Effect of heuristic anchoring and adjustment, and optimism bias, in stock market forecasts (original) (raw)
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Effect of the anchoring and adjustment heuristic and optimism bias in stock market forecasts
2020
Stock market forecasting is an important and challenging process that influences investment decisions. This paper presents an experimental design that aims to measure the influence of the anchoring and adjustment heuristic and optimism bias in these forecasts. The study was conducted using information from the S&P MILA Pacific Alliance Select financial index; this was presented to 670 students from the cities of Concepción (Chile), Cali (Colombia), and Lima (Peru). Data was collected and presented through an instrument that asked participants to make a forecast judgment of the said financial index, based on the presented graphics, representing a year, a month, a week, and the last closing value of the index. Thus, it was possible to measure the influence of the anchor and adjustment heuristic in order to establish whether the presence of an initial value affected the financial forecast. Similarly, the study sought to determine whether the judgment issued was biased toward an optimis...
Do Investors Experience Heuristics in Earnings Forecasting?
2020
This research aims to examine empirically the overreliance on representativeness heuristic and anchoring-adjustment influences experienced by investors in forecasting future earnings. This research was a laboratory experiment with a design of 2x2 full factorial between subject. The results showed that representativeness heuristics were only experienced by investors who obtained positive information. Besides, this study also shows that investors do not overreliance on anchoring-adjustment heuristics. Generally, this research shows that cognitive biases occur when the information presented is of good value so that it can be taken into consideration for investors to be more careful in making predictions. Multiple benchmark information can be used as a consideration in evaluating the company’s earnings and stock performance.
An exploratory analysis of portfolio managers' probabilistic forecasts of stock prices
Journal of Forecasting, 1994
This study reports the results of an experiment that examines (1) the effects of forecast horizon on the performance of probability forecasters, and (2) the alleged existence of an inverse expertise effect, i.e., an inverse relationship between expertise and probabilistic forecasting performance. Portfolio managers are used as forecasters with substantive expertise. Performance of this 'expert' group is compared to the performance of a 'semi-expert' group composed of other banking professionals trained in portfolio management. It is found that while both groups attain their best discrimination performances in the four-week forecast horizon, they show their worst calibration and skill performances in the 12-week forecast horizon. Also, while experts perform better in all performance measures for the one-week horizon, semi-experts achieve better calibration for the four-week horizon. It is concluded that these results may signal the existence of an inverse expertise effect that is contingent on the selected forecast horizon. KEY WORDS Probabilistic forecasting Stock price forecasts Calibration Inverse expertise effect Forecasting accuracy of financial variables attracts considerable research attention, with predominantly conflicting findings. Liljeblom (1989) shows expirical evidence displaying the predictive power of financial analysts' forecasts of earnings per share for short-term forecast horizons in the Scandinavian securities market. DeBondt and Thaler (1990), on the other hand, conclude that the earnings forecasts provided by the security analysts are basically overreactive, i.e., when large earnings increases are forecasted actual earnings are lower than predictions and vice versa. Keane and Runkle (1990) use the ASA-NBER survey of economic forecasters and show that the price forecasts for GNP deflator by expert forecasters are rational demonstrating improved performance. Zarnovitz (1985) uses the same data set and rejects the rational expectations hypothesis for inflation forecasts. Summarizing the relevant work on financial forecasting, DeBondt (1991) concludes that 'finance should attempt to model the behavior of representative investors and the nature of their errors' (p. 90). Contradictory results obtained in these studies may be viewed as resulting from data revisions, biases in aggregating data, and the definitions of forecast error that are employed.
The Effect of Forecast Bias on Market Behavior: Evidence from Experimental Asset Markets
: This paper reports the results of 15 experimental asset markets designed to investigate the effect of optimistic forecast bias on market behavior. Each market is organized as a double oral auction in which participants trade a single-period asset with uncertain value. Traders are informed of the asset value distribution and, prior to trading, given the opportunity to acquire a forecast of the asset's periodend value. The degree of forecast bias is manipulated across experimental sessions so that in some sessions the forecast contains a systematic, upward (low or high) bias. We conduct sessions with inexperienced and experienced traders. The results suggest that market prices are supportive of a full revelation unbiased price in the unbiased markets and the experienced, low-bias markets. The results from the low-bias markets indicate that as long as traders have sufficient experience with such forecasts, asset prices reflect the debiased forecasts. In contrast, we find no evide...
