BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing

This systematic literature review summarizes the extant research in the Behavioral Finance (BeFi) and digital asset spaces to understand better the interactions of behavioral effects on the pricing of assets constructed, enabled, and exchanged in Decentralized Finance (DeFi) markets. We find that asset pricing in these rapidly evolving markets is better explained through BeFi than through traditional finance (TradFi) theory. Investor attention, sentiment, heuristics and biases, and network effects interact to form a highly volatile and dynamic market. We offer a deterministic research framework with propositions for future research. We further provide investors with a theoretically and empirically supported structure to better inform their decisions through an understanding of BeFi applications to DeFi.     

Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks

This paper examines the relevance of sentiment in predicting overall financial system instability using long-run short-term memory networks. Weekly data on the US financial system, consumer sentiment, producer sentiment, and investor sentiment is collected from 21 January 1994 to 27 December 2019, and different models are developed to predict the one-week-ahead levels of financial stress in the US financial system. We find that models using sentiment indices outperform those relying solely on historical financial stress and risk data. This result is robust to comparisons with an alternative deep learning method and out-of-sample predictions. It constitutes an argument in favor of behavioral finance and Minsky’s (Knell, 2015) financial instability hypothesis against the Efficient Market Hypothesis. As it concretely identifies the main indicators for predicting US financial stress one week in advance, the study provides relevant recommendations for policymakers and investors in terms of macroprudential policies and portfolio management.     

Belief-based momentum indicator and stock market return predictability

Li et al. (2022) propose a new momentum indicator that combines past returns and consistent belief information, and show that the indicator positively predicts cross-sectional stock returns. Based on the momentum indicator of Li et al. (2022), we further develop a conditional past return (CPR) indicator that additionally adds the direction information for the investors' consistent belief. We examine the effectiveness of CPR as a predictor for stock market returns. Our evidence shows that CPR significantly and positively predicts future one-month market returns. And CPR provides unique predictive information that is not related to the other popular predictors. The abundant out-of-sample evidence further supports CPR’s predictive ability. Additionally, we detect the asymmetric role of CPR in predicting market returns and find that much of the predictive ability of CPR is attributed to the interaction between the positive past returns and the positive consistent belief.     

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

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

Disclosure of internal control material weaknesses and optimism in analyst earnings forecasts

This paper investigates how analyst forecast optimism is associated with disclosures of internal control material weaknesses (ICMWs) and their remediation under Section 404 of the Sarbanes–Oxley Act (SOX). Drawing on agency theory, I hypothesize that analysts are likely to issue earnings forecasts that are more optimistic for firms with ICMW disclosures than for those without ICMW disclosures. Using a sample of 20,875 firm-year observations with 10-K (10-Q) reports from 2004 to 2018, I find a positive association between ICMW disclosures and analyst forecast optimism. This positive association is partially driven by investors’ inability to unravel analyst forecast bias and analysts’ intentions to curry favor with management for private information. In addition, analysts are found to issue less optimistic forecasts for firms with ICMW remediation disclosures compared with those without ICMW remediation disclosures. A series of propensity score matching and regression analyses are conducted to test the robustness of my inferences. Overall, the paper suggests that analysts have incentives to take the opportunity of firms disclosing ICMWs to bias their forecasts upward for self-interest. The findings have the potential to assist regulators in guiding analyst behavior and educating investors to unravel positive bias in analyst forecasts.     

Pricing,issuance volume,and design of innovative securities: The role of investor information

This study investigates the role of asymmetric information for the pricing, issuance volume, and design of innovative securities. By analyzing the information that structured product issuers provide to the investors of those products, we can identify specific sources of asymmetric information between the issuers and investors in this market. We show that issuers exploit this information friction to offer products to investors that appear more profitable for the issuer. In addition, we find that the friction induces issuers to design products with higher information asymmetry. Our results suggest that product issuers’ behavior increases information frictions in the financial system.     

低轨时差频差系统对运动目标的定位能力

战场高速运动目标往往具有高威胁性，现有天基电子侦察手段对此类目标的定位跟踪能力已经难以适应现代战场感知需要。通过研究时差频差定位原理，在现有双星体制基础上构建了三星时差频差定位体制；同时结合卡尔曼滤波算法，实现了对空中高速运动目标的定位和速度估计。经计算仿真，系统对空中高速运动目标的定位精度达到了几公里量级，速度估计误差达到了10 m/s量级，充分验证了方法的工程可行性。通过上述改进，大幅扩展了天基时差频差系统的应用范围，将有力提升天基系统的战场感知能力。     

How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis

This paper studies the time–frequency, nonlinear quantile relationship between investor attention (GSVI) and crude oil over the period from January 2000 to April 2020. To do so, the wavelet coherency, wavelet-based causality-in-quantiles test and quantile-on-quantile method are employed. The results indicate that first, the correlation between investor attention and crude oil is relatively high, and the highly correlated regions are concentrated from 8 to 16 months. In most cases, the GSVI is negatively correlated with the crude oil market. Additionally, under extreme market conditions, the explanatory ability is stronger than in the normal market, and it is greater in the low-frequency domain than in the high-frequency domain. Finally, investor attention has an apparent asymmetric impact on crude oil prices and returns at each scale, displaying a positive effect on the low quantiles of crude oil but a negative effect on the high quantiles across all quantiles of the GSVI. In the short term, when crude oil prices and returns are in a bear market, the larger volume of the GSVI has a greater impact on them. Moreover, the impact becomes greatest under extreme market conditions.     

Do investors turn a blind eye to risk-factor disclosures by state-supported firms?

There is scant empirical evidence on how government involvement affects investor reactions toward firm-specific information. Our study provides new evidence on how investors respond to risk-factor disclosures in IPO prospectuses in China, where state-supported firms presumably receive government-offered implicit insurance against bankruptcy risk while bearing significant agency risks. We find an insignificant association between risk-factor disclosure quality and IPO underpricing (or post-IPO stock return volatility) among state-supported firms. The finding suggests that state-offered implicit insurance becomes the predominant consideration when investors value IPO shares of state-supported firms, thereby weakening investor reactions to high-quality risk-factor disclosures. Our study expands the scope of IPO underpricing literature by implying that simply increasing disclosure transparency in the IPO prospectus may not resolve the IPO underpricing issue in a government-dominated economy such as China.