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Navigating The Taiwanese Forex Landscape: Strategies For Success

Navigating The Taiwanese Forex Landscape: Strategies For Success

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Foreign Exchange Market Size, Share, Trends

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Mei-Li Shen Mei-Li Shen Scilit Google Scholar 1, Chen-Feng Li Cheng-Feng Li Scilit Google Scholar 2, *, Hsiou-Hsiang Liu Hsiou-Hsiang Liu Scilit Google Scholar 1 , Po-Yin Chan Po-Yin Chan Skillet Google Scholar 3 and Cheng-Hong Yang Cheng-Hong Yang Skillet Google Scholar 3, 4, *

Received: 27 December 2020 / Revised: 5 February 2021 / Accepted: 23 February 2021 / Published: 4 March 2021

Chinese Real Estate Investors

Accurately predicting the movement of exchange rates is of interest in various fields such as international business, financial management, and monetary policy, but it is not an easy task due to the sudden changes due to political and economic events. In this study, we develop a new forecasting approach called FSPSOSVR, which combines particle optimization (PSO), random forest feature selection, and vector regression (SVR) to accurately forecast exchange rates. PSO is used to obtain optimal SVR parameters for forecasting exchange rates. Our analysis covers monthly exchange rates from January 1971 to December 2017 for seven countries including Australia, Canada, China, the European Union, Japan, Taiwan and the United Kingdom. The out-of-sample forecast performance of the FSPSOSVR algorithm is compared with six forecasting models, including mean absolute percentage error (MAPE) and root mean square error (RMSE), including random walk, exponential smoothing, and autoregressive integrated moving average (ARIMA). ), seasonal ARIMA, SVR, and PSOSVR. Our empirical results show that the FSPSOSVR algorithm provides consistent forecast accuracy that is comparable to competing models for all currencies. These findings suggest that the proposed algorithm is a promising method for empirically forecasting exchange rates. Finally, we demonstrate the empirical relevance of exchange rate forecasts derived from FSPSOSVR using currency carry trades and the proposed trading strategy can generate positive returns of more than 3% per year for most currencies, except AUD and NTD.

Forecasting the movement of exchange rates has long been a hot topic in various fields of application, attracting the interest of academics, financial traders, and monetary authorities. For foreign exchange traders and stock market investors, the ability to accurately predict exchange rates helps in reducing risk and maximizing returns from transactions [1, 2]. From the point of view of monetary authorities, reliable exchange rate forecasting also helps in managing exchange rates and conducting monetary policy. Under a managed exchange rate regime, exchange rates are allowed to fluctuate within an unspecified band, and the government can intervene in this regime based on its expectations of future exchange rates [3]. Furthermore, when the government uses monetary policy to stimulate the economy, such as lowering interest rates, it will increase income and demand for a country’s imported goods, depreciating the currency, and ultimately negatively affecting the competitiveness of exported goods. Hence, accurate forecasting of exchange rates can help the government to determine the sufficient level of interest rate reduction, which is related to the evaluation of monetary policy performance [4, 5]. To accurately predict exchange rates, academic researchers begin to study the behavior of exchange rates from a theoretical perspective. Many studies have been devoted to the development of various exchange rate determination models that relate the level of the exchange rate to macroeconomic variables [6, 7]. International debt theory, purchasing power parity, interest rate parity, and active market theory provide theoretical explanations for the relationship between certain variables, exchange rates, and economic fundamentals, such as a country’s balance of payments, price level, and real income. levels, money supply, interest rates and other economic factors [8, 9].

To understand whether these theories can adequately approximate the behavior of exchange rates, Meese and Rogoff examined them and found that most economic forecasting models performed worse at predicting exchange rates than a simple random walk without drift. The (RW) model assumes that exchange rate forecasts are at the same level as previous exchange rates [10]. Subsequent literature has also shown that the relationship between the nominal exchange rate and money supply, output, and interest rates is clear during periods of floating exchange rates. this is called the “exchange rate breaker puzzle” [11]. So predicting exchange rates seems like a difficult task.

Navigating The Taiwanese Forex Landscape: Strategies For Success

In recent years, significant progress has been made in developing sophisticated exchange rate forecasts. The former, for example, used a custom-built quarterly real-time database to estimate the forecast performance of linear models using purchasing power parity and Taylor’s rule to outperform the previous quarter’s 16. quarter horizon [12]. Cavusoglu and Nevu investigated the role of consensus forecast variance in exchange rate forecasting; they found that consensus forecasts consistently predict exchange rates in the long run, but most do not in the short run [13]. Pirdzioch and Rülke examined whether expert exchange rate forecasts reliably predict the future behavior of exchange rates in emerging markets; however, they obtained different results for different currencies. Their general conclusion is that forecasts are often informative about directional changes in exchange rates [14]. Dick et al. They used survey data on forecasting collected by individual professionals and showed that good performance in forecasting short-term exchange rates correlates with good performance in forecasting, especially interest rates [15]. Ahmed et al. linear factor models using unconditional and conditional expectations of three currency-based risk factors to examine the predictability of exchange rates. They found that all models performed worse than a random walk in forecasting monthly exchange rate returns, and that information incorporated into currency-based risk factors did not provide systematic economic value for investors. .

Brexit, Taiwan And The Global Semiconductor Industry

Recent studies have shown that the relationship between exchange rates and fundamentals can be difficult to detect using the Meese and Rogoff approach. Amat et al. abandoned conventional circular or recursive regressions to obtain exchange rate forecasts and adopted a machine learning estimation method to demonstrate that it can provide useful information to improve forecasts at a 1-month horizon [17]. Chung et al. comprehensively examined exchange rate forecasts from a large set of models and compared the forecast performance against the RW model at different horizons and found that model/specification/currency combinations that may perform well in one period and indicators of one indicator may not perform well in other periods and/or performance indicators [18].

Considering the shortcomings of the above models, this paper uses another approach using machine learning (ML) to predict changes in the exchange rate. ML has received attention from academia and industry. In particular, artificial neural networks (ANN) and statistical learning methods are frequently used in the recent exchange rate forecasting literature. Several sophisticated artificial intelligence (AI) techniques can deal with nonlinear and non-stationary data in various fields. In particular, they can be used in managing health insurance costs [19], improving multivariate regression methods [20], managing missing IoT data [21], and analyzing cancer mortality and survival data [22, 23]. ].

Nosratabadi et al. conducted a comprehensive review of state-of-the-art ML and advanced deep learning (DL) methods in emerging economic and financial applications [24]. Recent novel ML methods include: Lin et al. Discrete sampling and ensemble learning to improve bankruptcy forecasting accuracy. Chen et al. [25] proposed a nested-pSVM and a boosted-pSVM for bankruptcy prediction. Lee et al. used vector regression for commercial aircraft safety monitoring [26]. Husejinovic used naïve

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