世界経済・社会統計 = World development indicators

In this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitative and qualitative methods. Paradoxically, the fuzzy set is a powerful tool because it replaces an unwieldy, "fuzzy" instrument—the variable, which establishes only the positions of cases relative to each other, with a precise one—degree of membership in a well-defined set. Ragin argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible. They let quantitative researchers abandon "homogenizing assumptions" about cases and causes, they extend diversity-oriented research strategies, and they provide a powerful connection between theory and data analysis. Most important, fuzzy sets can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry. This book will revolutionize research methods not only in sociology, political science, and anthropology but in any field of inquiry dealing with complex patterns of causation.

Fuzzy-Set Social Science

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Using a sample from 22 countries, I investigate the relations between the accuracy of analysts’ earnings forecasts and the level of annual report disclosure, and between forecast accuracy and the degree of enforcement of accounting standards. I document that firm-level disclosures are positively related to forecast accuracy, suggesting that such disclosures provide useful information to analysts. I construct a comprehensive measure of enforcement and find that strong enforcement is associated with higher forecast accuracy. This finding is consistent with the hypothesis that enforcement encourages managers to follow prescribed accounting rules, which, in turn, reduces analysts’ uncertainty about future earnings. I also find evidence consistent with disclosures being more important when analyst following is low and with enforcement being more important when more choice among accounting methods is allowed. ∗University of Toronto. I appreciate the helpful comments on various versions of this paper by Joel Amernic, Hollis Ashbaugh, Ray Ball (editor), Sudipta Basu, Jeff Callen, Bjørn Jørgensen, Peter Pope (referee), Beverly Walther, and participants at the 2001 EIASM Workshop on Accounting and Regulation, 2002 European Accounting Association Meeting, 2002 Journal of Accounting Research conference, 2002 Canadian Academic Accounting Association meeting, and 2002 American Accounting Association annual meeting. All errors are my own. I gratefully acknowledge the financial support of the Norwegian School of Economics and Business Administration. I thank IBES International Inc. for providing earnings forecast data.

Disclosure Practices, Enforcement of Accounting Standards, and Analysts' Forecast Accuracy: An International Study

ADRs, Analysts, and Accuracy: Does Cross Listing in the United States Improve a Firm's Information Environment and Increase Market Value?

The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets.

Does financial development influence renewable energy consumption to achieve carbon neutrality in the USA

CatBoost model and artificial intelligence techniques for corporate failure prediction

Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.

Salma Mefteh-Wali

Papers

The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets.

Does financial development influence renewable energy consumption to achieve carbon neutrality in the USA

CatBoost model and artificial intelligence techniques for corporate failure prediction

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

The safe-haven property of precious metal commodities in the COVID-19 era