Exchange Rate Forecasting with Information Flow Approach

Abstract

and increasing international trade turnovers lead to great demand for exchange rate forecasts. On the other hand, the amount of factors which influence the fluctuations of exchange rates, the heterogeneity of market participants, the amount of financial instruments which allow to gain exposure to a particular currency and the organizational form (over the counter system) of the market complicate the process of generating exchange rate forecasts.In the second part of 20th century there were many exchange rate forecasting models introduced. Rasekhi, Rostamzadeh (2011) classify the models to fundamen tal and technical. Fundamental models try to predict the exchange rate based on the fundamental factors with the purpose to distinguish the intrinsic value of the two cur rencies, while technical models forecast the exchange rate based on the price fluctuations from the past. It is argued that fundamental exchange rate forecasting models cannot predict the exchange rates for short term period. Even in nowaday research a paper of Meese, Rogoff (1983) Abstract. The purpose of this article is to assess exchange rate forecasting possibilities with an information flow approach model. In the model the three types of information flows are distinguished: fundamental analysis information flow through particular macroeconomic determinants, microstructure approach information flow through dealer clients' positioning data, technical analysis information flow through technical indicators. By using regression analysis it is shown that the composed model can forecast the exchange rate, the most significant information flows are distinguished. The results lead to further development of the information flow approach as a tool to forecast exchange rate fluctuations.

Conjectural Connection: Tourism Enterprises Competitiveness : A Macro Perspective Through the Lens of Knowledge Capital Management

“…Yeh-Yun Lin and Edvinsson (2008) compare the Nordic countries in terms of their national intellectual capital. Mačerinskienė and Balčiūnas (2016) investigate exchange rate forecasting using an information flow approach, offering empirical evidence from Lithuania. Salonius and Lönnqvist (2012) assess the policy relevance of national intellectual capital information, evaluating its usefulness for decision-makers.…”

“…Additional drivers for economic expansion through competitiveness, particularly in service-oriented sectors such as tourism, would be described as follows: a highly and relevantly knowledgeable workforce that, combined with the appropriate infrastructure, can transform and accumulate unsalable surplus knowledge overs; knowledge capital (Bérubé and Mohnen, 2007). A study by (Oprescu and Oprescu, 2012) linking knowledge-based capital to national economic growth, equivalent research aiming to link wealth and/or national competitiveness was published by (Bontis, 2004 Mačerinskienė and Balčiūnas, 2016). Several studies attempt to understand the dynamics of intellectual and knowledge capital at the national level, while others focus on how national knowledge capital can be optimized and managed to spur progress in trade and industry (Roos, 2017).…”

Prediction of Malaysian Exchange Rate Using Microstructure Fundamental and Commodities Prices: A Machine Learning Method

“…The introduction of the microstructure approaches brought dramatic developments in short run horizon and has a significant impact on the exchange rate dynamics. The literature showed that the microstructure approaches to exchange rates have achieved substantial improvements due to the availability of high quality and high frequency data [4,5]. Moreover, besides the microstructure fundamentals, the commodities prices have also considered as an important determinant of the exchange rate in export oriented economies.…”