Risk Preferences Of Investors Research Articles

Dutch auction dynamics in non-fungible token (NFT) markets

Non-fungible tokens (NFTs) rose to prominence as a wide-scale implementation of blockchain technology to support the emergence of crypto-asset markets. These nascent digital markets raise questions about the behaviours of investors in the digital economy and their appetite for risk. Using 28,919 auction listings, 4937 sales, and 30,197 Telegram messages, we conduct a field study on the bidding and selling behaviours of NFT investors in a Dutch auction system. We reveal risk-seeking behaviours in our sample of Dutch auction sales. We document that time pressures and value propositions significantly influence NFT investors: fast clock speeds and greater price separations induce underbidding behaviours and are associated with low value retention for sellers. These results are confirmed using a matched-pairs analysis. Our study raises further questions on the risk preferences of investors in emergent digital marketplaces. We propose value maximisation strategies for marketplace developers and participants, while drawing attention to the presence of potentially exploitable biases and heuristics amongst participants, courtesy of bidding incentivisation schemes significantly altering how investors value NFTs.

Culture imprint and gambling preference: Evidence from individual investors' trading in the Chinese stock market

Using the stock transaction records of individual investors from a large brokerage firm in China, we explore the impact of Confucian culture on the gambling preferences of individual investors. After a series of tests, we find a significant negative relationship between Confucianism and the lottery-type stock preference of individual investors, indicating that Confucian culture inhibits gambling preferences in stock investment. This effect is more pronounced for female investors, middle-aged investors and investors in regions with higher gambling sentiment. Investor risk preference mediates the relationship between Confucian culture and lottery-type stock preference.

A short-term price prediction-based trading strategy.

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions.

Detecting turning points of stock markets in China and the United States

ABSTRACT Choosing the appropriate method to identify turning points is critical for investors and policymakers. We propose a novel hybrid model combining dual long memory with structural breaks in the mean to identify the turning points of the stock market in China and the United States. The results show that models accounting for long memory and structural break perform well in the turning point detection. Notably, the proposed hybrid model generates superior in-sample turning point matching and out-sample forecasts for SSECI over those obtained from the competing ARFIMA models. In contrast, DJIA is better explained by the dual memory model with structural breaks in volatility. Finally, our analysis extends to encompass FTSE, HSI, IBOVESPA, and SENSEX30. The results demonstrate the superior performance of the hybrid model in markets exhibiting fractal characteristics. Such heterogeneity indicates differences in investor risk preference across stock markets and offers elaborate international evidence to the adaptive market hypothesis.

Using investor utility to determine portfolio choice with REITs

This article examines the decision of individual investors to allocate a portion of their existing investment portfolios to REITs. It first derives the risk preferences of investors represented by their benchmark portfolios of stocks and bonds. Such risk preferences are then used for portfolio decisions regarding REITs. The analysis shows that investors with lower risk aversion tend to have a more substantial stock component in their benchmark portfolio and will obtain higher risk-return benefits from adding REITs. In addition to the theoretical analysis, the article provides a practical solution to evaluate the benefit of investing in REITs.

Do financial markets predict macroeconomic performance? US evidence from risk‐based measures

AbstractFinancial markets are expected to predict macroeconomic conditions as movement in the former depends upon expectations of future performance for the latter. However, existing evidence is mixed. We argue that this arises because the stock return and term structure series typically used in studies, fail to sufficiently capture investor risk preferences. For US data, we use the variance risk premium (VRP) and default yield (DFY) to better capture such a risk measure and demonstrate that these variables exhibit greater evidence of predictive power for key macroeconomic series. In addition to VRP and DFY, we include further variables that may also capture market risk. Given similar dynamics between different risk measures and the potential for multicollinearity in estimation, we consider variable combinations. Using results obtained through predictive regressions, out‐of‐sample forecasting and a probit model designed to capture periods of expansion and contraction, we show that these combination variables can predict future movements in macroeconomic conditions as well as results using individual variables. Of key interest, combinations that include the VRP and DFY are preferred across all macro‐series.

