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This paper computes the pricing errors of S&P 500 index by employing the valuation model developed by Doran et al. (2009) and investigates its response to individual and institutional investor sentiments. This study contributes to the literature by looking at both rational and quasi-rational sentiments and how noise trading and investments based on fundamentals affect pricing errors.

This paper computes the pricing errors of S&P 500 index by employing the valuation model developed by Doran et al. (2009) and investigates its response to individual and institutional investor sentiments.

Results show that pricing errors are persistent and stock prices systematically deviate from their intrinsic values. The authors also find that both individuals and institutional investors form their expectations based on risk factors as well as noise; however, institutional investors seems to be more driven by rational factors. The findings also suggest that institutional investors have a significant power to cause pricing errors due to unpredictable changes in their sentiments while small investors lack such ability to move stock prices away from their intrinsic values. Additionally, this paper finds that quasi-rational (rational) investor sentiments have positive (negative) impact on pricing errors suggesting that trading based on noise is an important determinant of pricing errors while investors' expectations stemming from fundamentals play an important role in improving market efficiency.

The impact of rational outlook due to changes in fundamentals seems to be greater than that of noise on the pricing errors, consistent with both risk-based and behavioral models of the asset pricing literature.

Our study contributes to the existing literature in the following ways: first, the authors employ most recent data to compute mispricing for the market index and investigate if it is persistent and systematic. Second, the authors decompose sentiment variables into rational and quasi-rational components and trace their dynamics to better understand the role of risk factors and noise in the formation of sentiments. Third, the authors investigate the relative impact of individual and institutional investor sentiments on mispricing. Lastly, the authors examine the response of pricing errors to both rational and quasi-rational sentiments of individual and institutional investors.

The purpose of this paper is to investigate the existence of behavioral biases, disposition effect and house money effect in investment decisions of defined benefit pension funds. It investigates the determinants of portfolios by examining whether pensions display risk seeking or risk aversion behavior in reaction to prior gains and losses.

The first research question is to examine the impact of prior period’s return and αs on existing portfolio allocation in equity, debt, real estate and other assets. In order to test this relationship, four separate regressions are estimated using the pooled data. Regression helps in examining the relationship between prior gains with current allocation in four categories of assets of varying degrees of riskiness (stocks, debt, real estate and other assets). In order to investigate the second research question on whether pension funds increase (decrease) their investments in risky (safer) assets due to prior gains and αs, the four variables representing the changes in portfolio allocation for each asset class over one period are employed. These changes in allocation are regressed against the prior year’s actual return, expected return, αs and a set of control variables.

The results suggest significant negative (positive) relationship between prior positive returns and αs with portfolio allocation in risky (safer) assets. Also, there is an increased (decreased) investment in safer (risky) assets following prior period’s positive returns and αs. The findings confirm the existence of disposition effect, while there is no evidence of house money effect.

The portfolio allocation of pension plans provides unique setting to investigate the relevance of behavioral finance and examine the role of psychological biases on risk taking. This study attempts to contribute to the literature by empirically investigating whether the tenets of behavioral finance are relevant in defined benefit pension fund’s portfolio allocation decisions. Specifically, it focuses on the determinants of portfolio choices by directly investigating pension funds’ reaction to prior period’s actual as well as risk adjusted return (or αs – the difference between the actual and expected return).

Investors’ fear can be rational, emanating from the natural dynamics of economic fundamentals, or it can be quasi rational and not attributable to any known risk factors. Using VIX from Chicago Board Options Exchange as a proxy for investors’ fear, the purpose of this paper is to consider the following research questions: to what extent does noise play a role in the formation of investors’ fear? To what extent is the impact of fear on S&P 500 index returns driven by rational reactions to new information vs fear induced by noise in stock market returns? To what extent do S&P 500 index returns display asymmetric behavior in response to investor’s rational and quasi rational fear?

In a two-step process, the authors first decompose investors’ fear into its rational and irrational components by generating two additional variables representing fear induced by rational expectations and fear due to noise. The authors then estimate a three-vector autoregression (VAR) model to examine their relative impact on S&P 500 returns.

Impulse responses generated from a 13-variable VAR model show that investors’ fear is driven by risk factors to some extent, and this extent is well captured by the Fama and French three-factor and the Carhart four-factor models. Specifically, investors’ fear is negatively related to the market risk premium, negatively related to the premium between value and growth stocks, and positively related to momentum. The magnitude and duration of the impact of the market risk premium is almost twice that of the impact of the premium on value stocks and the momentum of investors’ fear. However, almost 90 percent of the movement in investors’ fear is not attributable to the 12 risk factors chosen in this study and thus may be largely irrational in nature. The impulse responses suggest that both rational and irrational fear have significant negative effects on market returns. Moreover, the effects are asymmetric on S&P 500 index returns wherein irrational upturns in fear have a greater impact than downturns. In addition, the component of investors’ fear driven by irrationality or noise has more than twice the impact on market returns in terms of magnitude and duration than the impact of the rational component of investors’ fear.

