|
on Computational Economics |
|
Issue of 2023‒01‒09
35 papers chosen by |
| By: | Kühl, Niklas; Schemmer, Max; Goutier, Marc; Satzger, Gerhard |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:135656&r=cmp |
| By: | Zheng Cao; Raymond Guo; Wenyu Du; Jiayi Gao; Kirill V. Golubnichiy |
| Abstract: | This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.15912&r=cmp |
| By: | Christian B. Hansen (University of Chicago); Mark E. Schaffer (Heriot-Watt University); Thomas Wiemann (University of Chicago); Achim Ahrens (ETH Zürich) |
| Abstract: | We introduce the Stata package ddml, which implements double/debiased machine learning (DDML) for causal inference aided by supervised machine learning. Five different models are supported, allowing for multiple treatment variables in the presence of high-dimensional controls and instrumental variables. ddml is compatible with many existing supervised machine learning programs in Stata. |
| Date: | 2022–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:boc:csug22:02&r=cmp |
| By: | Meisenbacher, Stephen; Norlander, Peter |
| Abstract: | Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected "no poach" clauses in franchise documents. |
| Keywords: | machine learning, natural language processing, text classification, big data |
| JEL: | B41 C38 C81 C88 J08 J41 J42 J47 J53 Z13 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1214&r=cmp |
| By: | Rameshwar Garg; Shriya Barpanda; Girish Rao Salanke N S; Ramya S |
| Abstract: | Time series data is being used everywhere, from sales records to patients' health evolution metrics. The ability to deal with this data has become a necessity, and time series analysis and forecasting are used for the same. Every Machine Learning enthusiast would consider these as very important tools, as they deepen the understanding of the characteristics of data. Forecasting is used to predict the value of a variable in the future, based on its past occurrences. A detailed survey of the various methods that are used for forecasting has been presented in this paper. The complete process of forecasting, from preprocessing to validation has also been explained thoroughly. Various statistical and deep learning models have been considered, notably, ARIMA, Prophet and LSTMs. Hybrid versions of Machine Learning models have also been explored and elucidated. Our work can be used by anyone to develop a good understanding of the forecasting process, and to identify various state of the art models which are being used today. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.14387&r=cmp |
| By: | Li, Ziqi |
| Abstract: | Ridesharing, compared to traditional solo ride-hailing, can reduce traffic congestion, cut per-passenger carbon emissions, reduce parking infrastructure, and provide a more cost-effective way to travel. Despite these benefits, ridesharing only occupies a small percentage of the total ride-hailing trips. This study provides a reproducible and replicable framework that integrates big trip data, machine learning models, and explainable artificial intelligence (XAI) to better understand the factors that influence people's decisions to take or not to take a shared ride. |
| Date: | 2022–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:chy4p&r=cmp |
| By: | Pierre Brugière (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique); Gabriel Turinici (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We present in this paper a method to compute, using generative neural networks, an estimator of the "Value at Risk" for a nancial asset. The method uses a Variational Auto Encoder with a 'energy' (a.k.a. Radon- Sobolev) kernel. The result behaves according to intuition and is in line with more classical methods. |
| Date: | 2022–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03880381&r=cmp |
| By: | Sara Salamat; Nima Tavassoli; Behnam Sabeti; Reza Fahmi |
| Abstract: | Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such as document classification. One major shortcoming of such models is that they mainly work on homogeneous graphs, while representing text datasets as graphs requires several node types which leads to a heterogeneous schema. In this paper, we propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model. The proposed model is capable of processing heterogeneous graphs to produce unified node embeddings which are then utilized for node classification or link prediction as the downstream task. The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain. Experiments, which are carried away using a constructed dataset, demonstrate the ability of the model in embedding extraction and the downstream tasks. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.16103&r=cmp |
| By: | Jie Zou; Jiashu Lou; Baohua Wang; Sixue Liu |
| Abstract: | More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings. In this paper, to capture the hidden information, we propose a DRL based stock trading system using cascaded LSTM, which first uses LSTM to extract the time-series features from stock daily data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Experiments in DJI in the US market and SSE50 in the Chinese stock market show that our model outperforms previous baseline models in terms of cumulative returns and Sharp ratio, and this advantage is more significant in the Chinese stock market, a merging market. It indicates that our proposed method is a promising way to build a automated stock trading system. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.02721&r=cmp |
| By: | Morteza Tahami Pour Zarandi; Mehdi Ghasemi Meymandi; Mohammad Hemami |
