stock's short-term value. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.03868&r= |
| By: | Maximilien Germain (EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF, EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UP - Université de Paris); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UP - Université de Paris, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique, FiME Lab - Laboratoire de Finance des Marchés d'Energie - EDF R&D - EDF R&D - EDF - EDF - CREST - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres); Xavier Warin (EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF, EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, FiME Lab - Laboratoire de Finance des Marchés d'Energie - EDF R&D - EDF R&D - EDF - EDF - CREST - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres) |
| Abstract: | We consider the control of McKean-Vlasov dynamics (or mean-field control) with probabilistic state constraints. We rely on a level-set approach which provides a representation of the constrained problem in terms of an unconstrained one with exact penalization and running maximum or integral cost. The method is then extended to the common noise setting. Our work extends (Bokanowski, Picarelli, and Zidani, SIAM J. Control Optim. 54.5 (2016), pp. 2568–2593) and (Bokanowski, Picarelli, and Zidani, Appl. Math. Optim. 71 (2015), pp. 125–163) to a mean-field setting. The reformulation as an unconstrained problem is particularly suitable for the numerical resolution of the problem, that is achieved from an extension of a machine learning algorithm from (Carmona, Laurière, arXiv:1908.01613 to appear in Ann. Appl. Prob., 2019). A first application concerns the storage of renewable electricity in the presence of mean-field price impact and another one focuses on a mean-variance portfolio selection problem with probabilistic constraints on the wealth. We also illustrate our approach for a direct numerical resolution of the primal Markowitz continuous-time problem without relying on duality. |
| Keywords: | mean-field control,state constraints,neural networks |
| Date: | 2021–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03498263&r= |
| By: | Linyi Yang; Jiazheng Li; Ruihai Dong; Yue Zhang; Barry Smyth |
| Abstract: | Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.01770&r= |
| By: | Xiao-Yang Liu; Jingyang Rui; Jiechao Gao; Liuqing Yang; Hongyang Yang; Zhaoran Wang; Christina Dan Wang; Jian Guo |
| Abstract: | Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently. However, due to the highly complex and dynamic nature of real-world markets, raw historical financial data often involve large noise and may not reflect the future of markets, degrading the fidelity of DRL-based market simulators. Moreover, the accuracy of DRL-based market simulators heavily relies on numerous and diverse DRL agents, which increases demand for a universe of market environments and imposes a challenge on simulation speed. In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning. First, FinRL-Meta separates financial data processing from the design pipeline of DRL-based strategy and provides open-source data engineering tools for financial big data. Second, FinRL-Meta provides hundreds of market environments for various trading tasks. Third, FinRL-Meta enables multiprocessing simulation and training by exploiting thousands of GPU cores. Our codes are available online at https://github.com/AI4Finance-Foundation /FinRL-Meta. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.06753&r= |
| By: | Jiequn Han; Yucheng Yang; Weinan E |
| Abstract: | We propose an efficient, reliable, and interpretable global solution method, $\textit{Deep learning-based algorithm for Heterogeneous Agent Models, DeepHAM}$, for solving high dimensional heterogeneous agent models with aggregate shocks. The state distribution is approximately represented by a set of optimal generalized moments. Deep neural networks are used to approximate the value and policy functions, and the objective is optimized over directly simulated paths. Besides being an accurate global solver, this method has three additional features. First, it is computationally efficient for solving complex heterogeneous agent models, and it does not suffer from the curse of dimensionality. Second, it provides a general and interpretable representation of the distribution over individual states; and this is important for addressing the classical question of whether and how heterogeneity matters in macroeconomics. Third, it solves the constrained efficiency problem as easily as the competitive equilibrium, and this opens up new possibilities for studying optimal monetary and fiscal policies in heterogeneous agent models with aggregate shocks. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.14377&r= |
| By: | Anthony Coache; Sebastian Jaimungal |
| Abstract: | We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.13414&r= |
| By: | Igor Halperin; Jiayu Liu; Xiao Zhang |
| Abstract: | We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.01874&r= |
| By: | Yiheng Sun; Tian Lu; Cong Wang; Yuan Li; Huaiyu Fu; Jingran Dong; Yunjie Xu |
| Abstract: | The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion. It has non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation caused by the distinct characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promoting financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling real-world data with high dimensional features and sparsity in $O(n)$ time complexity. We conduct extensive experiments on two public datasets and a unique large-scale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-of-the-art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, shows stronger robustness to data sparsity, and provides meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.02365&r= |
