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on Computational Economics |
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Issue of 2021‒07‒26
27 papers chosen by |
| By: | Helmut Wasserbacher; Martin Spindler |
| Abstract: | This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP\&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.04851&r= |
| By: | Supriya Bajpai |
| Abstract: | In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.16088&r= |
| By: | Juyong Lee; Youngsang Cho |
| Abstract: | As the volatility of electricity demand increases owing to climate change and electrification, the importance of accurate peak load forecasting is increasing. Traditional peak load forecasting has been conducted through time series-based models; however, recently, new models based on machine or deep learning are being introduced. This study performs a comparative analysis to determine the most accurate peak load-forecasting model for Korea, by comparing the performance of time series, machine learning, and hybrid models. Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) is used for the time series model. Artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM) are used for the machine learning models. SARIMAX-ANN, SARIMAX-SVR, and SARIMAX-LSTM are used for the hybrid models. The results indicate that the hybrid models exhibit significant improvement over the SARIMAX model. The LSTM-based models outperformed the others; the single and hybrid LSTM models did not exhibit a significant performance difference. In the case of Korea's highest peak load in 2019, the predictive power of the LSTM model proved to be greater than that of the SARIMAX-LSTM model. The LSTM, SARIMAX-SVR, and SARIMAX-LSTM models outperformed the current time series-based forecasting model used in Korea. Thus, Korea's peak load-forecasting performance can be improved by including machine learning or hybrid models. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.06174&r= |
| By: | Angelo Garangau Menezes; Saulo Martiello Mastelini |
| Abstract: | Forecasting financial time series is considered to be a difficult task due to the chaotic feature of the series. Statistical approaches have shown solid results in some specific problems such as predicting market direction and single-price of stocks; however, with the recent advances in deep learning and big data techniques, new promising options have arises to tackle financial time series forecasting. Moreover, recent literature has shown that employing a combination of statistics and machine learning may improve accuracy in the forecasts in comparison to single solutions. Taking into consideration the mentioned aspects, in this work, we proposed the MegazordNet, a framework that explores statistical features within a financial series combined with a structured deep learning model for time series forecasting. We evaluated our approach predicting the closing price of stocks in the S&P 500 using different metrics, and we were able to beat single statistical and machine learning methods. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.01017&r= |
| By: | Sohrab Mokhtari; Kang K. Yen; Jin Liu |
| Abstract: | This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market's forecast. The results show a median performance, implying that with the current technology of AI, it is too soon to claim AI can beat the stock markets. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.01031&r= |
| By: | Priyank Sonkiya; Vikas Bajpai; Anukriti Bansal |
| Abstract: | The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the sentiment analysis - the emotion of the investors that shows the willingness to invest. A variety of techniques have been used by people around the globe involving basic Machine Learning and Neural Networks. Ranging from the basic linear regression to the advanced neural networks people have experimented with all possible techniques to predict the stock market. It's evident from recent events how news and headlines affect the stock markets and cryptocurrencies. This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. Firstly sentiment analysis of the news and the headlines for the company Apple Inc, listed on the NASDAQ is performed using a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP). Afterward, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores. Comparison is done with baseline models like - Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), vanilla GAN, and Auto-Regressive Integrated Moving Average (ARIMA) model. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.09055&r= |
| By: | Francesca Micocci (IMT School for Advanced Studies Lucca); Armando Rungi (IMT School for advanced studies) |
| Abstract: | In this contribution, we exploit machine learning techniques to predict out-of-sample firms' ability to export based on the financial accounts of both exporters and non-exporters. Therefore, we show how forecasts can be used as exporting scores, i.e., to measure the distance of non-exporters from export status. For our purpose, we train and test various algorithms on the financial reports of 57,021 manufacturing firms in France in 2010-2018. We find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with a prediction accuracy of up to 0:90. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporters. Eventually, we argue that exporting scores can be helpful for trade promotion, trade credit, and to assess firms' competitiveness. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to 44% more cash resources and up to 2:5 times more capital expenses to reach full export status. |
| Keywords: | exporting; machine learning; trade promotion; trade finance; competitiveness |
| JEL: | F17 C53 C55 L21 L25 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:ial:wpaper:3/2021&r= |
| By: | Wing Fung Chong; Haoen Cui; Yuxuan Li |
