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The intention of the empirics is to contribute to the general understanding of investor responses to market price shocks. The authors review assumptions about investor behavior in response to price shocks and investigate alternative rebalancing heuristics.

The authors use market data over 40 years to define market shocks. Portfolio rebalancing implements constrained Markowitz mean-variance (MV) heuristics.

Momentum rebalancing in portfolio management outperforms contrarian rebalancing in the study interval. Sensitivity analysis by decade, sector constraints and proportion of security holdings bought or sold continue to support momentum rebalancing.

The results are consistent with under-responding to price shocks at consensus levels in financial markets. The theoretical background provides a basis for experimental lab studies of shocks of different magnitudes under conditions in which participants have information on the levels of other participants and a condition in which they can only observe their previous estimates.

Managing portfolios in the face of price disturbances of different magnitudes is informed by empirical studies and their implications for investor behavior.

This is the first study the authors can locate that uses market data with alternative rebalancing heuristics to estimate price returns from the respective heuristics over a time interval of 40 years. The authors support the results with sensitivity estimates and consider implications for the underlying agent heuristics in light of background studies.

Computerising inventory control procedures is usually an attempt to gain better control over stock availability. The effectiveness of the procedures depends on the time delays imparted by such events as order processing and delivery. Through these time delays, much of a finished goods physical distribution system is linked together through the inventory control procedures. Changing the length of any one time element through changes in inventory stocking rules, order processing methods or selected transportation services impacts on the economics of the entire physical distribution system. Little is understood about the effects of time change in such complex systems. In this article, the actual computer inventory control procedures of a chemical company were computer simulated. Physical distribution system design decisions and their associated time delay effects were explored by interrogating the model. Surprising effects were discovered, some of them being counter‐intuitive to what simple theory would predict. Management guidelines were provided as to the system‐wide economic consequences of change in individual elements of a physical distribution system.

The purpose of this paper is to present a discrete quantitative trading strategy to directly control a portfolio's maximum percentage of drawdown losses while trying to maximize the portfolio's long‐term growth rate.

The loss control target is defined through a Rolling Economic Drawdown (REDD) with a constant look‐back time window. The authors specify risk aversion in the power‐law portfolio wealth utility function as the complement of maximum percentage loss limit and assume long‐term stable Sharpe ratios for asset class indexes while updating volatility estimation in dynamic asset allocation implementation.

Over a test period of the past 20 years (1992‐2011), a risk‐based out‐of‐sample dynamic asset allocation among three broad based indexes (equity, fixed income and commodities) and a risk free asset, is robust against variations in capital market expectation inputs, and out‐performs the in‐the‐sample calibrated model and traditional asset allocation significantly.

The current proposal can lead to a new mathematical framework for portfolio selection. Besides investors' liquidity and behavioural constraints, macroeconomic and market cycle, and the potential of central bank interventions following a market crash, could be additionally considered for a more rigorous dynamic asset allocation model.

Besides the benefit of a clear mandate to construct suitable client portfolios, the portfolio approach can be applied to design invest‐able securities, such as principal‐guaranteed investment products, target risk asset allocation ETFs, and target‐date mutual funds with a glide path, etc. The formulation can also be implemented as a managed futures hedge fund portfolio.

The paper introduces the Rolling Economic Drawdown (REDD) concept and specifies risk aversion as the floor of maximum percentage loss tolerance. Dynamic asset allocation is implemented through updating estimation of asset class volatilities.

The purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.

Energy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.

The results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.

This study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.

This chapter presents a data envelopment analysis (DEA) based relative financial strength (RFS) indicator using accounting data that is predictive of stock market performance of public firms. Such an indicator is indispensable in the fundamental analysis of firms for stock portfolio selections. This methodology requires optimally configuring inputs and outputs for the DEA model such that the strength indicator is maximally correlated with observed stock returns. This optimized RFS indicator providing the maximum predictive strength of stock returns is determined by factors such as asset utilization, leverage, profitability, and growth rates, in addition to the well-known factor, book-to-market ratio. Computational evidence is provided using more than 800 firms covering all major sectors of the U.S. stock market. Using quarterly financial data, we employ the RFS indicator to devise portfolios that yield superior financial performance relative to using portfolios of sector-based funds.

