|
on Big Data |
| By: | Nguyen, Phong Thanh |
| Abstract: | Machine Learning is a subset and technology developed in the field of Artificial Intelligence (AI). One of the most widely used machine learning algorithms is the K-Nearest Neighbors (KNN) approach because it is a supervised learning algorithm. This paper applied the K-Nearest Neighbors (KNN) algorithm to predict the construction price index based on Vietnam's socio-economic variables. The data to build the prediction model was from the period 2016 to 2019 based on seven socio-economic variables that impact the construction price index (i.e., industrial production, construction investment capital, Vietnam’s stock price index, consumer price index, foreign exchange rate, total exports, and imports). The research results showed that the construction price index prediction model based on the K-Nearest Neighbors (KNN) regression method has fewer errors than the traditional method. |
| Keywords: | Artificial Intelligence, K-Nearest Neighbors (KNN), machine learning, price index, construction management |
| JEL: | C53 C8 E0 L16 L74 |
| Date: | 2020–12–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109899&r= |
| By: | Bali, Turan G.; Beckmeyer, Heiner; Moerke, Mathis; Weigert, Florian |
| Abstract: | Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions, costly arbitrage, and option mispricing. |
| Keywords: | Machine learning,big data,option return predictability |
| JEL: | G10 G12 G13 G14 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2108&r= |
| By: | Xingzuo Zhou; Yiang Li |
| Abstract: | A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data-involving an autoregressive model with autoregressive integrated moving average (ARIMA)- and innovative web search queries-involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.13971&r= |
| By: | Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez |
| Abstract: | Events such as the Financial Crisis of 2007-2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.12621&r= |
| By: | Roland Tricot |
| Abstract: | New analysis of global investments by venture capitalists (VC) in private companies focused on artificial intelligence (AI) found VC investments in AI to be growing at a dramatic pace. The United States and the People’s Republic of China are leading this wave of investments that tend to concentrate on a few key industries. The data showed that the European Union, United Kingdom and Japan increased investments, but lag behind the two dominant players. The study analysed venture capital investments in 8 300 AI firms worldwide, covering 20 549 transactions between 2012 and 2020, based on data provided by Preqin, a private capital-markets analysis firm in London. The data did not capture every deal and required some extrapolation, yet the timeliness of the findings provides a valuable source of information as national governments, international organisations, public and private sectors develop policies and strategies to capture the benefits of AI for all. |
| Date: | 2021–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:oec:stiaab:319-en&r= |
| By: | Fatime Barbara Hegyi (European Commission - JRC); Manran Zhu (Central European University); Milan Janosov (Datapolis) |
| Abstract: | Despite their significant impact on social and economic development, innovation districts are facing challenges due to inadequacy of policies in terms of horizontal and vertical coordination or due to the lack of integrative policy approach. Strategic and targeted policy support leads to the acceleration of the growth of innovation districts, impacting the development of cities in general. To reach the potential of innovation districts in benefiting their local communities and in enabling greater collaboration, in creating jobs, and in promoting regional competitiveness, it is important to facilitate the positive externalities created by innovation districts through targeted policies. Hence the publication proposes a generic and algorithmic methodology to identify and measure the success of innovation districts. To achieve this, different sets of large-scale geospatial data have been combined with well-established machine learning methods and in-depth statistical analysis. As a result, a quantitative methodology is presented that can support the policy-making process in the identification of urban areas with a high concentration of innovation activities and with high potential for growth. First, this methodology allows the identification of such areas. Second, an evaluation framework is proposed that captures the success of these areas based on their economic performance. Third, these results are combined with descriptive statistical features to understand the main differentiators between successful and unsuccessful areas. This exploratory research aims at providing a set of methods and findings that heavily build on recent advances on using large-scale datasets and data science to understand social problems, and in particular, the key driving indicators of deprivation and success of various entities, such as urban areas with high concentration of innovation activities. |
| Keywords: | innovation districts, cities, urban development, data science |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc125559&r= |
