Adaptive Factor:

The use of alternative methods of analysis to modify alphas for nonlinear and behavioral biases.

Affect Heuristic:

The tendency of investors to make decisions using feelings over data.

Anchoring:

The habit of people of focusing on one data point and ignoring other important data.

Anchoring and Adjustment:

The habit of people to anchor on a piece of data that they know to be incorrect or random, and adjust off that data point.

Base rate effect:

The neglecting of the underlying possibility of an event happening. For example, if doing X happens to double your chance of getting disease Y, the important factor is the chance of getting Y (base rate), if it is low, your chance of getting the disease is still small. Most people dramatically overestimate the probability by neglecting the base rate.

Behavioral Cycle:

Stocks cycle through periods of euphoria and despair where they are subsequently overvalued and undervalued. This time it takes to move from one extreme to the other is the behavioral cycle and is usually completed over a period of years.

Behavioral Finance:

Understanding how our brain’s thought processes and emotions affect our investing.

Behavioral Biases:

Thought processes that cause investors to make less than rational decisions. These biases cause stocks to be mispriced.

Biases:

Preconceived judgments that may or may not be true or rational.

Cognitive Biases:

See Behavioral Biases.

Confirmation Biases:

A behavior that causes investors to look for and over emphasize data that supports our current belief, and ignore data that contradicts it.

Data Mining Errors:

If you slice and dice the data enough you can find evidence to support almost anything. Also, data mining can find data that is only relevant for the time period that is tested (see framing bias).

Disposition Effect:

The tendency of investors to avoid selling stocks at a loss, and be prone to selling stocks that have increased in value too early.

Endowment Effect:

The tendency of people to put a higher value on objects they own than objects they do not own.

Emotional Biases:

See behavioral biases.

Overconfidence:

People are excessively confident in their own abilities.

Framing:

The perception of a situation on how information is presented.

Framing Bias:

The method the data is presented can influence the response. For instance, risk data that is shown as an average volatility over a past time period may give very different responses than risk data shown as a probability of losing money.

Herding:

The tendency of people to prefer the popular choice especially in periods of uncertainty.

Heuristics:

A process of learning that is not expected to produce an optimal solution but a satisfactory one. Examples are rules of thumb and educated guessing.

Hindsight Bias:

The belief that we predicted past events although tests show that people are terrible at predicting future events.

Investor Biases:

See Behavioral Biases.

Loss aversion:

The tendency of many investors to avoid taking a loss for any reason. Also: see Disposition Effect.

Prospect Theory:

A theory that says people make behavioral errors even when the probabilities of outcomes are known. They value losses and gains differently thus leading to non-optimal results.

Recency Effect:

The tendency of investors to value recent data with a greater weight than earlier data that may be just as important.

Regret:

We avoid situations that we may regret. See loss aversion and disposition effect.

Risk Aversion:

Investors prefer known returns to unknown returns, even if on average the unknown returns are higher.

Selective Perception:

People may perceive similar situations very differently, but assume everyone else sees it like they do.

Value Effect:

The tendency of value stocks to outperform the market over the long term. Many papers have been written trying to explain this anomaly.