METHODS OF FORECASTING EXCHANGE RATES IN DIGITAL ASSET TRADING

Аннотация

In digital asset trading, there are different ways of forecasting exchange rates, among which fundamental, quantitative and sentimental analysis occupy the main place. Fundamental analysis predicts exchange rate fluctuations by studying economic, political and financial factors. Quantitative analysis, on the other hand, focuses on predicting based on past data using mathematical models and artificial intelligence algorithms. Sentimental analysis, on the other hand, allows you to predict the future actions of investors by analyzing the mood of market participants. The combination of these methods serves to more accurately predict the movements ofexchangerates. Keywords: digital assets, exchange rates, fundamental analysis, quantitative analysis, sentimental analysis, market forecast, economic indicators, artificial intelligence, investment strategies. Fundamental analysis is an approach based on the study of economic, political and social factors in predicting exchange rates, which provides a direction for traders and investors in making important decisions. The main purpose of this analysis is to identify factors affecting thevalue of the currency and assess their impact on the market. Economic factors include GDP growth, inflation rate, interest rates, and unemployment rates. If the economic growth of the country is stable and the gross domestic product increases regularly, the national currency is strengthened. An increase in inflation, on the other hand, can lead to depreciation of the currency, since its purchasing power decreases. The central bank's increase in interest rates makes this country's currency attractive to investors, as in countries with higher interest rates, revenues on deposits and bonds also rise. And when the unemployment rate decreases, economic activity can increase andstrengthen theexchange rate. Practically fundamental analysis allows you to forecast exchange rates based on real events and economic indicators. For example, when the U.S. Federal Reserve system raises interest rates in an attempt to lower inflation, the U.S. dollar is strengthened against other currencies as investors tend to invest in U.S. assets to generate higher returns. Another example is the depreciation of the Turkish lira. When the central bank lowers interest rates, investors give up their investment in the lira and convert capital into other currencies. This reduces the demand for the lyre and lowers its course. The depreciation of the Russian ruble also depends on geopolitical factors, and with the introduction of international sanctions against Russia in 2022, investors lost confidence in the Russian economy and caused the ruble to weaken. Later, after the Central Bank of Russia took emergency measures and sharply increased interest rates, the ruble exchange rate was consolidated fora short period. Fundamental analysis is an important tool for understanding and analyzing long-term currency trends. Traders and investors make decisions on currencies, taking into account economic, political and geopolitical factors. More effective results can be achieved when this analysis is https://ijmri.de/index.php/jmsi volume 4,issue2,2025 245 used in conjunction with technical analysis. Quantitative analysis is an approach to predicting exchange rates that aims to achieve accurate results based on statistical and mathematical models. This method helps to predict the movements of future exchange rates by analyzing past data. In this case, a large amount of data is processed, and future trends are determined using specific algorithms. The main principle of quantitative analysis is to understand the movement of exchange rates and determine the factors affecting their change, relying on historical data. This method makes it possible to make decisions based on economic models, statistical analysis and artificial intelligence, relying on accurate calculations rather than subjectiveconclusions. Regression analysis is widely used among statistical models. This method analyzes whether exchange rates are related to different economic indicators. For example, how inflation rates, interest rates, or oil prices affect currency value is estimated through mathematical formulas. With this model, future exchange rates are predicted based on available data. Networked time series (time series analysis) is another important area of quantitative analysis. This method is aimed at identifying their recurrent patterns by analyzing the previous movements of exchange rates in depth. For example, the Autoregressive Integrated Moving Average (ARIMA) model or Long Short-Term Memory (LSTM) neural networks are applied to determine exchange rates. Thesemethods allowalgorithms to predict what changes willoccur inthefuture. Machine learning algorithms and artificial intelligence are also an important part of quantitative analysis. In this approach, large volumes of historical data are analyzed and the model more accurately predicts market movements th