We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -- 2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectednes…
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48 results for “FOREX market”
Study compares LSTM and ANN architectures for forex prediction, finding ANN more efficient.
problem Forex time series prediction efficiency and resource usage.
method Comparison of Long Short-Term Memory (LSTM) and specialized ANN architectures.
result Specialized ANN architecture performs better with fewer resources and faster execution.
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
Wavelet denoised-ResNet with LightGBM predicts Forex rate of change.
problem Forecasting Foreign Exchange (Forex) rate of change for trading opportunities.
method Wavelet denoising, ResNet, LightGBM, technical indicators, image features.
result The model outperforms baseline models with low MAE, MSE, and RMSE.
We show how bad and good volatility propagate through forex markets, i.e., we provide evidence for asymmetric volatility connectedness on forex markets. Using high-frequency, intra-day data of the most actively traded currencies over 2007 - 2015 we document the dominating asymmetries in spillovers that are due to bad r…
The study reveals the hierarchical structure of the international FOREX market using currency fluctuation distribution similarities.
problem Understanding the hierarchical structure of the international FOREX market.
method Using Jensen-Shannon divergence to quantify the similarity between normalized logarithmic return distributions of currencies.
result Clusters of currencies are consistent with the nature of underlying economies but diverge during crises.
The paper examines how randomness in forex returns increases during financial crises.
problem Measuring randomness in forex returns during financial crises.
method Approximate Entropy and Sample Entropy statistics.
result Randomness in forex returns decreases during financial crises, as evidenced by lower entropy values.
Comparative study of neural networks for short-term FOREX forecasting.
problem Simulating expert judgment in foreign exchange market forecasting.
method Implemented and compared LSTM and ANN architectures for short-term FOREX forecasting.
result ANN custom architecture outperforms LSTM in prediction quality and resource efficiency.
Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the pe…
Developed Forex trading heuristics with high profit potential.
problem Reduced trade opportunities due to technical indicator values.
method Machine learning simulation of 10 years of Forex data.
result Optimized trade parameters for 118 pips daily profit.
A novel approach combines feature importance scores with deep learning for forex price prediction.
problem Improving forex price prediction using deep learning models.
method Feature importance recap combined with stacking models.
result Proper feature selection significantly improves model performance.
Paper proposes a deep reinforcement learning model for forex trading that considers transaction costs.
problem Trading in forex markets with high transaction costs and non-stationary data.
method Deep reinforcement learning model considering transaction costs and online learning.
result Maximizes profit while keeping transaction costs low in non-stationary markets.
Study improves forex forecasting accuracy using machine learning models.
problem Improving accuracy in predicting foreign exchange rates.
method Employed LSTM neural networks and Gradient Boosting Classifier for forecasting.
result Achieved 99.449% accuracy in forecasting USD/BDT exchange rates.
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant d…
Event-driven features improve forex price prediction accuracy.
problem Inaccurate predictions in forex due to market volatility.
method Developed event-driven features and used LSTM, BiLSTM, GRU models.
result Improved prediction system with minimal risk.
In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support …
GA-MSSR optimizes forex trading rules for higher returns and reduced risk.
problem Noisy market data affects the consistency and profitability of trading algorithms.
method Optimized trading rules derived from technical indicators using a Genetic Algorithm.
result GA-MSSR achieved superior performance with significant positive returns and reduced risk factors.
Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We…
Machine learning models show intermarket data can predict stock market performance better than expected.
problem Evaluating the semi-strong form of the Efficient Market Hypothesis.
method Used machine learning techniques on various intermarket data sets to predict stock market performance.
result Intermarket data significantly outperforms baselines in predicting stock market movement, contradicting the semi-strong EMH.
Stochastic model of financial markets reproducing scaling and memory in volatility return intervalsq-fin.GN
We investigate the volatility return intervals in the NYSE and FOREX markets. We explain previous empirical findings using a model based on the interacting agent hypothesis instead of the widely-used efficient market hypothesis. We derive macroscopic equations based on the microscopic herding interactions of agents and…
Flexible algorithm of multicurrency trade on Forex market has been built on the grounds of non-linear stochastic wavelets (NSW) model. Probability of the loss-free trade has been evaluated. Results of the algorithm's real-time testing and issues of the algorithm's development are discussed.
Study compares RNN, LSTM, and BP neural networks for forex rate prediction.
problem Improving real-time forex rate prediction accuracy.
method Analyzes RNN, LSTM, and BP neural networks' characteristics and advantages.
result Provides insights for selecting the best price-prediction model.
Paper presents a modular RL framework for Forex trading, addressing limitations of prior studies.
problem Challenges in applying RL to Forex trading, including unrealistic environments, simplified rewards, and restricted action spaces.
method Integrates three components: a friction-aware execution engine, a decomposable reward architecture, and a discrete action interface.
result Empirical evaluation shows strong non-monotonic reward interactions and optimal Sharpe ratio with the full reward configuration.
