Technical paper/Algorithmic trading
The relative entropy of expectation and price
The replacement of risk-neutral pricing with entropic risk optimisation
The cost of mis-specifying price impact
Expected returns can be significantly affected by the wrong use of impact models
Assessing the potential profitability of automated power market trading using event signals sourced from grid frequency data
The authors put forward a profitable trading strategy based on power grid events, demonstrating that minimized reaction times can increase profits.
Getting more for less: better A / B testing via causal regularisation
A causal machine learning algorithm is used to estimate trades’ price impact
Enhanced expected impact cost model under abnormally high volatility
The authors extend their impact cost model beyond the typical factors to address the larger transaction costs brought on by stock market crowding effects in times of market turbulence.
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Linking performance of vanilla options to the volatility premium
A framework to account for vanilla options' performance in trading strategies is presented
Deep learning profit and loss
The P&L distribution of a complex derivatives portfolio is computed via deep learning
A principled approach to clean-up costs in algo trading
The opportunity cost associated with the cancelled portion of an order is quantified
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
An approximate solution for options market-making
An algorithm for the market-making of options on different underlyings is proposed
Quant investing in cluster portfolios
This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters.
Optimal dynamic strategies on Gaussian returns
It is hoped that this paper will form a foundational approach to the study of dynamic strategies and how to optimize them. We make efforts to understand their properties without claiming to understand why they work (ie, why there are stable…
The joint S&P 500/Vix smile calibration puzzle solved
SPX and Vix derivatives are modelled jointly in an arbitrage-free setting
Connecting equity and foreign exchange markets through the WM “Fix”: a trading strategy
In this paper, the authors show the connection between equities and foreign exchange markets via this window, they leverage this connection using an algorithmic trading strategy and rank various statistical techniques used to make predictions for trading…
An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
This paper focuses on feature selection methods for support vector machine (SVM) classifiers, checking their optimality by comparing them with some statistical and baseline methods.
The swap market model with local stochastic volatility
An easy to calibrate and accurate swap market model is proposed
Fast and precautious: order controls for trade execution
Algo traders propose a new optimal execution algorithm with both limit and market orders
Optimal trading trajectories for algorithmic trading
This paper derives explicit formulas for the optimal implementation shortfall trading curve with linear and nonlinear market impact.
Optimal trading under proportional transaction costs
The theory of optimal trading under proportional transaction costs has been considered from a variety of perspectives. In this paper, Richard Martin shows that all results can be interpreted using a universal law through trading algorithm design
Cutting Edge introduction: The trouble with algorithmic execution
New set-up allows fast, tractable optimisation of trade execution, without neglecting downside risk