The Framing Effect and Confirmation Bias

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The Framing Effect and Confirmation Bias

The framing effect describes our tendency to react to, judge, or interpret the exact same information in distinctly different ways depending on how it is presented to us, or “framed” (most commonly, whether the information is framed as a loss or as a gain). Building off of the previously discussed concepts of loss aversion and Prospect Theory, people tend to avoid risk when information is presented in a positive frame but seek risk when information is presented in a negative frame.

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The most commonly cited example of this is a 1981 Tversky and Kahneman study that asked participants to choose between two treatments, A and B, for 600 people affected by a deadly disease. Treatment A was predicted to result in a guaranteed total of 400 deaths, while treatment B had a 33% chance that no one would die but a 66% chance that everyone would die. The same two alternatives were then presented to the study's participants either under a positive frame (how many peoples' lives would be saved) or under a negative frame (how many people would die).

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When the alternatives were framed positively, 72% of participants chose Treatment A (“saves 200 lives”). When the exact same alternatives were framed negatively, however, only 22% of participants chose Treatment A (now presented as “400 people will die”). Saving 200 of the 600 lives is the exact same outcome as letting 400 of the 600 die, but the manner in which this identical treatment option was framed resulted in a massive decrease in the number of participants who chose it. Under the positive frame, the majority of participants avoided risk by choosing the treatment that resulted in a sure saving of 200 lives. Under the negative frame, however, the majority of participants sought the riskier alternative treatment that offered a 33% chance of saving all 600 lives.

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Another famous example that demonstrates the impact of framing is a study that found 93% of PhD students registered for classes early when a penalty fee for late registration was emphasized, but only 67% did so when the same number was presented as a discount for early registration.

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It is no secret that investors in the financial markets are under a constant barrage of information from all different sides - bullish, bearish, and everything in between. The exact same information can be framed by multiple sources in many different ways, biasing your interpretation of it. As you filter the stream of news and financial data that comes your way, consider the manner in which those numbers, statistics or reports are framed and think about the impact that their presentation has on the opinions they lead you to form.

Confirmation Bias and Forex Signals

Confirmation bias is the tendency to overweight, favor, seek out, exaggerate or more readily recall information or alternatives in a way that confirms our preconceived beliefs, hypotheses or desires, while simultaneously undervaluing, ignoring or otherwise giving disproportionately less consideration to information or alternatives that do not confirm our preconceived beliefs, hypotheses or desires. This inherent flaw in our cognitive reasoning leads to misconstrued interpretations of information, errors in judgment, and poor decision making. The effects of confirmation bias have been shown to be much stronger for emotionally-charged issues or beliefs that are deeply entrenched. In addition to overvaluing information that confirms our preexisting beliefs, confirmation bias also includes our tendency to interpret ambiguous evidence as supporting existing positions, even if no true relationship exists. In short, this concept says that individuals are biased towards information that confirms their existing beliefs and biased against information that disproves their existing beliefs, leading to overconfidence in our opinions and our decisions even in the face of strong contrary evidence.

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As an investor in the financial markets, it can be difficult to maintain a separation between informed estimates or expectations and emotional judgments based on hopes or desires. By causing us to overweight information that confirms such hopes or desires, confirmation bias can affect our abilities to make sound assessments and form well-reasoned opinions about, for example, a stock's upside potential. Awareness of our natural biases towards confirming information and, perhaps more importantly, our biases against disproving information is the first step in combating the unwanted effects of confirmation bias.

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Hindsight Bias and the Availability Heuristic

Hindsight bias describes our inclination, after an event has occurred, to see the event as having been predictable, even if there had been little to no objective basis for predicting it. This is the psychological tendency that causes us, after witnessing or experiencing the outcome of even an entirely unforeseeable event, to exclaim “I knew it all along!”



