Genetic Programming in Statistical Arbitrage

Saks, Philip; Maringer, Dietmar

doi:10.1007/978-3-540-78761-7_8

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…The last condition is applied only when there exists a positive probability of losing money. Saks and Maringer (2008) -statistical arbitrage accepts negative payoffs as long as the expected positive payoffs are high enough, and the probability of losses is small enough. Stefanini (2006) uses the expected value while states that statistical arbitrage seeks to capture imbalances in expected value of financial instruments, while trying to be market neutral.…”

Section: Literature Reviewmentioning

confidence: 99%

Interest Rates Sensitivity Arbitrage – Theory and Practical Assesment for Financial Market Trading

Stádník

2021

Business, Management and Economics Engineering

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Purpose – Nowadays popular algorithmic trading uses many strategies which are algoritmizable and promise profitability. This research assess if it is possible successfully use interest rates sensitivity arbitrage in bond portfolio (also known as convexity arbitrage) in financial praxis. This arbitrage is sparsely described in literature and an assessment about its practical success is missing. Research methodology – Methodology steps: mathematical definition of given arbitrage; construction of sufficient portfolio; backtesting on USD zero-coupon curves. Portfolio of two bonds is constructed (theoretically and practically) to have the same Macaulay duration and price, but a different convexity at certain YTM point. Therefore, being long the first bond while shorting the second (of higher convexity) would result in a market-directional bet for parallel zero-coupon yield curve shifts. Findings – To construct practically the portfolio which is sufficient for the convexity arbitrage could be unrealistic on markets with low liquidity; the presumptions necessary to practically succeed are not fulfilled enough to ensure the arbitrage is profitable. Research limitations – The backtesting is limited to USD market, testing other markets is recommended, but different result is not expected. Practical implications – The research helps practitioners considering this strategy for its implementation to algorithmic trading. Originality/Value – New important results for financial practitioners; states that practical and profitable utilization of convexity arbitrage is unrealizable and save costs during implementation of the strategy.

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Section: Literature Reviewmentioning

confidence: 99%

Interest Rates Sensitivity Arbitrage – Theory and Practical Assesment for Financial Market Trading

Stádník

2021

Business, Management and Economics Engineering

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“…Additionally, it is not flexible enough to include SA strategies based on specific ratios, see for example the Sharpe ratios used by Bertram [101], Cummins and Bucca [67] and Goncu [100]. Our definition reformulates the definition of Saks and Maringer [53] adding relative value. This addition is fundamental to rule out investing in short term government bonds (with positive expected return and low probability of a loss) as a SA strategy.…”

Section: Definition Of Sa Strategymentioning

confidence: 99%

What Is Statistical Arbitrage?

Lazzarino¹,

Berrill²,

Šević³

2018

TEL

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Statistical Arbitrage (SA) is a common financial term. However, there is no common definition in the literature while investors use the expression SA for a variety of different strategies. So, what is SA? In order to answer this question, we investigate SA strategies across equity, fixed income and commodity. The analysis of strategies' key features indicates that no existing definition fully describes them. To bridge this gap, we identify a general definition and propose a classification system that encompasses the current forms of SA strategies while facilitating the inclusion of new types as they emerge.

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“…In 2008, Philip et al designed a statistical arbitrage strategy for the banking sector using genetic algorithms [5]. In 2013, Bogomolov pointed out that paired trading strategies are market neutral strategies and the steps of the strategy trading process [6].…”

Section: Introductionmentioning

confidence: 99%

Trading Strategies for Cryptocurrencies Based on Machine Learning Scenarios

Gao¹,

Hou²

2023

BCPBM

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A Cryptocurrency is a peer-to-peer digital exchange system in which cryptography is used to generate and distribute currency units. Bitcoin as the foremost digital currency, using asymmetric cryptographic algorithms, blockchain technology, was conceptualized by Satoshi Nakamoto in 2008 and born in 2009. In 14 years, digital currency has gone from being initially controversial and worthless to rapid increase in value. The huge fluctuations in its price have attracted worldwide attention, and more people have begun to pay attention to the investment strategy of digital currency. Starting from the attributes of Bitcoin, this paper objectively compares the application effect of arbitrage strategy and trend strategy in machine learning on Bitcoin, analyzes and summarizes and predicts the future of Bitcoin's investment. To be specific, the arbitrage strategy involves three methods, i. e. , cash arbitrage, cross-exchange arbitrage and related variety arbitrage; trend strategy involves two methods, i. e. , the timing method and the multi-factor method. These results shed light on guiding further exploration of potential of investing digital currencies, which provides an in-depth summary analysis of risk-free arbitrage and digital currency value forecasts.

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Genetic Programming in Statistical Arbitrage

Cited by 7 publications

References 17 publications

Interest Rates Sensitivity Arbitrage – Theory and Practical Assesment for Financial Market Trading

Interest Rates Sensitivity Arbitrage – Theory and Practical Assesment for Financial Market Trading

What Is Statistical Arbitrage?

Trading Strategies for Cryptocurrencies Based on Machine Learning Scenarios

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