Psychological determinants of financial buffer saving: the influence of financial risk tolerance and regulatory focus

With governments redistributing more responsibilities unto citizens, individuals have an increasing need for financial resources acting as a buffer against life’s setbacks and unexpected expenditures. The purpose of this study was to examine psychological determinants of saving for a financial buffer, for which a theoretical model was formulated based on the theory of planned behaviour with three new, domain-specific psychological constructs: financial risk tolerance, regulatory focus and perceived saving barriers. Data were collected with an online questionnaire that utilised convenience and snowball sampling to target both students and working individuals (N = 272). Regression analyses offered support for the proposed model, showing that participants’ financial risk tolerance (i.e. an individual’s attitude towards financial risk taking) was significantly associated with their subjective financial knowledge and regulatory focus. Furthermore, perceived financial self-efficacy and financial risk tolerance both predicted participants’ intention to save for a financial buffer. In turn, perceived financial self-efficacy and saving intention predicted self-reported saving behaviour. Importantly, perceived saving barriers mediated the relationship between saving intention and self-reported saving behaviour. In line with the proposed model, results also showed that a specific attitude-based construct (financial risk tolerance) is a considerably better predictor of saving intention than general measures of attitude towards saving. This study is also the first to demonstrate that regulatory focus influences financial risk tolerance. Implications of these findings for stimulating saving behaviour are discussed.     

Financial risk tolerance revisited: the development of a risk assessment instrument☆

This paper explores conceptual, methodological, and empirical issues related to the development of a financial risk-tolerance assessment instrument. Financial risk tolerance is a significant factor in a number of household financial decisions, yet few recognized, valid, and reliable methods of assessment are available for use by financial service providers and educators. Empirical results from a multistage development of a 13-item risk assessment instrument are discussed. The multidimensional instrument is presented as the foundation for the development of a more widely used and accepted index. Future use by practitioners and researchers is encouraged to further validate the usefulness of the instrument.JEL classification:D81     

Team earnings forecasting

While brokerage houses use both teams of sell-side analysts and individual analysts to conduct earnings research, there is no empirical research examining whether teams and individuals differ with regard to their forecasting performance or purpose. We first examine the most-often researched dimension of forecasting performance, earnings forecast accuracy, and show that teams are less accurate than individual analysts in general and their own individual team members in particular. We conjecture that teams focus their efforts on an alternative dimension of forecasting performance, timeliness, and show that team forecasts are timelier than those of individual analysts in general and their own individual team members in particular. Consistent with the notion that teams trade-off forecast accuracy for timeliness to comply with a market research demand, we show that team forecast revisions are associated with larger market responses than those of individuals. Finally, we illuminate the nature of team assignments by documenting that the firms that teams follow are in greater financial distress and larger in size.     

Measuring executive personality using machine-learning algorithms: A new approach and audit fee-based validation tests

We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs’ and CFOs’ responses to questions raised by analysts during conference calls. We obtain the Big Five personality traits – openness, conscientiousness, extraversion, agreeableness and neuroticism – based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk-tolerance measure varies with existing inherent and behavioural-based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised-vested options) in predictable ways. Second, we show that variation in firm-year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk-tolerance – as an innate personality trait – on audit fees is incremental to the effect of increased risk appetite from equity risk-taking incentives (Vega). Measuring executive personality using machine-learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct.     

Risk preferences and the marketing of financial services: Segmentation by birth order

Traditional investment questionnaires may yield an incomplete measure of clients’ risk tolerance. Birth order has the potential to provide additional insight into the true nature of customers’ risk aversion, thereby assisting financial advisors to formulate the optimal investment portfolio for each client. We summarize research findings on birth order-related personality traits that have potential impact on the financial services industry. Marketing implications for investment firms are discussed in a framework that considers customers’ birth order differences in risk tolerance, patience, financial goals and conformity.     

The joint influence of financial risk perception and risk tolerance on individual investment decision‐making

The increasing complexity of the investment environment has accelerated the need for better quality financial advice services. Central to quality advice is advisers’ accurate assessment of their clients’ risk characteristics. Typically a client's risk characteristic is assessed by measuring the client's risk tolerance but not risk perception. To assess whether this practice fails to fully capture the client's risk profile, we explore both risk tolerance and risk perception in the investment decision‐making context. Using Australian online survey data of financial adviser clients (n = 364), our results reveal that risk tolerance influences risky‐asset allocation directly and indirectly through risk perception. These results thus clarify the joint role of both risk constructs in the investment making decision and highlight the importance of assessing both in the provision of client financial advice services. Importantly, our results validate a new comprehensive risk perception measure applicable in the financial advice context.     

Risk-based forecasting and planning and management earnings forecasts

This study examines the association between a firm’s internal information environment and the accuracy of its externally disclosed management earnings forecasts. Internally, firms use forecasts to plan for uncertain futures. The risk management literature argues that integrating risk-related information into forecasts and plans can improve a firm’s ability to forecast financial outcomes. We investigate whether this internal information manifests itself in the accuracy of external earnings guidance. Using detailed survey data and publicly disclosed management earnings forecasts from a sample of publicly traded U.S. companies, we find that more sophisticated risk-based forecasting and planning processes are associated with smaller earnings forecast errors and narrower forecast widths. These associations hold across a variety of different planning horizons (ranging from annual budgeting to long-term strategic planning), providing empirical support for the theoretical link between internal information quality and the quality of external disclosures.     

