Leading Indicators’ Applicability to Forecast Profitability of Commercial Bank: Case Study from Lithuania


  • Darius Rauličkis Mykolas Romeris University Faculty of Economics and Business Institute of Finance Ateities g. 20, Vilnius, 08303 Lithuania Tel.: +370 5 271 4625 E-mail: darius.raulickis@gmail.com
  • Daiva Jurevičienė Vilnius Gediminas Technical University Faculty of Business Management The Department of Economics Engineering Saulėtekio al. 11, LT-10223 Vilnius Lithuania Tel.: +370 5 274 4873 E-mail: daiva.jureviciene@vgtu.lt




Profitability, financial ratios, leading indicators, commercial banks.


Purpose of the article: Profitability is one of the most important ratios for performance measurement in any competitive commercial bank and key source to fund future working capital and investments needs. This leads to necessity to investigate topics related to profitability and applicability of factors, which would enable to capture latest trends in economy. In scientific literature, leading economic indicators (in addition to financial and lagging/coinciding economic indicators) are suggested as able to capture trends of economic development. However, there is still a discussion going on applicability of these indicators as well as on financial ratios and economic indicators. The problem is relevant from theoretical and practical point of view. Methodology/methods: Quantitative factors for forecasting commercial banks’ profitability were identified and tested employing methods of detailing, grouping and quantitative analysis (GMM estimator) in empirical research. Scientific aim: To identify applicability of leading economic indicators for bank’s profitability forecasting. Findings: Regression analysis of models using blend of bank, industry, economic ratios improves explanatory power in both dimensions – time (higher scores received for all forecasting horizons) and alternatives (different models that use different blends of determinants). Such improvement was found for all forecasting horizons (one, two and three-quarters) resulting improved explanatory power for one, two and three quarters in comparison to models without leading economic indicators. Conclusions: Leading economic indicators can help to better capture forwardd-looking signals, however, to avoid volatility in forecasts they should be employed with careful analysis of their methodologies and in combination with bank and industry specific, lagging and coinciding economic factors.