Building resilience for sustainability of MSMEs post COVID-19 outbreak: An Indian handicraft industry outlook

The handicraft business constitutes concept selling rather than mere product selling, which is highly dependent on demand. Handicrafts' Micro Small and Medium Enterprises (MSMEs) have an expanding market in developed countries. The impact of the pandemic on this industry is severe due to the industry's informal nature and seasonal demand. The survival and resilience of these handicraft MSMEs face many challenges in the post-COVID-19 outbreak. The focus of the present study is to understand and analyze the key challenges for building resilience in handicraft MSMEs by scrutinizing the existing literature and interactions with stakeholders. EFA and the Grey DEMATEL approach are used to analyze the challenges for the adoption of resilience. EFA is used to categorize the challenges into various dimensions. The study has divided the challenges for the inclusion of resilience into survivable, sustainable, and viable categories using EFA to plan for short- and long-term business growth. Grey DEMATEL is being utilized for understanding these contextual relationships for each resilience dimension. Grey systems theory is a methodology that enables the incorporation of improbability and vagueness into the analysis. Findings of the study revealed the influencing challenges for each of the dimensions such as competition from machine-made products, insufficient government support and incentives for export, and inefficient managerial concern and response to internationalization as the prominent challenges. The results of this study illustrate the causal relationships amongst the identified resilience challenges to the various stakeholders. These findings offer practical insights for the government to allocate resources and impose measures to ensure resilience, as well as understanding the cause-effect relationship. Managerial implications and Policy insights for building the resilience of handicraft MSMEs are discussed in detail.     

新财税政策下的职工基本养老保险最优缴费率测算研究

企业职工基本养老保险缴费率作为养老保险制度的关键性要素,受到专家学者的普遍关注,企业缴费率高、缴费压力大已成为制约企业发展的重要因素。本文综合运用灰色关联、因子分析与回归、动态调整和模拟预测的方法,构建企业基本养老保险缴费率适度水平的因子测算模型,以上海市2008—2019年的数据作为样本进行测算与预测。结果显示:缴费率的适度水平处于19%～23%;增加国有资产划转10%补充社保基金以及中央调剂金制度等参数后,模拟预测2018—2025年的缴费率适度水平在14.8%～18.6%之间,说明企业职工基本养老保险的缴费率有进一步下降的空间。     

Quantifying the uncertainty of long-term macroeconomic projections

We propose a practical approach to measuring the uncertainty of long-term economic projections. The presented method quantifies the uncertainty of economic variables by using simulations from a multivariate unobserved components model in which variables are formulated as sums of stationary and nonstationary components. The method captures the correlations between both the stationary and nonstationary components of the variables and offers a seamless analysis of short- and long-term uncertainty. Experiments on artificial data demonstrate that, despite its simplicity, the method performs fairly well compared with alternative methods in terms of long-term predictive accuracy and coverage.     

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity; (ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak; and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.     

How well do investor sentiment and ensemble learning predict Bitcoin prices?

Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR significantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.     

Group penalized logistic regressions predict up and down trends for stock prices

Stock prices are influenced by many economic factors, investors psychology and expectations, movement of other stock markets, political events, etc. Therefore, correctly predicting up and down trends for stock prices is an important puzzle in the financial field. In this paper we combine technical analysis with group penalized logistic regressions, and propose group SCAD/MCP penalized logistic regressions with technical indicators to predict up and down trends for stock prices. Firstly, we screen out 24 important technical indicators, divide them into the five different indicator groups, and construct group SCAD/MCP penalized logistic regressions for the three listed companies. Secondly, we apply the training set to learn the parameter estimators and the probability estimators for the two group penalized logistic regressions, adopt the test set to obtain confusion matrices and ROC(Receiver Operating Characteristic) curves to assess their prediction performances, and found that the AUC values to the three companies all exceed 0.78. Finally, we compare group SCAD/MCP penalized logistic regressions with SCAD/MCP penalized logistic regressions, and found that the two group penalized logistic regressions perform better than the two penalized logistic regressions in terms of prediction accuracy and AUC. Therefore, in this paper we develop a new prediction method by combining group SCAD/MCP penalized logistic regressions with technical indicators to improve the prediction accuracy and bring huge economic benefit for investors.     

Beating the market with a bad predictive model

It is a common misconception that in order to make consistent profits as a trader, one needs to possess some extra information leading to an asset value estimation that is more accurate than that reflected by the current market price. While the idea makes intuitive sense and is also well substantiated by the widely popular Kelly criterion, we prove that it is generally possible to make systematic profits with a completely inferior price-predicting model. The key idea is to alter the training objective of the predictive models to explicitly decorrelate them from the market. By doing so, we can exploit inconspicuous biases in the market maker’s pricing, and profit from the inherent advantage of the market taker. We introduce the problem setting throughout the diverse domains of stock trading and sports betting to provide insights into the common underlying properties of profitable predictive models, their connections to standard portfolio optimization strategies, and the commonly overlooked advantage of the market taker. Consequently, we prove the desirability of the decorrelation objective across common market distributions, translate the concept into a practical machine learning setting, and demonstrate its viability with real-world market data.     

基于改进的GM_Markon模型对北京创意产业发展的预测

创意产业产值受周边环境因素影响较大，在某些年份甚至出现局部较大畸变，通过相关文献学习，首先通过对灰色模型预测，然后经过残差修正增加模型规律性（采用两次残差累加），最后在残差修正灰色模型基础上应用改进的马尔科夫链提高预测精度，对2005-2014年北京创意产业产值数据验证算法有效性进行研究。此算法优点在于弥补灰色模型数据少，随机性大的缺陷，很好修正畸变数据和提高拟合度。     

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal $do(.)$ operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.     

Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model

Aggregating predictions from multiple judges often yields more accurate predictions than relying on a single judge, which is known as the wisdom-of-the-crowd effect. However, a wide range of aggregation methods are available, which range from one-size-fits-all techniques, such as simple averaging, prediction markets, and Bayesian aggregators, to customized (supervised) techniques that require past performance data, such as weighted averaging. In this study, we applied a wide range of aggregation methods to subjective probability estimates from geopolitical forecasting tournaments. We used the bias–information–noise (BIN) model to disentangle three mechanisms that allow aggregators to improve the accuracy of predictions: reducing bias and noise, and extracting valid information across forecasters. Simple averaging operates almost entirely by reducing noise, whereas more complex techniques such as prediction markets and Bayesian aggregators exploit all three pathways to allow better signal extraction as well as greater noise and bias reduction. Finally, we explored the utility of a BIN approach for the modular construction of aggregators.