Research on the Application of Machine Learning Technology in Intelligent Stock Selection

With the development of big data era and artificial intelligence, intelligent investment has gradually replaced artificial investment consultants for consulting and financial services. Robo-advisor can also be called robot investment, intelligent financial management, automated financial management and so on. The traditional quantitative investment model mostly depends on the indicators designed by the financial knowledge and experience of the researchers and cooperates with the economic model or strategy. Financial knowledge and experience play a major role in the construction of the quantitative investment model, so the efficiency of model development and updating is low and the human influence factors are large. The quantitative model is established through big data and intelligent algorithms, and the market is judged according to the risk preference of investors, intelligent allocation and investment of assets are carried out, and automatic strategic trading services are implemented. Based on machine learning theory, this paper studies the two types of investment in quantitative investment: quantitative timing and quantitative stock selection, and puts forward corresponding quantitative investment models. Through testing the models, it is verified that they have good profitability and risk control ability, which is a quantitative investment model that can provide guidance for investors.

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.

Dynamic Implied Risk Aversion Term Structure: An Empirical Analysis Based on Shanghai Stock Exchange 50 ETF Option

Risk preference has constantly been one of the vital issues in economics and finance. In this study, the time series and term structure of Chinese investors’ implicit risk aversion are investigated using the implicit risk aversion extraction method, and the dynamic term structure of Chinese investors’ implicit risk aversion is modeled using the Vasicek model. In the empirical process, the SSE 50 ETF option data from March 2015 to July 2018 are adopted to extract model‐free risk‐neutral skewness. The standard deviation, skewness, and kurtosis of reality measure are extracted using the Shanghai 50 ETF data, and then the monthly implied risk aversion time series and term structure of Chinese investors are obtained. As indicated by the results of this study, the risk preference of Chinese investors exhibits significant time series characteristics, and it will show risk‐loving and risk‐averse phenomena at certain times. Moreover, for different periods in the future, differences are generated in investors’ risk preferences, suggesting an aversion to short‐term risk and a certain tolerance for long‐term risk, i.e., there are significant characteristics of the term structure. Besides, the term structure of implied risk aversion of Chinese investors is dynamically modeled using the Vasicek model. To be specific, its first principal component can account for 90% of the change in the term structure. The “level factor” refers to the critical factor load, and the level of long‐term implied risk aversion reaches 0.658. Furthermore, the term structure of implied risk aversion exhibits the characteristics of mean regression. Next, more effective research results on investors’ risk preference are achieved, and the time‐varying characteristics and term structure characteristics of investors’ risk preference are investigated. The result suggests that the long‐term risk preference of Chinese investors approaches 1, and there is a significant feature of “mean regression,” i.e., a vital finding of this study.

Development of Optimal Stock Portfolio Selection Model in the Tehran Stock Exchange by Employing Markowitz Mean-Semivariance Model

In an increasingly complex financial market, selecting the optimal stock portfolio has become a subject of intense debate. This study aims to develop a model for optimal stock portfolio selection. We apply Markowitz's mean-semivariance approach to determine the downside risk of portfolios, which reflects investors' intuitive perception of risk. In the first stage, the combination of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with interval data is employed to identify and rank good quality stocks according to the recommended criteria by experts. After selecting qualified stocks, in the second phase, we create portfolios, and the weight invested in each stock is determined. Then, three portfolios are created for three groups of risk-averse, neutral to risk, and risk-taker investors. The mean-semivariance optimization model is used in this phase. The proposed approach in the paper is implemented in a real case study of the Tehran stock exchange (TSE). Three portfolios for three groups of investors were evaluated and compared to the market performance using sharp criteria. All three portfolios outperformed the market portfolio both in terms of risk and return. The proposed model of this study can be utilized as a decision support tool when forming an optimal stock portfolio by considering both experts’ opinions on stock evaluation and investor risk preferences simultaneously.

Robust Portfolio Optimization with Respect to Spectral Risk Measures Under Correlation Uncertainty

This paper proposes a distributionally robust multi-period portfolio model with ambiguity on asset correlations with fixed individual asset return mean and variance. The correlation matrix bounds can be quantified via corresponding confidence intervals based on historical data. We employ a general class of coherent risk measures namely the spectral risk measure, which includes the popular measure conditional value-at-risk (CVaR) as a particular case, as our objective function. Specific choices of spectral risk measure permit flexibility for capturing risk preferences of different investors. A semi-analytical solution is derived for our model. The prominent stochastic dual dynamic programming (SDDP) algorithm adapted with intricate modifications is developed as a numerical method under the discrete distribution setting. In particular, our new formulation accounts for the unknown worst-case distribution in each iteration. We verify the convergence property of this algorithm under the setting of finite scenarios. Our results show that the optimal solution favours a certain degree of anti-diversification due to dependence ambiguity and exhibits its protection ability during the financial crisis period.

