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.     

Teaching courses in macroeconomics and monetary policy with Bloomberg analytics

In this article, the authors illustrate the use of Bloomberg for analyzing topics in macroeconomics and monetary policy in economics and finance courses. The hands-on experience that students gain from such a course has many benefits, including deeper learning and clearer understanding of data. The authors describe goals and learning objectives, then compare Bloomberg with Federal Reserve Economic Data (FRED). In addition, they provide examples of how to use Bloomberg in the classroom, describe how to have students perform sector analysis, show how Bloomberg tools are useful for analyzing monetary policy, discuss how to use Bloomberg to analyze the financial sector, and illustrate the platform’s use in a case study.     

LGB-BAG模型在P2P网贷借款者信用风险评估中的应用

立足于P2P平台,利用P2P平台个人借款人的信息建立了一套系统的信用风险评估指标体系来甄别可能违约的借款人。本文基于LightGBM（一种基于决策树的Boosting模型）和Bagging而提出一种新的LGB-BAG模型,有效结合了Boosting和Bagging的优势。结果表明,在N增大到一定程度的时候,LGB-BAG的F1均值（预测效果）要高于LightGBM和随机森林;并且LGB-BAG的F1方差也要小于其余两种模型。LGB-BAG的F1均值最高可达到0.71175,且LGB-BAG模型能够显著提高信用风险预测效率。     

The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual-level Wealth

This article provides comparative estimates of the gender wealth gaps for 22 European countries, employing data from the Household Finance and Consumption Survey. The data on wealth are collected at the household level, while individual-level data are needed for the estimates of gender wealth gaps. We propose a novel approach using machine learning and model averaging methods to predict individual-level wealth data for multi-person households. Our results suggest that random forest performs best as the predicting tool for this exercise, outperforming elastic net and Bayesian model averaging. The estimated gender wealth gaps tend to be in favor of men, especially at the top of the wealth distribution. Men have 24 percent more wealth than women on average. We also find that a high home ownership rate is associated with a smaller country-level gender wealth gap. Our estimates suggest that the individual-level wealth inequality is on average 3 pp higher than the household-level wealth inequality in multi-member households.     

大数据能力对农业科技企业创新的影响：一个有调节的中介效应模型

目前学者们较为关注大数据能力对于企业创新的影响研究,但大多笼统探究大数据能力对企业创新的直接影响,深入探究其中介机制和调节机制的研究尚未完善,立足于农业科技企业的研究更是缺乏。本文基于动态能力理论,深入探究大数据能力对农业科技企业创新的影响机制,以双元组织学习为中介变量,以市场导向为调节变量,建立可调节的中介模型。基于181家农业科技企业样本,结果表明：大数据能力对企业创新具有显著正向影响;探索式学习和利用式学习在大数据能力和企业创新间均具有正向中介作用;市场导向增强了利用式学习在大数据能力与企业创新之间的中介作用;而对探索式学习在大数据能力与企业创新之间的中介作用未发挥显著的调节作用。     

基于工作过程的数控编程与操作课程学习情境设计

现代制造企业对高职院校学生的职业技能要求越来越高,开展课程教学改革,实施理实一体化教学非常重要。数控编程与加工课程是数控技术专业核心课程,开展基于工作过程的数控编程与操作课程学习情境设计,实施理实一体化教学,有利于提高学生职业技能的培养。     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

Judgment aggregation in search for the truth

We analyze the problem of aggregating judgments over multiple issues from the perspective of whether aggregate judgments manage to efficiently use all voters' private information. While new in judgment aggregation theory, this perspective is familiar in a different body of literature about voting between two alternatives where voters' disagreements stem from conflicts of information rather than of interest. Combining the two bodies of literature, we consider a simple judgment aggregation problem and model the private information underlying voters' judgments. Assuming that voters share a preference for true collective judgments, we analyze the resulting strategic incentives and determine which voting rules efficiently use all private information. We find that in certain, but not all cases a quota rule should be used, which decides on each issue according to whether the proportion of ‘yes’ votes exceeds a particular quota.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

Converging scientific fields and new technological paradigms as main drivers of the division of scientific labour in drug discovery process: the effects on strategic management of the R&D corporate change

The thesis of this study is that the convergence of genetics, genomics and proteomics spurs new technological paradigms in medicine, which are generating a R&D corporate change: division of scientific labour of the drug discovery process by strategic alliances among firms in order to reinforce the integrative capabilities in different biomedical research fields and collective and cumulative learning between in-house R&D and external sources of innovation. This study shows, by key a case study of pharmaceutical companies, as scientific and technological paradigms in medicine are main drivers of industrial and R&D corporate change to enhance and accelerate the discovery process of ground-breaking drugs for more and more personalised healthcare.