| Abstract: | This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equation system, GARCH families procedures, and Artificial Neural Networks that determine the characteristics of cotton price trend duration 1990-2020. It is established that in the structural method, the best procedure is AR (2) by Markov switching estimation. Based on the MS-AR procedure, it concludes that tending to regime change from decreasing trend to an increasing one is more significant than a reverse mode. The simultaneous equation system investigates three procedures based on the acreage cotton, value-added, and real cotton price. Finally, prediction with the GARCH families TARCH procedure is the best-fitting model, and in the LSTM neural network, the results show an accurate prediction by the training-testing method. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.01584&r=cmp |
| By: | Jan Ditzen (Free University of Bozen-Bolzano, Italy); Francesco Ravazzolo (Free University of Bozen-Bolzano, Italy) |
| Abstract: | For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D^{2}ML to identify drivers of national inflation. D^{2}ML combines machine learning for model selection with time dependent data and graphical models to estimate the inverse of the covariance matrix, which is then used to identify dominant drivers. Using a dataset of 33 countries, we find that the US inflation rate and oil prices are dominant drivers of national in ation rates. For a more general framework, we carry out Monte Carlo simulations to show that our estimator correctly identifies dominant drivers. |
| Keywords: | Time Series, Machine Learning, LASSO, High dimensional data, Dominant Units, Inflation. |
| JEL: | C22 C23 C55 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:bzn:wpaper:bemps97&r=cmp |
| By: | Biagini Luigi; Severini Simone |
| Abstract: | Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers of factors affect participation, making evaluation difficult. These interrelated factors can mask the influence on adhesion predictions, making them misleading.This study evaluated how 66 common characteristics affect insurance participation choices. We relied on individual farm data from FADN from 2016 to 2019 with type 1 (Fieldcrops) farming with 10,926 observations.We use three Machine Learning (ML) approaches (LASSO, Boosting, Random Forest) compare them to the GLM model used in insurance modelling. ML methodologies can use a large set of information efficiently by performing the variable selection. A highly accurate parsimonious model helps us understand the factors affecting insurance participation and design better products.ML predicts fairly well despite the complexity of insurance participation problem. Our results suggest Boosting performs better than the other two ML tools using a smaller set of regressors. The proposed ML tools identify which variables explain participation choice. This information includes the number of cases in which single variables are selected and their relative importance in affecting participation.Focusing on the subset of information that best explains insurance participation could reduce the cost of designing insurance schemes. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.03092&r=cmp |
| By: | Damian Kisiel; Denise Gorse |
| Abstract: | Previous attempts to predict stock price from limit order book (LOB) data are mostly based on deep convolutional neural networks. Although convolutions offer efficiency by restricting their operations to local interactions, it is at the cost of potentially missing out on the detection of long-range dependencies. Recent studies address this problem by employing additional recurrent or attention layers that increase computational complexity. In this work, we propose Axial-LOB, a novel fully-attentional deep learning architecture for predicting price movements of stocks from LOB data. By utilizing gated position-sensitive axial attention layers our architecture is able to construct feature maps that incorporate global interactions, while significantly reducing the size of the parameter space. Unlike previous works, Axial-LOB does not rely on hand-crafted convolutional kernels and hence has stable performance under input permutations and the capacity to incorporate additional LOB features. The effectiveness of Axial-LOB is demonstrated on a large benchmark dataset, containing time series representations of millions of high-frequency trading events, where our model establishes a new state of the art, achieving an excellent directional classification performance at all tested prediction horizons. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.01807&r=cmp |
| By: | Ziwei Mei (The Chinese University of Hong Kong); Zhentao Shi (The Chinese University of Hong Kong); Peter C. B. Phillips (Cowles Foundation, Yale University) |
| Abstract: | The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2348&r=cmp |
| By: | Massimiliano Fessina; Giambattista Albora; Andrea Tacchella; Andrea Zaccaria |
| Abstract: | Tree-based machine learning algorithms provide the most precise assessment of the feasibility for a country to export a target product given its export basket. However, the high number of parameters involved prevents a straightforward interpretation of the results and, in turn, the explainability of policy indications. In this paper, we propose a procedure to statistically validate the importance of the products used in the feasibility assessment. In this way, we are able to identify which products, called explainers, significantly increase the probability to export a target product in the near future. The explainers naturally identify a low dimensional representation, the Feature Importance Product Space, that enhances the interpretability of the recommendations and provides out-of-sample forecasts of the export baskets of countries. Interestingly, we detect a positive correlation between the complexity of a product and the complexity of its explainers. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.03094&r=cmp |