| By: | Gharad T. Bryan; Dean Karlan; Adam Osman |
| Abstract: | We experimentally study the impact of substantially larger enterprise loans, in collaboration with an Egyptian lender. Larger loans generate small average impacts, but machine learning using psychometric data reveals dramatic heterogeneity. Top-performers (i.e., those with the highest predicted treatment effects) substantially increase profits, whereas profits for poor-performers drop. The magnitude of this difference implies that an individual lender’s credit allocation choices matter for aggregate income. Evidence on two fronts suggests large loans would be misallocated: top-performers are predicted by loan officers to have higher default rates; and, top-performers grow less than others when given small loans, implying that allocating larger loans based on prior performance is not efficient. Our results have important implications for credit expansion policy and our understanding of entrepreneurial talent: on the former, the use of psychometric data to identify top-performers suggests a pathway towards better allocation that revolves around entrepreneurial type more than firm type; on the latter, the reversal of fortune for poor-performers, who do well with small loans but not large, indicates a type of entrepreneur that we call a “go-getter” who performs well when constrained but poorly when not. |
| JEL: | D22 D24 L26 M21 O12 O16 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29311&r= |
| By: | Ioanna Arkoudi; Carlos Lima Azevedo; Francisco C. Pereira |
| Abstract: | This study proposes a novel approach that combines theory and data-driven choice models using Artificial Neural Networks (ANNs). In particular, we use continuous vector representations, called embeddings, for encoding categorical or discrete explanatory variables with a special focus on interpretability and model transparency. Although embedding representations within the logit framework have been conceptualized by Camara (2019), their dimensions do not have an absolute definitive meaning, hence offering limited behavioral insights. The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative. Thus, our approach brings benefits much beyond a simple parsimonious representation improvement over dummy encoding, as it provides behaviorally meaningful outputs that can be used in travel demand analysis and policy decisions. Additionally, in contrast to previously suggested ANN-based Discrete Choice Models (DCMs) that either sacrifice interpretability for performance or are only partially interpretable, our models preserve interpretability of the utility coefficients for all the input variables despite being based on ANN principles. The proposed models were tested on two real world datasets and evaluated against benchmark and baseline models that use dummy-encoding. The results of the experiments indicate that our models deliver state-of-the-art predictive performance, outperforming existing ANN-based models while drastically reducing the number of required network parameters. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.12042&r= |
| By: | Angell, Mintaka; Gold, Samantha; Hastings, Justine S.; Howison, Mark; Jensen, Scott; Keleher, Niall; Molitor, Daniel; Roberts, Amelia |
| Abstract: | Technology may displace tens of millions of workers in the coming decades. Part of the explanation for the projected displacement is an expanding mismatch in skills that employers seek and the skills that workers possess. Effects of labor force displacement disproportionately affect low-income workers and workers within industries where technological change replaces labor. As a result, a great deal of emphasis is placed on training and reskilling workers to ease transitions into new careers. However, utilization of training programs may be below optimal levels if workers are uncertain about the returns to their investment in training. While the U.S. spends billions of dollars annually on reskilling programs and unemployment insurance, there are few measures of program effectiveness that workers and government can use to guide training investment decisions and ensure delivery of valuable reskilling and improved outcomes. In a nationwide conjoint survey experiment, we find job seekers prefer information on the value-added returns to earnings following enrollment in training and reskilling programs. We identify a clear demand for value-added measures. For every 10% increase in expected earnings, workers are 17.4% more likely to express interest in a training program. To meet this demand for information, governments can provide return on investment measures. Fortunately, the data to estimate these returns are available in state administrative data. We demonstrate a causal machine learning method that provides these missing causal estimates of value-added that workers prefer and that can provide correct incentives in the market for labor training. Focusing on a set of workforce training programs in Rhode Island, our causal machine learning estimates suggest that training increases enrollees’ future quarterly earnings by \$605. We estimate that return on investment ranges between -\$1,570 in quarterly earnings for the lowest value-added program to \$3,470 in quarterly earnings for the highest value-added program. |