金融业不当行为风险致因与监管策略研究——基于文献综述与问卷调查实证

不当行为风险是金融业面临的一种新型风险,它源于组织机构及员工个体的不端行为,具有典型的负外部性;有效识别金融机构不当行为风险致因及其规律,是金融监管部门实施行为监管的重要前提。首先,基于文献综述,界定和梳理金融业不当行为风险的内涵边界、特征以及各类致因;其次,基于金融生态系统理论,构建起金融生态体系变量要素并分析其作用关系;再次,设计并向金融从业人员发放调查问卷,获取了514份有效问卷数据,对问卷及结果数据进行交叉分析和信度、效度检验,并运用频次分析、交叉分析等统计方法,发现和挖掘不当行为风险关键致因及其规律;最后提出不当行为风险监管策略建议,归纳出相关研究结论和展望。研究可为金融监管部门实施不当行为风险监管提供重要参考。     

Gender stereotyping in financial advisors’ assessment of customers

This article presents the results of a comparison of male and female advisors’ assessment of their customers. The findings from the empirical material, consisting of 361 advisors’ answers to a questionnaire, show significant evidence that advisors assess their customers differently depending not only on customer gender, but also according to their own gender. The investigated variables are the advisors’ assessment of consumers’ perception of their own risk tolerance, customer satisfaction with the advisor, customer trust in the advisor, customer likelihood to follow the advice given and advisors’ ratings of customer financial literacy. Male advisors rated consumers’ answers higher than did their female colleagues for all variables, with the exception of advisors’ ratings of consumer financial literacy. Advisors and their employers in the financial services industry, as well as policymakers, should be aware of the possible association between advisor gender and potential gender stereotyping of clients.     

Women are scaredy-cats and men are conquerors?

The European legal framework requires that financial consultants assess their clients’ risk tolerance before advising them on investments. The study aims at further testing an alternative measure to quantify the risk attitudes of investors: The Implicit Association Test (IAT). This study examines differences in implicit, unconscious, and explicit cognitive attitudes of men and women. The results of the IAT are compared with their answers from a risk tolerance questionnaire used by practitioners and self-selected asset allocation (portfolio). The data show that implicit attitude, and, thus, unconscious and uncontrolled thinking, can differ from conscious processes. Furthermore, in this study, there is no gender gap in risk attitude and risk tolerance. The findings suggest that experience and financial knowledge are likely the most important determinants of risk attitudes with no significant difference between men and women.     

Why are older investors less willing to take financial risks?

We investigate the link between age and tolerance of financial risks in the context of attitude to risk questionnaires completed by clients when meeting their financial advisors. Using a unique database comprising the responses to over half a million such questionnaires, we show that risk tolerance declines at an increasing, albeit slow, rate with age. We investigate the explanatory power of the ability to bear losses, declining investment horizon and retirement effects, finding that these variables have considerably greater explanatory power for the cross-section of risk aversion than age, and that they are only able to partially mediate the link between age and risk tolerance. We are unable to uncover any evidence that declining cognitive abilities among older investors are able to explain their lower willingness to take financial risks. Overall, our results are indicative of a modest age effect in risk tolerance that cannot be attributed to changes in other observable characteristics that differ between younger and older investors.     

Effects of market returns and market volatility on investor risk tolerance

The 2008 credit market debacle and subsequent “Great Recession” accompanied by the stock market crash of 2008 has caused many investors and their advisors to reevaluate their risk tolerance and investment asset allocation choices. Additionally, marketers for many financial institutions and investment advisors are rethinking the strategies and tactics they use for both individual and corporate clients about the level of risk that is appropriate to meet their investment objectives. This research shows that an investor’s risk tolerance is not as stable as it has been portrayed previously in the literature and can be affected by both the direction of movement and the volatility in the market. In addition, this research provides some suggestions on how to frame investment decisions for individual investors to better assess their actual risk tolerance in the face of a volatile market.     

Improved Estimates of Correlation Coefficients and their Impact on Optimum Portfolios

To implement mean variance analysis one needs a technique for forecasting correlation coefficients. In this article we investigate the ability of several techniques to forecast correlation coefficients between securities. We find that separately forecasting the average level of pair‐wise correlations and individual pair‐wise differences from the average improves forecasting accuracy. Furthermore, forming homogenous groups of firms on the basis of industry membership or firm attributes (e.g. size) improves forecast accuracy. Accuracy is evaluated in two ways: First, in terms of the error in estimating future correlation coefficients. Second, in the characteristics of portfolios formed on the basis of each forecasting technique. The ranking of forecasting techniques is robust across both methods of evaluation and the better techniques outperform prior suggestions in the literature of financial economics.     

