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Financial systemic risk is often assessed by the interconnectedness of financial institutes (FI) in terms of cross-ownership, overlapping investment portfolios, interbank credit exposures, etc. Less is known about the interconnectedness between FIs through the lens of consumer credits. Using detailed consumer credit data in Canada, this chapter constructs a novel banking network to measure FIs’ interconnectedness in the consumer credit markets. Results show that FIs on average are more connected to each other over the sample period, with the interconnectedness measure increases by 19% from 2013 Q4 to 2019 Q4. FIs with more diversified portfolios are more connected in the network. Among various types of FIs, secondary FIs have the notable increase in interconnectedness. Domestic Systemically Important Banks and secondary FIs offering a broad range of loan products are more connected to large FIs, while those specialized in single loan types are more connected to their industry peers. FI connectedness is also significantly related to their participation in the mortgage markets.

The purpose of this paper is to analyze the trading behaviors of retail investors and investigate their impacts on stock returns.

As retail investors are considered as the main noise traders in the capital market, using the trading records of Chinese retail investors from 2005 to 2009, the authors study their trading preferences and the correlation of their trades. Then, they use a multifactor model to test whether the co‐movement of stock returns could be explained by individual sentiment.

The authors' results show that the small‐cap stocks are obviously preferred by retail investors. Meanwhile, the net stock demands of retail investors are systematically correlated, even when the effect of market risk is excluded. In the perspective of the net stock demands, the authors use BSI to measure the individual sentiment, finding that individual sentiment plays an important role in the formation of the cross‐section of stock returns. However, the authors' results imply that BSI is a reverse indicator to predict the future returns, which implies that the trading behaviors of retail investors are irrational.

Consistent with behavioral theory, the authors' findings support the viewpoint that stock returns could be affected by the systematic correlated trading of retail investors. To some extent, their findings highlight the need to know more details of individual investors' trading behaviors through which the fluctuations of asset prices can be better understood.

This paper aims to examine how various academic disciplines shape the field of event portfolio management. Given the complex nature of portfolios comprising different genres that are studied separately from their respective disciplinary realms, the academic event portfolio landscape remains fragmented. This is against the nature of portfolios, which requires inter-disciplinarity and novel integration of genres, stakeholders and perspectives.

Based on a scoping literature review, this conceptual paper sets up a common ground for the academic study and industrial development of event portfolio management.

A comprehensive view of event portfolio literature across disciplines reveals its hypostasis as a compound transdisciplinary field. The authors suggest a set of foundational premises whereby they identify 22 principal thematic areas that comprise this emerging field.

The establishment of event portfolio management as a distinct field will help in the osmosis and diffusion of new ideas, models and best practices to run and leverage portfolios. The portfolio perspective highlights the need for cohesive learning to design comprehensive systems of events, implement joint strategies, solidify social networks, coordinate multiple stakeholders and develop methods of holistic evaluation.

By examining comprehensively event portfolio management as a transdisciplinary field, the authors have been able to identify principal research directions and priorities. This comprehensive analysis provides a synergistic ground, which at this embryonic stage of development, can be used to set out joint trajectories and reciprocal foci across the whole span of scholarship studying planned series of events.

The purpose of this paper is to analyze long-term prior return patterns in stock returns for India.

The methodology involves portfolio generation based on company characteristics and long-term prior return (24-60 months). The characteristic sorted portfolios are then regressed on risk factors using one factor (capital asset pricing model (CAPM)) and multi-factor model (Fama-French (FF) model and four factor model involving three FF factors and an additional sectoral momentum factor).

After controlling for short-term momentum (up to 12 months) as documented by Sehgal and Jain (2011), the authors observe that weak reversals emerge for the sample stocks. The risk model CAPM fails to account for these long-run prior return patterns. FF three-factor model is able to explain long-term prior return patterns in stock returns with the exception of 36-12-12 strategy. The value factor plays an important role while the size factor does not explain cross-section of average returns. Momentum patterns exist in long-term sector returns, which are stronger for long-term portfolio formation periods. Further, the authors construct sector factor and observe that prior returns patterns in stock returns are partially absorbed by this factor.

The findings are relevant for investment analysts and portfolio managers who are continuously tracking global markets, including India, in pursuit of extra normal returns.

