A study of optimal portfolio selection using quadratic programming modelling: Evidence from Indian pharmaceutical industry during COVID-19 times

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically. Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio. Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks. Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model. Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks. © 2023 by Chetna and Dhiraj Sharma.

Investigation into the International Financial Reporting Standards 9 model flaws exposed during the pandemic

Orientation: Market events during the coronavirus disease 2019 (COVID-19) pandemic exposed flaws in the econometric models used to derive International Financial Reporting Standards (IFRS) 9 impairments. Models were unable to capture the level of government intervention or predict the economic recovery process because of the unprecedented nature of the pandemic. Research purpose: This study examines the causes of the challenges experienced with the IFRS 9 models during the pandemic and approaches to minimise this risk in the future. Motivation for the study: Structural correlation breaks forced banks to replace the IFRS 9 models with expert overlays or rapidly rebuild the models to reduce impairment volatility and manage the impact on earnings. Expert judgement may lead to biased outcomes. Research approach/design and method: Behavioural finance theory suggests that emotion and cognitive biases often lead to irrational investment decisions with disastrous consequences to the market. The link between market sentiment and economic outcomes is tested with natural language processing. Archimedean copulas are used to compare the dependence structures of market variables between different stress periods. Main findings: Market sentiment is closely related to the trends observed in major macroeconomic indicators. The nature of the dependence structures differs between stress periods. Practical/managerial implications: Sentiment may be a valuable exogenous variable to incorporate into economic forecast models. Learnings from one stress period are not necessarily applicable to another. Contribution/value-add: Government intervention and market sentiment had a significant impact on the economic outcomes and correlation breaks observed during the pandemic. Developing bespoke models for the different phases of the economic cycle may not necessarily lead to improved outcomes.

Predicting the antecedents of consumers' intention toward purchase of mutual funds: A hybrid PLS‐SEM‐neural network approach

The current study intends to identify the behavioural antecedents of investors' attitude and investment intention toward mutual funds using a robust SEM‐ANN approach. It focuses on novel factors in the purview of the COVID‐19 pandemic, increasing digitalization and social media usage. The research outcome indicates that attitude (ATB), awareness (AW) and investment decision involvement (IDI) have a significant positive relation with investment intention (BI). In contrast, perceived barrier (PBR) negatively relates to investment intention. Herd behaviour (HB) and social media influence (SMI) do not influence investment intention toward mutual funds. Moreover, all the tested predictors share direct relation with the attitude toward mutual fund investment, barring perceived risk (PR), which has an inverse relationship. As per the outcome of ANN sensitivity analysis, attitude is the most crucial determinant of investment intention. It is followed by awareness (AW), perceived barriers (PBR) and investment decision involvement (IDI). Among the significant determinants of attitude, self‐efficacy (SE) is the most important determinant, followed by perceived usefulness (PU), perceived emergency (PEMER), subjective norms (SN) and perceived risk (PR).

Behavioural finance and cryptocurrencies

Purpose>The present study sets out to examine the empirical literature on the behavioural aspects of cryptocurrencies, showing the findings of related studies and discussing the various results. A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effects.Design/methodology/approach>Systematic literature review.Findings>A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment (investor's, market's) and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effect.Originality/value>The authors' survey paper complements recent papers in the area by offering a systematic account on the influence of behavioural factors on cryptocurrencies. Further, this study's purpose is not just to index the relevant literature, but rather to showcase and pinpoint several research areas that have emerged in the field of behavioural cryptocurrency research. For all these reasons, a systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important.

Personality Traits and Investment in the New Normal

Are all the investors the same or does each investor exhibit a different personality trait? Investing is as old as human civilization itself and is always focused on getting higher returns. Rationality is always a prerequisite for making a profitable and meaningful investment decision, but it is constantly challenged by Behavioral Finance. There is a characteristic that makes each one a unique investor - it's one's Personality. The reasoning and judgment in investment decision-making are constantly inhibited by the investors' personality. The COVID-19 crisis has changed people's lives drastically and everyone is adapting to a new normal. What's remarkable in the new normal is how the investors are finding new ways of meeting and fulfilling their investment needs. Empirical research methodology has been used for this study and both primary and secondary sources of information have been extensively analysed. The five major personality traits considered herein are extraversion, agreeableness, conscientiousness, openness to experience and neuroticism. The primary data has been collected using a structured online questionnaire;the sample size consists of 385 individual investors. The Chi- square test has been used to analyse the data and to find out the significance of association between the personality traits and the individual investment decisions based on risk preference, in the new normal. The findings of the study and the conclusion will provide an insight into how investors' personality traits could be employed to enhance their investment experience and welfare.

