Which return regime induces overconfidence behavior?Artificial intelligence and a nonlinear approach

Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.

Network correlation between investor’s herding behavior and overconfidence behavior

It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.

Overconfidence and the adoption of robo-advice:why overconfident investors drive the expansion of automated financial advice

Adaptive online platforms,powered by artificial intelligence,commonly referred to as robo-advice,steadily increase their market share.Yet these comparably new financial services are critically understudied.Little is known about why some investors adopt robo-advice for something as essential as asset allocation.The current paper tries to close this gap by shedding light on the causal effect of investor overconfidence on the propensity of using robo-advice.The study proposes a theoretical framework that combines the divergence of opinion hypothesis with consumer behavior insights and information technology diffusion research.The framework is empirically tested on the Investor Sample of the 2015 National Financial Capability Study,a subsample of 2000 US investors.The results from a series of generalized linear,structural,and semipara-metric models show that in a pre-chasm market,overconfident investors have a significantly higher propensity of adopting robo-advice.While higher financial literacy seems to decrease robo-advice uptake,unjustified confidence in one’s knowledge causally increases it.Willingness to take financial risk cannot account for the significantly increased adoption of robo-advice among overconfident investors.The findings help managers to better position robo-advice by offering behavioral insights into their user base.In addition,the results outline a managerial tool to take demand-side actions to increase the likelihood of an end-user innovation crossing the chasm.

Research on Property Rights, Revenue Transparency and Equity Financing Costs ——Based on the Perspective of CEO Overconfidence

This paper takes the Chinese listed company with the equity refinancing qualification from 2012 to 2013 as the research object, and uses the residual revenue model to calculate the equity financing cost. This paper discusses the impact of the overconfidence of executives on the equity financing cost and its impact mechanism. The unique institutional background examines the differences in property rights characteristics. The research found that:(1) executive overconfidence has a negative impact on the cost of equity financing, executives tend to be overconfident, the higher the equity financing cost of the company;(2) the overconfidence of executives to state-owned enterprises compared to private enterprises The negative impact of financing costs is more significant;(3) in addition, this paper also examines the potential impact mechanism of executive overconfidence on the cost of equity financing. The quality of information disclosure and the risk of investor prediction have a mediating effect on the impact of executive overconfidence on equity financing costs.

Biased Learning Creates Overconfidence

The aim of this paper is to develop a multi-period economic model to interpret how the people become overconfident by a biased learning that people tend to attribute the success to their abilities and failures to other factors.The authors suppose that the informed trader does not know the distribution of the precision of his private signal and updates his belief on the distribution of the precision of his knowledge by Bayer's rule.The informed trader can eventually recognize the value of the precision of his knowledge after an enough long time biased learning,but the value is overestimated which leads him to be overconfident.Furthermore,based on the definition on the luckier trader who succeeds the same times but has the larger variance of the knowledge,the authors find that the luckier the informed trader is,the more overconfident he will be;the smaller the biased learning factor is, the more overconfident the informed trader is.The authors also obtain a linear equilibrium which can explain some anomalies in financial markets,such as the high observed trading volume and excess volatility.

Analysis on the Psychology and Behavior of Individual Investors under the Influence of COVID-19

Due to the relatively short history of the development of the Chinese stock market,the investment philosophy and psychology of most individual investors are not particularly mature.Especially under the influence of public health emergencies,the individual investors’characteristic of the investment behavior in the stock market has become more obvious.This paper combines questionnaire and psychological experiments to study the factors that affect investment decisions of individual investors,and then takes COVID-19 as an example to analyze the impact of public emergencies on individual investors’investment decisions in the stock market.

Concentrated Trading and the Survival of Overconfident Traders

We build a strategic trading model where the overconfident trader earns more than the rational trader and the mechanism for this result differs from that of Kyle and Wang(1997). In this paper, discretionary and nondiscretionary liquidity traders coexist. The overconfident insider is less worried by the market maker and thus induces a lower liquidity cost. In this way, he attracts all the trading from the discretionary liquidity traders, which enables the survival of the overconfident trader.