Suboptimal Investment Research Articles

UNLOCKING THE INVESTMENT PUZZLE: THE INFLUENCE OF BEHAVIORAL BIASES & MODERATING ROLE OF FINANCIAL LITERACY

This study aims to investigate the influence of behavioral biases on investors' investment decisions and explore the moderating role of financial literacy in this relationship. Research adopts quantitative approach, utilizing a survey questionnaire distributed among a sample of individual investors actively trading in Pakistan stock exchange. Survey assesses prominent behavioral biases like overconfidence, disposition effect, risk-aversion bias, and herding behavior, while measuring participants' level of financial literacy. Findings of study proves significant impact of behavioral biases on investors' decisions. Respondents validate prevalence of shared biases, including overconfidence, disposition effect, risk-aversion bias, and herding behavior. These biases tend to influence the decision-making process of investors, leading to suboptimal investment choices. Results suggest that financial literacy plays moderating role in linking behavioral biases and investment decisions. The implications of findings suggest importance of promoting financial literacy amid investors over guidance and support of financial professionals, ultimately improving quality of investment decision-making.

The Role of Behavioral Bias in Investment Outcomes

Investment decisions are made by individuals who are subject to behavioral biases. The biases could lead to a significant impact on the outcome of investments. This research paper aims to investigate the role of behavioral biases in investment decisions and outcome. The study draws on the theories of Rational Behavioral Finance, which recognizes that human behavior is influenced by both rational and irrational factors. The paper reviews several key behavioral biases that can affect investment decisions, including loss aversion, endowment bias, framing, overconfidence, and the illusion of control. The analysis of these biases is based on the context of different investment settings, including stock markets, mutual funds, and real estate. The paper introduces the significance of each bias, and how they can lead to suboptimal investment decisions. The latter part of the paper highlights how the biases can be addressed to improve the outcome of investments. Eventually, the research paper concludes that behavioral biases can significantly impact investment outcomes, but the application of Rational Behavioral Finance theories and strategies can mitigate the impact of these biases.

Identifying the Factors Influencing IPO Underpricing using Explainable Machine Learning Techniques

We study the underpricing phenomenon in the U.S. Initial Public Offering (IPO) market using an interpretative machine learning approach. Inadequate conventional modelling of IPOs efficiency can lead to suboptimal investment decisions and a poor understanding of underlying factors that drive IPO underpricing. We employ interpretative machine learning for both predicting the numeric underpricing levels and classifying IPOs into underpriced and overpriced categories. We use the SHapley Additive exPlanations method to provide concise insights into the underlying factors that contribute to underpricing. Our numerical study reveals that offer price has the most predictive power for the models’ output, followed by equity retained and assets. Additionally, underpricing is more pronounced in technology-based sectors, and a higher dispersion in quality among firms during IPO surges results in higher levels of underpricing. Our analyses emphasize the importance of considering both the industry sector and market conditions when evaluating the firms going public.

Coordination of care is facilitated by delayed feeding and collective arrivals in the long-tailed tit

When multiple carers invest in a shared brood, there is likely to be conflict among individuals over how much each carer invests. This conflict results in suboptimal investment to the detriment of all carers. It has been proposed that conditional cooperation, that is, ‘turn taking’ or ‘alternation’, may resolve this conflict by preventing exploitation. This contentious idea has received some empirical support, but distinguishing active alternation from that expected via passive processes has proved challenging. The aim of this study was to use detailed observations of provisioning to examine whether carers at biparental (parents only) and cooperative (parents and helpers) nests of the long-tailed tit, Aegithalos caudatus, behave in a context-dependent manner that increases the level of alternation. First, we show that carers that had been the last to feed waited near the nest (loitering) for longer before feeding when they next arrived at the nest and allowed others to feed first, thus facilitating alternation. Second, we found that the arrival of carers near the nest and their subsequent feeds were tightly synchronized, with overlapping loitering periods, allowing them to monitor the effort of other carers. Finally, we show that measures of coordination were influenced by carers arriving in a status-dependent order, with breeding females consistently arriving first and helpers last. Together, these results show how patterns of alternation and synchrony arise in long-tailed tits, and reveal the behavioural mechanisms underpinning coordination of care.

Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood.

Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond ("bad stock") or expected value greater than the bond ("good stock"). Five measures of behavior (choice inflexibility, risk seeking, suboptimal investment) and beliefs (absolute error, optimism) were computed and D2-like binding potential was extracted from four brain regions of interest (midbrain, amygdala, anterior cingulate, insula). We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension (r = 0.76) revealed that the strongest associations were between measures of choice inflexibility, incorrect choice, optimism, amygdala binding potential, and age. Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.

Quantitative models in asset management: A review of efficacy and limitations

This review provides a comprehensive overview of the efficacy and limitations associated with quantitative models in the field of asset management. Over the past few decades, the financial industry has witnessed a significant shift towards algorithmic and data-driven approaches in investment decision-making. Quantitative models, ranging from traditional risk-return frameworks to sophisticated machine learning algorithms, play a crucial role in shaping investment strategies and portfolio management. The review begins by examining the strengths of quantitative models, highlighting their ability to process vast amounts of financial data efficiently and objectively. These models enable investors to make data-driven decisions, manage risk, and optimize portfolio allocations. Furthermore, quantitative models facilitate the identification of patterns and trends in market behavior, allowing for timely adjustments to investment strategies. However, the efficacy of quantitative models is not without its limitations. The review explores challenges such as model risk, data quality issues, and the inherent complexity of financial markets. Model risk refers to the possibility of errors or inaccuracies in the mathematical models used, leading to suboptimal investment decisions. Additionally, the reliance on historical data assumes that future market conditions will resemble the past, a presumption that may be invalidated during unforeseen events or structural shifts. The review also delves into the ongoing debate surrounding the balance between human expertise and algorithmic decision-making. While quantitative models offer objectivity and systematic processes, the human element remains crucial for interpreting results, adapting to dynamic market conditions, and exercising judgment in situations where models may fall short. In conclusion, this review provides valuable insights into the evolving landscape of asset management through the lens of quantitative models. Recognizing their efficacy in processing vast datasets and identifying patterns, it also underscores the importance of acknowledging and addressing the limitations inherent in these models. Achieving a harmonious integration of human judgment and quantitative methodologies is crucial for enhancing the overall effectiveness of asset management strategies in a rapidly changing financial environment.

Strategic deviation and investment inefficiency

We examine the association between strategic deviation and investment inefficiency. We conceptualize strategic deviation as the extent to which the pattern of a firm’s resource allocation deviates from its industry peers. We posit that firms pursuing deviant strategies are prone to increased information asymmetry and hence, are able to engage in self-serving behaviour as manifested in inefficient investments. Our results suggest that deviant firms have sub-optimal investments. A battery of robustness tests validates our findings. We further provide evidence to suggest that weaker monitoring, high product market competition and a low-quality information environment moderate the relation between strategic deviation and investment inefficiency. JEL Classification: M41, G41

Overcoming home- and overconfidence bias: an effective guideline for private investors based on meta-analysis

Private investors’ underperformance compared to institutional investors is attributed to a combination of factors, including home bias and overconfidence bias. Home bias refers to the tendency of private investors to overinvest in their domestic markets, which can result in missed opportunities for diversification and exposure to international markets. Institutional investors are less likely to exhibit this bias as they have the resources and expertise to invest globally. Overconfidence bias, on the other hand, refers to private investors’ tendency to believe they have an informational advantage and can outperform the market. This can lead to excessive trading and suboptimal investment decisions. Institutional investors, with their experience and disciplined investment processes, are less likely to fall prey to overconfidence bias. Together, these biases contribute to the underperformance of private investors compared to institutional investors. The following abstract presents four strategies to overcome home- and overconfidence bias derived from the insights of this literature reviewed meta analysis.

Sub-optimal Investments in Public Health: A Case for Persisting Poverty

Investment in Human capital is one of the most important variables that determine long run economic growth of an economy, even more so for low income, capital deficient, labour abundant economies. In the context of additions to value added, endowments of education and health of workers take prominence. This paper theoretically and empirically shows that suboptimal investments in health care lead to slowing down of economic growth in the long run and causes poverty and inequality to persist. This persistence of poverty can be despite rapid rates of economic growth in the short run. The paper uses endogenous growth models to argue that investments in health are necessary for long run, sustainable economic growth. It also uses secondary data to draw an overview of the public health scenario in India, bringing to fore the inadequacies of the structure and the need for a greater and more dedicated intervention by the state.

Optimal asset allocation for commodity sovereign wealth funds

This paper studies the dynamic asset allocation problem faced by an infinitively lived commodity-based sovereign wealth fund under incomplete markets. Assuming that the fund receives a non-tradable stream of commodity revenues until a predetermined date, the optimal consumption and investment strategies are state and time-dependent. Using data from the Norwegian Petroleum Fund, we find that the optimal demand for equity should decrease gradually from 60% to 40% over the next 60 years. However, the solution is particularly sensitive to the correlation between oil and stock price changes. We also estimate wealth-equivalent welfare losses, relative to the optimal rule, when following alternative suboptimal investment rules.