| By: | Thi Thu Giang Nguyen (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance) |
| Abstract: | The study compares the use of various Long Short-Term Memory (LSTM) variants to conventional technical indicators for trading the S&P 500 index between 2011 and 2022. Two methods were used to test each strategy: a fixed training data set from 2001–2010 and a rolling train–test window. Due to the input sensitivity of LSTM models, we concentrated on data processing and hyperparameter tuning to find the best model. Instead of using the traditional MSE function, we used the Mean Absolute Directional Loss (MADL) function based on recent research to enhance model performance. The models were assessed using the Information Ratio and the Modified Information Ratio, which considers the maximum drawdown and the sign of the annualized return compounded (ARC). LSTM models' performance was compared to benchmark strategies using the SMA, MACD, RSI, and Buy&Hold strategies. We rejected the hypothesis that algorithmic investment strategy using signals from LSTM model consisting only from daily returns in its input layer is more efficient. However, we could not reject the hypothesis that signals generated by LSTM model combining daily returns and technical indicators in its input layer are more efficient. The LSTM Extended model that combined daily returns with MACD and RSI in the input layer generated a better result than Buy&Hold and other strategies using a single technical indicator. The results of the sensitivity analysis show how sensitive this model is to inputs like sequence length, batch size, technical indicators, and the length of the rolling train - test window. |
| Keywords: | algorithmic investment strategies, machine learning, testing architecture, deep learning, recurrent neural networks, LSTM, technical indicators, forecasting financial-time series, technical indicators, hyperparameter tuning S&P 500 Index |
| JEL: | C15 C45 C52 C53 C58 C61 G14 G17 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2022-29&r=cmp |
| By: | Marc Wildi; Branka Hadji Misheva |
| Abstract: | Artificial intelligence is creating one of the biggest revolution across technology driven application fields. For the finance sector, it offers many opportunities for significant market innovation and yet broad adoption of AI systems heavily relies on our trust in their outputs. Trust in technology is enabled by understanding the rationale behind the predictions made. To this end, the concept of eXplainable AI emerged introducing a suite of techniques attempting to explain to users how complex models arrived at a certain decision. For cross-sectional data classical XAI approaches can lead to valuable insights about the models' inner workings, but these techniques generally cannot cope well with longitudinal data (time series) in the presence of dependence structure and non-stationarity. We here propose a novel XAI technique for deep learning methods which preserves and exploits the natural time ordering of the data. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.02906&r=cmp |
| By: | Edouard Ribes (CERNA i3 - Centre d'économie industrielle i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Background context. The retail side of the finance industry is currently undergoing a deep transformation associated to the rise of automation technologies. Wealth management services, which are traditionally associated to the retail distribution of financial investments products, are no stranger to this phenomena. Specific knowledge gap the work aims to fill. The retail distribution of financial instruments is currently normalized for regulatory purposes but yet remains costly. Documented examples of the use of automation technologies to improve its performance (outside of the classical example of robo-advisors) remain sparse. Methods used in the study. This work shows how machine learning techniques under the form of classification algorithms can be of use to automate some activities (i.e. client expectations analysis) associated to one of the core steps behind the distribution of financial products, namely client discovery. Key findings. Once calibrated to a proprietary data-set owned by one of the leading french productivity tools providers specialized on the wealth management segment, standard classification algorithms (such as random forests or support vector machines) are able to accurately predict the majority of households financial expectations (ROC either above 80% or 90%) when fed with standard wealth information available in most of the database of financial products distributors. Implications. This study thus shows that classifications tools could be easily embedded in digital journey of distributors to improve the access to financial expertise and accelerate the sales of financial products. |
| Keywords: | Wealth Management Brokerage Machine learning Classification, Technological Change, Wealth Management, Brokerage, Machine learning, Classification |
| Date: | 2022–12–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03887759&r=cmp |
| By: | Kim Ristolainen (Department of Economics, Turku School of Economics, University of Turku, Finland) |