Predicting the antecedents of consumers' intention toward purchase of mutual funds: A hybrid PLS-SEM-neural network approach

The current study intends to identify the behavioural antecedents of investors' attitude and investment intention toward mutual funds using a robust SEM-ANN approach. It focuses on novel factors in the purview of the COVID-19 pandemic, increasing digitalization and social media usage. The research outcome indicates that attitude (ATB), awareness (AW) and investment decision involvement (IDI) have a significant positive relation with investment intention (BI). In contrast, perceived barrier (PBR) negatively relates to investment intention. Herd behaviour (HB) and social media influence (SMI) do not influence investment intention toward mutual funds. Moreover, all the tested predictors share direct relation with the attitude toward mutual fund investment, barring perceived risk (PR), which has an inverse relationship. As per the outcome of ANN sensitivity analysis, attitude is the most crucial determinant of investment intention. It is followed by awareness (AW), perceived barriers (PBR) and investment decision involvement (IDI). Among the significant determinants of attitude, self-efficacy (SE) is the most important determinant, followed by perceived usefulness (PU), perceived emergency (PEMER), subjective norms (SN) and perceived risk (PR). © 2022 John Wiley & Sons Ltd.

How do investors price accrual risk during crises?

Under rational asset pricing theory, and in efficient, frictionless market, risk should be priced contemporaneously and, thus, the market meltdown during the COVID-19 pandemic must have been a contingent valuation of newly created risk. In contrast, we find that the reduction in equity value during the pandemic was stronger for stocks with higher pre-pandemic accrued risk. This lends support to the discrete pricing proposition, which is a form of behavioural bias where investors price accrued risk during significant corporate or macroeconomic events. Furthermore, we compare the pricing of accrued risk during the pandemic with the pricing of accrued risk during non-pandemic events and during past financial crises. We report evidence that pricing of accrued risk results in a premium in normal times and a discount during financial turmoil. Finally, we report evidence that investors price accrued stocks discriminately, that is, they are more likely to price accrued risk of stocks of larger firms, smaller B/M, and weaker momentum. Several theoretical and practical implications are discussed inside the paper.

The wisdom of the Twitter crowd in the stock market: Evidence from a fragile state

The classical finance theory postulates that markets are informationally efficient and that the actions of arbitrageurs always bring stock prices to their correct values. Behavioural finance, on the other hand, emphasises the role of investor sentiment in the formulation of asset prices. In this study, we provide insights into the relationship between textual sentiment extracted from Twitter and stock returns in the fragile market of Zimbabwe between 24 February 2019 and 22 June 2020. Wavelet analysis is used to find the linkages between sentiment and returns in a frequency-time domain. The results from this study show that coherence is persistent and significant in highly volatile periods characterised by increasing inflation as well as during the time COVID-19 was declared a global pandemic. The findings also show that macroeconomic instability, especially hyperinflation, induces fear in investors while the onslaught of black swan events like the COVID-19 pandemic leads to greed in the financial markets as investors become uncertain about the future. The government could, therefore, prioritise macroeconomic stability as the high coherence between sentiment and returns during a crisis like the COVID-19 pandemic may lead to a crashing of the stock market. Classical finance theory, therefore, falls short in explaining the stock market returns as the evidence in the study shows that investors are susceptible to investor sentiment.

COVID-19 LED TO PRICE SLUMPS IN THE GERMAN STOCK MARKET. IS SENTIMENT APPLICABLE AS AN EXPLANATORY FACTOR?

Explaining and forecasting returns and other statistical moments of returns in the stock market have always been critical challenges. Recent studies postulate a relation between investor sentiment and future stock market returns. Supported by evidence from other countries, this study explores the statistical moments of stock returns in Germany and analyses to what extent an explanation can be found through investor sentiment. The recent COVID-19 induced market distortions provide an opportunity to investigate the suitability of predictive sentiment-based analyses. These are presented in this study and appear to be meaningful. The main concept behind the sentiment-based return explanation is built on the assumption that stock returns are linked to investor psychology. This theory often serves as an explanation for market movements that cannot be explained by fundamental data which are directly linked to stocks. However, the extraction of various sentiment proxies for further analysis in statistical models remains challenging. Problems occur because sentiment proxies do not have a constant influence and depend greatly on what currently drives the market. Furthermore, the correlation between sentiment indicators varies over time, especially in times of market distress. In this study, 73 sentiment indicators were examined in the aggregate with regard to the explainability of future stock market return distribution moments such as mean, variance, skewness, and kurtosis. This study examines 169 one-month periods from 2006 to 2020 and shows a potential solution to these challenges by applying a neural network based on long short-term memory (LSTM) neurons. The authors were able to identify a good model fit and reasonable forecasting power, which seem to work particularly well in trend forecasting. The results can be valuable in the area of portfolio risk management.

