Risk-averse Investors Research Articles

Tracking Smart Beta indices during different market phases: A “smarter” option for passive investors?

Purpose This study aims to examine whether tracking Smart Beta (SB) indices during bullish, bearish and stagnant market phases is a better choice for passive investors compared to Cap-Weighted (CW) indices. As investors’ strategies differ with market movements, this study analyses how single-factor and multi-factor SB indices perform during different market phases, in relation to CW indices. It also attempts to determine which SB factors are more suitable for investors in these phases. Design/methodology/approach Using various return and risk indicators, this study analyses how SB indices perform vis-à-vis CW indices during bullish, bearish and stagnant phases. The authors also evaluate the upside and downside participation advantage of SB indices and assess their ability to capture upside returns and limit downside risk. The authors attempt to determine the cyclical or defensive nature of SB indices using Average Participation values. Findings This study found that SB indices outperform CW indices during the bearish and stagnant phases. Multi-factor SB indices have lower risk levels in all market phases, providing downside protection to risk-averse investors. Dividend, Low Volatility, Quality and multi-factor SB indices are defensive portfolios offering better payoffs during the down market phases, while Alpha, Beta, Equal Weight and Value SB indices provide higher payoffs during the up-market phases. Originality/value To the best of the authors’ knowledge, this is the first study that examines the performance of single-factor and multi-factor Indian SB indices in different market phases. It determines the suitability of various factors to passive investors during these phases and also identifies whether SB indices are cyclical or defensive.

Exploring information demand in the maritime industry: a Google Trends approach

ABSTRACT The fundamental impetus for this research is the documented wide use of Google and the ensuing applications of Google Trends in behavioural economics. Expanding the existing literature, we focus on addressing the question of whether information demand from individual investors can reflect a freight rates sentiment. This is achieved by analysing Google queries referring to investment in maritime stocks and other related markets. These searches comprise essentially a blend of information and intuition. More specifically, we examine whether the most frequently searched term and topic referred to Baltic Dry Index (BDI) can serve as a reliable and valid positive or negative sentiment market proxies. We find a negative impact on freight rates, capital markets, and the number of available dry bulk fleet, providing evidence aligned with the risk-averse investor theory. We unveil bidirectional interactions between our sentiment index and the two out of three examined maritime markets, supporting the rejection of the efficient market hypothesis. Our study also verifies the similar effects of information demand and media-produced sentiment indexes on the corresponding market. We succeed in confirming sentiment’s predictability out-of-sample for BDI. Beyond the academic value, our findings suggest a manner of enhancing the accuracy of freight rates predictions for practitioners.

Perception of Women Towards Investment in Different Investment Alternatives

Women are empowered to take active participation in investment activities through financial education and an inclusive investment environment. Several alternatives to investing are available, including mutual funds, real estate, precious metals, savings accounts, and stocks, which offer potential returns through regular income or capital appreciation. Investment alternatives are differentiated by their unique features, risks, returns, and ease of management. Investors, before investing, must take these features into account in relation to their risk tolerance, goals, and time horizon when considering any particular alternative. As women are recognized for making risk-averse investment decisions, their investments are impacted by numerous factors. The primary goal of this study is to understand the investment perception of women towards different investment alternatives. While the main factors influencing investment decisions are return, risk, and liquidity, several other factors specifically influence women investors, such as social and cultural norms, peers, and family members. Primary data was collected through a well-structured questionnaire consisting of Likert scale questions. SPSS software was used for data analysis. The initial results of the survey showed that 70% of the respondents had an average understanding of different investment options, whereas only 5% had high knowledge of different investment alternatives. When it comes to interest in a particular investment, mutual funds are the most preferred alternative. Additionally, 72% of women respondents show a moderate level of risk tolerance among all respondents in the study.