The study contributes to the asset pricing and behavioral literature from emerging markets.

The purpose of this paper is to compare the risk and return characteristics as well as the allocation of mean‐variance (MV) and downside risk (DR) optimized portfolios of international real estate stock markets and to discuss implications for portfolio management.

The analysis focuses on real estate markets only and examines the appropriateness of the Markowitz approach based on MV optimization in comparison to the DR framework suggested by Estrada. Therefore, the two frameworks are presented before the properties of the return distributions are analyzed. Afterwards, the risk and return characteristics as well as the allocation of the efficient portfolios in both frameworks and the divergences are analyzed.

Because of non‐normally distributed returns, negative skewness, and probably non‐quadratic utility functions of investors, MV optimization is not appropriate and the alternative approach by Estrada has its merit compared with other DR frameworks. Furthermore, MV‐efficient and DR‐efficient portfolio allocation differ, as shown by a similarity index. Summarizing, MV optimization is inherent with misleading results and DR optimization shows stronger out‐of‐sample performance – at least during time periods characterized by high market volatility and financial market turmoil.

This study provides some interesting and valuable insights into the DR of international securitized real estate portfolios and the limitations for portfolio management based on MV optimization.

Traditionally, the measure of risk used in portfolio optimisation models is the variance. However, alternative measures of risk have many theoretical and practical advantages and it is peculiar therefore that they are not used more frequently. This may be because of the difficulty in deciding which measure of risk is best and any attempt to compare different risk measures may be a futile exercise until a common risk measure can be identified. To overcome this, another approach is considered, comparing the portfolio holdings produced by different risk measures, rather than the risk return trade‐off. In this way we can see whether the risk measures used produce asset allocations that are essentially the same or very different. The results indicate that the portfolio compositions produced by different risk measures vary quite markedly from measure to measure. These findings have a practical consequence for the investor or fund manager because they suggest that the choice of model depends very much on the individual's attitude to risk rather than any theoretical and/or practical advantages of one model over another.

The purpose of this paper is to investigate whether the special items (SI) mispricing reported in Burgstahler et al. is distinct from the accruals (ACC) mispricing documented in Sloan.

This paper employs the control hedge‐portfolio test, non‐overlap hedge‐portfolio test, and regression analysis to determine whether the SI anomaly is distinct from the ACC anomaly. In addition, the Mishkin test is used to examine the impact of SI on the ACC anomaly.

This paper has four main findings. First, one‐year‐ahead abnormal returns to the special‐items‐based hedge portfolio are much diminished when holding ACC constant, whereas those to the ACC‐based hedge portfolio remain significantly positive when holding SI constant. Second, the special‐items‐based hedge portfolio loses much of its ability to earn future abnormal returns without the help of extreme ACC, whereas the ACC‐based hedge portfolio remains profitable without the help of extreme SI. Third, SI are no longer negatively associated with future abnormal returns after controlling for ACC, whereas ACC remain negatively associated with future abnormal returns after controlling for SI. Finally, SI affect the extent to which the market overprices ACC, with negative (positive) SI aggravating (alleviating) ACC overpricing.

This is the first paper to show that the SI anomaly is dependent on the ACC anomaly.

I am interested in clarifying the discussion of how researchers might try to isolate real option effects to identify whether managerial decisions are guided by a real option heuristic. If we are to claim that the theory of real options illuminates managerial behavior, then as a field, we must converge on an understanding as to what constitutes a real option effect, and what does not. The discussion centers on hypothesis development, measurement issues, and research methodology.

Firms frequently need to update their capabilities in changing environments but face significant barriers to accomplish this goal due to the stickiness of their routines, local search constraints, bounded rationality, uncertain imitability, and causal ambiguity. Under high levels of uncertainty, dynamic capabilities are often externally oriented, involving acquisitions and alliances. However, nonunique but competitive predictions about the behavior of these capabilities arise from the evolutionary theory. We test these competitive hypotheses analyzing portfolios of acquisitions and alliances made by pharmaceutical firms in search of portfolios of biotech capabilities. The analysis of portfolios enables us to better identify “common practices” in the pharmaceutical industry than using a transactional‐level focus. We develop implications for the evolutionary theory and for managerial practice.

In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.