Hybridising metaheuristics and exact methods for portfolio optimisation problem

This thesis focuses on the portfolio optimisation problems, which concern with allocating the limited capital to invest in a number of potential assets (investments) in order to achieve the investors risk appetites and the return objectives. In the 1950s, Harry Markowitz proposed a mean-variance portfolio optimisation model, which is widely regarded as the foundation of the modern portfolio theory. However, the basic Markowitz mean-variance model has limited practical utilities since it omits many constraints existed in real world trading. The problem quickly becomes more complex with the additional real-world trading constraints involved. One main problem of the mean-variance portfolio optimisation framework is that it relies on the perfect information. In practice, the problems faced in portfolio optimisation are more complex since many sources of market uncertainty are involved. Moreover, different risk measures need to be adopted in order to have a better reflection of the asymmetry nature of asset returns. The thesis firstly studies the single-period mean-variance portfolio optimisation model with two practical trading constraints. Hereafter, a two-stage scenario-based stochastic portfolio optimisation model is developed. The two-stage stochastic programming model minimises the excess shortfall of portfolios which are captured by the CVaR risk measure. The two-stage stochastic programming model can capture the market uncertainty in terms of future asset prices and it enables the investors rebalancing the assets in a dynamic setting. A copula-based method is applied to generate scenarios to represent uncertainty in future asset prices in accordance with their historical information. Stability tests are also performed and the results confirm that the scenario generation method is appropriate for the model. Three hybrid algorithms which hybridise metaheuristics and exact methods in an integrated manner are presented to solve the two models. The principle of designing hybrid methods in this thesis can be described as: metaheuristic algorithms are adopted to search for the assets combination heuristically and exact methods are applied to calculate the corresponding assets weights optimally. For the cardinality constrained mean-variance model, a combinatorial algorithm which hybridises a PSO and the mathematical programming method is proposed to address the problem. For the two-stage stochastic programming model, a hybrid algorithm which integrates a GA and a LP solver is presented to address the problem and a hybrid combinatorial approach which integrates a PBIL-based metaheuristic and a LP solver is developed to address the problem with a large number of scenarios. One main advantage of the hybridisation approach is that it can guarantee the optimal weight allocation of the identified asset combinations. Some useful strategies for different metaheuristics are investigated in order to keep a balance between algorithms' exploration and exploitation. Some useful mechanisms are also adopted in order to enhance the search efficiency and achieve a global better performance. The results have shown that such hybridisation strategy can achieve synergetic effects through the integration of multiple components

Forecasting stock market return with nonlinearity: a genetic programming approach

The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading

Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos

Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic

Supply chain management based on volatility clustering: The effect of CBDC volatility

A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction

The oil price-inflation nexus: The exchange rate pass-through effect

Crude oil prices have been considered one of the key drivers of inflation worldwide, reaching a peak in 2022. Inflation targeting plays a pivotal role in such a high inflation episode. In this vein, the exchange rate is a key channel in transmitting the high commodity price into the domestic price level, known as the exchange rate pass-through effect. On this basis, this paper scrutinizes the connection between oil prices and inflation through RMB exchange rates. We verify that the covariance between exchange rates and oil prices are sound factors in explaining and predicting inflation in China. We advocate that policymakers can use the exchange rate as an inflation stabilizer by reducing the covariance between the exchange rate and oil price, especially for emerging economies and during the turmoil periods. This could be extremely helpful to frustrate the exchange rate pass-through effect of high commodity prices globally, which sheds new insights into stabilizing inflation and assisting inflation targeting for emerging economies

A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method

In this work, the impact of chemical additions, especially nano‐particles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)‐response surface methodology (RSM) algorithm. Fe‐based and Ni‐based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe‐based NPs and ions, but not for Ni‐based NPs and ions. An optimal range of particle size (86–120 nm) and Ni‐ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ \u3e Mg2+ \u3e Cu2+ \u3e NH4+ \u3e K+

Commodity market stability and sustainable development: The effect of public health policies

This study explores the influence of public health policies on commodity market volatility during public health emergencies, such as pandemics, using data from China and the US. We investigate how stringent public health measures can mitigate the effects of pandemics on the stability of commodity markets by stabilizing domestic demand and supply of natural resources. Our findings highlight the interconnectedness between commodity market stability and oil production, showing that firms increase their oil inventories in response to oil market volatility as a precautionary measure. This action, in turn, affects the amount of oil available for production, impacting oil consumption and extraction rates. We demonstrate that stability in the oil market significantly influences not only oil consumption but also has broader implications for sustainable development, green asset markets, and carbon emissions

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators