I examine the behavior of forex prices around the setting of the 4:00 pm WMR Fix. Numerous banks have been fined by regulators for their trading activities around the Fix, but the overall impact of ...their actions is not known. I first examine trading patterns around the Fix in a microstructure model of competitive trading. I then compare the model with the empirical behavior of forex prices across 21 currencies over a decade. Contrary to the predictions of the model, forex price changes display extraordinary volatility and negative serial correlation around the Fix.
The transformer architecture with its attention mechanism is the state-of-the-art deep learning method for sequence learning tasks and has achieved superior results in many areas such as NLP. ...Utilizing the transformer architecture for the prediction of sequential time series such as financial time series has hardly been investigated in previous studies. In this research paper, the transformer architecture with time embeddings is used in foreign exchange (FX) trading, the world’s largest financial market, and tests its suitability. A systematic comparison is made between transformer and benchmark models. It also examined which influence multivariate, cross-sectional input data have on the forecasting performance of the various models. The goal of the paper is to contribute to the empirical literature on FX forecasting by introducing a transformer with time embeddings to the forecasting community and assessing the accuracy of corresponding models by forecasting exchange rate movements. Empirical results indicate the suitability of transformer models for FX-Spot forecasting in general but also evidence the need for transformer models for multivariate, cross-sectional input data to outperform other state-of-the-art neural networks such as LSTM.
•Review of essential concepts for market prediction based on online text-mining.•Review of the cutting-edge work in the literature.•Identification of main differentiating factors among the available ...solutions.•Observations on possible opportunities for future work.
The quality of the interpretation of the sentiment in the online buzz in the social media and the online news can determine the predictability of financial markets and cause huge gains or losses. That is why a number of researchers have turned their full attention to the different aspects of this problem lately. However, there is no well-rounded theoretical and technical framework for approaching the problem to the best of our knowledge. We believe the existing lack of such clarity on the topic is due to its interdisciplinary nature that involves at its core both behavioral-economic topics as well as artificial intelligence. We dive deeper into the interdisciplinary nature and contribute to the formation of a clear frame of discussion. We review the related works that are about market prediction based on online-text-mining and produce a picture of the generic components that they all have. We, furthermore, compare each system with the rest and identify their main differentiating factors. Our comparative analysis of the systems expands onto the theoretical and technical foundations behind each. This work should help the research community to structure this emerging field and identify the exact aspects which require further research and are of special significance.
The interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. ...Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.
•Low-complexity machine learning models are used trade in the FOREX market.•A six year trading simulation in USDJPY, EURGPB and EURUSD are assessed.•Periodic retraining, number of attributes and ...retraining set size are varied and studied.•Middle range accuracies are obtained with high financial returns in the long term.
Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.
The goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including ...Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized. The results obtained showed that, GRU performed better than LSTM with a 0.950 Test R2 score and an adjusted R2 score of 0.122. The RMSE is way lower than LSTMs at 0.105 and MAE is even lower at 0.950.
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This study examines the spillover effect between financial technology (Fintech) stocks and other financial assets (gold, Bitcoin, a global equity index, crude oil, and the US Dollar) ...during the COVID-19 crisis. Employing daily data from June 2019 to August 2020, our empirical analysis shows that the outbreak of COVID-19 exacerbated volatility transmission across asset classes, while subsequent decreases in new confirmed cases globally reduced the intensity of these spillovers. The evidence for the USD and gold supports their safe haven properties during catastrophic events, while innovative technology products as represented by a financial technology index (KFTX) and Bitcoin were highly susceptible to external shocks. These results show that when push comes to shove, the buck stops with the USD and gold and that the exorbitant privilege enjoyed by the USD prevailed during the COVID-19 pandemic.
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