Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Performance and learning in an ambiguous environment: A study of cryptocurrency traders

We investigate the performance and learning ability of traders in an environment governed by ambiguity, such as the cryptocurrency market. Using a profit decomposition methodology, we find significant cross-sectional and temporal heterogeneity in performance. Traders do not learn to progressively increase the magnitude of returns; however, they are able to improve on their ability to realise profits as a mechanism of adaptation to survive through ambiguity. This adaptation increases as traders progress through their career. Moreover, we find evidence in support of the gambler’s fallacy. We argue that learning in ambiguous environments has limitations, allowing traders primarily to survive.     

The contractionary effects of tax shocks on productivity: An empirical and theoretical analysis

I analyze the effects of tax policy changes on US Total Factor Productivity. VAR estimates show that permanent and exogenous tax increases have strong, permanent, and negative effects on TFP which represent about 80% of change in output following the tax increase. I then build a DSGE model which has learning-by-doing and endogenous TFP evolution. The benchmark model is able to replicate the empirical impulse responses. However, when I calibrate the model as in the literature, the effect of taxes on TFP is substantially less elastic than in the data. I argue that this divergence may arise because tax changes labeled as exogenous can give spurious results or because of a mis-specified model.     

Creating competitive advantage: Linking perspectives of organization learning,innovation behavior and intellectual capital

Drawing on learning theories and the intellectual capital (IC) theory, organizational learning research has discussed the benefits of searching for market opportunities and maintaining a competitive advantage in dynamic environments. To explain organizational performance and survival, the previous literature has focused mainly on what organizations do, but it has failed to address how and what they should do. This study argues that if hotel management is open-minded about exploratory and exploitative learning, it can open the door to capturing opportunity and competitive advantage through increased innovation behavior and human capital accumulation. Further, IC theory has also confirmed that social capital and relational capital will strengthen the relationship between innovation behavior and human capital. This study analyzes data from 595 hotel managers and finds strong support for the proposed hypotheses.     

SC针梳机铁炮结构优化

SC针梳机在腈纶毛条生产中控制单位长度毛条重量,对产品质量起决定性作用。针梳机匀整部位铁炮故障的频繁发生,直接影响毛条产品的产量和质量。根本原因在于零部件结构不合理及轴承型号不匹配。因此解决铁炮故障意义重大。     

Nonparametric learning rules from bandit experiments: The eyes have it!

How do people learn? We assess, in a model-free manner, subjectsʼ belief dynamics in a two-armed bandit learning experiment. A novel feature of our approach is to supplement the choice and reward data with subjectsʼ eye movements during the experiment to pin down estimates of subjectsʼ beliefs. Estimates show that subjects are more reluctant to “update down” following unsuccessful choices, than “update up” following successful choices. The profits from following the estimated learning and decision rules are smaller (by about 25% of average earnings by subjects in this experiment) than what would be obtained from a fully-rational Bayesian learning model, but comparable to the profits from alternative non-Bayesian learning models, including reinforcement learning and a simple “win-stay” choice heuristic.     

Economic corollaries of personalized recommendations

The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.     

Passive learning: a critique by example

In models of learning by experimentation that exhibit signal dependence, a benchmark using a passive learner has been proposed. The use of this benchmark is flawed – first, passive learning does not disentangle the effects of knowing that beliefs, as well as other state variables, might change, and we address this issue directly by introducing a naïve learner. Secondly, and more tellingly, passive learning does not do what it is supposed to do, namely help measure the gains from active experimentation; the naïve learner enables us to illustrate this point in the context of a particular example.     

二语词汇习得过程研究

词汇是二语学习的一项重要内容。学习者在二语词汇学习过程中必然会受到母语学习策略、母语认知和母语思维的影响。在词汇学习过程中,二语学习者要逐步减少对母语的依赖,逐渐适应二语词与概念的直接对应复习并运用所学词汇,对词汇信息进行深入全面的加工,培养二语词汇能力。     

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.