There are many studies dealing with the analysis of similarity among currencies in foreign exchange market by using network analysis approach. In those studies, each currency is represented by a univariate time series of exchange rate return. This is the standard practice to analyze the underlying information in the fo…
The aim of this paper is to determine the Value at Risk (VaR) of the portfolio consisting of long positions in foreign currencies on an emerging market. Basing on empirical data we restrict ourselves to the case when the tail parts of distributions of logarithmic returns of these assets follow the power laws and the lo…
Algorithm of multicurrency trading at the market of Forex is realized on the basis of nonlinear stochastic wavelets. The distinctive feature of the algorithm is the possibility of weakly- and strongly connected horizontal self-assemblies, as well as use of nested structures. On-line trading with eight currency couples …
Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts …
Deep learning predicts currency volatility accurately.
problem Predicting future volatility in Forex trading.
method Constructed a deep-learning network using multiscale LSTM with multi-currency pairs.
result Multiscale LSTM model outperforms conventional models.
Critical Crashescond-mat.stat-mech
We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced f…
Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. Fluctuations of high frequency exchange rates of eight major world currencies over 2010-2018 period are used to study cross-correlations. The study is motivated by fundamental questions in compl…
A new oscillator measures trending behavior of financial instruments.
problem Detecting underlying deterministic components in financial market prices.
method Financial market geometry and tube oscillator derived from past history.
result Simple trading strategy based on tube oscillator leads to consistent positive returns.
In this work, we consider the optimal portfolio selection problem under hard constraints on trading volume amounts when the dynamics of the risky asset returns are governed by a discrete-time approximation of the Markov-modulated geometric Brownian motion. The states of Markov chain are interpreted as the states of an …
Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Marketq-fin.ST
New method detects currency contagion sources using causal inference.
problem Lack of causal interpretation in quantifying contagion among currencies.
method Network-based causal inference to identify contagion paths.
result Identifies sources of contagion and diversification options.
Survey of deep learning methods for forex and stock price prediction.
problem Improving accuracy and return in financial prediction.
method Classification of papers based on different deep learning methods.
result Recent models combining LSTM with other methods yield great returns and performances.
We decompose the exchange rates returns of 41 currencies (incl. gold) into their sign and amplitude components. Then we group together all exchange rates with a common base currency, construct Minimal Spanning Trees for each group independently, and analyze properties of these trees. We show that both the sign and the …
Cryptocurrency market becomes more cross-correlated over time.
problem Understanding the inter-market dependencies of cryptocurrencies.
method Time series analysis, spectral analysis, topological analysis of minimal spanning trees.
result Cryptocurrency market becomes more cross-correlated with other markets over time.
We address the problem of long-range memory in the financial markets. There are two conceptually different ways to reproduce power-law decay of auto-correlation function: using fractional Brownian motion as well as non-linear stochastic differential equations. In this contribution we address this problem by analyzing e…
Study compares nine deep learning architectures for multi-horizon financial forecasting.
problem Evaluating the performance of deep learning architectures for multi-horizon financial forecasting.
method Conducted 918 experiments across cryptocurrency, forex, and equity markets using nine architectures.
result ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate.
Paper introduces non-adversarial training for Neural SDEs using signature kernel scores.
problem Stability and mode collapse issues in adversarial training of Neural SDEs.
method Uses signature kernel scores as objective function for non-adversarial training.
result Non-adversarial training leads to better performance and more stable models.
Causal-NECO VaR improves financial risk assessment under market turbulence.
problem Inaccurate risk assessment in volatile markets.
method Causal Network Contagion Value at Risk (Causal-NECO VaR) using causal network framework.
result Robust and invariant predictive power in unstable financial environments.
Study analyzes cryptocurrency market complexity, comparing it to traditional markets.
problem Understanding the dynamics and characteristics of cryptocurrency markets.
method Statistical physics methods and analysis of price fluctuations.
result Cryptocurrency market exhibits complexity similar to traditional markets but with slower information flow.
Cryptocurrencies are increasingly correlated with traditional financial markets.
problem Determining the independence of cryptocurrencies from traditional financial markets.
method High-frequency detrended cross-correlation analysis over various time scales and market periods.
result Cryptocurrencies have become more aligned with traditional financial markets, especially during bear phases.
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial beha…
Cross-correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015, through March 31, 2019, are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others la…
Unified model explains volatility memory in stocks and forex.
problem Understanding the components of volatility memory in financial markets.
method Developed a three-dimensional decomposition of volatility memory into level, shape, and tempo.
result Unified model shows that volatility memory is state-dependent, with different gates prevailing in equities and forex.
Possible distributions are discussed for intertrade durations and first-passage processes in financial markets. The view-point of renewal theory is assumed. In order to represent market data with relatively long durations, two types of distributions are used, namely, a distribution derived from the so-called Mittag-Lef…
Correlation matrices of foreign exchange rate time series are investigated for 60 world currencies. Minimal Spanning Tree (MST) graphs for the gold, silver and platinum are presented. Inverse power like scaling is discussed for these graphs as well as for four distinct currency groups (major, liquid, less liquid and no…
From positions, attained by modern theoretical physics in understanding of the universe bases, the methodological and philosophical analysis of fundamental physical concepts and their formal and informal connections with the real economic measurings is carried out. Procedures for heterogeneous economic time determinati…