| Abstract: | Economic research has shown that debt markets have an information sensitivity property that allows these markets to work properly when price discovery is absent and opaqueness is maintained. Dang, Gorton and Holmström (2015) argue that sufficiently âbad newsâ can switch debt to become information sensitive and start a financial crisis. We identify narrative triggers in the news by utilizing machine learning methods and daily information about firm default probability, the publicâs information acquisition and newspaper articles. We find state-specific generalizable triggers whose effect is determined by the language used by journalists. This language is associated with different psychological thinking processes. |
| Keywords: | information sensitivity, debt markets, financial crisis, machine learning, news data, primordial thinking process |
| JEL: | G01 G14 G41 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:tkk:dpaper:dp156&r=cmp |
| By: | Lokman Abbas-Turki; St\'ephane Cr\'epey; Bouazza Saadeddine |
| Abstract: | We consider the computation by simulation and neural net regression of conditional expectations, or more general elicitable statistics, of functionals of processes $(X, Y )$. Here an exogenous component $Y$ (Markov by itself) is time-consuming to simulate, while the endogenous component $X$ (jointly Markov with $Y$) is quick to simulate given $Y$, but is responsible for most of the variance of the simulated payoff. To address the related variance issue, we introduce a conditionally independent, hierarchical simulation scheme, where several paths of $X$ are simulated for each simulated path of $Y$. We analyze the statistical convergence of the regression learning scheme based on such block-dependent data. We derive heuristics on the number of paths of $Y$ and, for each of them, of $X$, that should be simulated. The resulting algorithm is implemented on a graphics processing unit (GPU) combining Python/CUDA and learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark shows that the hierarchical simulation technique is key to the success of the learning approach. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.17005&r=cmp |
| By: | Tamay Besiroglu; Nicholas Emery-Xu; Neil Thompson |
| Abstract: | Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction. As more scientists and engineers adopt deep learning, it is important to consider what effect widespread deployment would have on scientific progress and, ultimately, economic growth. We assess this impact by estimating the idea production function for AI in two computer vision tasks that are considered key test-beds for deep learning and show that AI idea production is notably more capital-intensive than traditional R&D. Because increasing the capital-intensity of R&D accelerates the investments that make scientists and engineers more productive, our work suggests that AI-augmented R&D has the potential to speed up technological change and economic growth. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.08198&r=cmp |
| By: | Edward I. Altman; Marco Balzano; Alessandro Giannozzi; Stjepan Srhoj |
| Abstract: | SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management- and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model’s predictive accuracy. |
| Keywords: | Default prediction modeling; small and medium-sized enterprises; machine learning techniques; LASSO; logit regression |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:inn:wpaper:2022-19&r=cmp |
| By: | Luigi Biagini |
| Abstract: | This paper evaluates Machine Learning (ML) in establishing ratemaking for new insurance schemes. To make the evaluation feasible, we established expected indemnities as premiums. Then, we use ML to forecast indemnities using a minimum set of variables. The analysis simulates the introduction of an income insurance scheme, the so-called Income Stabilization Tool (IST), in Italy as a case study using farm-level data from the FADN from 2008-2018. We predicted the expected IST indemnities using three ML tools, LASSO, Elastic Net, and Boosting, that perform variable selection, comparing with the Generalized Linear Model (baseline) usually adopted in insurance investigations. Furthermore, Tweedie distribution is implemented to consider the peculiarity shape of the indemnities function, characterized by zero-inflated, no-negative value, and asymmetric fat-tail. The robustness of the results was evaluated by comparing the econometric and economic performance of the models. Specifically, ML has obtained the best goodness-of-fit than baseline, using a small and stable selection of regressors and significantly reducing the gathering cost of information. However, Boosting enabled it to obtain the best economic performance, balancing the most and most minor risky subjects optimally and achieving good economic sustainability. These findings suggest how machine learning can be successfully applied in agricultural insurance.This study represents one of the first to use ML and Tweedie distribution in agricultural insurance, demonstrating its potential to overcome multiple issues. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.03114&r=cmp |
| By: | Mathias Valla (LSAF - Laboratoire de Sciences Actuarielles et Financières [Lyon] - ISFA - Institut de Science Financière et d'Assurances); Xavier Milhaud (I2M - Institut de Mathématiques de Marseille - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Anani Ayodélé Olympio (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon) |
