Applying the Teachings of Behavioural Finance to Risk Arbitrage Strategy

2012 ◽  
Author(s):  
Stephane Dieudonne ◽  
Fabienne Cretin ◽  
Slimane Bouacha
Keyword(s):  
Behavioural Finance ◽  
Risk Arbitrage ◽  
Arbitrage Strategy

Macroeconomic Drivers Behind Risk Arbitrage Strategy

2010 ◽  
Author(s):  
Stephane Dieudonne ◽  
Slimane Bouacha ◽  
Fabienne Cretin
Keyword(s):  
Risk Arbitrage ◽  
Arbitrage Strategy

A Risk Arbitrage Strategy for Lotteries

Wilmott ◽  
2019 ◽  
Vol 2019 (100) ◽  
pp. 52-63
Author(s):  
Steven D. Moffitt ◽  
William T. Ziemba
Keyword(s):  
Risk Arbitrage ◽  
Arbitrage Strategy

What Is the Nature of Hedge Fund Manager Skills? Evidence from the Risk-Arbitrage Strategy

2016 ◽  
Vol 51 (3) ◽  
pp. 929-957 ◽  
Author(s):  
Charles Cao ◽  
Bradley A. Goldie ◽  
Bing Liang ◽  
Lubomir Petrasek
Keyword(s):  
Hedge Funds ◽  
Hedge Fund ◽  
Merger And Acquisition ◽  
Superior Performance ◽  
Fund Manager ◽  
Fund Managers ◽  
Portfolio Holdings ◽  
Risk Arbitrage ◽  
Arbitrage Strategy ◽  
Hedge Fund Manager

AbstractTo understand the nature of hedge fund managers’ skills, we study the implementation of risk arbitrage by hedge funds using their portfolio holdings and comparing them with those of other institutional arbitrageurs. We find that hedge funds significantly outperform a naive risk-arbitrage portfolio by 3.7% annually on a risk-adjusted basis, whereas non–hedge fund arbitrageurs fail to outperform the benchmark. Our analysis reveals that hedge funds’ superior performance does not reflect fund managers’ ability to predict or affect the outcome of merger and acquisition deals; rather, hedge fund managers’ superior performance is attributed to their ability to manage downside risk.

Analysis of Improved Bids in the Scope of a Risk Arbitrage Strategy

2011 ◽  
Author(s):  
Stephane Dieudonne ◽  
Fabienne Cretin ◽  
Slimane Bouacha
Keyword(s):  
Risk Arbitrage ◽  
Arbitrage Strategy

The profitability of algorithmic trading systems based on football sentiment

2020 ◽  
Vol 42 (1) ◽  
pp. 33-46
Author(s):  
Raúl Gómez-Martínez ◽  
Camila Marqués-Bogliani ◽  
Jessica Paule-Vianez
Keyword(s):  
Financial Markets ◽  
Investment Decisions ◽  
Futures Market ◽  
Algorithmic Trading ◽  
Investment Strategies ◽  
Measurable Effect ◽  
Behavioural Finance ◽  
Trading Systems ◽  
European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

A behavioural finance-based tick-by-tick model for price and volume

2010 ◽  
Vol 14 (1) ◽  
pp. 57-80 ◽  
Author(s):  
Garud Iyengar ◽  
Alfred Ka Chun Ma
Keyword(s):  
Behavioural Finance

Fuzzy Probabilistic Sets as a Tool for Behavioural Finance

2011 ◽  
Author(s):  
Krzysztof Maciej Piasecki
Keyword(s):  
Behavioural Finance

scholarly journals Machine Learning in Futures Markets

2021 ◽  
Vol 14 (3) ◽  
pp. 119
Author(s):  
Fabian Waldow ◽  
Matthias Schnaubelt ◽  
Christopher Krauss ◽  
Thomas Günter Fischer
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Learning Models ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Arbitrage ◽  
Out Of Sample ◽  
Sample Testing ◽  
Arbitrage Strategy ◽  
Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

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