Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

Changing International Financial Architecture: Growing Chinese Influence?

It is often said that the 21st century will be the Asian century. Based on population and gross domestic product projections, it certainly looks as if this will be so. Will the existing international financial institutions give more voices, votes, and top positions to Asia? Or will Asia create its own institutions that would rival the old architecture? This present paper argues, first, that China, and possibly India, will be in a position to be so influential that the international financial architecture may have to go through significant changes. Second, the three large crises in the last 20 years have made Asian countries more confident that they can manage capital flows by accumulating large foreign reserves and by adopting sound macrofinancial policies. After 2009, China started to push various initiatives that will amount to creating its own sphere of influence with new regional institutions in the future.     

Pricing used books on Amazon.com: a spatial approach to price dispersion

This paper estimates a spatial autoregressive (SAR) model of price dispersion using publicly available internet bookselling data. It uses a semiparametric adaptive estimator that does not require the usual Gaussian assumption of maximum likelihood (ML) estimators. The results suggest that both price competition and seller heterogeneity are key drivers of the observed price dispersion. The paper finds that sellers with large sales volume, newly established sellers and US mainland states-based sellers tend to price lower. The identified significant spatial interaction is evidence of spatial price competition. Controlling for everything else, a seller asks a lower price when large sellers charge relatively high prices, which is also evidence of price-based selling and undercutting.     

我国林业扶贫工作的主要问题及优化措施研究

林业扶贫是当前我国扶贫工作的重要内容。通过介绍开展林业扶贫工作的政策背景、理论背景以及经济背景,分析林业扶贫工作开展过程中存在的基础设施落后、针对性差等问题,提出了因地制宜,互联网+林业扶贫,鼓励社会资本进入扶贫工作、志智双扶等应对措施。     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Forecast of realized covariance matrix based on asymptotic distribution of the LU decomposition with an application for balancing minimum variance portfolio

We derive the asymptotic distribution for the LU decomposition, that is, the Cholesky decomposition, of realized covariance matrix. Distributional properties are combined with an existing generalized heterogeneous autoregressive (GHAR) method for forecasting realized covariance matrix, which will be referred to as a generalized HARQ (GHARQ) method. An out-of-sample forecast comparison of a real data set shows that the proposed GHARQ method outperforms other existing methods in terms of optimizing the variances of portfolios.     

Bidding for Firms with Unknown Characteristics

When a region successfully attracts a firm by offering subsidies, the firm often commits itself to performance targets in terms of employment. In this paper, we interpret these firm‐specific targets as a consequence of incomplete information. We analyze a model of two regions that compete for a firm, assuming that the firm's productivity is ex ante unknown. We show that performance targets often induce overemployment in high‐productivity firms, and that tax credits are often superior to lump‐sum payments. Moreover, when regions differ in wage rates, the low‐wage region wins the bid and has a higher surplus than under complete information. Finally, we show that, under incomplete information, bidding might not lead to efficient firm location.     

政府间竞争影响城市土地市场化水平的双边效应研究：基于财政竞争和引资竞争的不同作用

研究目的:本文将政府间竞争划分为财政竞争和引资竞争,比较两者对城市土地市场化水平影响的大小,分析政府间竞争与城市土地市场化水平之间的关系。研究方法:基于2003—2016年全国284个地级市的面板数据,利用双边随机前沿模型测算财政竞争和引资竞争对城市土地市场化水平的影响。研究结果:财政竞争对城市土地市场化水平具有正向效应,提高了城市土地市场化水平1.61%;引资竞争对城市土地市场化水平具有负向效应,降低了城市土地市场化水平12.51%;政府间竞争总体上降低了城市土地市场化水平10.90%。时间趋势表明,政府间竞争对城市土地市场化水平的抑制作用呈现波动下降的状态。研究结论:政府间竞争降低了城市土地市场化水平。     

Determinants of the allocative,cost and scope efficiencies of Indian banks

ABSTRACT

This study aims to investigate the impact of competition on determinants of allocative, scope and cost efficiencies of Indian scheduled commercial banks (SCBs). Specifically, the study, analyzes the impact of the second round of licensing on the efficiency of Indian SCBs. This is the first paper to measure scope efficiency of Indian banks and analyze its determinants. A two-stage analysis is performed on a balanced panel dataset of Indian SCBs for the period 1999–2016. In the first stage, the allocative, cost and scope efficiencies for each bank are estimated following the data envelopment analysis approach. In the second stage, internal determinants of the stated efficiency measures are estimated following the system of the generalized method of moments approach. The findings suggest that competition has not resulted in enhancing the efficiency of Indian SCBs. Among factors that can influence efficiency, it is seen that size does matter. Larger banks can enhance the efficiency of SCBs. It is also seen that having more foreign banks improves the overall efficiency of SCBs. However, before embarking on further rounds of licensing, the study posits that market-driven correction to succeed, it is imperative to address sunspots in the form of investor or borrower repression.