Using Binary Prediction Markets as Hedging Instruments: Strategies for Renewable Generators

Renewable energy sources face volumetric risk in their revenue streams, in any electricity sale structure, due to changeability associated with weather conditions. Weather/power derivatives are often employed to hedge against such financial risks. This letter proposes that a binary prediction market, if is adequately liquid, has the potential to resemble the function of such derivatives to improve the financial profile of renewable sources. The size and the price of shares (contracts) to be purchased by the renewable generator are determined analytically in the methodology of this paper. To this end, two different approaches have been considered: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indifference</i> utility condition and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximisation of utility</i> function to reflect different risk preferences of investors.

Lifetime consumption and investment with housing, deferred annuities and home equity release

We develop a life-cycle optimal investment and consumption model with deferred annuities, housing, mortgages and home equity release. The investor can hold cash, bonds, stocks, annuities and can invest in housing, through renting, purchasing, or a mix of both, with access to variable-rate mortgages. In retirement, the investor can access his housing equity through a form of home equity release called a home reversion contract. Transaction costs, taxes and management fees are explicitly included. The investor's risk preferences are represented by standard power utility derived from housing and non-housing consumption, both before and after retirement, and from bequest. We use multi-stage stochastic programming to solve the optimization problem numerically. Our results show that both non-housing and housing consumption may be higher in retirement when deferred annuities and home reversion are available than when they are absent. The bequest motive has little effect on home reversion and results in a small reduction in overall annuitization.

Risk Preference Elicitation and Financial Advice Taking

Financial advisors rely on accurate measures of investor risk preferences. This study compares different risk elicitation methods (REMs) in terms of their perceived suitability and impact on financial advice taking. The results suggest that the perceived suitability of the suggested risk profile strongly predicts delegation to an advisory tool. REMs differ in terms of their perceived process similarity with the investor, which positively affects suitability (and thus, delegation) directly and through its positive effect on source credibility. Differences were also found with regards to the perceived complexity of the risk profiling task, which is positively related to suitability. In summary, the findings imply that applying suitable REMs matters not only because it avoids misrepresentation of an investor’s true risk preferences, but because it directly affects the propensity to delegate financial decision-making.

Robo-advisor using closed-form solutions for investors’ risk preferences

ABSTRACT In this article, we design a robo-advisor which has a bi-level framework. The framework enables it to handle a large amount of assets using fast algorithms in the lower level. The proposed robo-advisor can utilize the closed-form solutions for investors’ risk preferences based on corresponding portfolio choices. A dynamic weight is applied to update investors’ risk preferences. Numerical results based on real data in Chinese stock market show that our proposed robo-advisor can accurately estimate the risk preferences of investors and outperform the benchmark formed by market indexes.

Prudence and temperance in portfolio selection with Shariah-compliant investments

PurposeThis paper aims to explore the impact of Sharīʿah-compliant stocks on other investor risk preferences beyond the risk aversion, namely, prudence and temperance.Design/methodology/approachThis paper uses the non-parametric model data envelopment analysis with the shortage function as a measure of performance. The model uses three specifications considering skewness and kurtosis that describe according to expected utility theory, prudence and temperance.FindingsResults show that first, efficient portfolios consist mainly of conventional stocks in the three-model specification. Second, the skewness improvement is achieved only when considering conventional stocks while Sharīʿah-compliant assets do not exhibit any impact on the third moment. Finally, diversification through both conventional and Sharīʿah-compliant stocks does not lead to kurtosis reduction. Sharīʿah-compliant stocks in efficient portfolios are sensitive to return and risk solely, and hence, prudence and temperance as related to skewness and kurtosis measures can be ignored in optimal portfolio selection during normal market conditions.Research limitations/implicationsFindings suggest the same conclusions for four Islamic screening methods; however, readers should be prudent due to the limited sample. Results show that Sharīʿah-compliant assets do not have an impact on higher-order moments optimal portfolio returns, and hence, question the commonly admitted assumption of non-normality return distributions at least for Sharīʿah-compliant stocks.Practical implicationsThe research findings suggest that Islamic investor preferences are described only by return and risk along with Sharīʿah criteria for stock selection and portfolio allocation. Portfolio managers should not care about higher-order moments to manage Sharīʿah-compliant funds. The traditional mean-variance Markowitz framework will be sufficient for investment or allocation decision-making. Description of Sharīʿah-compliant portfolio returns with only the first two order moments gives such asset more resiliency to extreme events like a crisis.Originality/valueThis research is the first in literature exploring whether prudence and temperance defined by higher-order moments can be drivers, besides Sharīʿah criteria, in portfolio allocation decision-making. This study is unique in terms of methodology and application. It uses individual stock data on the Casablanca Stock Exchange.