| Abstract: | A retention strategy based on an enlightened lapse modelization can be a powerful profitability lever for a life insurer. Some machine learning models are excellent at predicting lapse, but from the insurer's perspective, predicting which policyholder is likely to lapse is not enough to design a retention strategy. Changing the classical classification problem to a regression one with an appropriate validation metric based on Customer Lifetime Value (CLV) has recently been proposed. In our paper, we suggest several improvements and apply them to a sizeable real-world life insurance dataset. We include the risk of death in the study through competing risk considerations in parametric and tree-based models and show that further individualization of the existing approach leads to increased performance. We show that survival tree-based models can outperform parametric approaches and that the actuarial literature can significantly benefit from them. Then, we compare how this framework leads to increased predicted gains for the insurer regardless of the retention strategy. Finally, we discuss the benefits of our modelization in terms of commercial and strategic decision-making for a life insurer. |
| Keywords: | Lapse, Lapse management strategy, Tree-based models, Competing risks, Customer lifetime value, Machine Learning |
| Date: | 2022–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03903047&r=cmp |
| By: | Laura Bonacorsi; Vittoria Cerasi; Paola Galfrascoli; Matteo Manera |
| Abstract: | We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Machine Learning (ML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968).We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm’s probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies. |
| Keywords: | credit risk, z-scores, ESG factors, Machine learning. |
| JEL: | C5 D4 G3 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:mib:wpaper:507&r=cmp |
| By: | Jingruo Sun; Asaf Cohen |
| Abstract: | We consider an optimal control problem where the average welfare of weakly interacting agents is of interest. We examine the mean-field control problem as the fluid approximation of the N-agent control problem with the setup of finite-state space, continuous-time, and finite-horizon. The value function of the mean-field control problem is characterized as the unique viscosity solution of a Hamilton-Jacobi-Bellman equation in the simplex. We apply the DGM to estimate the value function and the evolution of the distribution. We also prove the numerical solution approximated by a neural network converges to the analytical solution. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.01719&r=cmp |
| By: | Massimo Ferrari Minesso; Maria Sole Pagliari |
| Abstract: | This paper presents DSGE Nash, a toolkit to solve for pure strategy Nash equilibria of global games in general equilibrium macroeconomic models. Although primarily designed to solve for Nash equilibria in DSGE models, the toolkit encompasses a broad range of options including solutions up to the third order, multiple players/strategies, the use of user-defined objective functions and the possibility of matching empirical moments and IRFs. When only one player is selected, the problem is re-framed as a standard optimal policy problem. We apply the algorithm to an open-economy model where a commodity importing country and a monopolistic commodity producer compete on the commodities market with barriers to entry. If the commodity price becomes relevant in production, the central bank in the commodity importing economy deviates from the first best policy to act strategically. In particular, the monetary authority tolerates relatively higher commodity price volatility to ease barriers to entry in commodity production and to limit the market power of the dominant exporter. |
| Keywords: | DSGE Model, Optimal Policies, Computational Economics |
| JEL: | C63 E32 E61 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:884&r=cmp |
| By: | BARRIOS Salvador (European Commission - JRC); REUT Adriana; RISCADO Sara; VAN DER WIELEN Wouter |
| Abstract: | In this paper, we propose a novel approach for the ex-ante assessment of tax reforms accounting for second-round effects, i.e. the dynamic scoring of tax reforms. We combine a microsimulation model for selected European countries with VAR estimates of macro responses and, exploiting a unique database of tax reforms in the EU, compare our estimates with the real-time assessment of tax reforms conducted by the EU Member States as well as with ex-post realisations. This is the first time dynamic scoring of tax reforms is conducted in real-time and compared to ex-post realizations in a systematic way. The novelty of our approach hinges on the use of a macro-econometric model combined with a microsimulation model which represents a more flexible tool than (computable) general equilibrium models in order to conduct real-time dynamic scoring analysis. Our results suggest that on average personal income tax cuts resulted in medium-term increases in output and employment; however, the second-round revenue impact is found to be small relative to the first-round microsimulation results. |
| Keywords: | Fiscal policy, tax reforms, real-time, microsimulation, EUROMOD, VAR models |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:taxref:202214&r=cmp |
| By: | Taylan Kabbani |
