The Tobin Tax A Review of the Evidence

The debate about the Tobin Tax, and other financial transaction taxes (FTT), gives rise to strong views both for and against. Unfortunately, little of this debate is based on the now considerable body of evidence about the impact of such taxes. This review attempts to synthesise what we know from the available theoretical and empirical literature about the impact of FTTs on volatility in financial markets. We also review the literature on how a Tobin Tax might be implemented, the amount of revenue that it might realistically produce, and the likely incidence of the tax. We conclude that, contrary to what is often assumed, a Tobin Tax is feasible and, if appropriately designed, could make a significant contribution to revenue without causing major distortions. However, it would be unlikely to reduce market volatility and could even increase it.Tobin tax, financial transaction taxes, volatility, revenue, incidence, feasibility

FOREX Microstructure, Invisible Price Determinants,and the Central Bank's Understanding of Exchange Rate Formation

The paper investigates the transmission of macroeconomic factors into the price-setting behavior of a specific dealer in the FX market. This problem is viewed from the perspective of a central banker who observes the price evolution but does not make the market in the home currency. The central banker's task is to explain the forex behavior in terms of conventional economic logic. The analysis is based on a model of a multiple dealer market under two organizations - direct inter-dealer and brokered. The model is constructed in such a way as to reflect the most prominent features of the market for the Czech koruna and, accordingly, to address some issues of key relevance to the Czech National Bank's exchange rate policy. We show that the totality of the exchange rate-relevant fundamental factors influence the market maker's behavior through a single sufficient statistic, his 'marginal' valuation of foreign currency holdings. Under the two studied trading mechanisms, the marginal valuations across market participants determine the equilibrium exchange rate by means of different trade patterns. Specifically, the brokered market is inferior to the direct one in terms of welfare improvement through trade. It takes a higher inter-dealer trade volume in the brokered market to absorb a new price impulse. Therefore, the central banker would do best by monitoring the brokered segment (as the only partially transparent one available), but by conducting interventions in the direct segment, where the desired impact is easier to achieve.forex microstructure, multiple dealership, order flow, pricing schedule.

Components of the Czech Koruna Risk Premium in a Multiple-Dealer FX Market

The paper proposes a continuous time model of an FX market organized as a multiple dealership. The model reflects a number of salient features of the Czech koruna spot market. The dealers have costly access to the best available quotes. They interpret signals from the joint dealer-customer order flow and decide upon their own quotes and trades in the inter-dealer market. Each dealer uses the observed order flow to improve the subjective estimates of the relevant aggregate variables, which are the sources of uncertainty. One of the risk factors is the size of the cross-border dealer transactions in the FX market. These uncertainties have diffusion form and are dealt with according to the principles of portfolio optimization in continuous time. The model is used to explain the country, or risk, premium in the uncovered national return parity equation for the koruna/euro exchange rate. The two country premium terms that I identify in excess of the usual covariance term (a consequence of the 'Jensen inequality effect') are the dealer heterogeneity-induced inter-dealer market order flow component and the dealer Bayesian learning component. As a result, a 'dealer-based total return parity' formula links the exchange rate to both the 'fundamental' factors represented by the differential of the national asset returns, and the microstructural factors represented by heterogeneous dealer knowledge of the aggregate order flow and the fundamentals. Evidence on the cross-border order flow dependence of the Czech koruna risk premium, in accordance with the model prediction, is documented.Bayesian learning, FX microstructure, optimizing dealer, uncovered parity.