Interdependent investments in attached and movable assets under insecure land rights

The literature concludes that insecure land rights cause farms to make suboptimal capital investment in attached assets, but not in movable assets, implicitly assuming attached and movable assets to be independent. However, the two asset types can be interdependent because the operational efficiency of movable assets, such as farm machinery, typically depends on field conditions, such as land fragmentation. Thus, investment in attached assets may affect investment in movable assets via farm infrastructure. I develop a conceptual model to explain why tenure insecurity may lead a farm to under-invest in attached assets and over-invest in low-efficiency movable assets relative to the secure-land-right scenario. To quantify the economic significance of investment inefficiency, I collect unique survey data on large-scale farms, for which machinery is of particular importance in production, in Southwest China where land tenure remained insecure in 2016. Simulations based on the large-farm survey data suggest that the suboptimal investment occurs given fairly small probabilities of losing some farmland and results in considerable economic losses. The findings have important policy implications regarding land reforms and farm infrastructure in developing economies.

The Information-Leveling Role of Management Forecast Consistency in Facilitating Investment Efficiency

ABSTRACT This study examines whether voluntary disclosure enhances investment efficiency through its information-leveling role in the capital markets. We argue that investment efficiency improves with the quality of managers’ past earnings forecasts because past forecast quality alleviates information frictions that inhibit firm access to investment capital. Our empirical results are consistent with this argument. To start, we document a baseline positive association between management forecast consistency and investment efficiency. Building on this result, we find that the consistency effect strengthens when cross-sectional attributes indicate higher information frictions and when industry-wide accounting quality is negatively shocked. In addition, we find that consistency effects are stronger for financially constrained firms and that consistency is associated with lower bid-ask spreads and positive changes in equity issuance. Overall, we show that building a reputation for high-quality management earnings forecasts can help firms overcome information frictions that contribute to suboptimal investment.

Private benefits of corporate philanthropy and distortions to corporate financing and investment decisions

AbstractResearch Question/IssueThis study examines whether private benefits of control, as measured by corporate philanthropic activities, distort corporate financing and investment decisions.Research Findings/InsightsWe find that corporate giving represents a private benefit of control that distorts corporate investment and financing activity, consistent with free cash flow agency theory. Corporate giving discourages managers from pursuing external financing, especially debt issuance, to minimize outside monitoring. It creates preferences for internally financed cash acquisitions for the same reason. These distortions reduce shareholder wealth. Following both the 2003 dividend tax cut and hedge fund activism, corporate charitable contributions fall while investment rises, suggesting suboptimal investment caused by managerial private benefit extraction. Acquisition announcements of firms making large charitable contributions show negative stock market reactions that are more pronounced for acquirers with poor corporate governance.Theoretical/Academic ImplicationsOur findings suggest that due to agency problems and managerial rent extraction, corporate philanthropic activities can distort corporate financing and investment decisions and yield corporate charitable contribution allocations that personally benefit a firm's CEO at shareholder expense.Practitioner/Policy ImplicationsAs corporate philanthropy imposes a significant cost to shareholders, managers may seek shareholder approval on corporate philanthropic activities. The Securities and Exchange Commission may also require timely disclosure of corporate donations.

Policy suggestions from a simple framework with extreme outcomes

The experience of financial crises in recent decades has reinforced the critical need for economic policy to anticipate and reduce the impact of unexpected, extreme, events. In this paper, we develop a simple framework based on latent regime shifts to analyze why investors may undertake overly risky investment strategies and how policymakers may attempt to constrain such behavior. This framework explains, in particular, why banks, investors, and policymakers may decide not to hedge against extreme events, even when those events are exogenously determined and have well understood probabilities and consequences. We also examine cases in which the extreme events are endogenously created by the investment strategies themselves. Our main finding is that the private costs of the sustained suboptimal investment may be bounded and small. Thus, investors may knowingly ignore or exacerbate the likelihood of extreme events, especially if they face costs to learning the structure of the financial environment in which the events are created. These results obtain both in the theoretical model and upon calibration to the US economic experience, including the recent financial crisis. Our findings provide a strong motivation for policymakers to ensure full disclosure and dissemination of information regarding probabilities and consequences of extreme outcomes.

Economic analysis of vehicle infrastructure cooperation for driving automation

The current approach to driving automation has been primarily vehicle-centric. However, a vehicle-infrastructure cooperative approach, in which infrastructure and vehicles cooperate to perform the different driving tasks, may prevail in enabling automated driving. This paper conducts an economic analysis of vehicle infrastructure cooperation for automated driving. In doing so, we present a model that captures investment decisions in vehicle automation and infrastructure digitalization and their effect on travelers’ purchase and travel decisions. Our analysis shows that, under certain conditions, equipping both infrastructure and vehicles is socially optimal. However, by analyzing strategic interactions between infrastructure support service providers and automakers, we show that lack of coordination between these two actors results in suboptimal investment in vehicle automation and infrastructure digitalization. Especially, when these two technologies are complementary, service providers are reluctant to invest in digital infrastructure and vehicle manufacturers tend to over equip their vehicles so as to avoid relying on infrastructure technology. Thus, we conclude by showing that better coordination between automakers and service providers – under the form of profit sharing – is welfare-improving and could potentially yield the socially optimal levels of automation and digitalization.