| By: | Schmidt, Lorenz; Odening, Martin; Ritter, Matthias |
| Abstract: | Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers, but suffer from considerable basis risk. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in forecasting precision by using machine learning techniques compared with traditional estimation approaches is substantial and that the use of regionalized models and disaggregated high-resolution weather data improve the performance of artificial neural networks. |
| Keywords: | Agricultural Finance, Crop Production/Industries, Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2021–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:ags:haaepa:316598&r= |
| By: | Andrew Butler; Roy Kwon |
| Abstract: | Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. Integrating medium and large scale quadratic programs into a deep neural network architecture, however, is challenging as solving quadratic programs exactly by interior-point methods has worst-case cubic complexity in the number of variables. In this paper, we present an alternative network layer architecture based on the alternating direction method of multipliers (ADMM) that is capable of scaling to problems with a moderately large number of variables. Backward differentiation is performed by implicit differentiation of the residual map of a modified fixed-point iteration. Simulated results demonstrate the computational advantage of the ADMM layer, which for medium scaled problems is approximately an order of magnitude faster than the OptNet quadratic programming layer. Furthermore, our novel backward-pass routine is efficient, from both a memory and computation standpoint, in comparison to the standard approach based on unrolled differentiation or implicit differentiation of the KKT optimality conditions. We conclude with examples from portfolio optimization in the integrated prediction and optimization paradigm. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.07464&r= |
| By: | Anas Aboutaoufik (UIT - Université Ibn Tofaïl) |
| Abstract: | Big data is revolutionizing many fields of business, and logistics analytics is one of them, In this article, we aim to establish the level of use of big data in the transport and logistics sector in Morocco, we will define in a first place the concept of big data and its emergence, then we will try to illustrate the positioning of Morocco in this area, we will return after the results of a study conducted among logistics and transport operators in Morocco about the adoption of big data. |
| Keywords: | Morocco,Transport,Logistics,Big Data |
| Date: | 2021–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03266969&r= |
| By: | Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Amir Khalilzadeh (Ecole Polytechnique Fédérale de Lausanne) |
| Abstract: | We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that machine learning methods can capture the stylized facts about volatility without relying on any assumption about the distribution of stock returns. Finally, we show that our long short-term memory model outperforms other models by properly carrying information from the past predictor values. |
| Keywords: | Volatility Prediction, Volatility Clustering, LSTM, Neural Networks, Regression Trees. |
| JEL: | C51 C52 C53 C58 G17 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2195&r= |
| By: | Xiao Huang |
| Abstract: | We consider a two-stage estimation method for linear regression that uses the lasso in Tibshirani (1996) to screen variables and re-estimate the coefficients using the least-squares boosting method in Friedman (2001) on every set of selected variables. Based on the large-scale simulation experiment in Hastie et al. (2020), the performance of lassoed boosting is found to be as competitive as the relaxed lasso in Meinshausen (2007) and can yield a sparser model under certain scenarios. An application to predict equity returns also shows that lassoed boosting can give the smallest mean square prediction error among all methods under consideration. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.08934&r= |
| By: | Du, Qianqian; Mieno, Taro; Bullock, David; Edge, Brittani |
| Abstract: | A methodology is introduced that combines data from on-farm precision experimentation (OFPE) with remotely sensed vegetative index (VI) data to derive site-specific economically optimal side-dressing N rates (EONRs). An OFPE was conducted on a central Illinois field in the 2019 corn growing season; the trial design targeted six side-dressing N rates ranging from 0 and 177 kg ha-1 on field plots, and yields were recorded at harvest using a standard GPS-linked yield monitor. NDRE values were calculated from Sentinel-2 satellite imagery during the V10 to V12 corn growth stages of the experiment’s crop. After partitioning the field by NDRE quartile, economically N side-dressing rates were calculated after estimating each quartile’s yield response function. Consistent with agronomic expectations, results showed that the parts of the field with lower NDRE values had higher yield; but the impact of increasing NDRE levels on the side-dressing rate’s marginal product and EONR was not monotonic. Simulations predicted that compared to the side-dressing strategy the farmer would have implemented if not participating in the OFPE, net revenues could have been increased by $54 ha-1 by using the methodology presented, suggesting high potential value of combining OFPE and VI data. A key advantage of the proposed methodology is that the data’s inference space is the field to be managed. Further study is needed to improve the featured methodology. |
| Keywords: | Crop Production/Industries, Land Economics/Use, Research and Development/Tech Change/Emerging Technologies |
| Date: | 2021–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:ags:haaewp:316596&r= |