Stock Returns and Volatility: Comparative Investigation from SAARC Countries

This research article analyses the stock return and volatility of three selected SAARC countries (Pakistan, India, and Bangladesh), focusing on the role of natural resources in influencing market performance. This study aims to determine the robustness of the performance, considering the economic significance of these countries' natural resources. The daily data from the PSX 100 index (Pakistan Stock Exchange), BSE index (Bombay Stock Exchange), and DSE index (Dhaka Stock Exchange) for the years 2019 to 2021 has been obtained from their websites. The data is treated in MS Excel to compute the daily Geometric Returns for BSE, PSX, and DSE indices. Furthermore, the data were analyzed using the specialized package EViews for Time Series Graph, Descriptive Statistics, Unit Root Test, and GARCH Model. The findings of this research show that the BSE index has the highest return value of 6.78 percent compared with 4.56 percent and 3.75 percent for DSE and PSX, respectively. The Half-life model has been applied to calculate the volatility observed by the indices. The result shows that BSE is the most volatile and takes the highest time of 45 days to revert to its mean position, followed by PSX and DSE, which take 19 and 10 days, respectively. Therefore, investing in the BSE index is better for risk-lover investors. At the same time, DSE has the fastest mean reversion compared to the other two indices. Therefore, it is feasible for risk-averse investors.

Unveiling asymmetric return spillovers with portfolio implications among Indian stock sectors during Covid-19 pandemic

This paper aims to provide a systematic inquiry into the return spillover dynamics between a network of Indian sectoral indices during the pre- and post-pandemic periods. To analyze the same, this paper uses the asymmetric time-varying parameter vector autoregressions (TVP-VAR) framework. Furthermore, in the spirit of Broadstock et al. (2020), we perform dynamic portfolio exercises based on common hedging techniques and the minimum connectedness portfolio approach to determine what better captures asymmetry. Our daily dataset includes 12 sectoral stocks spanning from January 01, 2017, to May 5, 2023. The findings reveal that negative connectedness dominates throughout the sample period, demonstrating that profit-maximizing agents and risk-averse investors are more likely to react negatively to news. We also show that in the network, the average net transmitters are the banking and other financial service sectors, whereas the net receivers are the information technology, pharmaceutical, and fast-moving consumer goods sectors throughout the period under consideration. Our results show that the minimum connectedness portfolio (MCoP) approach is a very useful method based on Sharpe ratios, as it is either the first or second most profitable among these three competing methods. These results, therefore, yield valuable insights for policymakers and investors

Risk and Return Analysis of Stocks of Nepalese Commercial Banks

This research article analyzes the impact of risk and return of Nepalese commercial bank stocks on portfolio returns to assist investors in making informed investment decisions. It aims to examine the effects of individual bank stock returns, risks, market return, and market risk (beta) on overall portfolio performance. The study utilizes a quantitative approach, employing descriptive statistics, comparative (trend) analysis, correlation analysis, and hypothesis testing. Secondary data was collected from 18 commercial banks in Nepal, including post-merger banks, covering the fiscal years from 2015/16 to 2021/22. Statistical tools such as SPSS and EViews were used for analysis. The findings indicate that investors seeking higher returns might consider Prabhu Bank, which offers high mean returns but also comes with higher volatility. In contrast, risk-averse investors may prefer banks like NMB and Nabil, which provide lower mean returns but more stable performance. Notably, this research contributes to the understanding of the risk-return relationship in the context of 18 Nepalese commercial banks post-merger, providing new insights for investors. This study highlights the importance of analyzing individual bank stocks and their associated risks for portfolio construction. By understanding the dynamics of risk and return, investors can optimize their investment strategies and enhance portfolio performance. Overall, this research fills a gap in existing literature by focusing on the Nepalese commercial banking sector and its implications for investor decision-making. The insights gained from this study can guide investors in navigating the complexities of the stock market, ultimately leading to more informed and strategic investment choices. Future research could expand on this work by exploring additional factors influencing stock performance and investor behavior in the Nepalese context.

Monetary policy and equity returns: The role of investor risk aversion

AbstractThis article examines the role of investor risk aversion in the transmission of monetary policy to stock returns based on U.S. data. Our results show that following an expansionary monetary policy shock, investor risk aversion falls, leading to a decrease in the equity risk premium and an increase in equity returns. Moreover, the returns of high‐beta stocks increase much more than those of low‐beta stocks. Finally, we investigate the mechanism through mutual fund flows. We find that high‐beta funds attract greater inflows in response to lower interest rates, and there is a positive relationship between fund returns and flows. Our findings have policy implications for financial stability.