The results are consistent with the view that one of the most important drivers of stock market returns is irrational fear that is not rooted in economic fundamentals.

The purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments.

The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact).

These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

The learning outcomes are as follows: to benchmark and compare the theoretical models of the performance management and appraisal processes. (Questions 1 and 2) Remembering-in Bloom’s Taxonomy; to understand the importance of practicing fair performance appraisal process. (Question 4) Understanding-in Bloom’s Taxonomy; to analyze the implementation and effectiveness of 180-degree performance appraisal method and rating system prevalent in the IT Sector. (Question 1) Applying and Analyzing-in Bloom’s Taxonomy; to assess the impact of perceptual biases on human behavior and performance (Questions 2 and 3) Evaluating-in Bloom’s Taxonomy.

The case study entitled “Is HR Blind? Why do People Leave Managers Not Companies? A Case of Unfair Performance Appraisal and Biases” is a classic example of a flawed and biased performance appraisal process and perceptual biasness, which resulted in the loss of a valuable and talented resource in a leading Indian IT MNC. The present case had been based upon the real-life experience of an employee (i.e. Rahul Verma), who worked with the company from year 2010 to 2021. It was among the top ten IT MNCs employing about 0.1 million people. The objective of the case was to highlight real time issues existing with HR practices, mainly in IT sector organizations. For example, in the present case, do the HR seek proper justification from the manager before taking a harsh decision like forcibly asking an employee to sign a termination contract without looking at the contributions of his qualitative performance or even performance rating (refer to the transcript) for that matter? Was the job of the HR to only ensure how to fit in employees in the faulty bell curve system? Whether the performance appraisal system being followed at the company is adequately capable of identifying and recognizing the talent. Do the different functions really work cohesively and organically toward achieving the intended goals and objectives of the organization? Was this a failure of the manager in recognizing talent or something went wrong at the employee’s part? Was this a failure of the entire HR system or performance management process at the organization that was unable to filter out the capable and skilled resources out of the crowd? Was this a problem of organizational culture that put on stake its most critical resource – the human capital – by allowing the appraisers to evaluate them just because of the hierarchical structure, and not because they are not being competent enough to perform this most critical job objectively? Who ensures the appraiser is free from any kind of prejudice or bias and is capable of fairly assessing the talent resource? So, the present case was a deliberate attempt to throw out these burning questions to the practitioners and students to ponder upon. Does HR really follow the blind process merely acting on the feedback received from the different units of the organization?

With the help of strong theoretical foundation and practical applications, the following objectives and questions have been framed to deliberate and propose the workable solutions for the benefits of the relevant stakeholders.

HR practitioners, HR managers, supervisors, senior management and HR students, IT heads, project managers.

Teaching notes are available for educators only.

CSS 6: Human Resource Management.

The purpose of this paper is to investigate the forecasting power of the conditional relationship between beta and international stock returns.

Using the market model, the individual betas for each country in the sample are estimated by ordinary least square. The conditional relation between beta and return is tested by estimating cross‐sectional regressions for each month (i.e. 343 cross‐sectional regressions). Mean value of coefficients and t‐statistics (one tail) are computed to test whether the mean values of coefficients are significantly positive and negative. Whether there is a systematic relationship between the bull market and the bear market is also tested.

Overall, no support of the model was found. Although positive, there is insignificant relationship between beta of current period when excess market return is positive with next period stock return. Moreover, although negative, there was insignificant relationship between beta of current period when excess market return in negative with next period stock return. Similar results were found when the sample was divided into January and non‐January months.

This contribution is to test the validity of conditional relationship between beta and stock returns in international setting by considering the effect of current period up‐ and down‐markets on the next period stock return. If the conditional version of capital asset pricing model (CAPM) holds, there must be a consistent result, i.e. similar to those obtained by the studies using contemporaneous relationship. It is important to perform such tests on the conditional version of CAPM since an ex post state dependent model may not be used as a forecasting model.