Are Stock-Market Anomalies Anomalous After All?

We propose a stochastic spanning to evaluate whether anomalies are genuine under factor-model framework. Our approach is nonparametric and does not rely on any assumption of return distribution and investor risk preferences. It depends on the whole distribution of returns, rather than only on the first two moments. Of the anomalies we consider, only a few expand the opportunity set of the risk-averter and have real economic content. Our approach is consistent in identifying genuine anomalies in and out of samples. This is in contrast to mean-variance (MV) spanning tests where anomalies identified in-sample, not out-of-sample.

Ordering Arbitrage Portfolios and Finding Arbitrage Opportunities

Concepts are introduced for analyzing arbitrage portfolios in the face of ambiguity about investor risk preferences and initial portfolio holdings. A Stochastic Arbitrage Opportunity is a self-financing overlay portfolio which enhances every feasible host portfolio for all relevant utility functions. The logical alternative to the existence of such opportunities is the solvability of a system of asset pricing restrictions for the base assets. An asymptotic statistical theory is developed to analyze the behavior of the empirical optimal arbitrage portfolio if the latent parameters of the joint payoff distribution are estimated using time-series data. An empirical application to one-month S&P500 index options uncovers significant arbitrage opportunities in the form of short strip strangle spreads, for investors with reasonable levels of relative risk aversion. The optimal option combinations enhance not only passive index portfolios but also portfolios with existing volatility risk exposure.

Practical Applications of Portfolio Optimization with Active, Passive, and Factors:Removing the Ad Hoc Step

In <b>Portfolio Optimization with Active, Passive, and Factors: <i>Removing the Ad Hoc Step</i></b>, from the March 2020 issue of <b><i>The Journal of Portfolio Management</i>, Roger Aliaga-Diaz, Giulio Renzi-Ricci, Ankul Daga</b>, and <b>Harshdeep Ahluwalia</b>, all with <b>The Vanguard Group</b>, discuss a more streamlined way to optimize portfolios. They present a quantitative asset-allocation framework that incorporates active, passive, and factor strategies—and propose a utility optimization model that concurrently allocates assets among investments in these three dimensions. Their approach condenses portfolio optimization into one step that integrates investors’ risk tolerance into asset allocation. The model incorporates investor risk preferences shaped by the uncertainties inherent with active, passive, and factor investment returns. By clarifying investors’ decision-making, this approach steers them away from ad hoc portfolio allocations and permits them to customize their portfolios more deliberately. The authors highlight practical applications, including how the approach can be used to substitute lower-cost factor strategies for higher-cost active strategies and to create factor-tilted portfolios aligned with investors’ varying risk thresholds. <b>TOPICS:</b>Analysis of individual factors/risk premia, factor-based models, portfolio theory, portfolio construction

Pricing European Option Under Fuzzy Mixed Fractional Brownian Motion Model with Jumps

As we all know, the financial environment on which option prices depend is very complex and fuzzy, which is mainly affected by the risk preferences of investors, economic policies, markets and other non-random uncertainty. Thus, the input data in the options pricing formula cannot be expected to be precise. However, fuzzy set theory has been introduced as a main method for modeling the uncertainties of the input parameters in the option pricing model. In this paper, we discuss the pricing problem of European options under the fuzzy environment. Specifically, to capture the features of long memory and jump behaviour in financial assets, we propose a fuzzy mixed fractional Brownian motion model with jumps. Subsequently, we present the fuzzy prices of European options under the assumption that the underlying stock price, the risk-free interest rate, the volatility, the jump intensity and the mean value and variance of jump magnitudes are all fuzzy numbers. This assumption allows the financial investors to pick any option price with an acceptable belief degree to make investment decisions based on their risk preferences. In order to obtain the belief degree, the interpolation search algorithm has been proposed. Numerical analysis and examples are also presented to illustrate the performance of our proposed model and the designed algorithm. Finally, empirically studies are performed by utilizing the underlying SSE 50 ETF returns and European options written on SSE 50 ETF. The empirical results indicate that the proposed pricing model is reasonable and can be treated as a reference pricing tool for financial analysts or investors.