| Date: | 2021–09–24 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:thg23&r= |
| By: | Pengzhou Wu; Kenji Fukumizu |
| Abstract: | As an important problem of causal inference, we discuss the estimation of treatment effects (TEs) under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying TEs. Our VAE also naturally gives representation balanced for treatment groups, using its prior. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings. Based on the identifiability of our model, further theoretical developments on identification and consistent estimation are also discussed. This paves the way towards principled causal effect estimation by deep neural networks. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.15062&r= |
| By: | Genz, Sabrina; Gregory, Terry; Janser, Markus; Lehmer, Florian; Matthes, Britta |
| Abstract: | We investigate how workers adjust to firms' investments into new digital technologies, including artificial intelligence, augmented reality, or 3D printing. For this, we collected novel data that links survey information on firms' technology adoption to administrative social security data. We then compare individual outcomes between workers employed at technology adopters relative to non-adopters. Depending on the type of technology, we find evidence for improved employment stability, higher wage growth, and increased cumulative earnings in response to digital technology adoption. These beneficial adjustments seem to be driven by technologies used by service providers rather than manufacturers. However, the adjustments do not occur equally across worker groups: IT-related expert jobs with non-routine analytic tasks benefit most from technological upgrading, coinciding with highly complex job requirements, but not necessarily with more academic skills. |
| Keywords: | technological change,artificial intelligence,employment stability,wages |
| JEL: | J23 J31 J62 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:21073&r= |
| By: | Jaeyoung Cheong; Heejoon Lee; Minjung Kang |
| Abstract: | Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.15045&r= |
| By: | Olubusoye, Olusanya E; Akintande, Olalekan J.; Yaya, OlaOluwa S.; Ogbonna, Ahamuefula; Adenikinju, Adeola F. |
| Abstract: | The study investigates the impact of uncertainties on energy pricing during the COVID-19 pandemic using five uncertainty measures that include the COVID-Induced Uncertainty (CIU), Economic Policy Uncertainty (EPU), Global Fear Index (GFI); Volatility Index (VIX), and the Misinformation Index of Uncertainty (MIU). The data, which span between 2-January, 2020 and 19-January, 2021, corresponding to the period of the COVID-19 pandemic. The study finds energy prices to respond significantly to the examined uncertainty measures, with EPU seen to affect the prices of most energy types during the pandemic. We also find predictive potentials inherent in VIX, CIU, and MIU for global energy sources. |
| Keywords: | Coronavirus pandemic; Energy market; Machine Learning; Uncertainty |
| JEL: | D8 D81 Q41 |
| Date: | 2021–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109838&r= |
| By: | Nicolas Gavoille; Anna Zasova |
| Abstract: | The interactions between minimum wage policy and tax evasion remain largely unknown. We study firm-level employment effects of a large and biting minimum wage increase in Latvia conditional on labor tax compliance. The Latvian labor market is characterized by the prevalence of envelope wages, i.e. unreported cash-in-hand complements to the official wage. We apply machine learning to classify firms between compliant and tax-evading using a unique combination of administrative and survey data. We then show that firms engaged in labor tax evasion are insensitive to the minimum wage shock. Our results suggest that these firms use wage underreporting as an adjustment margin, converting (part of) the envelope into legal wage. Increasing minimum wage contributes to tax rule enforcement, but this comes at the cost of negative employment consequences for compliant firms. |
| Keywords: | Minimum wage; Employment; Tax evasion |
| JEL: | J08 H26 E26 |
| Date: | 2021–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:sol:wpaper:2013/331990&r= |
| By: | Shuo Sun; Rundong Wang; Bo An |
| Abstract: | Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques' potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.13851&r= |
| By: | Nicolas Gavoille (Stockholm School of Economics in Riga (SSE Riga)); Anna Zasova (Baltic International Centre for Economic Policy Studies (BICEPS)) |
| Abstract: | The interactions between minimum wage policy and tax evasion remain largely unknown. We study firm-level employment effects of a large and biting minimum wage increase in Latvia conditional on labor tax compliance. The Latvian labor market is characterized by the prevalence of envelope wages, i.e., unreported cash-in-hand complements to the official wage. We apply machine learning to classify firms between compliant and tax-evading using a unique combination of administrative and survey data. We then show that firms engaged in labor tax evasion are insensitive to the minimum wage shock. Our results suggest that these firms use wage underreporting as an adjustment margin, converting (part of ) the envelope into legal wage. Increasing minimum wage contributes to tax rule enforcement, but this comes at the cost of negative employment consequences for compliant firms. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:bic:rpaper:6&r= |
