Purpose This investigation aims to determine the effect of trade uncertainty on domestic investment (DI) and foreign direct investment (FDI) for the Turkish economy from the first quarter of 2005 to the first quarter of 2020. Design/methodology/approach The authors adopt the vector autoregression (VAR) model augmented with Fourier terms. Using this methodology, the authors obtain the empirical results of the impulse-response functions and the variance decomposition analysis. Findings The empirical results demonstrate that a shock to trade uncertainty has a slight negative impact on DI for up to approximately 1.5 years, whereas its impact on FDI is negative but long-lasting. Moreover, the contribution of trade uncertainty to FDI is relatively higher than to DI in the error variance decomposition for the investigated period. These empirical results can be beneficial for shaping the Turkish authorities' trade policies in the following periods. Research limitations/implications These findings have implications within the macroeconomic setting. Government authorities can provide tax exemptions for specified sectors and debureaucratize investment processes for both domestic and foreign entrepreneurs. Additionally, institutional quality and property rights should be protected strictly and developed gradually. Originality/value This study is the first to examine the impact of world trade uncertainty on Turkiye’s DI and FDI. Because trade uncertainty might act as fixed costs, this creates the option value of waiting and seeing the market, and firms hesitate to incur investment.

Purpose This study examines the impact of structural shocks and policy interventions on the India/US exchange rate post the 1991 economic reforms in India. The study aims to improve forecasting accuracy by incorporating macroeconomic and microeconomic factors into the analysis using the threshold regression model (TRM), a nonlinear approach to estimation. Design/methodology/approach Extending Dornbusch’s (1976) overshooting model, the study incorporates micro factors, such as investor behaviour, beliefs and preferences, alongside traditional macroeconomic variables. Additionally, it introduces a capital control variable to assess monetary policy interventions. Using quarterly data from 1996Q2 to 2019Q3, TRM identifies two distinct economic regimes, providing a comprehensive understanding of India’s exchange rate dynamics. Findings The study reveals that macro and micro factors have varying effects on the exchange rate across regimes, reflecting India’s different economic conditions and policies. Furthermore, the TRM-based model achieves superior out-of-sample forecasting accuracy compared to the random walk model across all forecast horizons. Originality/value Unlike prior studies, where not all variables were deemed significant, our analysis demonstrates that all factors significantly influence the exchange rate. The innovative use of TRM deepens understanding of exchange rate behaviour, particularly in response to structural shocks and policy shifts. By identifying distinct economic regimes, the model offers insights into targeted policy measures tailored to India’s economic conditions, a previously unexplored perspective.

Purpose This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Purpose – The factors that determine foreign direct investment (FDI) are important to policy-makers, investors, the banking industry and the public at large. FDI in Ghana has received increased attention in recent times because its relevance in the Ghanaian economy is too critical to gloss over. The purpose of this paper is to examine the determinants of FDI in Ghana between the period of 1990 and 2015. Design/methodology/approach – The study employed a causal research design. The study used the Johansen’s approach to cointegration within the framework of vector autoregressive for the data analysis. Findings – The study found a cointegrating relationship between FDI and its determinants. The study found that both the long-run and short-run results found statistically significant negative effects of inflation rate, exchange rate and interest rate on FDI in Ghana while gross domestic product, electricity production and telephone usage (TU) had a positive effect on FDI. Research limitations/implications – The study found a cointegrating relationship between FDI and its determinants. The study found that both the long-run and short-run results found statistically significant negative effects of inflation rate, exchange rate and interest rate on FDI in Ghana whiles gross domestic product, electricity production and TU had a positive effect on FDI. Practical implications – This study has potential implication for boosting the economies of developing countries through its policy recommendations which if implemented can guarantee more capital inflows for the economies. Social implications – This study has given more effective ways of attracting more FDI into countries which in effect achieve higher GDP and also higher standard of living through mechanisms and in the end creating more social protection programs for the people. Originality/value – Although studies have been conducted to explore the determinants of FDI, some of the core macroeconomic variables such as inflation, interest rate, telephone subscriptions, electricity production, etc., which are unstable and have longstanding effects on FDI have not been much explored to a give a clear picture of the relationships. Therefore, a study that will explore these and other macroeconomic variables to give clear picture of their relationships and suggest some of the possible ways of dealing with these variables in order to attract more FDI for the country to achieve its goal is what this paper seeks to do.

Purpose – The purpose of this paper is to examine the issue of high current account deficit (CAD) from various perspectives focussing its behaviour, financing pattern and sustainability for India. Design/methodology/approach – To begin with the trends, composition and dynamics of CAD for India are analysed. Next, the influence of capital flows on current account is investigated using Granger noncausality test proposed by Toda and Yamamoto (1995) between current account balance (CAB) to GDP ratio and financial account balance to GDP ratio. Also, the sustainability of India’s current account is examined using different econometrics techniques. In particular, Husted’s (1992), Johansen’s cointegration and vector error correction model (VECM) is applied along with conducting unit root and structural break tests wherever applicable. Further, long-run and short-run determinants of the CAB are estimated using Johansen’s VECM. Findings – The study found that the widening of CAD is due to fall in household financial savings and corporate investments. Also, it was found that a large part of India’s CAD has been financed by FDI and portfolio investments which are partly replaced by short-term volatile flows. The unit root and cointegration tests indicate a sustainable current account for India. Further, econometric analysis reveals that India’s current account is driven by fiscal deficit, terms of trade growth, inflation, real deposit rate, trade openness, relative income growth and the age dependency factor. Practical implications – Since India’s CAD has widened and is expected to widen primarily due to rise in gold and oil imports, policy makers should focus on achieving phenomenal export growth so that a sustainable current account is maintained. Also, with rising working-age and skilled population, India should focus more on high-value product exports rather than low-value manufactured items. Further, on the structural side it is important to correct fiscal deficit as it is one of the important factors contributing to large CAD. Originality/value – The paper is an important empirical contribution towards explaining India’s CAD over time using latest and comprehensive data and econometric models