Transforming under deep uncertainty: A strategic perspective on risk management

Companies increasingly face the need for transformation in today’s rapidly changing business environment, characterized by major shifts in technology, regulation, and customer behavior. A lack of strategic risk insight and foresight leaves many incumbents insufficiently prepared in the face of such deep uncertainty. We argue that traditional risk management falls short because it predominantly focuses on strategy execution while leaving strategy formulation largely untouched. Moreover, an administrative-heavy risk management process can create strategic inertia and a misleading sense of control. In today’s dynamic business context, companies must not only increase the speed and impact of their strategy execution but also continuously explore the development of new strategies in response to disruptive events or emerging opportunities. Our research shows how leading companies develop a strategic risk management (SRM) capability to increase their resilience and agility in response to deep uncertainty. SRM takes a strategic, forward-looking perspective and focuses on strengthening processes, people, and practices for purposefully integrating risk into the strategy formulation process. This article offers a framework with three proven configurations of content and timing integration, risk management roles, and leading practices that enable effective SRM.     

Size and information revelation in securities trading

We examine differences in information content between order submission sizes and trade sizes by U.S. equity traders. Increasing (decreasing) order submission (trade) size is reflective of information. The result suggests that better-informed traders want to trade in a large size, but that they engage in stealth trading practices or break larger orders into smaller sizes in order to conceal information. While prior studies tend to narrowly focus on trade executions at the market-centre level, our findings indicate that order submission size varies significantly from trade size and that both sizes are informative about future prices, albeit in an inverse manner.     

High frequency momentum trading with cryptocurrencies

Over the past few years, cryptocurrencies have increasingly been discussed as alternatives to traditional fiat currencies. These digital currencies have garnered significant interest from investment banks and portfolio managers as a potential option to diversify the financial risk from investing in other assets. This interest has also extended to the general public who have seen cryptocurrencies as a way of making a quick profit. This paper provides a first insight into the applicability of high frequency momentum trading strategies for cryptocurrencies. We implemented two variations of a signal-based momentum trading strategy: (i) a time series method; (ii) a cross sectional method. These strategies were tested on a selection of seven of the largest cryptocurrencies ranked by market capitalization. The results show that there exists potential for the momentum strategy to be used successfully for cryptocurrency trading in a high frequency setting. A comparison with a passive portfolio strategy is proposed, which shows abnormal returns when compared with the momentum strategies. Furthermore, the robustness of our results are checked through the application of the momentum strategies other sample periods. We also compare the performances of the signal-based momentum strategies with returns-based versions of the strategies. It is shown that the signal-based strategy outperforms the returns-based strategy. However, there appears to be no single parameterization of the signal-based strategies that can generate the greatest cumulative return over all sample periods.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

结构性违约、信用分层与评级机构的策略性反应——来自中国信用债市场的证据

2014年以来，以主要集中于民企的"结构性违约"宣告了中国信用债市场刚性兑付的"结构性打破"，债券市场进入了新的发展阶段。利用2013-2017年债券数据，本文深入分析了结构性违约对评级机构行为和效率的影响，主要有如下发现：首先，信用评级的市场公信力会因政府隐性担保导致的"刚性兑付"而削弱；其次，结构性违约爆发之后，信用评级的整体效率有所提升，但在不同发行主体呈现分化，对于非城投类企业，信用评级对发行利差的影响显著增大，意味着评级公信力的显著提升，而对于城投类企业，评级效率并未明显改善；进一步研究表明，出现这种情况的原因在于评级机构在违约后采取了差异化的评级策略，对于违约风险较大的非城投类债券，评级机构倾向采取"收紧评级"的策略；对于违约风险较低的城投债，评级机构倾向于采取"放宽评级"的策略。这种策略性行为是导致市场"信用分层"的重要原因，并有可能推升民营企业的融资成本。     

A new assessment of economic integration in East Asia: Application of an industry-specific G-PPP model

It is well known that intra-industry trade and cross-border production networks have promoted economic growth and regional integration in East Asia. However, regional supply and production chains may have been formed differently across industries, reflecting different degrees and a different scope of regional economic linkages at an industry level. This paper makes a threefold contribution. First, to assess industry-level differences, this study adopts the generalized purchasing power parity (G-PPP) model using industry-specific producer prices. Second, the momentum threshold autoregressive (M-TAR) model is employed to allow for possible nonlinearity arising from the dynamic nature of regional economic growth and development. Third, the Granger causality test is also conducted to assess whether regional economies have autonomously integrated. The empirical results reveal that economic integration has progressed more autonomously in the electrical industry, as well as in the transportation equipment industry, as China and the ASEAN countries have become the final destination markets for finished products in these two industries.     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Cognitive ability and human behavior in experimental ultimatum games

Recent researches have shed light on the effect of cognitive ability on economic decision-making. By measuring cognitive ability applying Raven's progressive matrix test, we obtain two significant results that this effect affects decision-making in two types of experimental ultimatum games. First, the higher the cognitive ability, the larger the amount a sender offers when the offer is smaller than or equal to the half split. Second, the higher the responders’ cognitive ability, the smaller the offer they accept, when they accept it or not with the strategy method. This study not only finds new factors that affect decision-making in experimental ultimatum games, but also provides more evidences that cognitive ability influences economic decision-making.     

创业型企业话语框架策略与合法性构建:基于成长阶段的纵向案例分析

网络信息时代的组织话语能够以较低的成本迅速传播，是创业型企业在资源局限、时间紧迫情况下实现合法化的重要工具。本研究以创业期的淘宝网（2003-2014年）为研究对象，基于其代表性广告话语，尝试刻画、剖析组织合法性的话语构建过程。基于系统功能语法与系统图像语法的多模态话语分析显示，创业型企业在初创期主要运用发挥话语框架策略构建基本的实用合法性与道德合法性，在快速扩张阶段同时运用发挥与延伸策略进一步维护、增强这两种合法性，在稳定成长期则综合运用延伸、桥接与转变策略重点构建道德合法性与认知合法性。从企业成长阶段来看，话语框架策略与组织合法性之间呈现出协同演进关系。本研究通过跨学科理论与方法丰富了关于话语与组织合法性动态关系的研究，同时为创业型企业的合法性构建带来实践启示。