| Abstract: | This paper applies a deep reinforcement learning approach to revisit the hedging problem of variable annuities. Instead of assuming actuarial and financial dual-market model a priori, the reinforcement learning agent learns how to hedge by collecting anchor-hedging reward signals through interactions with the market. By the recently advanced proximal policy optimization, the pseudo-model-free reinforcement learning agent performs equally well as the correct Delta, while outperforms the misspecified Deltas. The reinforcement learning agent is also integrated with online learning to demonstrate its full adaptive capability to the market. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.03340&r= |
| By: | Kyle Colangelo (Institute for Fiscal Studies); Ying-Ying Lee (Institute for Fiscal Studies) |
| Abstract: | We propose a nonparametric inference method for causal e?ects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial e?ects are asymptotically normal with nonparametric convergence rates. The nuisance estimators for the conditional expectation function and the conditional density can be nonparametric kernel or series estimators or ML methods. Using doubly robust in?uence function and cross-?tting, we give tractable primitive conditions under which the nuisance estimators do not a?ect the ?rst-order large sample distribution of the DML estimators. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation to support the theoretical results and demonstrate the usefulness of our DML estimator in practice. |
| Date: | 2019–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:72/19&r= |
| By: | Naudé, Wim (University College Cork); Bray, Amy (Zindi); Lee, Celina (Zindi) |
| Abstract: | In this paper, we study the crowdsourcing of innovation in Africa through a data science contest on an intermediated digital platform. We ran a Machine Learning (ML) contest on the continent's largest data science contest platform, Zindi. Contestants were surveyed on their motivations to take part and their perceptions about AI in Africa. In total, 614 contestants submitted 15,832 entries, and 559 responded to the accompanying survey. From the findings, we answered several questions: who take part in these contests and why? Who is most likely to win? What are contestants' entrepreneurial aspirations in deploying AI? What are the obstacles they perceive to the greater diffusion of AI in Africa? We conclude that crowdsourcing of AI via data contest platforms offers a potential mechanism to alleviate some of the constraints in the adoption and diffusion of AI in Africa. Recommendations for further research are made. |
| Keywords: | crowdsourcing, innovation, data science, artificial intelligence, Africa |
| JEL: | O31 O33 O36 O55 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp14545&r= |
| By: | Dan Wang; Zhi Chen; Ionut Florescu |
| Abstract: | In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the "effort" required to further improve credit rating. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10306&r= |
| By: | Cynthia Pagliaro; Dhagash Mehta; Han-Tai Shiao; Shaofei Wang; Luwei Xiong |
| Abstract: | Modeling investor behavior is crucial to identifying behavioral coaching opportunities for financial advisors. With the help of natural language processing (NLP) we analyze an unstructured (textual) dataset of financial advisors' summary notes, taken after every investor conversation, to gain first ever insights into advisor-investor interactions. These insights are used to predict investor needs during adverse market conditions; thus allowing advisors to coach investors and help avoid inappropriate financial decision-making. First, we perform topic modeling to gain insight into the emerging topics and trends. Based on this insight, we construct a supervised classification model to predict the probability that an advised investor will require behavioral coaching during volatile market periods. To the best of our knowledge, ours is the first work on exploring the advisor-investor relationship using unstructured data. This work may have far-reaching implications for both traditional and emerging financial advisory service models like robo-advising. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.05592&r= |
| By: | Ren\'e Carmona; Mathieu Lauri\`ere |
| Abstract: | Financial markets and more generally macro-economic models involve a large number of individuals interacting through variables such as prices resulting from the aggregate behavior of all the agents. Mean field games have been introduced to study Nash equilibria for such problems in the limit when the number of players is infinite. The theory has been extensively developed in the past decade, using both analytical and probabilistic tools, and a wide range of applications have been discovered, from economics to crowd motion. More recently the interaction with machine learning has attracted a growing interest. This aspect is particularly relevant to solve very large games with complex structures, in high dimension or with common sources of randomness. In this chapter, we review the literature on the interplay between mean field games and deep learning, with a focus on three families of methods. A special emphasis is given to financial applications. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.04568&r= |
| By: | Hengxu Lin; Dong Zhou; Weiqing Liu; Jiang Bian |