| By: | Kea BARET |
| Abstract: | This paper studies the side-effects of fiscal rules’ compliance on the economy and social welfare. It considers Budget Balance Rules’ (BBR) compliance effects on maroeconomic indicators and social welfare proxy indicators in sixteen countries between 2004 and 2015. Instead of fiscal rules strength or fiscal rules presence effectiveness, we focus on fiscal rules’ compliance to assess the impact of governments behavior on the social area. The paper shows that governments go beyond the expected trade-off between BBR’s compliance and GDP Growth by operating a reallocation of their spending. Such choices in public expense lead to an increase in social inequalities highlighted that governments finally face a trade-off between fiscal rules’ compliance and social objectives. The analysis constitutes the first use of double/debiased machine learning for treatment recently developed by Chernozhukov et al. [2018] applied to fiscal discipline issues. Through this method we are able to highlight key determinants for BBR’s compliance and assess the compliance’s effect on different macroeconomic and social indicators. We take care of Voter Preferences by computing a new proxy though Latent Factor Analysis Approach, and show that Voter prefenreces appear as a key variable for BBR’s compliance, giving an empirical proof that Wyplosz [2012]’s bias matters. |
| Keywords: | Fiscal rules’ compliance; Social Welfare; Fiscal Surveillance; Machine learning. |
| JEL: | E61 H11 H50 H61 H62 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ulp:sbbeta:2021-38&r= |
| By: | Wurm, Daniel; Zielinski, Oliver; Lübben, Neeske; Jansen, Maike; Ramesohl, Stephan |
| Abstract: | Im Zeitalter der Machine Economy ist der maschinelle Dialog allgegenwärtig - das bietet neue Chancen für Nachhaltigkeit, erhöht gleichzeitig aber durch die zugrundeliegenden Technologien auch den Druck auf unsere Umwelt. Internet of Things (IoT), Künstliche Intelligenz (KI) und Distributed Ledger Technology (DLT) sind das technologische Fundament der Machine Economy. Damit verbunden sind Infrastrukturen, Datenströme und Anwendungen, die hohe Energie- sowie Ressourcenaufwände erzeugen. Der derzeitige politische Diskurs sowie die Nachhaltigkeitsforschung fokussieren sich auf Umweltwirkungen durch digitale Infrastrukturen. Daten, Applikationen sowie die Rolle von Akteuren als Treiber der Umweltwirkung werden zu wenig beleuchtet. In diesem Papier sprechen wir uns für eine 'Grüne Governance der Machine Economy' aus. Adressiert werden Annahmen zu systemübergreifenden Treibern von Umweltbelastungen und ihrer Wirkung. Ziel ist es, ein Gesamtsystem nachhaltiger Entscheidungen und ein ökologisches Zusammenspiel aller beteiligten Technologien in der Wertschöpfung zu ermöglichen. Zukünftige Forschung soll die hier vorgestellten Hypothesen weiter ausarbeiten und konkrete Handlungsoptionen für eine Stakeholder übergreifende Roadmap erarbeiten. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wuprep:22&r= |
| By: | Claude Crampes (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Yassine Lefouili (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | This paper investigates the trade-offs associated with the digitalization of the energy sector. Arguing that digitalization has both bright and dark sides, we study the extent to which it can help make energy systems efficient and sustainable. We first discuss how digitalization affects the responsiveness of demand, and explore its implications for spot pricing, load shedding, and priority service. In particular, we highlight the conditions under which digital technologies that allow demand to be more responsive to supply are likely to be used. We then turn to the way digitalization can contribute to the decarbonization of the energy sector, and discuss the promises and limitations of artificial intelligence in this area. Finally, we contend that policymakers should pay special attention to the privacy concerns raised by the digitalization of the energy sector and the cyberattacks that it enables. |
| Keywords: | Digitalisation,Dynamic pricing,Electricity,Artificial Intelligence |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03352748&r= |
| By: | Pedro Garcia-del-Bario (Universidad de Navarra, Pamplona, Spain); J. James Reade (Department of Economics, University of Reading) |
