RISK DECOUPLING STRATEGIES IN CORPORATE LAW: THEORETICAL MODEL AND PRACTICAL IMPLICATIONS (first part)

The principal focus of the paper is the risk-decoupling phenomenon in corporate law. Key strategies for achieving decoupling are considered, such as those, in which the amount of risk of a shareholder is less than the amount of her participatory rights in the corporation, as well as those, in which the risk of a shareholder is higher than the rights of participation belonging to her. This effect is achieved through the use of derivatives, swaps, the record date capture as well as through contracts for difference. As a result of the analysis of the theoretical model of these strategies and the application of the Law & Economics methodology solutions for correcting negative effects arising from the use of the above mentioned mechanisms of risk-decoupling are proposed.

The use of Contracts for difference (‘CFD’) Spread Bets and Binary Options (‘forbin’) to Trade Foreign Exchange (‘forex’) Commodities and Stocks and Shares in Volatile Financial Markets

Finally, it is argued that whilst forex is the most popular asset traded, its price movements are more difficult to predict and are much smaller compared with stocks and shares and commodities, making it even more difficult for traders to trade them successfully.

Betting on bitcoin: a profitable trading between directional and shielding strategies

In this paper, we come up with an original trading strategy on Bitcoins. The methodology we propose is profit-oriented, and it is based on buying or selling the so-called Contracts for Difference, so that the investor’s gain, assessed at a given future time t, is obtained as the difference between the predicted Bitcoin price and an apt threshold. Starting from some empirical findings, and passing through the specification of a suitable theoretical model for the Bitcoin price process, we are able to provide possible investment scenarios, thanks to the use of a Recurrent Neural Network with a Long Short-Term Memory for predicting purposes.

Observation Time Effects in Reinforcement Learning on Contracts for Difference

In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) and act on the basis of different observation time spans. Each agent tries to maximize trading profit by buying or selling one of a number of contracts in a simulated market environment for Contracts for Difference (CfD), considering correlations between individual assets by architecture. To decide which action to take on a specific contract, an agent develops a policy which relies on an observation of the whole market for a certain period of time. We investigate whether or not there exists an optimal observation sequence length, and conclude that such a value depends on market dynamics.

Contracts for Difference: A Reinforcement Learning Approach

We present a deep reinforcement learning framework for an automatic trading of contracts for difference (CfD) on indices at a high frequency. Our contribution proves that reinforcement learning agents with recurrent long short-term memory (LSTM) networks can learn from recent market history and outperform the market. Usually, these approaches depend on a low latency. In a real-world example, we show that an increased model size may compensate for a higher latency. As the noisy nature of economic trends complicates predictions, especially in speculative assets, our approach does not predict courses but instead uses a reinforcement learning agent to learn an overall lucrative trading policy. Therefore, we simulate a virtual market environment, based on historical trading data. Our environment provides a partially observable Markov decision process (POMDP) to reinforcement learners and allows the training of various strategies.