A Computable General Equilibrium (CGE) model in a bottom-up approach - based on microfoundations - and a Social Accounting Matrix (SAM) for the regional economy of Chiapas are built. Methodology: This research applies a Computable General Equilibrium (CGE) model. It is a system of equations that describes an entire economy and all the interactions between productive sectors, commodity and factor markets, and institutions. All of the equations are solved simultaneously to find an economy-wide equilibrium in which demand and supply quantities are equal in every market at a certain level of prices (Burfisher, 2011). Two of the features of this model are that, on one hand, it implements a “bottom-up” approach, that is, it is focused on individual markets and economic agents. On the other hand, it is partially synthetic. In other words, most parameters can be calibrated with data from the SAM. Data framework: A Social Accounting Matrix (SAM) is a balanced square matrix that represents all income and expenditure flows between productive sectors, markets, and economic agents of an economy at a given period of time (Müller, Perez & Hubertus, 2009). It is based on the double entry bookkeeping in accounting, which requires that total revenue equals total expenditure in each single account included in the SAM (Breisinger, Thomas & Thurlow, 2010). The main features of the Chiapas SAM are that production activities are broken down in 10 sectors, according to the North American Industry Classification System (NAICS). There is one commodity per economic activity. Factors of production are disaggregated into formal and informal labor, and capital. Direct taxes are broken up into activity tax, social security contributions, household and corporate income taxes, ‘tenencia’ tax (ownership tax, i.e. a tax associated with the possession or use of vehicles), and regional payroll tax (‘nomina’). Indirect taxes, in turn, are value-added, sales and export taxes, and import tariffs. Subsidies on production by economic activity are also included. Households are disaggregated by income quintiles. Social transfers are split in non-conditional (Procampo, universal pension, PAL-Sin Hambre , temporary employment program, and the regional program Amanecer ) and Oportunidades. The latter is also broken down into its five components: food, elderly, education, child, and energy. The introduction of conditional cash transfers in the SAM is particularly relevant because it allows assessing the impact of changes in their amount and distribution on household income, poverty reduction, income inequality, and economic growth at the regional level. Data sources: - National Institute of Statistics and Geography (INEGI): 2012 National Employment and Occupation Survey 2013 Chiapas Statistical Yearbook 2012 National Household Income-Expenditure Survey 2012 Chiapas Statistical Perspective 2003-2012 Goods and Services Accounts (SCNM) 2003-2012 Institutional Sector Accounts (SCNM) 2008 Input-Output Table 2008 Supply and Use Tables - Chiapas State Committee of Statistical and Geographical Information (CEIEG): 2012 Chiapas Employment and Occupation Survey 2012 Chiapas Monthly Statistical Reports of IMSS-insured Workers - Federal Ministry of Labor and Social Welfare (STYPS): 2012 IMSS-registered Daily Salary by Economic Activity 2012 IMSS-insured Workers Quality/Lineage: With the raw data a Social Accounting Matrix for the regional economy of Chiapas was built Features: - Oportunidades broken down by component - Other non-conditional social transfers such as Procampo, PAL-Sin Hambre, Employment program, Universal pension, and the regional program 'Amanecer' - Informal wages - Satellites tables of formal and informal employment - Productive activities according to the North American Industry Classification System (NAICS) used in Mexico, Canada, and the United States of America - 10 economic activities - 10 Commodities (one per economic activity) - Factors of production: formal and informal labor and capital Purpose: 1. To assess the opportunity cost of financing "Oportunidades", Mexico's conditional cash transfers program, and its implications for rural development and rural economic growth in the regional setting of Chiapas. Moreover, 2. Pro-growth and pro-poor tax structures are also evaluated by applying standard economic analysis tools and modeling to substantially raise the federal non-oil tax revenue to finance social policy for poverty and inequality reduction. Dissertation: Viveros Añorve, J. L. (2015): The opportunity cost of financing "Oportunidades": a general equilibrium assessment for poverty reduction in Mexico. Ph.D. dissertation. Center for Development Research, Faculty of Agriculture, University of Bonn

Enhancing Financial Market Predictions: Causality-Driven Feature Selection This paper introduces FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.

Our FinSen Dataset

This repository contains the dataset for Enhancing Financial Market Predictions: Causality-Driven Feature Selection, which has been accepted in ADMA 2024.

If the dataset or the paper has been useful in your research, please add a citation to our work:

@article{liang2024enhancing, title={Enhancing Financial Market Predictions: Causality-Driven Feature Selection}, author={Liang, Wenhao and Li, Zhengyang and Chen, Weitong}, journal={arXiv e-prints}, pages={arXiv--2408}, year={2024} }

Datasets [FinSen] can be downloaded manually from the repository as csv file. Sentiment and its score are generated by FinBert model from the Hugging Face Transformers library under the identifier "ProsusAI/finbert". (Araci, Dogu. "Finbert: Financial sentiment analysis with pre-trained language models." arXiv preprint arXiv:1908.10063 (2019).)

We only provide US for research purpose usage, please contact w.liang@adelaide.edu.au for other countries (total 197 included) if necessary.

We also provide other NLP datasets for text classification tasks here, please cite them correspondingly once you used them in your research if any.

20Newsgroups. Joachims, T., et al.: A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In: ICML. vol. 97, pp. 143–151. Citeseer (1997) AG News. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015) Financial PhraseBank. Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65(4), 782–796 (2014)

Dataloader for FinSen We provide the preprocessing file finsen.py for our FinSen dataset under dataloaders directory for more convienient usage.

Models - Text Classification

DAN-3.

Gobal Pooling CNN.

Models - Regression Prediction

LSTM

Using Sentiment Score from FinSen Predict Result on S&P500 Dependencies The code is based on PyTorch under code frame of https://github.com/torrvision/focal_calibration, please cite their work if you found it is useful.

:smiley: ☺ Happy Research !

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials’ optimization, we performed the joint analysis using prediction-based models in four different scenarios: i) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trial’s allocation considering the counties’ economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed genotype-by-environment interaction. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.