| Abstract: | In this paper, a heuristic method based on TabuSearch and TokenRing Search is being used in order to solve the Portfolio Optimization Problem. The seminal mean-variance model of Markowitz is being considered with the addition of cardinality and quantity constraints to better capture the dynamics of the trading procedure, the model becomes an NP-hard problem that can not be solved using an exact method. The combination of three different neighborhood relations is being explored with Tabu Search. In addition, a new constructive method for the initial solution is proposed. Finally, I show how the proposed techniques perform on public benchmarks |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.17193&r=cmp |
| By: | Kühl, Niklas; Goutier, Marc; Baier, Lucas; Wolff, Clemens; Martin, Dominik |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:135657&r=cmp |
| By: | Jo\"el Terschuur |
| Abstract: | Inequality of Opportunity (IOp) is considered an unfair source of inequality, an obstacle for economic growth and a determinant of preferences for redistribution. IOp can be estimated in two steps: (i) fitted values are estimated by predicting an outcome given some circumstances out of the control of the individual, (ii) the inequality in the distribution of the fitted values is measured by some inequality index. Using machine learners in the prediction step allows to consider a rich set of circumstances but it leads to biases in the estimation of IOp. We propose to use debiased estimators based on the Gini coefficient and the Mean Logarithmic Deviation (MLD). Further, we measure the effect of each circumstance on IOp and provide valid standard errors. To stress the usefulness of inference, we provide a test to compare IOp in two populations and a group test to check joint significance of a group of circumstances. We use the debiased estimators to measure IOp in 29 European countries. Romania and Bulgaria are the countries with highest IOp. Southern countries tend to have high levels of IOp while Nordic countries have low IOp. Debiased estimators are more robust to the choice of the machine learner in the first step. Mother's education and father's occupation are key circumstances to explain inequality. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.02407&r=cmp |
| By: | Griselda Deelstra; Lech A. Grzelak; Felix Wolf |
| Abstract: | Exposure simulations are fundamental to many xVA calculations and are a nested expectation problem where repeated portfolio valuations create a significant computational expense. Sensitivity calculations which require shocked and unshocked valuations in bump-and-revalue schemes exacerbate the computational load. A known reduction of the portfolio valuation cost is understood to be found in polynomial approximations, which we apply in this article to interest rate sensitivities of expected exposures. We consider a method based on the approximation of the shocked and unshocked valuation functions, as well as a novel approach in which the difference between these functions is approximated. Convergence results are shown, and we study the choice of interpolation nodes. Numerical experiments with interest rate derivatives are conducted to demonstrate the high accuracy and remarkable computational cost reduction. We further illustrate how the method can be extended to more general xVA models using the example of CVA with wrong-way risk. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.17026&r=cmp |
| By: | Tauchert, Christoph |
| Abstract: | Artificial intelligence (AI) is fundamentally changing our society and economy. Companies are investing a great deal of money and time into building corresponding competences and developing prototypes with the aim of integrating AI into their products and services, as well as enriching and improving their internal business processes. This inevitably brings corporate and private users into contact with a new technology that functions fundamentally differently than traditional software. The possibility of using machine learning to generate precise models based on large amounts of data capable of recognizing patterns within that data holds great economic and social potential—for example, in task augmentation and automation, medical diagnostics, and the development of pharmaceutical drugs. At the same time, companies and users are facing new challenges that accompany the introduction of this technology. Businesses are struggling to manage and generate value from big data, and employees fear increasing automation. To better prepare society for the growing market penetration of AI-based information systems into everyday life, a deeper understanding of this technology in terms of organizational and individual use is needed. Motivated by the many new challenges and questions for theory and practice that arise from AI-based information systems, this dissertation addresses various research questions with regard to the use of such information systems from both user and organizational perspectives. A total of five studies were conducted and published: two from the perspective of organizations and three among users. The results of these studies contribute to the current state of research and provide a basis for future studies. In addition, the gained insights enable recommendations to be derived for companies wishing to integrate AI into their products, services, or business processes. The first research article (Research Paper A) investigated which factors and prerequisites influence the success of the introduction and adoption of AI. Using the technology–organization–environment framework, various factors in the categories of technology, organization, and environment were identified and validated through the analysis of expert interviews with managers experienced in the field of AI. The results show that factors related to data (especially availability and quality) and the management of AI projects (especially project management and use