| Abstract: | Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance. Within the modern portfolio construction framework that built on Markowitz's theory, the covariance matrix of stock returns is required to model the portfolio risk. Traditional approaches to estimate the covariance matrix are based on human designed risk factors, which often requires tremendous time and effort to design better risk factors to improve the covariance estimation. In this work, we formulate the quest of mining risk factors as a learning problem and propose a deep learning solution to effectively "design" risk factors with neural networks. The learning objective is carefully set to ensure the learned risk factors are effective in explaining stock returns as well as have desired orthogonality and stability. Our experiments on the stock market data demonstrate the effectiveness of the proposed method: our method can obtain $1.9\%$ higher explained variance measured by $R^2$ and also reduce the risk of a global minimum variance portfolio. Incremental analysis further supports our design of both the architecture and the learning objective. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.05201&r= |
| By: | Gautier Marti; Victor Goubet; Frank Nielsen |
| Abstract: | We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.10606&r= |
| By: | Jean-Fran\c{c}ois Chassagneux; Mohan Yang |
| Abstract: | In this work, we study the numerical approximation of a class of singular fully coupled forward backward stochastic differential equations. These equations have a degenerate forward component and non-smooth terminal condition. They are used, for example, in the modeling of carbon market[9] and are linked to scalar conservation law perturbed by a diffusion. Classical FBSDEs methods fail to capture the correct entropy solution to the associated quasi-linear PDE. We introduce a splitting approach that circumvent this difficulty by treating differently the numerical approximation of the diffusion part and the non-linear transport part. Under the structural condition guaranteeing the well-posedness of the singular FBSDEs [8], we show that the splitting method is convergent with a rate $1/2$. We implement the splitting scheme combining non-linear regression based on deep neural networks and conservative finite difference schemes. The numerical tests show very good results in possibly high dimensional framework. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.15496&r= |
| By: | Orkun Saka ⓡ; Barry Eichengreen ⓡ; Cevat Giray Aksoy |
| Abstract: | We ask whether epidemic exposure leads to a shift in financial technology usage within and across countries and if so who participates in this shift. We exploit a dataset combining Gallup World Polls and Global Findex surveys for some 250,000 individuals in 140 countries, merging them with information on the incidence of epidemics and local 3G internet infrastructure. Epidemic exposure is associated with an increase in remote-access (online/mobile) banking and substitution from bank branch-based to ATM-based activity. Using a machine-learning algorithm, we show that heterogeneity in this response centers on the age, income and employment of respondents. Young, high-income earners in full-time employment have the greatest propensity to shift to online/mobile transactions in response to epidemics. These effects are larger for individuals in subnational regions with better ex ante 3G signal coverage, highlighting the role of the digital divide in adaption to new technologies necessitated by adverse external shocks. |
| JEL: | G0 G20 G59 I10 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29006&r= |
| By: | Li, Wei; Paraschiv, Florentina; Sermpinis, Georgios |
| Abstract: | The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field. |
| Keywords: | Case-based reasoning,Financial risk detection,Multiple-criteria decision-making,Feature scoring,Particle swarm optimization,Parallel computing |
| JEL: | C51 C52 C53 C61 C63 D81 G21 G32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021010&r= |
| By: | Naftali Cohen; Srijan Sood; Zhen Zeng; Tucker Balch; Manuela Veloso |
| Abstract: | In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. To assess the robustness and quality of our approach, we examine various datasets and multiple evaluation metrics. Our experiments show that our forecasting tool is effective for cyclic data but somewhat less for irregular data such as stock prices. Importantly, when using image-based evaluation metrics, we find our method to outperform various baselines, including ARIMA, and a numerical variation of our deep learning approach. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.01273&r= |
| By: | Claudia Noack; Tomasz Olma; Christoph Rothe |
| Abstract: | Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement, as tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.07942&r= |
| By: | Khalid El-Awady |
| Abstract: | We demonstrate the use of Adaptive Stress Testing to detect and address potential vulnerabilities in a financial environment. We develop a simplified model for credit card fraud detection that utilizes a linear regression classifier based on historical payment transaction data coupled with business rules. We then apply the reinforcement learning model known as Adaptive Stress Testing to train an agent, that can be thought of as a potential fraudster, to find the most likely path to system failure -- successfully defrauding the system. We show the connection between this most likely failure path and the limits of the classifier and discuss how the fraud detection system's business rules can be further augmented to mitigate these failure modes. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.03577&r= |
| By: | González-Bustamante, Bastián (Universidad de Santiago de Chile); Astete Olmos, Matías Ignacio; Orvenes, Berenice Issabella |