Investment in CCUS under technical uncertainty considering investor's risk aversion: An exotic compound real-options approach

Carbon capture, utilization, and storage (CCUS) technology is effective and value-adding solution for reducing emissions. However, the development and commercialization of these technologies are challenging due to high investment costs and several uncertainties. This study develops a novel comprehensive real-options-based model to evaluate investment in CCUS projects considering the technical risk and the investor's risk aversion. This study proposed an exotic compound real options model that combines American and barrier options. First, applying the Poison process, the technical risk is explicitly modeled. Secondly, the investor's risk aversion is defined as a barrier level for the barrier option part of the proposed model. Thirdly, the value of the project is evaluated through the exotic compound real option. Finally, we assess the economic viability of the project under multiple scenarios. The results of implementing the model for a real case show that the integrated technical risk assessment and the barrier option appropriately address investors' risk aversion. Furthermore, the comparison indicates that the proposed compound real options model is more effective than the traditional NPV (Net Present Value). Regarding policymaking, the results reveal that setting an appropriate carbon tax that considers the costs of carbon capture would be more beneficial. Further, the model provides investors helpful guidance to make proper investment decisions for CCUS technology projects under uncertainties.

Research on the Investment Portfolio in Technology and Consumer Goods Industries

This research investigates investment portfolios in the U.S. technology and consumer goods sectors, employing Modern Portfolio Theory (MPT) and the Markowitz Model to analyze and compare their risk-return profiles. The study highlights that the technology sector demonstrates a high-risk and high-return profile, making it attractive for risk-seeking investors aiming for significant gains amidst market volatility. In contrast, the consumer goods sector, with lower volatility, appeals to risk-averse investors who prefer stable and predictable returns. Key findings strongly emphasize the importance of aligning investment choices with individual investor preferences, illustrating how specific industry characteristics can influence investment strategies. The research suggests that investment success is considerably enhanced when portfolios are tailored to match individual risk tolerance and investment goals. This strategic approach provides a robust framework for guiding investors to make informed decisions that reflect their personal preferences and financial objectives, thereby helping them navigate market complexities and enhance portfolio performance.

Right and Duty: Investment Risk Under Different Renewable Energy Support Policies

AbstractRenewable energy projects are subject to risk due to the uncertainty of future electricity prices and the amount of energy produced. Public support usually consists of some form of guarantee, either on the price received per MWh supplied or the return on investment per MW installed. This support reduces the investor’s risk, which is then assumed by the regulator. However, most of these policies do not only grant the investor the right to receive a guaranteed payment but also limit its potential benefits (i.e., impose an obligation). We propose a model with analytical solutions in which, considering the randomness of the market price, as well as that of the energy production, we quantify the risk removed under different types of regulations and assign a value to both the rights and the obligations that each policy entails. Finally, we apply the model to the case of Spain, which has undergone several changes in its green energy support system in recent years. Our results indicate that in the context of low electricity prices, the obligations imposed by most of these policies are negligible compared to the rights received. By contrast, in the context of high electricity prices and increasingly competitive renewable energy sources, the assumed obligations become more notable to the point of the support policy becoming a liability. Nevertheless, a sufficiently risk-averse investor may be incentivized by a policy with negative expected regulatory costs.

Analisis Pembentukan Portofolio Optimal pada Indeks Saham LQ-45 dengan Metode Safety First Criterion

An optimal portfolio of stocks is a combination of various stock investment assets chosen to provide the maximum level of return for a specified level of risk or provide a minimal level of risk for a specified level of return. Investors form an optimal stock portfolio with the aim of minimizing the risk of investment activities. This research discusses the formation of the optimal portfolio on LQ-45 index stocks with the Safety First Criterion method. There are three criteria in the Safety First method, namely Roy Safety First, Kataoka Safety First, and Telser Safety First. The three criteria of Safety First have the main similarity in focusing on investment risk and have different objectives. The optimal portfolio with Roy Safety First criteria aims to reduce the possibility of a high level of risk. Then, the optimal portfolio with Kataoka Safety First criteria, has the goal of maximizing returns, with a level of risk determined by investors. While the optimal portfolio of Telser Safety First criteria aims to achieve the highest expected return within a predetermined risk level. The data in this study are secondary data on the weekly closing price of the LQ-45 index for the period February 2021 to January 2023, which is 105 weeks. Based on the results of the analysis, the optimal portfolio formation for risk-loving investors is the Telser criteria portfolio. This portfolio consists of ADRO, BBNI, BMRI, ITMG, and MEDC stocks. Then, the optimal portfolio for risk-averse investors is the Roy criteria portfolio consisting of ADRO, BBNI, BMRI, MDKA, and MEDC stocks.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Unpacking the financial attributes of blue-chip non-fungible tokens (NFTs) against traditional and digital assets