Terrorism, an important component of Political risk as a possible determinant of ADRs (American Depository Receipts) returns have received little attention in academic literature. To address this issue and examine whether political risk is a major determinant of ADR returns of emerging market countries, this paper empirically examines market valuation of Indian ADRs around acts of terrorism. Using a sample of 52 such events in the sample period Jan 2003‐Dec 2003 we empirically analyze returns of Indian ADRs. The results from our study indicate a marginally negative significant effect, failing to indicate that event of terrorist attacks severely affect the Indian ADRs listed on the US stock market. This may be explained by a combined effect of; (a) the optimism of US investors towards emerging markets, and (b) market participants becoming more resilient and making informed choices around the “general” events of terrorism.

After completion of the case study, the students will be able to understand the different risks associated with a business, focusing on price risk and the importance of price risk management in business; understand and evaluate the products available for hedging price risk through exchange-traded derivatives in the Indian scenario; and understand and evaluate the different strategies for price risk management through exchange-traded derivatives in the Indian scenario.

The case study pertains to a small business, M/s Sethi Jewellers. The enterprise is being run by Shri Charan Jeet Sethi and his son Tejinder Sethi. The business is located in Jain Bazar, Jammu, UT, in Northern India. The business was started in 1972 by Charan Jeet’s father. They deal in a wide range of jewelry products and are well-established jewelers known for selling quality ornaments. Tejinder (MBA in marketing) was instrumental in revamping his business recently. Under his leadership, the business has experienced rapid transformation. The business has grown from a one-room shop fully managed by Tejinder’s grandfather to a multistory showroom with several artisans, sales staff and security persons. Through his e-store, Tejinder has a bulk order from a client where the client requires him to accept the order with a small token at the current price and deliver the final product three months from now. Tejinder is in a dilemma about accepting or rejecting the large order. Second, if he accepts, should he buy the entire gold now or wait to buy it later at a lower price? He is also considering hedging the price risk through exchange-traded derivatives. However, he is not entirely sure, as he has a few apprehensions regarding the same, and he is also not fully aware of the process and the instruments he has to use for hedging the price risk on the exchange.

The case study is aimed to cater to undergraduate, postgraduate and MBA students in the field of finance. This case study can be used for students interested in commodity derivatives, risk management and market microstructure.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

The purpose of this paper is to quantify and evaluate transport sustainability from an integrated perspective. It is argued that transportation systems being interdisciplinary and complex, the integrated approach towards quantification and evaluation would present its sustainability goals’ attainment in a much better way.

The proposed integrated methodology is divided into five major steps that are sub-divided. It adopts a three-tier classification system i.e. pillar, sub-division and indicator. In total, 116 sustainable transportation indicators (STIs) were classified into 10 subdivisions corresponding to three sustainability pillars. The weighting and aggregation of STIs were done using a combination of a statistical tool called principal component analysis/factor analysis, a machine learning tool called fuzzy logic and equal weighting method. The final output that the proposed integrated methodology generates is a new composite index called the integrated sustainable transportation index (IIST).

To check the credibility of the proposed integrated methodology, it was applied to 26 states and the National Capital Territory (NCT) of Delhi, India. Based on the computed IIST, 27 study zones were ranked and mapped. The results showed that while Tamil Nadu (0.643), Telangana (0.574) and Maharashtra (0.564) were the best-performing states, Goa (0.347), Chhattisgarh (0.436) and Meghalaya (0.439) were the worst-performing states, as far as the relative transportation sustainability is concerned.

The proposed integrated methodology could be useful for transportation evaluators, planners and policymakers who can recognize, track and improve the strengths and overcome the weaknesses within a transportation system.

The novelty of this research work is that it uses a multi-stage multi-tool approach to quantify and evaluate transport sustainability that minimizes the demerits of single-stage individual tools while maximizing their merits.

The purpose of this paper is to identify the barriers halting the usage of sustainable modes of transportation in a university campus.

Two large-scale universities, namely, Quaid-i-Azam University and the National University of Sciences and Technology, in Islamabad have been taken as case studies with a sample population of 421. The questionnaire data, comprising 30 different barriers, were analysed by principal component analysis and mean value method.

The findings indicate that safety and infrastructural factors are the most influential ones affecting sustainable mobility within the campus. The provision of user-friendly and safe infrastructure and more frequent shuttle buses are recommended to promote the culture of sustainable transportation.

Inclusion of other large-scale universities of the country might give more generalized results. The sustainability of other areas associating with transportation can also be evaluated to explore other unfamiliar dimensions.

The outcome of this research expects to highlight important policy recommendations for the promotion of sustainable mobility inside the campus and also serves as a guiding principle for campus developers and policymakers for planning new campuses in other parts of the country.

This research can be considered as a starting point for achieving sustainability in the universities of Pakistan. The same research methodology can be opted in other campuses with the possibility of the addition of other related factors with different statistical analysis techniques as well.