TSFDC: A Trading strategy based on forecasting directional change

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy

A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk-Adjusted Performance of AI Trading Algorithms

The majority of existing artificial intelligence (AI) studies in computational finance literature are devoted solely to predicting market movements. In this paper we shift the attention to how AI can be applied to control risk-based money management decisions. We propose an innovative fuzzy logic approach which identifies and categorizes technical rules performance across different regions in the trend and volatility space. The model dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk-adjusted performance. By adopting a hybrid method in conjunction with a popular neural network (NN) trend prediction model, our results show significant performance improvements compared with both standard NN and buy-and-hold approaches

The AI Revolution: Opportunities and Challenges for the Finance Sector

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators

Developing trading strategies under the Directional Changes framework, with application in the FX Market

Directional Changes (DC) is a framework for studying price movements. Many studies have reported that the DC framework is useful in analysing financial markets. Other studies have suggested that, theoretically, a trading strategy that exploits the full promise of the DC framework could be astonishingly profitable. However, such a strategy is yet to be discovered. In this thesis, we explore, and consequently provide proof of, the usefulness of the DC framework as the basis of a profitable trading strategy. Existing trading strategies can be categorised into two groups: the first comprising those that rely on forecasting models; the second comprising all other strategies. In line with existing research, this thesis develops two trading strategies: the first relies on forecasting Directional Changes in order to decide when to trade; whereas the second strategy, whilst based on the DC framework, uses no forecasting models at all. This thesis comprises three original research elements: 1. We formalize the problem of forecasting the change of a trend’s direction under the DC framework. We propose a solution for the defined forecasting problem. Our solution includes discovering a novel indicator, which is based on the DC framework. 2. We develop the first trading strategy that relies on the forecasting approach established above (Point 1) to decide when to trade. 3. We develop a second trading strategy which does not rely on any forecasting model. This is trading strategy employs a DC-based procedure to examine historical prices in order to discover profitable trading rules. We examine the performance of these two trading strategies in the foreign exchange market. The results indicate that both can be profitable and that both outperform other DC-based trading strategies. The results additionally suggest that none of these two trading strategies outperforms the other in terms of profitability and risk simultaneously