基金投资行为能够预测经济吗？ ——基于宏观、中观、微观三个层面的实证检验

股市作为经济的晴雨表,基金因其投资行为的专业化被投资者所青睐,那么股票型基金在构建投资组合时是否会依据实体经济呢？本文从宏观、中观和微观三个层面,通过构建非平衡动态面板模型,实证检验我国基金超常规发展与经济增长之间的关系,以及基金投资行为对经济增长的预测作用。研究结果显示：从宏观层面来看,基金发展规模和机构投资者持股比例的增加与经济增长之间存在负相关关系;从行业层面来看,基金的行业持仓增加,则经济预期出现向好局面,表明基金具有一定的经济预判能力;从微观层面来看,预期经济上涨向好趋势时,基金管理人会在当期减持投资组合内的股票,并选择配置更多新股以寻求新的经济增长投资机会,这表明基金投资行为对经济增长具有一定的预测作用。     

A Social-Psychological Perspective on Herding in Stock Markets

A social-psychological perspective conceives of herding in stock markets as informative social influence resulting from heuristic or systematic information processing. In three laboratory experiments employing undergraduates we apply this perspective to investigate factors that prevent herd influence that would lead to inaccurate predictions of stock prices. In Experiment 1, we show that an economic reward for making the same predictions as the herd increases the influence of a majority but not the influence of a minority, and that an individual economic reward for making accurate predictions reduces the influence of the majority. In Experiment 2, we show a reduced influence of a majority herd's inaccurate predictions when requiring assessments of the accuracy of the majority herd´s predictions as compared to requiring judgments of their consistency. Experiment 3 shows that a lower volatility of stock prices reduces the influence of a majority herd´s inaccurate predictions.     

The role of machine learning analytics and metrics in retailing research

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.     

Explaining variance in the accuracy of prediction markets

Thus far, the focus in prediction market research has been on establishing its forecast accuracy relative to those of other prediction methods, or on the investigation of a few single sources of forecast error. This article is the first attempt to overcome the narrow focus of the literature by combining observational and experimental analyses of prediction market errors. It investigates the prediction error of a real money prediction market uusing a logarithmic market scoring rule for 65 direct democratic votes in Switzerland. The article distinguishes between prediction market error due to the setup of the market, features of the event to be predicted, and the participants involved, and finds that the prediction market accuracy varies primarily according to the setup of the market, with the features of the event and especially the composition of the participant sample hardly mattering.     

Data snooping in equity premium prediction

We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) sum-of-the-parts approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.     

Evaluating fund capacity: issues and methods

We examine the issues and methods involved in evaluating the size that an equity fund might attain before it becomes unable to create additional value for investors. We discuss how capacity is defined, identify ten drivers and outline methods for conducting capacity analysis. We detail models that predict capacity, assuming that a fund adjusts the manner in which it trades and constructs portfolios as funds under management grow. We also provide an overview of transaction cost modelling, which is integral to predicting capacity. This study is primarily intended as an aid for investment industry participants who wish to evaluate the capacity associated with a given investment signal.     

Longshots,overconfidence and efficiency on the Iowa Electronic Market

We study the forecast accuracy and efficiency of popular “binary” prediction markets. Such markets forecast probabilities for future states of the world (e.g., election winners) by paying off $0 or $1 depending on the realized state (e.g., who actually wins). To assess accuracy, forecast probabilities must be compared to realization frequencies, not individual realizations. We use Iowa Electronic Market (IEM) data to test efficiency against two alternative propositions from behavioral finance: the longshot bias and the overconfidence bias (which yield opposing predictions). No longshot bias appears in IEM markets. Nor does overconfidence influence prices at short horizons. However, overconfident traders may bias prices at intermediate horizons. While the markets are efficient at short horizons, non-market data indicate some intermediate-horizon inefficiency. We calculate Sharpe ratios for static trading strategies and document returns for dynamic trading strategies to assess the economic content of the inefficiencies.     

A brief history of forecasting competitions

Forecasting competitions are now so widespread that it is often forgotten how controversial they were when first held, and how influential they have been over the years. I briefly review the history of forecasting competitions, and discuss what we have learned about their design and implementation, and what they can tell us about forecasting. I also provide a few suggestions for potential future competitions, and for research about forecasting based on competitions.     

铁路物流基地智能化运营管理体系设计

在人工智能、大数据、云计算等技术日益成熟的背景下,社会物流企业以物流节点为核心载体,加快智能技术的商业化集成应用,极大地提升了需求响应效率与物流服务质量。结合转运节点型、仓配一体型等社会物流企业业态特征,分析其在服务升级、效率提升、效益增长等方面的发展经验,立足铁路物流基地运营管理现状,以提升铁路物流基地效率、效益、服务、安全水平为目标,以"信息自主感知、生产自动组织、管理智能决策"为主线,从平台建设、系统开发、交互接口、终端感知与设备创新等方面设计智能化运营管理体系架构,为我国铁路货运场站从信息化向智能化转型提供参考。     

Prediction versus accommodation in economics

ABSTRACT

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role of orthodox economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then (often) we should demand prediction rather than accommodation – against most current practice.