Developing Machine Learning Technique for Measuring Central Bank Credibility
Creators: name: Okiriza Wibisono
name: Zulen, Alvin Andhika
name: Anggraini Widjanarti
Publisher: name: Zenodo
Year: 2020
Subjects: central bank credibility
policy formulation
central bank communication
text mining
machine learning
big data analytics

description: The formulation of monetary policy by the central bank in order to achieve the target will only be effective with the presence of credibility. The role of credibility in supporting policy effectiveness has long been believed by economists and central bank practitioners. This is in-line with the existence of rational expectation which is believed to play a role in shaping the behaviour of economic agents. The credibility of monetary policy will determine the ability of central bank to manage the expectation of economic actors (Lyziak, 2016) and will be reflected in the expectation of long-term interest rates and other asset prices (Blinder, 2000). Based on the theory, credibility is about consistency in providing accurate and valuable information or always being responsible (Sobel, 1985). In the terminology of policy makers, credibility is the expectation that policies that are announced will be implemented, not only reflect the policy makers’ wishes, but also reflect the situation and conditions behind the policy so that if there is a shock beyond expectation, it will affect the credibility (Drazen &amp; Masson, 1994). Central bank credibility is formed by 7 (seven) factors that are considered important, i.e. honesty, independence, consistency (history of fighting inflation), transparency, maintaining fiscal deficit, the use of policy rules, and incentives (personal loss) (Blinder, 2000). To put it simply, credibility can be determined by competence/professionalism, independence, accountability, and transparency. Empirically, credibility is a qualitative concept, which is not easy to measure, and there are several approaches have been used in measuring credibility. In Cukierman &amp; Meltzer (1986) and Faust &amp; Svensson (2001), central bank independence is used as a proxy for credibility. In Blinder (2000), the credibility of a central bank is measured using survey where economists choose the main characteristics that contribute to the central bank credibility, which is then quantified to produce an index. While in Lyziak (2016) measurement is made using an index, which is built from several indicators including the achievement of the central bank's target, the deviation of inflation expectation, transparent targets and indicators, independence, and accountability. Bank Indonesia regularly conducts survey to external stakeholders to measure the credibility of monetary policy. The monetary policy credibility survey is prepared based on 6 (six) aspects of credibility, i.e. formulation, independence, communication, accountability, coordination, and effectiveness. In addition to measure credibility, the survey is also used as feedback to strengthen the implementation of the central bank's tasks in the future. In practice, the survey method has several weaknesses. Questions in the survey can be understood differently by respondents. In addition, the execution of survey by third party can influence the survey results, including in the selection of respondents. Based on these factors, it is necessary to strengthen the central bank’s credibility measurement method. Utilizing Big Data Analytics to gather public perception regarding central bank policy can be an alternative method that needs to be considered. This research aims to utilize Big Data Analytics method, particularly machine learning and text mining, to measure stakeholders' perceptions on the central bank credibility from news data. Utilizing the ability of computers to carry out complex analysis (advance analytics) is expected to be able to identify changes in public sentiment towards the credibility of central bank policy. The indicator is expected to be used as an additional measure of central bank credibility and to support the effectiveness of central bank monetary policy in the future. Measurement of central bank credibility is done by utilizing machine learning models that are built based on annotated news sentences (i.e. news with credibility sentiment information, labelled by experts). The news data source used in this research is Bank Indonesia’s Cyber Library, an internal curated news database that contains news related to economics and finance, which includes ± 19 print media and 3 online media. Approximately 12,600 sentences were obtained from the annotation steps, which were then separated into 11,600 sentences (92%) for training data and 1,000 sentences (8%) for test data. There are 4 (four) aspects of credibility that are classified using machine learning methods in this research, I.e. formulation, effectiveness, coordination, and communication. We used 6 (six) machine learning models as alternatives, namely Logistic Regression, Naive Bayes, Random Forest, XGBoost, FastText, and Deep Learning. The credibility index is the average of the credibility score from those four aspects. To measure the accuracy of classification and comparison between models, the classification results on the test data are compared with the annotated labels, with the F1 metric as the main evaluation criteria. Testing is done by using k-fold cross validation with a value of k = 5. From the out-of-sample evaluation results, we achieved a good machine learning model for coordination (F1=70%) and communication (F1=65.6%) aspects, whereas model for formulation and effectiveness aspects can be further developed. The resulting credibility index also moves in-line with the result of the policy credibility survey, with a correlation of ± 80%.