Is foreign exchange intervention effective? Some micro-analytical evidence from the Czech Republic

I estimate a two-equation system on the euro-Czech koruna exchange rate and order flow at hourly frequency within the framework of Evans-Lyons (JME 2002). I use transac-tions data from the Reuters Spot Matching market in the second half of 2002, during which the Czech National Bank conducted discreet interventions to stem the appreciation of the domestic currency. I find a significant impact of order flow on the exchange rate, equal on average to 7.6 basis points per €10 million, of which 80 percent persists through the day. The news of intervention increases the price impact of order flow by 3.9 basis points per €10 million, consistently with the notion of intervention efficacy. The order flow equation yields in-conclusive results.Foreign exchange, central bank intervention, Czech koruna, ERM II, empirical microstructure

SISTEM EVALUASI ROBOT TRADING DENGAN METODE ELECTRE BERBASIS REAL-TIME WEB SERVICE PADA PASAR VALAS

Penelitian ini bertujuan mengoptimalkan keuntungan perdagangan valas secara otomatis menggunakan robot trading namun tetap mempertimbangkan tingkat akurasi dan drawdown. Sistem evaluasi mengelompokkan kinerja robot trading berdasarkan sesi pasar perdagangan (Sydney, Tokyo, London dan New York) untuk menentukan robot trading yang tepat untuk digunakan pada sesi pasar tertentu. Sistem evaluasi ini berbasis web dengan perhitungan metode ELECTRE yang berinteraksi secara real-time dengan robot trading melalui web service dan mampu menyajikan grafik kinerja secara real-time pada dashboard dengan komunikasi protokol web socket. Aplikasi web diprogram menggunakan teknologi NodeJs. Pada periode pengujian, semua robot trading disimulasikan 24 jam di semua sesi pasar selama tiga bulan, robot trading terbaik dinilai berdasarkan kriteria laba, akurasi dan drawdown yang dihitung menggunakan metode ELECTRE berbasis web. Ide dari penelitian ini adalah membandingkan robot trading terbaik pada periode pengujian dengan kinerja kolaborasi empat robot trading terbaik di setiap sesi pasar. Penelitian ini menggunakan data historis pergerakan mata uang EURO terhadap USD sebagai periode pengujian dan 3 bulan berikutnya sebagai data validasi. Dari hasil penelitian, kinerja kolaborasi empat robot trading terbaik yang dikelompokkan berdasarkan sesi pasar dapat meningkatkan persentase keuntungan secara konsisten dengan tetap menjaga tingkat akurasi dan drawdown. Kata Kunci: Kinerja, Sistem Evaluasi, Valas, Robot Trading, ELECTRE This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York) to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs technology. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criterias that calculated using web-based ELECTRE method. The ideas of this research is to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR against USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels. Keywords: Performance, Evaluation System, Forex, EA, ELECTR

Forex Trading Signal Extraction with Deep Learning Models

The rise of AI technology has popularized deep learning models for financial trading prediction, promising substantial profits with minimal risk. Institutions like Westpac, Commonwealth Bank of Australia, Macquarie Bank, and Bloomberg invest heavily in this transformative technology. Researchers have also explored AI's potential in the exchange rate market. This thesis focuses on developing advanced deep learning models for accurate forex market prediction and AI-powered trading strategies. Three deep learning models are introduced: an event-driven LSTM model, an Attention-based VGG16 model named MHATTN-VGG16, and a pre-trained model called TradingBERT. These models aim to enhance signal extraction and price forecasting in forex trading, offering valuable insights for decision-making. The first model, an LSTM, predicts retracement points crucial for identifying trend reversals. It outperforms baseline models like GRU and RNN, thanks to noise reduction in the training data. Experiments determine the optimal number of timesteps for trend identification, showing promise for building a robotic trading platform. The second model, MHATTN-VGG16, predicts maximum and minimum price movements in forex chart images. It combines VGG16 with multi-head attention and positional encoding to effectively classify financial chart images. The third model utilizes a pre-trained BERT architecture to transform trading price data into normalized embeddings, enabling meaningful signal extraction from financial data. This study pioneers the use of pre-trained models in financial trading and introduces a method for converting continuous price data into categorized elements, leveraging the success of BERT. This thesis contributes innovative approaches to deep learning in algorithmic trading, offering traders and investors precision and confidence in navigating financial markets

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Reinforcement Learning Applied to Trading Systems: A Survey

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page