The Valuation of Illiquid Assets: A Focus on Private Equity and Real Estate

This article reviews methods that can be used to value illiquid investments, focusing on private equity and real estate. The authors discuss the traditional valuation methods, particularly the net present value (NPV) rule, and show in what circumstances these can lead to suboptimal investment decisions. Emphasis is placed on a real option valuation framework that can alleviate some of the drawbacks of traditional approaches. A new jump-diffusion option pricing model and a numerical example are presented to show the usefulness of such a framework in valuing illiquid assets, in particular venture capital funds. They maintain that, under harmonized parameter estimation techniques, the NPV rule can be viewed as the lower bound in a competitive market and the real option exercise criteria and valuation models as providing the upper bound to value and time investments in illiquid assets. The knowledge of such bounds should enhance the decision-making process concerning allocating funds to alternative investments.

Can Technology Solve the Principal-Agent Problem? Evidence from China’s War on Air Pollution

We examine the introduction of automatic air pollution monitoring to counter suspected tampering at the local level, a central feature of China’s “war on pollution.” Exploiting 654 regression discontinuity designs based on city-level variation in the day that monitoring was automated, we find an immediate and lasting increase of 35 percent in reported PM10 concentrations post-automation. Moreover, automation’s introduction increased online searches for face masks and air filters, which are strong predictors of purchases. Overall, our findings suggest that the biased and imperfect information prior to automation led to suboptimal investments in defensive measures, plausibly imposing meaningful welfare costs. (JEL D82, O13, P28, Q53, Q55, Q58)

Are Investment Opportunities Associated with Dividend Reduction?: Evidence from Korea

Purpose This paper examines associations among firm dividend reductions, investment opportunities(IOS), and share repurchases(SRP). Also, I examine the association between these factors and future operating performance. Design/Methodology/Approach This paper uses un-balanced panel data of 21,453 firm-year observations of non-financial Korean listed firms from 2003 to 2018. Findings Empirical evidence suggests that firms with higher IOS are more likely to reduce dividends to retain internally generated capital to prevent sub-optimal investments, while SRPs have a substitution effect on dividends in the dividend reducing context, and the effect is more pronounced with a less flexible SRP method. Also, dividend reductions associated with higher IOS are likely followed by enhanced operating performance. Additional tests suggest that the signaling effect of SRP varies according to the IOS among dividend-reducing entities over the long-run horizon. Research Implications Dividend reductions have been considered a ‘last resort’ for corporate managers, and thus manager reluctance to reduce dividends is taken for granted. However, there are firms reducing dividends despite good operating performance. This study provides a clue of such a phenomenon in practice.

International portfolio choice under multi-factor stochastic volatility

In this article, we develop an identifiable multi-factor stochastic volatility model for international portfolio choice problems in complete and incomplete markets. Allowing for stochastic covariance between financial asset returns and foreign exchange rates, optimal investment strategies are derived in closed form and welfare losses arising from suboptimal investment strategies are analysed. Moreover, we provide a two-step procedure for estimating as well as calibrating the model parameters and use this ansatz to illustrate optimal investment decisions for the S&P 500, the German blue chip index DAX, and the USD/EUR foreign exchange rate. We find, both theoretically and empirically, that the model satisfies various well-known stylized facts of equity and foreign exchange rate markets and that investors who invest myopically or ignore derivative assets can incur substantial welfare losses implying strong evidence for significant welfare benefits from international diversification across different asset classes.

Stochastic Planning of Integrated Energy System via Frank-Copula Function and Scenario Reduction

Uncertainty introduces both significant complexity and the high risk of suboptimal investment decisions into power system planning. Considering the multiple uncertainties of wind and solar power output, load demands, and energy prices as well as pollutant emission factors during the planning period, a multi-scenario stochastic programming model of an integrated energy system (IES) is constructed in this paper. Scenarios of wind and solar power output are generated based on non-parametric kernel density estimation and the Frank-Copula function; scenarios of load demands are generated through DeST software, and energy prices and pollutant emission factors are generated corresponding to a uniform distribution. Then the generated scenario results of wind and solar power output and load demands are reduced by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -means clustering; the generated scenarios of energy prices and pollutant emission factors are reduced by discrete approximation of continuous distribution based on Gaussian quadrature. An illustrative example with 8 cases is performed to analyze the influences of each uncertain parameter on the optimal configuration and economy of the IES.