投资者认知偏差导致的非理性行为分析

中西方学者在对资本市场的各种“异象”和投资者的非理性行为进行了详尽的研究,事实上,一些非理性、有限理性的行为产生归根结底都是由于人类本身所具有的心理偏差造成的,在资本市场中表现出过度自信、损失厌恶、赌博心理等非理性行为,从而影响股票价格的波动。本文将介绍投资者认知偏差的形成过程并分析投资者认知偏差在资本市场中的表现。     

个人投资者决策行为偏差探析及应用

资本市场是投资者追求财富的梦想之地，真正成功者凤毛麟角，其原因在于：个人投资者在投资中存在各种行为偏差，这些偏差包括认知偏差、情绪偏差、意志偏差等。     

我国股市投资者行为心理偏差分析

投资者是资本市场的经济行为主体,投资者行为会影响股市的价格定位和波动性。本文在回顾我国股市投资者行为研究现状的基础上,对交易环境决定论下投资者行为进行解读,并对投资者行为心理偏差的现象及诱因进行了分析,最后对政府监管层和投资者提出了若干建议。     

证券投资者行为偏差研究及在我国证券市场中的应用

行为金融学是自20世纪80年代兴起的金融学说，注重研究证券投资者在投资决策时的心理特征，利用投资者行为偏差所导致的证券定价误差，发展新的投资策略，来获取超越市场的超额收益。本文从行为金融学角度对证券投资者行为特征、行为偏差进行了分析，展望了行为金融学在我国证券市场中的应用。     

农户信贷行为的认知偏差及对其贷款困境的影响

文章分析了农户信贷行为的认知偏差，发现农户信贷行为的认知偏差往往加重了金融机构的外部性约束，最终，这种行为偏差加大了其贷款难度，文章还对农户信贷问题的现状进行了研究，最后，利用机制设计理论提出解决农户信贷认知偏差和金融机构缺位矛盾的政策建议。     

关于投资者框定依赖偏差的调查研究

行为金融学中对投资者心理的研究很多，本文主要探讨证券市场上投资者表现出的框定依赖偏差，主要包括以下内容：框定依赖偏差的定义、影响因素及行为特征；框定依赖偏差的问卷设计；实际调查结果分析和调查需要改进的地方。     

学生偏差行为的若干思考

学生偏差行为是指违反学校的相关规定以及老师的期望的行为。采用了定性研究的方法,以漳州市某中学的偏差行为学生作为研究对象,通过对这所学校的学生以及老师进行深入的访谈,对学生偏差行为形成的原因进行了分析,并且讨论了解决青少年偏差行为的对策。     

学生偏差行为的若干思考

学生偏差行为是指违反学校的相关规定以及老师的期望的行为。采用了定性研究的方法,以漳州市某中学的偏差行为学生作为研究对象,通过对这所学校的学生以及老师进行深入的访谈,对学生偏差行为形成的原因进行了分析,并且讨论了解决青少年偏差行为的对策。     

投资决策中管理者心理偏差的影响及其防范

在企业的有关财务决策中,投资决策是最主要的决策.错误的投资决策,轻则影响企业发展,重则导致企业破产.投资决策错误是由多种因素造成的,其中一个重要的因素是一系列认知或心理偏差.这些偏差渗透进人们的思考过程,影响人们推理.投资决策过程中常见的偏差有过度自信、损失厌恶、证实偏差等.这些偏差会导致企业投资决策错误以及非效率投资行为.因此,必须采取有效措施抑制认知或心理偏差对投资决策的影响.     

投资决策中心理偏差的分析

专注于投资心理学研究领域的一个分支即心理偏差如何影响投资者的决策,重点介绍了几个影响投资决策的心理偏差,即过分自信,代表性思维和熟识性思维,贪婪,恐惧,过度乐观。     

偏压隧道施工技术

江西泰井高速公路碧溪隧道以“偏压、软岩、富水、煤层、岩溶”不良地质著称,施工难度较大,文章介绍了其偏压段施工技术。     

Behind closed doors: Sex bias at professional and managerial levels

U.S. companies have made important strides in combating sex discrimination in the workplace over the last two decades, but more subtle forms of sex bias still exist, often in decisions and behaviors that occur behind closed doors. This paper focuses on sex bias at professional and managerial levels. It explores sources of sex bias in the informal culture, selection and recruitment, task assignment, performance appraisal, promotion, and salary allocation, and then suggests action steps to help reduce sex bias in each of these areas.     

Influence diagnostic in survey sampling: Estimating the conditional bias

The conditional bias has been proposed by Moreno Rebollo et al. (1999) as an influence diagnostic in survey sampling, when the inference is based on the randomization distribution generated by a random sampling. The conditional bias is a population parameter. So, from an applied point of view, it must be estimated. In this paper, we propose an estimator of the conditional bias and we study conditions that guarantee its unbiasedness. The results are applied in a Simple Random Sampling and in a Proportional Probability Aggregated Size Sampling, when the ratio estimator is used. Received October 2000     

Social desirability bias in PSM surveys and behavioral experiments: Considerations for design development and data collection

Social desirability issues are long known, but not long gone. Across major purchasing and supply management (PSM) research streams, surprisingly few empirical studies explicitly address social desirability bias (SDB), despite SDB constituting a potentially limiting factor. With regard to surveys and behavioral experiments as two of the most widely used empirical methods in PSM, SDB can represent a critical issue. A first step should therefore be to collect and fully report data on SDB in all such studies. The present note then continues by providing an overview of methodological considerations for PSM researchers to mitigate social desirability issues before they arise in their surveys and behavioral experiments. We describe eight potential mitigation approaches, namely disguising the study's research purpose, assuring anonymity and confidentiality, indirect questioning, adapting the wording of single items, broadening response modes, conducting preparatory cognitive interviews, using multiple sources, and applying multiple research methods. In describing this repertoire of preventive measures, we point authors and review teams to both the broader methodological literature and PSM studies that have used such approaches.     

