Fruit production forecasting by neuro-fuzzy techniques

Neuro-fuzzy techniques are finding a practical application in many fields such as in model identification and forecasting of linear and non-linear systems. This paper presents a neuro-fuzzy model for forecasting the fruit production of some agriculture products (olives, lemons, oranges, cherries and pistachios). The model utilizes a time series of yearly data. The fruit forecasting is based on Adaptive Neural Fuzzy Inference System (ANFIS). ANFIS uses a combination of the least-squares method and the backprobagation gradient descent method to estimate the optimal food forecast parameters for each year. The results are compared to those of an Autoregressive (AR) model and an Autoregressive Moving Average model (ARMA).Fruit forecasting, neuro-fuzzy, ANFIS, AR, ARMA, forecasting, fruit production, Agricultural Finance, Crop Production/Industries,

Neural Networks, Ordered Probit Models and Multiple Discriminants. Evaluating Risk Rating Forecasts of Local Governments in Mexico.

Credit risk ratings have become an important input in the process of improving transparency of public finances in local governments and also in the evaluation of credit quality of state and municipal governments in Mexico. Although rating agencies have recently been subjected to heavy criticism, credit ratings are indicators still widely used as a benchmark by analysts, regulators and banks monitoring financial performance of local governments in stable and volatile periods. In this work we compare and evaluate the performance of three forecasting methods frequently used in the literature estimating credit ratings: Artificial Neural Networks (ANN), Ordered Probit models (OP) and Multiple Discriminant Analysis (MDA). We have also compared the performance of the three methods with two models, the first one being an extended model of 34 financial predictors and a second model restricted to only six factors, accounting for more than 80% of the data variability. Although ANN provides better performance within the training sample, OP and MDA are better choices for classifications in the testing sample respectively.Credit Risk Ratings, Ordered Probit Models, Artificial Neural Networks, Discriminant Analysis, Principal Components, Local Governments, Public Finance, Emerging Markets

Metode K-nearest Neighbor Berbasis Forward Selection Untuk Prediksi Harga Komoditi Lada

Banyak peneliti termotivasi untuk meningkatkan kinerja prediksi. K-Nearest Neighbor (KNN) merupakan salah satu algoritma untuk regresi maupun klasifikasi sudah secara sukses diimplementasikan di berbagai bidang. Di sisi lain, penentuan variabel yang sesuai dapat memberikan performa yang semakin baik pada suatu model. Pada penelitian ini bertujuan mengembangkan model prediksi dengan menggabungkan algoritma K-Nearest Neighbor dengan metode seleksi atribut, khususnya forward selection untuk memprediksi komiditi lada. Model yang diusulkan dievaluasi dengan data time series lada hitam dan lada putih. Hasil penelitian menunjukkan bahwa algoritma K-Nearest Neighbor berbasis forward selection memberikan kinerja yang terbaik dibandingkan dengan KNN berbasis backward elimination dan SVM berbasis seleksi atribut

Does Fiscal Decentralization in Indonesia have Asymmetrical Information?: Principal-Agent Model, Primal-Dual, and a Neural Network Analysis

In reality, subnational governments suffer from moral hazard, creating uncertainty which, in turn, causes economic inefficiency. The behavior of subnational governments cannot be observed by the central government. An analysis which takes into account this phenomenon is therefore needed. Decentralization implies delegating authority from central government to subnational governments. In this study, the subnational government is represented by the local government. This study utilizes a model of principal-agent. The central government acts as a principal who delegates fiscal authority to subnational governments who act as agents. By applying principal-agent model, we can use the primal-dual approach to analyze both revenue and expenditure assignment associated with the tax effort of the subnational governments. The result from artificial neural network approach shows that asymmetric information between central and subnational governments exists in Indonesia.Keywords: Artificial Neural Network, Fiscal Decentralization, Local Tax Effort, Primal-Dual, Principal-Agent

Estimating bankruptcy using neural networks trained with hidden layer learning vector quantization

The Hidden Layer Learning Vector Quantization (HLVQ), a recent algorithm for training neural networks, is used to correct the output of traditional MultiLayer Preceptrons (MLP) in estimating the probability of company bankruptcy. It is shown that this method improves the results of traditional neural networks and outperforms substantially the discriminant analysis in predicting one-year advance bankruptcy. We also studied the effect of using unbalanced samples of healthy and bankrupted firms. The database used was Diane, which contains financial accounts of French firms. The sample is composed of all 583 industrial bankruptcies found in the database with more than 35 employees, that occurred in the 1999-2000 period. For the classification models we considered 30 financial ratios published by Coface available from Diane database, and additionally the Beaver (1966) ratio of Cash Earnings to Total Debt, the 5 ratios of Altman (1968) used in his Z-model and the size of the firms measured by the logarithm of sales. Attention was given to variable selection, data pre¬processing and feature selection to reduce the dimensionality of the problem

Multimodal Generative Models for Bankruptcy Prediction Using Textual Data

Textual data from financial filings, e.g., the Management's Discussion & Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA section for all public companies, which limits the use of MDA data in traditional bankruptcy models, as they need complete data to make predictions. The two main reasons for the lack of MDA are: (i) not all companies are obliged to submit the MDA and (ii) technical problems arise when crawling and scrapping the MDA section. To solve this limitation, this research introduces the Conditional Multimodal Discriminative (CMMD) model that learns multimodal representations that embed information from accounting, market, and textual data modalities. The CMMD model needs a sample with all data modalities for model training. At test time, the CMMD model only needs access to accounting and market modalities to generate multimodal representations, which are further used to make bankruptcy predictions and to generate words from the missing MDA modality. With this novel methodology, it is realistic to use textual data in bankruptcy prediction models, since accounting and market data are available for all companies, unlike textual data. The empirical results of this research show that if financial regulators, or investors, were to use traditional models using MDA data, they would only be able to make predictions for 60% of the companies. Furthermore, the classification performance of our proposed methodology is superior to that of a large number of traditional classifier models, taking into account all the companies in our sample

Forecasting Iowa gaming volume: A comparison of four time series forecasting methods

This study investigated the forecasting abilities of four forecasting techniques—(a) autoregressive integrated moving average (ARIMA), (b) artificial neural networks (ANNs), (c) simple moving average (SMA), and (d) the naïve method—as they apply to the Iowa monthly time series of slot coin-in and table drop. Mean squared error and mean absolute percentage error were adopted as evaluation criteria to compare the forecasting abilities of these various techniques. The results indicated that SMA outperformed the other three methods, which extends the conclusions of the M-competition to time series in the gaming field. Meanwhile, the ANN technique introduced without any modification was incapable of replacing the standing of ARIMA in the practice of gaming forecasting. This study is the first attempt to explore the forecasting abilities of four prevailing forecasting models based on gaming practices. It provides researchers and practitioners with a guide to and insights into the application of the ANN technique in gaming forecasting, selection of forecasting method, and effectiveness of the model for different horizons of gaming forecasting

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system