| By: | Li, Shiyuan; Hao, Miao |
| Abstract: | Based on the analysis of provincial-level data from 2001 to 2015, we find that regional inequality in China is not optimistic. Whether artificial intelligence, as a major technological change, will improve or worsen regional inequality is worthy of researching. We divide regional inequality into two dimensions: production and consumption, a total of three indicators. The empirical research is carried out to the eastern, central and western regions respectively. It is found that industrial intelligence improves the inequality of residents’ consumer welfare among regions, while at the same time there is the possibility of worsening regional inequality of innovation. We also clarify the heterogeneity of the mechanisms that artificial intelligence promotes innovation in different regions. |
| Keywords: | Artificial Intelligence; Regional Inequality; Innovation; Purchasing Power |
| JEL: | L25 O32 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110973&r= |
| By: | Rangel González Erick; Irving Llamosas-Rosas |
| Abstract: | This document presents an alternative to measure informal economic activity at the municipal level for the 2013-2020 period in Mexico. Using satellite images of nightlight and microdata from the 2019 Economic Census, the formal and informal Value Added at the municipal level is estimated using a modified version of the model proposed by Tanaka and Keola (2017). Although there are some measurements in Mexico of informal economic activity, these are not available at the municipal level or on an annual basis. The results indicate that at the national level, most of the municipalities show decreases in their levels of informal activity during the 2013-2019 period, with the North and North central regions concentrating a higher proportion of these, while in the Center the majority of the municipalities remained unchanged in the percentage of informal Value Added. In contrast, an important part of the Southern municipalities registered increases in the percentage of their informal activity during the same period. |
| JEL: | E01 E26 C53 C55 O54 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:bdm:wpaper:2021-18&r= |
| By: | Kieran Wood; Sven Giegerich; Stephen Roberts; Stefan Zohren |
| Abstract: | Deep learning architectures, specifically Deep Momentum Networks (DMNs) [1904.04912], have been found to be an effective approach to momentum and mean-reversion trading. However, some of the key challenges in recent years involve learning long-term dependencies, degradation of performance when considering returns net of transaction costs and adapting to new market regimes, notably during the SARS-CoV-2 crisis. Attention mechanisms, or Transformer-based architectures, are a solution to such challenges because they allow the network to focus on significant time steps in the past and longer-term patterns. We introduce the Momentum Transformer, an attention-based architecture which outperforms the benchmarks, and is inherently interpretable, providing us with greater insights into our deep learning trading strategy. Our model is an extension to the LSTM-based DMN, which directly outputs position sizing by optimising the network on a risk-adjusted performance metric, such as Sharpe ratio. We find an attention-LSTM hybrid Decoder-Only Temporal Fusion Transformer (TFT) style architecture is the best performing model. In terms of interpretability, we observe remarkable structure in the attention patterns, with significant peaks of importance at momentum turning points. The time series is thus segmented into regimes and the model tends to focus on previous time-steps in alike regimes. We find changepoint detection (CPD) [2105.13727], another technique for responding to regime change, can complement multi-headed attention, especially when we run CPD at multiple timescales. Through the addition of an interpretable variable selection network, we observe how CPD helps our model to move away from trading predominantly on daily returns data. We note that the model can intelligently switch between, and blend, classical strategies - basing its decision on patterns in the data. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.08534&r= |
| By: | Oscar Claveria (AQR IREA, University of Barcelona (UB). Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain. Tel.: +34-934021825); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC)); Petar Soric (Faculty of Economics & Business University of Zagreb.); Salvador Torra (Riskcenter–IREA, University of Barcelona (UB).) |
| Abstract: | This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as the tuning of the parameters and the regularisation techniques used to avoid overfitting. On the other hand, we design an out-of-sample forecasting experiment and evaluate the accuracy of three different deep neural networks to predict the US/UK foreign exchange rate in the days after the Brexit took effect. Of the three configurations, we obtain the best results with the deep feedforward architecture. When comparing the deep learning networks to time-series models used as a benchmark, the obtained results are highly dependent on the specific topology used in each case. Thus, although the three architectures generate more accurate predictions than the time-series models, the results vary considerably depending on the specific topology. These results hint at the potential of deep learning techniques, but they also highlight the importance of properly configuring, implementing and selecting the different topologies. |
| Keywords: | Forecasting, Exchange rates, Deep learning, Deep neural networks, Convolutional networks, Long short-term memory. JEL classification: C45, C58, E47, F31, G17. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:202201&r= |