Hierarchical GARCH

There is strong empirical evidence that the GARCH estimates obtained from panels of financial time series cluster. In order to capture this empirical regularity, this paper introduces the Hierarchical GARCH (HG) model. The HG is a nonlinear panel specification in which the coefficients of each series are modeled as a function of observed series characteristic and an unobserved random effect. A joint panel estimation strategy is proposed to carry out inference for the model. A simulation study shows that when there is a strong degree of coefficient clustering panel estimation leads to substantial accuracy gains in comparison to estimating each GARCH individually. The HG is applied to a panel of U.S. financial institutions in the 2007–2009 crisis, using firm size and leverage as characteristics. Results show evidence of coefficient clustering and that the characteristics capture a significant portion of cross sectional heterogeneity. An out-of-sample volatility forecasting application shows that when the sample size is modest coefficient estimates based on the panel estimation approach perform better than the ones based on individual estimation.     

投资者教育发挥作用了吗?——来自公募基金个人投资者调查数据的证据

投资者教育是保障资本市场平稳运行、良性发展的重要举措,也是我国资本市场重要的基础性制度建设。本文利用20000多份全国公募基金个人投资者调查数据,分析了投资者教育对基金投资收益的影响,并基于行为金融学框架探究了其背后的作用机制。以往文献研究表明,金融知识水平对投资收益会产生显著影响,本文研究发现,获取金融知识的渠道也会影响投资收益。相对于自己学习金融知识、相关工作经验累积金融知识、向亲戚朋友学习金融知识,投资者教育(如参加金融机构的投资教育活动、接受金融经济类课程或培训)更有助于投资者缓解趋势追逐、频繁交易、处置效应等交易行为偏差,从而获得更高的投资收益。进一步分析表明,投资者教育通过提高“理性程度”来提升基金投资盈利概率、投资总收益率和年均收益率的中介效应分别为19.41%、17.09%和12.75%。此外,不同群体参与投资者教育的积极性和受教育效果存在显著差异,投资者教育要更多采取“分类教育”的形式。本文研究对进一步加强投资者教育、更好地推动资本市场发展具有一定的参考意义。     

When it comes to the crunch: Retail investor decision-making during periods of market volatility

Attitude to risk questionnaires are widely used by financial advisors to recommend investments of appropriate risk levels to their clients. Yet the usefulness of this instrument to gauge how investors will react when faced with extreme volatility in the values of their assets remains untested. Using realistic scenarios and based on a large-scale survey in the UK, in this study we examine how the investing public reacts to actual portfolio losses. We find that conventional risk tolerance measures are inadequate for determining whether investors would ‘sell out’ or hold their portfolios in such circumstances. On the other hand, we find that past experience, emotions and personality characteristics, including measures of financial self-efficacy and extraversion, are significant predictors of investor reactions to market crashes.     

Individual financial risk tolerance and the global financial crisis

We investigate individual investors’ tolerance towards financial risk by focusing on changes associated with the global financial crisis (GFC) of 2007–2009. Financial risk tolerance (FRT) is analysed longitudinally controlling for demographic, socio‐economic and regional variations. In absolute terms, the change in FRT is small and contrasts with a popular view that risk tolerance is an elastic psychological state overly influenced by the pervading market conditions. Even in the presence of significant financial events, FRT tends to be a reasonably stable attribute in the shorter term but possibly influenced and reshaped by events more gradually over time.     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

Elimination of systemic risk in financial networks by means of a systemic risk transaction tax

Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function, and collapses. It has recently become possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (loans), liabilities and other obligations, such as derivatives. We demonstrate that it is possible to quantify the share of SR that individual liabilities within a financial network contribute to the overall SR. We use empirical data of nationwide interbank liabilities to show that the marginal contribution to overall SR of liabilities for a given size varies by a factor of a thousand. We propose a tax on individual transactions that is proportional to their marginal contribution to overall SR. If a transaction does not increase SR, it is tax-free. With an agent-based model (ABM) (CRISIS macro-financial model), we demonstrate that the proposed ‘Systemic Risk Tax’ (SRT) leads to a self-organized restructuring of financial networks that are practically free of SR. The SRT can be seen as an insurance for the public against costs arising from cascading failure. ABM predictions are shown to be in remarkable agreement with the empirical data and can be used to understand the relation of credit risk and SR.     

An empirical investigation of multiperiod tail risk forecasting models

In the context of multiperiod tail risk (i.e., VaR and ES) forecasting, we provide a new semiparametric risk model constructed based on the forward-looking return moments estimated by the stochastic volatility model with price jumps and the Cornish–Fisher expansion method, denoted by SVJCF. We apply the proposed SVJCF model to make multiperiod ahead tail risk forecasts over multiple forecast horizons for S&P 500 index, individual stocks and other representative financial instruments. The model performance of SVJCF is compared with other classical multiperiod risk forecasting models via various backtesting methods. The empirical results suggest that SVJCF is a valid alternative multiperiod tail risk measurement; in addition, the tail risk generated by the SVJCF model is more stable and thus should be favored by risk managers and regulatory authorities.