Industry-Level Stock Returns Response to COVID-19 News

The COVID-19 pandemic has impacted the stock markets of many countries. Understanding the impact of this pandemic on industries is an important and relevant basis for a thorough explanation of stock market movements during this period. The aim of this study is to examine how stock returns of non-financial sectors in Vietnamfs stock market react to information about the COVID-19 pandemic. The event study method is applied to analyze three main events related to the emergence and outbreak of this pandemic in Vietnam in 2020. The first event (January 23, 2020) and the second event (March 6, 2020), respectively, were the time when Vietnam officially announced that it had recorded the first case positive for COVID-19 in the Hochiminh city and Hanoi. The third event is on March 30, 2020, Vietnam announced that it will apply a blockade order in all provinces and cities nationwide to limit the outbreak of this pandemic. Closing price data from January 1, 2019 to April 14, 2020 for five industry indexes (Basic Materials, Consumer Goods, Consumer Services, Industry and Utilities), used in this study. The results show that the stock prices of all five sectors reacted in the same meaningful direction (negative/positive) after the event that Vietnam confirmed the first patient confirmed with COVID-19 in Hochiminh city and the nationwide blockade event was announced, proving that the stock market is affected by psychology. In industries, Industry and Consumer Services are the two sectors that respond the most to events, but Basic materials are the least affected. The study found that the Consumer Goods industry had the most positive results in the five industries for the following two events;The Utilities industry reacted negatively to the first information that could create potential risks of a COVID-19 outbreak in the community, especially in the two major economic centers of Vietnam. Conclusions from this study show that Vietnamfs stock market is inefficient, research results and insights on industry responses to disease information contribute to strategic planning for policymakers and investors in the future. © Phuong L. C.M., 2022.

Time-varying herding spillover for emerging countries: evidence from BRIC countries and Turkey

Purpose This study aims to investigate herding spillover in BRIC (Brazil, Russia, India and China) countries and Turkey under different regimes by using a time-varying approach. Design/methodology/approach The authors used the structural change model of Bai and Perron (1998). Findings The results indicate that there is an evidence of herding behaviour in the Chinese stock market in two different regimes. These regimes cover the recent global financial crisis and the period of Hong Kong protests. We also report the evidence of herding behaviour in the Turkish stock market in the regime covering the COVID-19 period. Findings of herding spillover show that there is a two-way herding among Russia and China during crises and high volatile regimes. Similarly, there exists a cross-country herding among Brazil and India during crisis regimes. Also, there is herding spillover from Turkey to Russia, China and Brazil during the global financial crisis, post-European debt crisis and COVID-19 periods respectively. Furthermore, it is also evident that there is a herding spillover from Russia and China to India during the period covering COVID-19. Originality/value To the best of the authors' knowledge, this is the first study that uses structural change approach to identify herding behaviour spillovers from the US stock market to BRIC countries and Turkey and to investigate the cross-country herding behaviour among BRIC countries and Turkey.

Investment strategies in pandemic situations: an analysis and comparison of prospective returns between developed and emerging markets

This study empirically analyzes return data from developed and emerging markets to assess whether emerging markets show superior performance during the COVID-19 pandemic in terms of cost of equity. It analyses panel data from eight country indices of developed and emerging countries as well as eight exemplary companies from developed and emerging countries, covering the period from 2000 to 2020. The results provide evidence that emerging markets do not perform in a better way than developed markets. The findings highlight the need for a reassessment of the generalized notion that emerging markets are more profitable than developed markets in such crises which affect the core of their economic structure. It provides investors with meaningful advice on the creation of an investment strategy if they wish to perform equity investments in similar periods like the COVID-19 pandemic. The study contributes to the literature by advancing this research area and is the first study which analyzes and compares the cost of equity of developed and emerging markets during the COVID-19 pandemic.