Triangular Arbitrage in the Foreign Exchange Market

Aiba, Yukihiro; Hatano, Naomichi; Takayasu, Hideki; Marumo, Kouhei; Shimizu, Tokiko

doi:10.1007/978-4-431-53947-6_2

Cited by 7 publications

(7 citation statements)

References 4 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3. A schematic image of the model [24]. The three random walkers with the restoring force working on the center of gravity.…”

Section: Review Of Macroscopic Model Of Triangular Arbitragementioning

confidence: 99%

“…From a physical viewpoint, we can regard [24] the model equation (10) as a one-dimensional random walk of three particles with a restoring force, by interpreting ln r x as the position of each particle (Fig. 3).…”

Section: Review Of Macroscopic Model Of Triangular Arbitragementioning

confidence: 99%

“…Otherwise, proceed to the step 3. (3) Check the arbitrage conditions (23) and (24). If the condition (23) is satisfied, renew the prices P X (t) and P Y (t) to max{B i,X (t)} and min{B i,Y (t)}+ Λ, respectively.…”

Section: Microscopic Model Of Triangular Arbitrage: Interacting Two Smentioning

confidence: 99%

See 2 more Smart Citations

A microscopic model of triangular arbitrage

Aiba

Hatano

2006

Physica A: Statistical Mechanics and its Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…3. A schematic image of the model [24]. The three random walkers with the restoring force working on the center of gravity.…”

Section: Review Of Macroscopic Model Of Triangular Arbitragementioning

confidence: 99%

Section: Review Of Macroscopic Model Of Triangular Arbitragementioning

confidence: 99%

See 1 more Smart Citation

A microscopic model of triangular arbitrage

Aiba

Hatano

2006

Physica A: Statistical Mechanics and its Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given the fact that the investor can calculate the final profit before making the transaction, this strategy is risk-free. Given the conditions of the perfect market and a large number of market participants, the arbitrage opportunities are increasingly challenging to find [3].…”

Section: Sniffing Algorithmsmentioning

confidence: 99%

High-Frequency Trading Strategy Based on Deep Neural Networks

Arévalo

Niño

Hernández

et al. 2016

Intelligent Computing Methodologies

View full text Add to dashboard Cite

Recent conceptual and engineering breakthroughs in Machine Learning (ML), particularly in Deep Neural Networks (DNN), have revolutionized the Computer Science field and have been responsible for astonishing breakthroughs in computer vision, speech recognition, facial recognition, transaction fraud detection, automatic translation, video object tracking, natural language processing, and robotics, virtually disrupting every aspect of our lives. The financial industry has not been oblivious to this revolution; since the introduction of the first ML techniques, there have been efforts to use them as financial modeling and decision tools rendering in some cases limited and other in cases useful results, but overall, not astonishing results as in other areas. Some of the most challenging problems for ML come form finance, for instance, price prediction whose solution will require not only the most advanced ML techniques but also other non-standard and uncommon methods and techniques, giving the origin of a new field called Financial ML, whose name has been coined by Lopez de Prado last year. Today, many hedge funds and investment banks have ML divisions, using all kinds of data sources and techniques, to develop financial modeling and decision tools. Consequently, ML is a part of the present and probably will be the future of the financial industry. In this thesis, we use the Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN), two of the most advanced ML techniques, whose learning capabilities are enhanced using the representational power of the Discrete Wavelet Transform (DWT), to model and predict short-term stock prices showing that these techniques allow us to develop exploitable highfrequency trading strategies. Since high-frequency financial (HF) data are expensive, difficult to access, and immense (Big Data), there is no standard dataset in Finance or Computational Finance. Therefore, the chosen testing dataset consists of the tick-by-tick data of 18 well-known companies from the Dow Jones Industrial Average Index (DJIA). This dataset has 348.98 millions of transactions (17 GB) from January 2015 to July 2017. After a long iterative process of data exploration and feature engineering, several features were tested and combined. The tick-by-tick data are preprocessed and transformed using the DWT with a Haar Filter. The final features consist of the sliding windows of two variables: one-minute pseudo-log-returns (the logarithmic difference between one-minute average prices) and the features generated by the DWT. viii These transformations, which are non-standard data transformations in finance, will better represent the high-frequency behavior of Financial Time Series (FTS). Moreover, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. These prices will be used to build a high-frequency trading strategy that buys (sells) when the next one-minute average price prediction is above (below) the last one-minute closing price. Result...

show abstract

“…The recent accumulation of high-frequency financial data due to the development and spread of information and communications technology has sparked interest in financial markets [1,2,3,4,5,6,7,8,9,10]. Many researchers expect new findings and insights into the worlds of both finance and physics.…”

Section: Introductionmentioning

confidence: 99%

Frequency analysis of tick quotes on the foreign exchange market and agent-based modeling: A spectral distance approach

Sato

2007

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

High-frequency financial data of the foreign exchange market (EUR/CHF, EUR/GBP, EUR/JPY, EUR/NOK, EUR/SEK, EUR/USD, NZD/USD, USD/CAD, USD/CHF, USD/JPY, USD/NOK, and USD/SEK) are analyzed by utilizing the Kullback-Leibler divergence between two normalized spectrograms of the tick frequency and the generalized Jensen-Shannon divergence among them. The temporal structure variations of the similarity between currency pairs is detected and characterized. A simple agent-based model in which N market participants exchange M currency pairs is proposed. The equation for the tick frequency is approximately derived theoretically. Based on the analysis of this model, the spectral distance of the tick frequency is associated with the similarity of the behavior (perception and decision) of the market participants in exchanging these currency pairs.

show abstract

Triangular Arbitrage in the Foreign Exchange Market

Cited by 7 publications

References 4 publications

A microscopic model of triangular arbitrage

A microscopic model of triangular arbitrage

High-Frequency Trading Strategy Based on Deep Neural Networks

Frequency analysis of tick quotes on the foreign exchange market and agent-based modeling: A spectral distance approach

Contact Info

Product

Resources

About