The discovery of hindsight bias emerged during the early 1970s as the field of psychology witnessed an expansion of investigations into heuristics and biases, largely led by Amos Tversky and Daniel Kahneman. Along with the uncovering of tendencies such as the hindsight bias came the discovery of the availability heuristic, a common mental shortcut that causes individuals to rely on immediate information or examples that come to mind first when evaluating a specific topic, concept, method or decision. According to the cognitive reasoning behind the availability heuristic, if something can be recalled, it must be important, or at least more so than alternatives that are not as readily recalled. As a result, individuals tend to more heavily weight recent or immediately-recalled information, creating a bias towards the latest news, events, experiences or memories.

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The Sunk Cost Fallacy

The sunk cost fallacy rests on the economic concept of a sunk cost: a cost that has already been incurred and cannot be recovered. While theoretical economics says that only future (prospective) costs are relevant to an investment decision and that rational economic actors therefore should not let sunk costs influence their decisions, the findings of psychological and behavioral finance research show that sunk costs do in fact affect real-world human decision making. Because of our tendencies towards Loss Aversion and other cognitive biases, we fall victim to the sunk cost fallacy, which describes our irrational belief that sunk costs should be considered a legitimate factor in our forward decision making when, in fact, their consideration often leads us towards inefficient outcomes.

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For example, let's say a gentleman named Fred is concerned about his weight and decides to go on a diet. As part of his cleanse, he empties his fridge of all tasty temptations. When he comes across an unopened tub of ice cream, however, he falls victim to the Sunk Cost Fallacy. Even though the $15.00 Fred spent on the ice cream is a sunk cost that has already been incurred and cannot be recovered, Fred convinces himself that he cannot let the ice cream go to waste because he previously spent his hard-earned dollars to buy it. Eating a full tub of ice cream is in no way in line with his current weight-loss objectives, as the calories he will take in by consuming it are many times the daily total target of his new diet. Still, despite the adverse consequences for his health goals, Fred is swayed into eating the ice cream because of the Sunk Cost Fallacy.

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In an investment setting, the consequences of the sunk cost fallacy can be much more severe than some unwanted calories. As the share price of a security falls, investors often begin to employ the logic that “I've already lost $XXX, it's too late to sell now.” As prices keep falling further and losses grow, the investor's commitment to the sunk cost continues to escalate. “Now I’ve lost $XXXXX, there's no way I can sell now. It has to come back eventually. I'll just hold on to it.” Improper or irrational considerations of sunk costs can lead to poor decisions that continue to spiral out of control, simply because of an incorrect perception of an expense that is irrecoverable.

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The Gambler's Fallacy

The gambler's fallacy, also known as the Monte Carlo Fallacy, is the mistaken tendency to believe that, if something happens more frequently than “normal” during a period of time, it must happen less frequently in the future, or that, if something happens less frequently than “normal” during a period of time, it must happen more frequently in the future. This tendency presumably arises out of an ingrained human desire for nature to be constantly balanced or averaged. In situations where the event being observed or measured is truly random (such as the flip of a coin), this belief, although appealing to the human mind, is false.

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The gambler's fallacy is, rather obviously, most strongly associated with gambling, where such errors in judgment and decision making are common. It can, however, arise in many practical situations, including investing. Winning and losing trades are in many ways similar to the flip of a coin and thus subject to the same psychological biases. If an investor has a series of losing trades, for example, he or she can begin to erroneously believe that, since the statistics feel unbalanced, his or her probability of making a profitable trade increases. In reality, the probability of his or her next trade being profitable is unaffected by previous losses.

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What is overweight in Portfolio Management?

An overweight investment is a sector of assets or industries that contains a larger portfolio than usual or an index. An investor may choose to dedicate most of his portfolio to a sector that appears to be particularly promising, or the investor may gain excess weight on defense stocks and bonds at a time when prices are fluctuating.

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Overweight and its opposite, underweight are also used by analysts and commentators in recommendations for acquiring or avoiding certain investments or sectors. For example, if federal defense spending is going to increase or decrease, the analyst may recommend that the investor gain excess weight or underdevelopment for defense-related businesses.

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In addition, many analysts are adding overweight recommendations to the stock, which they believe will surpass its sector in the coming months. Alternative ratings - the same weight (for average performers) or underweight (for performers below average).





KEY TAKES

Being overweight is a large investment in a particular asset, asset type or sector in a portfolio.