| Abstract: | The literature acknowledges \Uncertainty of Outcome" (UO) as a major factor to explain the degree of interest that sporting competitions draw from fans and the general public. Uncertainty about the championship winner is crucial insofar as nancial success depends on the capacity to attract potential consumers of spectacle. This paper focusses on one aspect of UO and examines to what extent reductions in the interest of followers is due to the removal of uncertainty about the world drivers' champion in Formula One. To study how certainty on the winner undermines the degree of attention generated by the Formula One world drivers' championship, we rely on two alternative indexes | similar although not identical | reported by Google Trends. Both of these appraisals are computed from data on users' search intensity in Google, where weekly records are normalized on the relative amount of searches per calendar year. Thus, as dependent variables for the empirical analysis we use two measures: Google Trends News (GTN), to capture the intensity with which individuals search news articles associated; and Google Trends Web (GTW), to get a wider overview based on all kind of Internet contents. The former empirical analysis is carried out on 10 years of available data; while the latter approach estimates the models for a larger period of 14 years. Our empirical strategy includes additionally adopting indicator saturation techniques to address this issue while controlling for outliers. |
| Keywords: | Global Sports, Outcome Certainty, Google Trends, Competitions' Multiple Prizes; Event Analysis |
| JEL: | J24 J33 J71 |
| Date: | 2021–09–27 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2021-18&r= |
| By: | Hélia Costa; Giuseppe Nicoletti; Mauro Pisu; Christina von Rueden |
| Abstract: | Despite the rising importance and economy-wide effects of online platforms, the paucity of cross-country comparable data still hampers understanding of the structural and policy determinants of their diffusion. This study contributes to the understanding of multi-sided online platforms in three main ways. First, we build a harmonised international dataset of online platforms and their use across 43 OECD and G20 countries, covering the 2013-19 period and nine areas of activity. Second, we describe main trends in the use of platforms in the past years, and third, we investigate the structural and policy determinants of online platforms diffusion across countries and over time. |
| Keywords: | Data collection, digitalisation, online platforms |
| JEL: | C80 M20 O33 |
| Date: | 2021–10–05 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1683-en&r= |
| By: | G. Mazzei; F. G. Bellora; J. A. Serur |
| Abstract: | In most real scenarios the construction of a risk-neutral portfolio must be performed in discrete time and with transaction costs. Two human imposed constraints are the risk-aversion and the profit maximization, which together define a nonlinear optimization problem with a model-dependent solution. In this context, an optimal fixed frequency hedging strategy can be determined a posteriori by maximizing a sharpe ratio simil path dependent reward function. Sampling from Heston processes, a convolutional neural network was trained to infer which period is optimal using partial information, thus leading to a dynamic hedging strategy in which the portfolio is hedged at various frequencies, each weighted by the probability estimate of that frequency being optimal. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.12337&r= |
| By: | Prem, Mounu; Purroy, Miguel E.; Vargas, Juan F. |
| Abstract: | Anti-personnel landmines are one of the main causes of civilian victimization in conflict-affected areas and a significant obstacle for post-war reconstruction. Demining campaigns are therefore a promising policy instrument to promote long-term development. We argue that the economic and social effects of demining are not unambiguously positive. Demining may have unintended negative consequences if it takes place while conflicts are ongoing, or if they do not lead to full clearance. Using highly disaggregated data on demining operations in Colombia from 2004 to 2019, and exploiting the staggered fashion of demining activity, we find that post-conflict humanitarian demining generates economic growth (measured with nighttime light density) and increases students’ performance in test scores. In contrast, economic activity does not react to post-conflict demining events carried out during military operations, and it decreases if demining takes place while the conflict is ongoing. Rather, demining events that result from military operations are more likely to exacerbate extractive activities. |
| Date: | 2021–09–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:3jzk6&r= |
| By: | Chongwoo Choe (Department of Economics, Monash University); Jiajia Cong (School of Management, Fudan University); Chengsi Wang (Department of Economics, Monash University) |
| Abstract: | We study how a data-rich firm can benefit by unilaterally sharing its customer data with a data-poor competitor when the data can be used for price discrimination. By sharing data on consumers that are more loyal to the competitor while keeping the data on the competitor's most loyal consumers to itself, the firm can induce the competitor to raise its price for consumers it does not have data on. This makes both firms better off than without data sharing. |
| Keywords: | customer data sharing, price discrimination |
| JEL: | L11 L13 L40 M30 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:mos:moswps:2021-10&r= |