The efficacy of hedging techniques can have significant implications for ACME. Ciorciari (2019) states that financial hedging refers to adopting an investment position to mitigate any adverse price fluctuations in other assets. Successful financial hedging allows for the pursuit of growth, expansion, and economic interdependence while mitigating the danger of financial meltdown (Ciorciari, 2019). ACME regarded financial hedging as an option and designed a five-step approach for financial hedging, which included exchange forecasts, reviewing the strategic plan’s impact, hedging rationale, financial instruments, and hedging program.
One of the most significant hazards in international trading is exchange rate risk. It is defined as losses caused by fluctuations in the local currency compared to the trading nation’s currency. The essential strategy for multinational trading enterprises like ECME to hedge exchange rate risks is to employ financial market instruments such as “financial derivatives (forward, futures, options, etc.) and foreign currency debt” (Lin et al., 2020, p. 2451). They will help decrease risks and avoid eroding revenues from international commerce. Another more direct and effective strategy to minimize exchange rate risks is to make accurate forecasts and choices about the direction and size of fluctuations. Lin et al. (2020) suggest a new method for improving the accuracy of short-term exchange rate forecasting to reduce exchange rate risks. Forecasting exchange rates use current and historical data from the foreign exchange market to anticipate future exchange rate behavior (Lin et al., 2020). Moreover, as a financial time series, exchange rate data possesses not only conventional time series characteristics such as autocorrelation, tendency, periodicity, and random noise, but also economic time series features such as nonlinearity, nonstationarity, and volatility grouping.
Consequently, ACME can use the novel hybrid model of complete ensemble empirical mode decomposition (CEEMDAN) based multilayer long short-term memory (MLSTM) networks. According to Lin et al. (2020), it solves the drawbacks of traditional approaches. CEEMDAN addresses the empirical mode decomposition (EMD) mode mixing problem and handles the residual issues included in the rebuilt data of ensemble empirical mode decomposition (EEMD) with reduced computing cost. Thus, MLSTM may learn more complicated dependencies from exchange rate data than the standard time series model.
Hedging is a valuable strategic plan that investors may employ to safeguard their investments against unexpected and rapid fluctuations in capital markets. CFI Team (2022) claims that the hedge ratio is the ratio or comparative value of the hedging of an open position to the absolute position. It is a crucial risk management metric used to assess the magnitude of any possible risk produced by a change in the hedging instrument (CFI Team, 2022). The optimal hedge ratio, computed dynamically by examining the trade-off between hedging costs and risk reduction, often outperforms a static approach to financial hedging in terms of risk-adjusted returns (Iborra & Chabane, 2020). The hedge ratio decomposition offers insight into the optimal hedge ratio’s drivers and allows investors to better tune their currency hedging policy by emphasizing the most relevant driver.
ACME should consider transaction exposure, a type of foreign exchange risk in global commerce that includes cross-currency transactions involving several currencies. In other words, transaction vulnerability is a problem a firm may face while conducting international business since currency exchange rates may fluctuate before the final agreement (Jayashree & Priya, 2019). As a result, the risk of shifting currency rates is known as transaction vulnerability. When businesses in two countries agree to a cross-currency contract in two currencies, the contract’s value may fluctuate due to changes in foreign exchange rates. Thus, hedging tactics might safeguard the firm against transaction risk.
The suggested hedging program for ACME spans three time periods. The first phase is the pre-acquisition period, during which the business assesses the target’s financial risk. The following stage is the interim period, during which the company evaluates the transaction risk. Significantly, the vulnerability to unknown factors affecting the anticipated return from a trade or transaction is referred to as transaction risk (CFI Team, 2021). It covers all unfavorable occurrences that might prohibit a transaction, such as a possibility that the US dollar valuation of a target’s pricing and external financing costs would fluctuate. Furthermore, fixed-income securities in developed markets should be utterly foreign currency (FX) hedged (Iborra & Chabane, 2020). The firm should emphasize integration, balance sheet, and cash flow risks during the post-acquisition stage to enable efficient acquisition operation.
Financial hedging will allow the company to seek more inorganic development possibilities in the form of acquisition. Alexandridis et al. (2021) acknowledge that enterprises that use interest rate (IR) and foreign currency (FX) derivatives have fewer investment limitations and lower interest rates in bank loan agreements, encouraging more significant organic investment for companies with financial hedging strategies. Acquiring businesses with financial hedging plans have lower borrowing costs and are more inclined to pay for their transactions with cash rather than with external borrowing Alexandridis et al. (2021). Enterprises with solid investment potential are more likely to hedge and, as a result, are less sensitive to exchange rate risk.
References
Alexandridis, G., Chen, Z., & Zeng, Y. (2021). Financial hedging and corporate investment. Journal of Corporate Finance, 67.
CFI Team. (2021). Transaction risk. Corporate Finance Institute.
CFI Team. (2022). Hedge ratio. Corporate Finance Institute.
Ciorciari, J. D. (2019). The variable effectiveness of hedging strategies. International Relations of the Asia-Pacific, 19(3), 523-555.
Iborra, R., & Chabane, I. (2020). Strategic currency hedging in multi-asset portfolios. The Journal of Investing, 29(5), 31-57.
Jayashree, G., & Priya, I. C. M. (2019). An empirical study on the various risks in foreign exchange market and its impact in global business transactions. International Journal of Trend in Scientific Research and Development, 3(5), 2613-2615. Web.
Lin, H., Sun, Q., & Chen, S.-Q. (2020). Reducing exchange rate risks in international trade: A hybrid forecasting approach of CEEMDAN and Multilayer LSTM. Sustainability, 12(6), 2451-2468.