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We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use historical volatility and liquidity measures to build our benchmark volatility model (Chandra & Thenmozhi, 2014). Moreover, we extend Bulut (2018) to incorporate indexes for 15 keywords (price-related, income-related, and liquidity-related) from Google Trends data into our benchmark volatility model to evaluate the forecasting ability of the models. Our results indicate that Google Trends data can improve volatility prediction and that among the groups of keywords that we consider, the price-related keywords have the best forecasting ability. Incorporating data on searches for “prices” into the model produces the highest reduction in the forecasting error: a 22.75% decrease compared to the level in the benchmark model. Hence, these empirical findings indicate that Google Trends data contain information that influences exchange rate movements.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

An essential part of the transfer of knowledge in the tourism and hospitality industry, destination image is defined as the expression of objective knowledge, imagination, and the subjective emotions of the tourist. Social media is profoundly changing the way the tourist images and interacts with the destination environment. In turn, firms in the industry are seeking to leverage the power of social media to gain insights into tourist cognition and behavior. In this chapter, we analyze various social media to investigate knowledge transfer relating to two groups of hotels in Philadelphia, and we propose a methodology to predict future lodging demand from empirical data in line with the objectives of the t-Forum.

Global economy, growing importance of innovations as well as wide use of technologies have changed the banking business worldwide. Financial technologies (FinTech) have become an integral part of banking, and nowadays banks have started to compete beyond financial services facing increasing competition from nonfinancial institutions providing, for example, payment services. Start-up service providers, search engines, and social networks have expanded their services “interfering” in the fields traditionally covered by banks. The rapid rise of FinTech has changed the business landscape in banking asking for more innovative solutions. These recent tendencies require the banks to increase investment in FinTech, rethink service distribution channels, especially the business-to-consumers models, increase further standardization of back-office functions, etc. Some members of the financial services industry see the boom in FinTech as a threat to traditional banking industry. Others believe that FinTech has become a challenge that can be turned into an opportunity as it provides more flexibility, better functionality in some areas, and aggregation of services. The aim of the paper is to analyze the recent trends in banking, identifying opportunities and risks of FinTech for banks. A timely integration of FinTech into business allows banks to get an advantage in growing competition. This paper provides an extensive analysis of recent trends in FinTech and banking, examining experience of leading European and US banks, as well as surveys conducted among members of the financial services industry in different countries. The authors have studied the development of the financial innovation and technology market, assessed the existing practices applied in the field of FinTech, identified the main risks related to development of FinTech and financial innovations the banks are exposed to on the micro- and macrolevel. The paper provides recommendations for regulators and banks to ensure reduction of risks associated with development of FinTech. Analysis of FinTech market has shown growing competition, including from nonfinancial institutions. The paper provides practical recommendations to commercial banks for strengthening the position in financial innovations and controlling the risks associated with introduction of financial innovations.