PurposeThis study aims to identify the financial attributes of non-fungible tokens (NFTs) as safe havens, hedges or diversifiers against traditional (stock indices, foreign exchange, gold and government bonds) and digital (Bitcoin and Ethereum) assets.Design/methodology/approachThe quantile via moments was utilized, and the data spanned from 20 September 2021 to 31 January 2022. The authors incorporated feasible generalized least squares (FGLS) and difference-generalized method of moments (diff-GMM) as the robustness check.FindingsOverall, NFTs offer strongly safe havens, hedging and diversifier attributes against cryptocurrencies, while weak properties for traditional assets. The specific findings are: (1) Bored Ape Yacht Club (BAYC) serves as a strong hedge for Bitcoin during market rise; (2) Mutant Ape Yacht Club (MAYC) serves as a strong safe haven against Bitcoin during market bull; (3) Crypto punk (CP) provides strong safe havens properties for gold during market turmoil while serving as a strong hedge against gold and Bitcoin on average and (4) the three blue-chip NFTs are powered by Ethereum blockchain, thus serving as a diversifier against Ethereum.Practical implicationsBitcoin investors are suggested to include NFTs in their investment portfolio to mitigate the losses when Bitcoin falls. Meanwhile, the inclusion of crypto punk is advised for risk-averse investors who invest in gold. NFTs are powered by the Ethereum blockchain, indicating co-movement among them and thus, serve as diversifiers. Policymakers and regulators are suggested to watch closely over NFTs' great development and restructure the existing policies and thus, stabilization of asset markets can be achieved.Originality/valueThe originality aspects are: (1) focusing on the three blue-chip NFTs (i.e. BAYC, MAYC and CP) that are categorized as the largest NFTs by floor market capitalization; (2) testing the NFT attributes (safe havens, hedges or diversifiers) against traditional and digital assets, a.k.a., cryptocurrencies and (3) panel setting on 14 countries with the highest NFT users.

Analysis of ESG information-disclosure strategies considering different risk-appetite investors

In response to the investment market and public, over 1,000 Chinese listed companies have started to provide environment, society, and governance (ESG) information of their own accord till 2022. How to develop an effective ESG information disclosure mechanism is important for providers of reliable external financing. Here, we designed a risk-management model to describe providers’ information disclosure strategy decisions under different risk-appetite investors, and analyse how information effectiveness, the information sensitivity and risk aversion of investors, and rating agency accuracy affect optimal ESG information-disclosure strategies. We found that: (1) Providers adopt appropriate under-disclosure strategies that can reduce their information collection and disclosure costs, thus maximising the benefits of ESG information disclosure; (2) Investors’ information sensitivity and risk aversion will affect the space for enterprises to adopt under-disclosure strategies; (3) More professional rating agencies can provide stronger credit backing for ESG information, prompting providers to reduce disclosure information.

The effects of policy uncertainty and risk aversion on carbon capture, utilization, and storage investments

Many model-based decarbonization pathways include substantial carbon capture, utilization, and storage (CCUS), and engineering cost estimates suggest that these technologies should be profitable to deploy under current incentives, such as the 45Q tax credits in the United States. However, CCUS investments have been slow to materialize. In this paper, we investigate the extent to which policy uncertainty and investor risk aversion may explain the limited buildout of CCUS infrastructure to date. To do so, we develop a two-stage stochastic programming model for optimal CCUS infrastructure network design that incorporates policy uncertainty and risk aversion as novel features. We then apply it to an empirically parameterized case study of the Texas-Louisiana Gulf Coast region. Our results show that the current 45Q incentive levels make a fair number of CCUS projects profitable even if they were to be discontinued after ten years. Moreover, while future policy uncertainty and risk aversion reduce CCUS development, the maximum effect size is only a 16% decline in expected total CO2 captured. We find policy uncertainty plays a more important role than investor risk aversion in the development of CCUS infrastructure, with increased uncertainty leading to reduced CO2 capture and investment.