cases) have been added to the framework, but regulatory factors have also emerged, such as the uncertainty caused by the General Data Protection Regulation. The focus of Research Paper B is companies’ motivation to host data science competitions on online platforms and which factors influence their success. Extant research has shown that employees with new skills are needed to carry out AI projects and that many companies have problems recruiting such employees. Therefore, data science competitions could support the implementation of AI projects via crowdsourcing. The results of the study (expert interviews among data scientists) show that these competitions offer many advantages, such as exchanges and discussions with experienced data scientists and the use of state-of-the-art approaches. However, only a small part of the effort related to AI projects can be represented within the framework of such competitions. The studies in the other three research papers (Research Papers C, D, and E) examine AI-based information systems from a user perspective, with two studies examining user behavior and one focusing on the design of an AI-based IT artifact. Research Paper C analyses perceptions of AI-based advisory systems in terms of the advantages associated with their use. The results of the empirical study show that the greatest perceived benefit is the convenience such systems provide, as they are easy to access at any time and can immediately satisfy informational needs. Furthermore, this study examined the effectiveness of 11 different measures to increase trust in AI-based advisory systems. This showed a clear ranking of measures, with effectiveness decreasing from non-binding testing to providing additional information regarding how the system works to adding anthropomorphic features. The goal of Research Paper D was to investigate actual user behavior when interacting with AI-based advisory systems. Based on the theoretical foundations of task–technology fit and judge–advisor systems, an online experiment was conducted. The results show that, above all, perceived expertise and the ability to make efficient decisions through AI-based advisory systems influence whether users assess these systems as suitable for supporting certain tasks. In addition, the study provides initial indications that users might be more willing to follow the advice of AI-based systems than that of human advisors. Finally, Research Paper E designs and implements an IT artifact that uses machine learning techniques to support structured literature reviews. Following the approach of design science research, an artifact was iteratively developed that can automatically download research articles from various databases and analyze and group them according to their content using the word2vec algorithm, the latent Dirichlet allocation model, and agglomerative hierarchical cluster analysis. An evaluation of the artifact on a dataset of 308 publications shows that it can be a helpful tool to support literature reviews but that much manual effort is still required, especially with regard to the identification of common concepts in extant literature. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:135700&r=cmp |
| By: | Axenbeck, Janna; Berner, Anne; Kneib, Thomas |
| Abstract: | The ongoing digital transformation has raised hopes for ICT-based climate protection within manufacturing industries, such as dematerialized products and energy efficiency gains. However, ICT also consume energy as well as resources, and detrimental effects on the environment are increasingly gaining attention. Accordingly, it is unclear whether trade-offs or synergies between the use of digital technologies and energy savings exist. Our analysis sheds light on the most important drivers of the relationship between ICT and energy use in manufacturing. We apply flexible tree-based machine learning to a German administrative panel data set including more than 25,000 firms. The results indicate firm-level heterogeneity, but suggest that digital technologies relate more frequently to an increase in energy use. Multiple characteristics, such as energy prices and firms' energy mix, explain differences in the effect. |
| Keywords: | digital technologies,energy use,manufacturing,machine learning |
| JEL: | C14 D22 L60 O33 Q40 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:22059&r=cmp |
| By: | Michael E. Mura |
| Abstract: | A novel approach called Moate Simulation is presented to provide an accurate numerical evolution of probability distribution functions represented on grids arising from stochastic differential processes where initial conditions are specified. Where the variables of stochastic differential equations may be transformed via It\^o-Doeblin calculus into stochastic differentials with a constant diffusion term, the probability distribution function for these variables can be simulated in discrete time steps. The drift is applied directly to a volume element of the distribution while the stochastic diffusion term is applied through the use of convolution techniques such as Fast or Discrete Fourier Transforms. This allows for highly accurate distributions to be efficiently simulated to a given time horizon and may be employed in one, two or higher dimensional expectation integrals, e.g. for pricing of financial derivatives. The Moate Simulation approach forms a more accurate and considerably faster alternative to Monte Carlo Simulation for many applications while retaining the opportunity to alter the distribution in mid-simulation. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.08509&r=cmp |