Search Results (5)

The era of low-interest rates is coming. In addition to their basic salary, people hope to increase their income by doing part-time work, understanding how to use assets already on hand, and investing in assets to earn extra rewards. Goldman Sachs reports that over the past 140 years, the 10-year stock market return has averaged 9.2%. The investment firm also noted that the S&P 500 outperformed its 10-year historical average with an annual average return of 13.6% between 2010 and 2020. Nowadays, with increased computing power and advancements in artificial intelligence algorithms, the effective use of computing power for investment has become an important topic. In the investment process, venture capitalists form portfolios, a practice that improves investment efficiency and reduces risks in a relatively safe situation. Current investments are not limited to one country, allowing for investments in other countries. Given this situation, we must pay attention to Uncovered Equity Parity (UEP) conditions. Thus, we aim to find optimal dynamic trading strategies with Deep Reinforcement Learning, considering the aforementioned properties. Full article

The purpose of this paper is to investigate how well contemporary exchange rate theories explain fluctuations in exchange rates of emerging economies, before and after the Global Financial Crisis (GFC). As an example, the EUR/PLN exchange rate in 1999–2015 was selected as the currency pair that was the most liquid in the region; it had a stable exchange rate regime in the given period. The whole analysis was performed within the selected linear vector error correction (VEC) model framework. VEC models incorporate such well-known theories as purchasing power parity (PPP), the uncovered interest rate parity (UIP), the Harrod–Balassa–Samuelson (HBS) effect, the terms of trade (TOT), the net financial asset (NFA) theory and risk premium. The results indicate the greater importance of external factors—in particular, the Euro Area (EA) short-term interest rates and EA price shocks after the GFC. The main sources of EUR/PLN variability were found to be exchange rate shocks, terms of trade shocks and foreign and domestic short-term interest rate shocks, as well as foreign price shocks. These results are of particularly high importance for our own exchange rate shocks and indicate that a large part of exchange rate fluctuations in EUR/PLN still remains unexplained. Full article

The study of the effectiveness of the currency market is one of the most important research problems in the field of finance. The paper aims to assess the efficiency of the Polish zloty exchange rate market. We test the market efficiency by applying two independent approaches, one based on the Uncovered Interest Parity theory, and another based on the fractal analysis of exchange rates series. The research results show that the Uncovered Interest Parity holds only on the USD/PLN market. For EUR/PLN, JPY/PLN, CHF/PLN, MXN/PLN and TRY/PLN, the Uncovered Interest Parity hypothesis is rejected and implies the existence of the forward premium anomaly and market inefficiency. The estimated Hurst coefficient provides insight into the long-range dependence of exchange rates. The MXN/PLN, TRY/PLN and EUR/PLN exchange rates exhibit anti-persistent behaviours suggesting mean-reverting characteristics. For JPY/PLN and CHF/PLN, a high value of the Hurst exponent indicates long memory in the time series. Only for USD/PLN, we achieve the Hurst exponent closest to 0.5, which implies market efficiency. The research results obtained based on the UIP hypothesis and fractal analysis are consistent. The study reveals that the market efficiency hypothesis holds only for the most tradable Polish zloty currency pair, i.e., USD/PLN. Full article

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical models can ever outperform the random walk in forecasting out-of-sample exchange rates has received scholarly attention. Recently, a combination of fundamental models with machine learning methodologies was found to outcompete the predictability of random walk (Amat et al. 2018). This paper focuses on combining modern machine learning methodologies with traditional economic models and examines whether such combinations can outperform the prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the monetary model, and the Taylor rule models). We performed a thorough robustness check using six government bonds with different maturities and four price indexes, which demonstrated the superior performance of fundamental models combined with modern machine learning in predicting future exchange rates in comparison with the results of random walk. These results were examined using a root mean squared error (RMSE) and a Diebold–Mariano (DM) test. The main findings are as follows. First, when comparing the performance of fundamental models combined with machine learning with the performance of random walk, the RMSE results show that the fundamental models with machine learning outperform the random walk. In the DM test, the results are mixed as most of the results show significantly different predictive accuracies compared with the random walk. Second, when comparing the performance of fundamental models combined with machine learning, the models using the producer price index (PPI) consistently show good predictability. Meanwhile, the consumer price index (CPI) appears to be comparatively poor in predicting exchange rate, based on its poor results in the RMSE test and the DM test. Full article

This paper investigates the predictability of exchange rate changes by extracting the factors from the three-, four-, and five-factor model of the relative Nelson–Siegel class. Our empirical analysis shows that the relative spread factors are important for predicting future exchange rate changes, and our extended model improves the model fitting statistically. The regression model based on the three-factor relative Nelson–Siegel model is the superior model of the extended models for three-month-ahead out-of-sample predictions, and the prediction accuracy is statistically significant from the perspective of the Clark and West statistic. For 6- and 12-month-ahead predictions, although the five-factor model is superior to the other models, the prediction accuracy is not statistically significant. Full article