| Abstract: | Where do appointments based on political trust end and meritocratic recruitments begin? This question dates to the end of the nineteenth century and is linked to civil service systems’ modernisation processes in the twentieth century. The Chilean civil service has been an example of modernisation in Latin America over the past decades, however, the existing evidence is descriptive and mainly evaluates its coverage. There is no clear, systematic empirical evidence on its stability. This paper presents a novel data set of senior public managers in Chile during the 2009-2017 period. The focus of this methodological article is demonstrating how data mining and machine learning processes could be useful in order to create the data set and its potential applications. First, we present how we created and validated this data set. Then, we present some descriptive applications and nonparametric survival estimates with Kaplan-Meier curves. We hope that this data set will be a relevant resource for deepening understanding of the Chilean civil service and making different comparisons to extend this research line on political and government personnel. |
| Date: | 2021–07–17 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:vshcz&r= |
| By: | Christian Meier; Lingfei Li; Gongqiu Zhang |
| Abstract: | We develop a new simulation method for multidimensional diffusions with sticky boundaries. The challenge comes from simulating the sticky boundary behavior, for which standard methods like the Euler scheme fail. We approximate the sticky diffusion process by a multidimensional continuous time Markov chain (CTMC), for which we can simulate easily. We develop two ways of constructing the CTMC: approximating the infinitesimal generator of the sticky diffusion by finite difference using standard coordinate directions, and matching the local moments using the drift and the eigenvectors of the covariance matrix as transition directions. The first approach does not always guarantee a valid Markov chain whereas the second one can. We show that both construction methods yield a first order simulation scheme, which can capture the sticky behavior and it is free from the curse of dimensionality. We apply our method to two applications: a multidimensional Brownian motion with all dimensions sticky which arises as the limit of a queuing system with exceptional service policy, and a multi-factor short rate model for low interest rate environment in which the stochastic factors are unbounded but the short rate is sticky at zero. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.04260&r= |
| By: | SAKKAS Stylianos (European Commission - JRC); CRUCITTI Francesca (European Commission - JRC); CONTE Andrea (European Commission - JRC); SALOTTI Simone (European Commission - JRC) |
| Abstract: | The Covid-19 crisis started in the EU at the beginning of 2020. The economic activity rebounded in summer, but there was another setback in late 2020 as the pandemic resurgence prompted a new round of containment measures. In 2020, the Rhomolo model was used to simulate the potential economic impact of Covid-19 across EU regions for the launch of NextGenerationEU (European Commission, 2020). This Policy Insight reports new territorial results based on up-to-date information on the effects of the crisis in the European economies in 2020. The results, in line with the latest country-level Eurostat official statistics, suggest that there is considerable regional heterogeneity in the impact of the Covid-19 crisis with clear implications for the policies to be put in place for recovery. |
| Keywords: | rhomolo, region, growth, cohesion policy, general equilibrium, spatial spillovers, Covid-19 |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc125536&r= |
| By: | James Hebden; Fabian Winkler |
| Abstract: | We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints. The procedure does not require knowledge of the structural or reduced-form equations of the model, its state variables, or its shock processes. Forecasts of the variables entering the policy problem, and impulse response functions of these variables to anticipated policy shocks under an arbitrary policy, constitute sufficient information to construct valid counterfactuals. We show how to compute solutions for instrument rules and optimal discretionary and commitment policies with multiple policy instruments, and discuss various extensions including imperfect information, asymmetric objectives, and limited commitment. Our procedure facilitates the comparison of the effects of policy regimes across models. As an application, we compute counterfactual paths of the US economy around 2015 for several monetary policy regimes. |
| Keywords: | Computation; DSGE; Occasionally Binding Constraints; Optimal Policy; Commitment; Discretion |
| JEL: | C61 C63 E52 |
| Date: | 2021–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-42&r= |
| By: | Shaojun Ma; Pengcheng Li |
| Abstract: | Predicting intraday trading volume plays an important role in trading alpha research. Existing methods such as rolling means(RM) and a two-states based Kalman Filtering method have been presented in this topic. We extend two states into various states in Kalman Filter framework to improve the accuracy of prediction. Specifically, for different stocks we utilize cross validation and determine best states number by minimizing mean squared error of the trading volume. We demonstrate the effectivity of our method through a series of comparison experiments and numerical analysis. |
| Date: | 2021–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2107.07678&r= |
| By: | Wynnona Steyn; Alexius Sithole; Winile Ngobeni; Eva Muwanga-Zake; Helen Barnes; Michael Noble; David McLennan; Gemma Wright; Katrin Gasior |
| Abstract: | In this paper we explore South Africa's personal income tax system using two microsimulation models. The first, SAMOD, simulates personal income tax and social benefits using a dataset derived from the nationally representative National Income Dynamics Study survey. The second, PITMOD, simulates the personal income tax system and is underpinned by a dataset comprising a full extract of anonymized individual-level administrative tax data especially constructed for this purpose. |
| Keywords: | Microsimulation, Personal income tax, Income distribution, South Africa |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2021-120&r= |