The purpose of this paper is to provide a brief review of three strands of the literature on exchange‐traded funds.

The paper starts with a review of the history of the growth of exchange‐traded funds and their characteristics. The paper then examines the key factors and findings of the existing studies on, respectively, the pricing efficiency, the tracking ability/performance, and the impact on underlying securities of exchange‐traded funds.

Although there has been a substantial amount of research conducted to advance our knowledge on the trading, management, and effect of exchange‐traded funds, the findings are still far from conclusive in addressing a number of research questions.

Investors and other market participants will find this review informative in enhancing the understanding of exchange‐traded funds.

By highlighting the general theme of the related research findings, the paper provides a systematic review of the existing literature that future researchers can utilize in developing their research agenda.

The paper seeks to investigate decision‐making processes within hospital improvement activity, to understand how performance measurement systems influence decisions and potentially lead to unsuccessful or unsustainable process changes.

A longitudinal study over a 33‐month period investigates key events, decisions and outcomes at one medium‐sized hospital in the UK. Process improvement events are monitored using process control methods and by direct observation. The authors took a systems perspective of the health‐care processes, ensuring that the impacts of decisions across the health‐care supply chain were appropriately interpreted.

The research uncovers the ways in which measurement systems disguise failed decisions and encourage managers to take a low‐risk approach of “symptomatic relief” when trying to improve performance metrics. This prevents many managers from trying higher risk, sustainable process improvement changes. The behaviour of the health‐care system is not understood by many managers and this leads to poor analysis of problem situations.

Measurement using time‐series methodologies, such as statistical process control are vital for a better understanding of the systems impact of changes. Senior managers must also be aware of the behavioural influence of similar performance measurement systems that discourage sustainable improvement. There is a risk that such experiences will tarnish the reputation of performance management as a discipline.

Recommends process control measures as a way of creating an organization memory of how decisions affect performance – something that is currently lacking.

The purpose of this study is to examine the influence of virtual currency (VC) development on financial stocks’ value in selected Asian equity markets and the moderating role of investor attention on this relationship.

The pooled ordinary least squares regression is used on a sample of 138 listed financial firms from four emerging Asian countries for the period 2016–2020.

This study finds that changes in VC values have greater spillover effects on the values of financial stocks in countries which do not recognize the legitimacy of VCs than in countries which do, due to the lack of breadth and depth of the former markets. Moreover, this paper also reports evidence of the greater moderating role of investor attention on this relationship in countries which do not recognize the legitimacy of VCs than in countries which do.

Although numerous studies have been conducted on the influence of VCs on stock performance, majority of these studies did not distinguish whether the sample countries being studied actually recognize the legitimacy of VC transactions or not. Moreover, extant literature has not considered the moderating role of investor attention on this relationship. It is the aim of this study to address these research voids by using a refined three-factor theory model of capital asset pricing model incorporating VCs to better represent stock performance in the digital economy era.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The author examine the performance of a number of high short interest stocks along with the prices of the GameStop stock and three major stock exchange indices, particularly for the period after the eruption of the Covid-19 crisis.

With the employment of the complexity–entropy causality plane approach, the author categorize the stock prices in terms of the level of informational efficiency.

The author reported that the efficiency level for the index of the high short interest stocks falls considerably, not only at the onset of the Covid-19 crisis but during the health crisis period at hand. This is translated into proof of less uncertainty in predicting the stock prices of these specific stocks. On the other hand, the GameStop prices exhibit the same behavior as those with the high short interest firms, but change considerably in the middle of the crisis. The reversal of the behavior, by obtaining higher informational efficiency levels, is attributed to the short squeeze frenzy that increased the price of the stock many times over. Among the stock market indices, the Dow Jones Industrial Average and the S&P 500 decreased their efficiency levels marginally, after the surge of the crisis, while the Russell 2000 index kept the level intact. The high and stable degree of randomness could be attributed to the measures taken concurrently by the Federal Reserve and the government immediately after the outbreak of the crisis.

This is one of the few studies that examine the impact of short selling behavior on the efficiency level of certain stocks' prices, particularly during the health public crisis. It provides an alternative approach to measuring quantitatively the degree of inefficiency and randomness.