Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

Information flow between prediction markets,polls and media: Evidence from the 2008 presidential primaries

Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Far from the madding crowd: collective wisdom in prediction markets

We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.     

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.     

Political uncertainty,risk of Frexit and European sovereign spreads

Using data from a prediction market (crowd-based forecasts), we build a daily measure capturing the risk of Frexit related to the 2017 French presidential elections. We study how unexpected changes in this new measure of political uncertainty in France affect European sovereign spreads vis-à-vis Germany. We show that our uncertainty proxy drives not only the French sovereign spread but also the spreads of those EU countries deemed the most vulnerable to the risk of desegregation of the Euro Zone. These results suggest that specific political uncertainty affects short-term investor’s expectations and may outweigh other economic determinants of sovereign spreads shortly prior to high stake elections     

个人投资者交易行为研究——来自台湾股市的证据

本文基于台湾股市数据,主要研究个人投资者的交易行为。参照Kaniel et al.(2008)构建了个人投资者交易不平衡性指标─净交易,以反映投资者股票交易的强度。采用这种交易不平衡性指标来构建投资组合研究个人投资者的交易行为。首先研究个人投资者交易和股票的收益之间的动态关系从而分析投资者的交易策略,然后研究个人投资者净交易的收益预测能力从而分析个人投资者交易的信息含量。本文研究发现:台湾股票市场的个人投资者采用负反馈的交易策略,并且个人投资者在交易中表现出很强的处置效应;个人投资者在交易中的信息含量不足;个人投资者交易中的盈利主要来自两个方面:过度反应和价格冲击。文章最后给出政策建议。     

对我国公共政策协调研究的思考

当前公共政策协调议题越来越受到关注。学术界关于公共政策不协调的现象、原因、影响、对策等方面已有一定的积累。有关政策协调的边界、理论借鉴、解释框架和实现路径等方面的研究有待进一步深化。     

沪通铁路建设背景下的南通区位形势及交通行业影响预测

沪通铁路建设的帷幕在南通提出加快沿江沿海开发和建设长三角北翼经济中心的背景下正式拉开。沪通铁路工程将对南通地方交通格局及交通运输行业形成重要影响。文章将首先对南通地方交通体系的历史和现状作简要陈述,在此基础上,围绕沪通铁路的建设对完善南通地方交通体系格局的作用展开分析,然后就该工程对南通地方交通行业的影响作综合分析和预测。