How can we correct for contingent valuation bias? A case study of the Macau Orchestra

The long‐standing contingent valuation method (CVM) is one of the most controversial techniques in economics. But it still remains the main means of assisting policymakers in understanding the preferences of ordinary citizens in valuing a range of non‐market goods believed to have positive external benefits. In the absence of viable alternatives, it is necessary that this method continues to be refined. This is especially important as CVM is increasingly being applied outside the area of environmental economics and outside the Western socio‐political context where theoretical advances have previously been made. This article examines three sources of bias in CVM in the context of valuing cultural goods: information, mixed good, and payment vehicle bias. A small case study of a publicly funded orchestra in an Asian city, the Macau Orchestra, is used to provide a preliminary exploration of the issues discussed.     

Inflationary bias in middle to late transition Czech Republic

A series of studies has now confirmed the Filer and Hanousek’s [Economic Systems 24 (2000) 285] suggestion that inflation mismeasurement during the transition is a serious problem of the same relative magnitude (and greater absolute magnitude), as in advanced market economies. During the 1990s in the Czech Republic, inflation was overstated by more than four percentage points a year. The largest portion of this bias is due to uncaptured quality changes. In effect, Czech consumers are living considerably better after the fall of communism, but this increase in living standards is manifested through better quality, rather than greater quantities of goods consumed.     

Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non-ignorable nonresponse?

Rising nonresponse rates in social surveys make the issue of nonresponse bias contentious. There are conflicting messages about the importance of high response rates and the hazards of low rates. Some articles (e.g. Groves and Peytcheva, 2008) suggest that the response rate is in general not a good predictor of survey quality. Equally, it is well known that nonresponse may induce bias and increase data collection costs. We go back in the history of the literature of nonresponse and suggest a possible reason to the notion that even a rather small nonresponse rate makes the quality of a survey debatable. We also explore the relationship between nonresponse rate and bias, assuming non-ignorable nonresponse and focusing on estimates of totals or means. We show that there is a ‘safe area’ enclosed by the response rate on the one hand and the correlation between the response propensity and the study variable on the other hand; in this area, (1) the response rate does not greatly affect the nonresponse bias, and (2) the nonresponse bias is small.     

Self-selection bias and cost-of-living estimates

Due to continued interest in geographic living-cost differentials, some researchers have used data from the ACCRA Cost of Living Index. This paper explores further the potential for using ACCRA data for cost-of-living research. In particular, it investigates the possibility of self-selection bias affecting OLS estimates using ACCRA data. The findings indicate that self-selection bias is a concern that researchers using ACCRA data should be aware of. Results using Heckman’s two-step procedure to estimate a cost-of-living model indicate promise for using ACCRA data to update and expand upon previous cost-of-living research. The author wishes to acknowledge Keith Ihlanfeldt and Cynthia Rogers for constructive comments on an earlier version of this paper presented at the 35th Meeting of the Southern Regional Science Association and also wishes to thank the two anonymous referees for their suggestions on improving the paper.     

Risk premium or irrational expectations? An investigation into the causes of forward discount bias across 27 developed and developing economies forward rates

This study investigates the causes of forward discount bias in 27 forward rates for the period 2007–2016. First, it analyzes the forward discount bias across two horizons: 3-month and 12-month; across two sub-periods: financial crisis and non-crisis; and across two country groups: developed and developing. The regression results show that forward discount bias is present and significant, but has become smaller compared to many earlier studies. In general, 12-month forward discount is less biased compared to the 3-month. The study confirms that during the non-crisis period, developing country forward discount is less biased compared to the developed country across both the horizons. The crisis period has impacted the magnitude of the bias. For the developed country, the bias is higher during the crisis period compared to the non-crisis period at the 3-month horizon but lower at the 12-month horizon. Developing country forward discount is less biased during the crisis period compared to the non-crisis period at the 3-month horizon, but slightly more biased at the 12-month horizon. Second, the study decomposes the bias into two components: irrational expectations and risk premium. The results show that both irrationality and time varying risk premium are present in the bias. However, the irrationality component plays a minor role in the 3-month forward discount bias, but plays a major role in the 12-month forward discount bias. Irrationality is high during the crisis period for both the country groups. The risk premium component of the bias is highly significant across both the periods, country groups and horizons, and thus, plays a major role in the overall bias. Further, the non-parametric tests of comparison of statistical significance of the beta coefficient values between country groups, periods and horizons indicate that many differences are statistically significant supporting our conclusions.     

Nonresponse Bias and Stimulus Effects in the Dutch National Election Study

Bias is a much-debated issue in survey research. Answer effects (respondents claim to have behaved differently than they did in reality), nonresponse bias (nonrespondents differ on important variables from the respondents) and stimulus effects (by participating in a previous wave of a study, respondents change their behavior or attitude) can seriously distort the results of survey research. By using data from the 1998 Dutch National Election Study the authors show that the results of election research can indeed be affected by bias. Not only are significant effects found in the distribution of political attitude and voting behavior variables as a result of both nonresponse bias and stimulus effects, it is also shown that relations between variables change as a result of bias.