Measuring Portfolio Gains: The Case of Earnings Announcement Trading Signals

ABSTRACT This study offers solutions to help address practical issues researchers generally overlook when assessing gains from a trading signal. Specifically, the methodologies used in previous research generally ignore investor risk aversion when forming portfolios, do not update portfolios as signals arrive, exploit look-ahead biases, do not assess the incremental gains of a new signal, and do not consider market frictions. We examine trading signals based on post-earnings announcement drift (PEAD), the earnings announcement premium (EAP), and earnings announcement rescheduling (RES). Using our proposed approach, we find that portfolios that incorporate the individual signals produce higher Sharpe ratios than equal-weighted portfolios; however, the gains for each signal are concentrated to a few days around the announcement. The EAP and RES signals do not provide incremental portfolio gains over the PEAD signal. After considering market frictions, portfolio performance rapidly attenuates and becomes similar to the SPY ETF as the portfolio size increases. JEL Classifications: G12; G14; G17.

Risks to Human Capital

We build a model with inalienable human capital, in which investors finance individuals who can potentially become skilled. Although investment in skill is always optimal, it does not take place in some states of the world, due to moral hazard. In intermediate states of the world, individuals acquire skill; however outside investors and individuals inefficiently share risk. We show that this simple moral hazard problem, combined with risk aversion of individuals and outside investors, amplifies the equity premium, lowers the risk-free rate, and leads to disaster states that fall especially heavily on some agents but not on others. We show that the possibility of disaster states distorts risk prices and affects wealth inequality, even under calibrations in which they never occur in equilibrium. This paper was accepted by Lukas Schmid, finance. Funding: M. Ebrahimian acknowledges funding from the Swedish House of Finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03068 .

The factors influencing the selection of fund management companies by Malaysian retail investors in the context of islamic unit trust funds

The purpose of this study is to analyze the impact of fund management companies' past performance, fees, perceived religiosity, and perceived risk on Malaysian Muslim investors' attitude toward them. The moderating effects of investor risk aversion, price sensitivity, religiosity, and Islamic financial literacy are considered. A model based on the unified theory of acceptance and use of technology is built to analyze the impact of the variables considered. The study data were collected using a survey of 550 Malaysian retail investors. Our findings show that Muslim investor attitudes are negatively affected by the perceived risk of a fund management company and positively affected by a fund management company's perceived religiosity and past performance. Moreover, the perceived religiosity impact is positively moderated by an investor's Islamic financial literacy and negatively moderated by the investor's price sensitivity. Practical implications of the results and avenues for further investigation in future research are discussed.

Ranking Investment Preferences in India's Dynamic Market: From SIPs to ETFs

This study examines the Indian stock market using the Weighted Point Method (WPM) to evaluate various investment options. The research focuses on five key investment vehicles: Exchange-Traded Funds (ETFs), Government Savings Schemes, Bonds and Debentures, Systematic Investment Plans (SIPs), and Fixed Deposits (FDs). These options were assessed across four critical indicators: Economic Indicators, Corporate Earnings, Market Sentiment, and Sectoral Analysis. The WPM analysis revealed that Systematic Investment Plans (SIPs) emerged as the most preferred investment option, achieving the highest preference score of 0.91795. SIPs demonstrated consistently strong performance across all evaluated criteria, suggesting they offer a balanced approach to investing with potential for steady growth and effective risk management. Bonds and Debentures ranked second with a preference score of 0.72492, showing particular strength in Market Sentiment. Fixed Deposits (FDs) secured the third position with a score of 0.64406, highlighting their continued relevance in the Indian investment landscape, especially for risk-averse investors. Government Savings Schemes and ETFs ranked fourth and fifth respectively, each showing strengths in specific areas. The study underscores the complex and dynamic nature of the Indian stock market, emphasizing the importance of considering multiple factors in investment decision-making. It highlights the value of quantitative methods like WPM in providing a structured approach to comparing diverse investment options. The findings suggest that while SIPs appear to be the most attractive option overall, the Indian market offers a range of investment vehicles catering to different risk appetites and financial goals. The research concludes that investors should consider their personal objectives, risk tolerance, and investment horizon when making decisions, and regularly review their strategies in light of evolving market conditions and economic indicators.

Risk-averse Reinforcement Learning for Portfolio Optimization

This investigation explores Reinforcement Learning (RL) for dynamic portfolio optimization with risk assessment. The challenges include market complexity, uncertain reactions, and regulatory requirements for risk-averse decisions. Our solution leverages Bayesian Neural Network (BNN) to capture uncertainties. We successfully implemented a risk-averse Reinforcement Learning algorithm, achieving 18 percent lower risk. Reinforcement Learning with risk-aversion shows promise for optimizing portfolios for risk-averse investors.