Empirical Testing of Heuristics Interrupting the Investor’s Rational Decision Making
European Scientific Journal, 2013
The study aimed to investigate the impact of behavioral biases on investor's financial decision making. Current research studies the behavioral biases including overconfidence, confirmation, and illusion of control, loss aversion, mental accounting, status quo and excessive optimism. The study is significant for the investors, policy makers, investment advisors, and bankers. Empirical data has been collected through administrating a questionnaire. Correlation and Linear regression model techniques are used to investigate whether investor decision making is affected by these biases. The study concluded that the Confirmation, Illusion of control, Excessive optimism, Overconfidence biases have direct impact on the investor's decision making while status quo, Loss aversion and Mantel accounting biases have no impact according to data collected from financial institutions.
International Journal of Academic Research in Business and Social Sciences, 2022
Purpose: The primary purpose of the study was to examine the roles of heuristic techniques and cognitive biases in Investment decision making and suggest directions for future research. Design/Methodology/Approach: The study adopted the literature review method to solicit an understanding of the heuristics and biases central to behavioural finance and influence investment decision-making. Findings: The paper provides conceptual insights into the influence of heuristic techniques and cognitive biases in investment decision-making. Results from the conceptual analysis show that in recent times, investors in their bid to minimise losses and maximize gains employ a range of heuristics which often lead to systematic errors in judgment. Practical Implications: The paper encourages investors to prioritise financial literacy as a prerequisite to making investment decisions in the capital market and minimise the overreliance on heuristic techniques which often lead to biases. Originality/Value: The current study is the first to focus on the influence of both heuristic techniques and cognitive biases in investment decision-making together with suggested future research directions. This article enhances understanding of the behavioural finance approach to investment decision-making.
The Psychology of Economic Forecasting
Is the imprecision of economic forecasts due to the judgments of ‘biased’ decision makers? This study explores decision-making among expert forecasters in Sweden using semi-structured interviews. The results indicate that forecasters’ decision processes are characterized by intuitive as well as calculating reasoning, gradually adopting mental models and conflicting goals. While forecasters make judgments that are non-optimal in terms of minimizing forecasting errors, these are not necessarily biased but can be described as ecologically rational decisions. The results indicate that behavioral forecasting research would benefit from taking into account the specific decision-making environment in which forecasters operate.
Do forecasts produced by organizations reflect anchoring and adjustment?
Journal of Forecasting, 1987
In attempting to improve forecasting, many facets of the forecasting process may be addressed including techniques, psychological factors, and organizational factors. This research examines whether a robust psychological bias (anchoring and adjustment) can he observed in a set of organizationally-produced forecasts. Rather than a simple consistent bias, biases were found to vary across organizations and items being forecast. Such bias patterns suggest that organizational factors may be important in determining the biases found in organizationally-produced forecasts, KEY WORDS Forecasting-organisational Behavioural decision-theory Anchoring Adjustment
Journal of Forecasting, 2002
This study aims to investigate the individual behaviour that underlies the overreaction hypothesis by conducting a controlled experiment. Two areas that were not captured by previous research on the validity of the overreaction hypothesis are investigated. First, actual portfolio managers are employed as forecasters. Second a real-world assessment task is given in the form of predicting the prices of stocks traded on the exchange on a real time basis. The purpose is to explore return expectations and risk perceptions of portfolio managers as well as financially unsophisticated investors by using point and interval forecasts provided for different forecast horizons in bull and bear markets. Contributions stem from three sources. (1) The use of financially sophisticated subjects for the first time in an experimental framework testing the overreaction hypothesis makes possible to control for the effect of expertise. (2) The use of different forecast horizons controls for the effect of forecast period. (3) The use of real-time forecasts of specific stocks traded at the stock exchange, for the first time in an experimental framework testing the overreaction hypothesis enables to control for ecological validity. Discussions will be given as to the portfolio managers' versus naive investors' interpolating asset prices from past trends and hedging behaviour, due to their caution in projections of ranges for future prices.