Testing of behavior bias

This study aims to (i) test the behavior bias of gamblers fallacy occurs at the time of uptrend and downtrend conditions; (ii) test the behavior bias of halo effect occurs at the time of uptrend and downtrend conditions; and (iii) test the behavior bias of familiarity effect occurs at the time of uptrend and downtrend conditions. The number of samples in the study was as many as 41 people. The test equipment used is One-Sample t-Test and Paired t-Test by using statistical package for social scientists (SPSS) as a static test tool. The results of this study show that: (i) Gamblers' fallacy that occurs when the uptrend condition is greater than when the condition is downtrend; (ii) Halo effect that occurs when the uptrend condition is greater than when the downtrend condition; (iii) Familiarity effect that occurs when the uptrend condition is greater than when the downtrend condition.

Geomorphic Apophenia: Inferring meaning where there could be none?

<p>Apophenia describes the experience of seeing meaningful patterns or connections in random or meaningless data. Francis Bacon was one of the first to identify its role as a "human understanding is of its own nature prone to suppose the existence of more order and regularity in the world than it finds". Since then, experiments using streams of randomly generated binary sequences show a propensity for people to believe random data fluctuates more than it actually does. A more mainstream example of this is gamblers fallacy, where lucky or unlucky streaks are identified in the random selection of a roulette wheel. Furthermore, humans can also be influenced by a pre-existing ideas or a narrative that they then transpose into their findings leading to tending to support a hypothesis instead of disproving (confirmation bias).  </p><p>As much of geomorphological science involves the interpretation of data, we argue that the persuasiveness of a narrative and human difficulties in recognizing genuinely random data could lead to apophenia. This presentation examines where apophenia might affect geomorphology, using examples from sediment stratigraphy, signal shredding, river meandering and the numerical modelling of landscape systems. In particular, we focus on how seductive it can be to link changes in landscape to drivers when there are potentially hazardous gaps in the data we are using.</p><p>In Geomorphology correlation has for long been substituted by causation. However, with emerging data rich methods including structure from motion, seismology, remote sensing and numerical modelling, former ‘classic’ techniques of qualitative interpretation can give way to quantitative hypothesis testing.</p>

Geomorphic Apophenia: Inferring meaning where there could be none?

<p>Apophenia describes the experience of seeing meaningful patterns or connections in random or meaningless data. Francis Bacon was one of the first to identify its role as a "human understanding is of its own nature prone to suppose the existence of more order and regularity in the world than it finds". Since then, experiments using streams of randomly generated binary sequences show a propensity for people to believe random data fluctuates more than it actually does. A more mainstream example of this is <em>gamblers fallacy</em>, where lucky or unlucky streaks are identified in the random selection of a roulette wheel. Furthermore, humans can also be influenced by a pre-existing ideas or a narrative that they then transpose into their findings leading to tending to support a hypothesis instead of disproving (<em>confirmation bias</em>). </p><p>As much of geomorphological science involves the interpretation of data, we argue that the persuasiveness of a narrative and human difficulties in recognizing genuinely random data could lead to apophenia. This presentation examines where <em>apophenia</em> might affect geomorphology, using examples from sediment stratigraphy, signal shredding, river meandering and the numerical modelling of landscape systems. In particular, we focus on how seductive it can be to link changes in landscape to drivers when there are potentially hazardous gaps in the data we are using.</p><p>In Geomorphology correlation has for long been substituted by causation. However, with emerging data rich methods including structure from motion, seismology, remote sensing and numerical modelling, former ‘classic’ techniques of qualitative interpretation can give way to quantitative hypothesis testing.</p><p> </p>

Establishing AN Association between Risk Tolerance and Behavioral Biases among Indian Investors.

Behavioral Finance Literature Has Shown A Mushroom Growth In The Recent Years. Literature Shed Specific Light On How The Concept Evolved And Later Developed To Various Stages Which Helped To Understand Various Market Anomalies And The Psychology Of Individuals Through Behavioral Biases. Behavioral Finance Tries To Explain The Logic Behind Applying Of Heuristics Or Shortcuts By Investors To Take Investment Decisions Which Still Need To Be Extensively Studied. The Study Here Attempts For Identify Presence Of Different Biases In Individual Decision Making And Their Association With The Risk Tolerance Capacity. The Results Indicate That Heuristic Biases (I.E. Representativeness Bias, Overconfidence Bias And Gamblers Fallacy Bias) Are Linked To Moderate To High Risk Tolerant Investors. While Herd Bias And Prospect Biases (Loss Aversion Bias And Mental Accounting Bias) Are Found To Be Linked With Low To Moderate Risk Tolerance Levels Of Investors. Heuristics Are Positively Correlated With Risk Tolerance However; Prospect And Herd Are Found To Be Negatively Correlated With Risk Tolerance.

BEHAVIOURAL BIASES IN BITCOIN TRADING

This paper aims to propose some behavioural biases of trading in Bitcoin. It is review literature in the areas of behavioural finance that address issues related to Bitcoin to underpin the conceptual model. A conceptual model for understanding the behavioural bias that affects investing in cryptocurrency is proposed. The biases are herding, optimism, overconfidence, confirmation bias, loss aversion, and gamblers’ fallacy. This paper ought to fill the research gap on cryptocurrency from the behavioral perspective. This paper implies that prices and Bitcoin transactions are more determined by psychological factors.

Heuristics Bias and Investment Performance: Does Age Matter? Evidence from Colombo Stock Exchange

This study investigates the existence of heuristics biases in Colombo Stock Exchange and their effect on investment performance from individual investor’s point of view. In specific, the effects of anchoring, availability bias, gamblers fallacy, overconfidence and representativeness are investigated. Further, the study inspects whether the heuristics biases differ between younger and older investors. The primary data were collected by survey from 425 individual investors. The data were analyzed using multivariate analysis such as Confirmatory Factor Analysis (CFA) and Structure Equation Modeling (SEM). The results show that there is a statistically significant effect of anchoring, availability bias, overconfidence and representativeness bias on investment performance. However, gamblers fallacy not significantly affects investment performance. Furthermore, statistically significant differences are found between the answers of younger and older investors. This study, hopefully, will help investors to be aware of the impact of their own heuristics bias on their decision making in the stock market, thus increasing the rationality of investment decisions for enhanced market efficiency.

Lucky Events and Beliefs in Luck: Paradoxical Effects on Confidence and Risk-Taking

The effects of a lucky event and irrational beliefs about luck were examined. In two experiments, some subjects experienced a lucky event, whereas others did not. All subjects then completed an unrelated decision task rated their confidence, and placed a bet. The effects of a lucky experience depended substantially on individual beliefs concerning the causal properties of luck. After the lucky event, those who believed in luck (i.e., thought of luck as a stable, personal attribute) were more confident and bet more, whereas those who did not believe in luck (i.e., thought luck was random) were less confident and bet less. A third experiment identified analogous effects using multiple-choice test questions that included a monetary penalty for errors. Increased expectations following initial luck were interpreted in terms of a lucky streak effect, whereas the paradoxical decrease in expectancy was viewed as an instance of the gamblers' fallacy.