Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Identifying changes and trends in Hong Kong outbound tourism

Despite the numerous research endeavors aimed at investigating tourists’ preferences and motivations, it remains very difficult for practitioners to utilize the results of traditional association rule mining methods in tourism management. This research presents a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism. Using datasets collected from five large-scale domestic tourism surveys of Hong Kong residents on outbound pleasure travel, both positive and negative contrasts are identified, thus enabling practitioners and policymakers to make appropriate decisions and develop more appropriate tourism products.     

变频技术在数控机床上的应用

介绍了采用数控车床的主轴驱动中变频控制的系统结构与运行模式，并简述了无速度传感器的矢量变频器的基本应用。     

Changing laundry habits in Norway

Maintenance is often the most energy‐demanding stage during clothes' life cycle. Therefore, a shift towards more sustainable washing habits has great potential to reduce the consumption of energy, water and detergent. This paper discusses the change in laundering practices during the past 10 years in Norway and suggests strategies to help consumers change their laundry habits to more sustainable ones. Quantitative information of consumers' experiences, habits and opinions concerning clothing maintenance was collected through three surveys in Norway in 2002, 2010 and 2011. The 2010 study was supplemented with qualitative in‐depth interviews of a strategic sample of households. The average washing temperature has decreased slightly during the studied time periods. Some products' washing frequencies remained the same, whereas other products such as jeans were used a few more days before washing. The cotton programme is the most used washing programme, but short programmes are gaining popularity. The laundry sorting processes vary greatly and are influenced by several factors such as washing temperature, colours, fibre type and use area. For some consumers, the use of several different sorting categories made it more difficult to collect a sufficient amount of clothing to fill the machine. They were also afraid that overfilling the machine would result in clothes that were not clean enough or had detergent residues. Detergent dosing practices are far from optimal. In 2010, although the majority of respondents only used eye measure and did not know the water hardness of their area, they still tried to vary detergent dosage based on the amount of laundry and the level of soiling. Different design for sustainable behaviour strategies could be used within detergent dosage systems, care labelling, machine programme selection (such as suggesting lower temperature and eco‐programme), machine filling grade indicators, storage systems for slightly used clothing and textile material choice.     

Probabilistic Models for Bacterial Taxonomy

We give a survey of different partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way.     

价值分析在机床改造中的应用

本文介绍了应用价值分析方法将MMB1312机床出手动改为半自动循环的过程,从而大大地降低了成本,提高了生产效率,创造了不菲的经济效益.     

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.     

HR——500ⅡA型自动温控红外热熔机整机结构设计

以红外热源的辐射热来热熔镀在印制电路板上的锡铅合金,具有很多的优点.本文着重对实施这一热熔工艺的专用设备——红外热熔机的整机造型、人机关系、各主要部件的功能和结构设计,作了较为详细的分析.