Overweight, rather than the same weight or underweight, also reflects the analyst's view that a particular stock will exceed the industry average over the next eight to 12 months.

Portfolio managers may be overweight in stocks or sectors if they think they will work well and increase overall returns.

Understanding overweight

Strictly speaking, overweight refers to the excess amount of a fund's asset or investment portfolio compared to the underlying index it tracks.

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Indices are weighed. That is, they monitor the effectiveness of the choice of stocks, each of which is a percentage of the index, which varies depending on its perceived impact as a whole.



Mutual funds are also weighed, and a certain percentage of the fund can be used for cash or interest-bearing bonds to reduce overall risk. That is why the efficiency of even index mutual funds may differ in part from each other and from the index itself.



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The purpose of the fund manager is to achieve or exceed the indicator with which he is compared. This can be achieved by being overweight or underweight by some parts of the whole.



Victory in the trend

Otherwise, there is no clear definition of overweight. It's just a deviation from the norm, whatever it was. For example, a global technology fund manager who predicts a downturn in the future may transfer some assets that are overweight to some of the most stable blue chip companies. An investor with a diversified portfolio that has predicted a downturn may lead to an overweight on interest-bearing bonds and dividends.

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Excess weight can also refer to the analyst's opinion that the stock is superior to others in its sector or market. In this sense, it is a purchase recommendation. When an analyst assumes that an asset is underweight, they say it now looks less attractive than other investment options.



Slip rate

Portfolio managers strive to create a balanced portfolio for each investor and personalize it for that person's risk tolerance. For example, a portfolio of 60% of stocks and 40% of bonds is best for a young investor with a moderate risk appetite. If the same investor decides to transfer 15% more balance on shares, the portfolio will be classified as excess reserves.



A portfolio may be overweight in a sector such as energy or in a particular country. This can be overweight in such categories as aggressive growth stocks or stocks with high dividends.

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Advantages and disadvantages

Actively managed funds or portfolios are overweight, in particular, securities, if it will help them achieve greater returns. For example, a fund manager may raise the weight of securities from the usual 15% of the portfolio to 25%, trying to increase the return on the total portfolio.



Another reason for the holding company's excess weight in the portfolio is hedging or reducing the risk of another overweight position. Hedging involves the acceptance of compensation or the opposite provision to the appropriate collateral. The most common method of hedging is through the derivatives market.

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For example, if you own shares in a company that currently sells for $ 20 per share, you can purchase a one-year option for that share for $ 10. A year later, if the stock sells for more than $ 10, you give an expiration date, losing only the purchase price. If the stock sells for less than $ 10, you can take advantage of the bid and get $ 10 for your stock.



The danger of overweight one investment is that it can reduce the overall diversification of their portfolio. Reducing diversification may expose the holding to additional market risk.



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Where to Set Your Stop-Loss



KEY TIPS

A stop-loss order is placed with a broker to sell securities when they reach a specific price.

Finding out where to place your stop loss depends on your risk threshold; the price should minimize and limit your loss.

The percentage method limits the stop loss to a specific percentage.

In the support method, an investor determines the most recent support level for the stock and places the stop loss just below that level.

The moving average method considers the stop loss to be placed just below a longer-term moving average price.

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Nobody wants to lose money when they play the market. That is why it is important to establish a floor for your position in a security. That's where stop-loss orders come in. But many investors have a hard time determining where to set their levels. Setting them too far can result in big losses if the market moves in the opposite direction. Set stop loss too close and you can exit a position too quickly.





So how can you know where to set your stop loss order? Keep reading to know more.







What is a stop loss order?

A stop-loss order is placed with a broker to sell securities when they reach a specified price.1 These orders help minimize the loss that an investor may incur on a securities position. Therefore, if you set the stop loss order 10% below the price at which you bought the security, your loss will be limited to 10%.



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For example, if you buy shares of Company X for $ 25 per share, you can enter a stop-loss order for $ 22.50. This will keep your loss at 10%. But if Company X's shares fall below $ 22.50, its shares will be sold at the current price.



Slippage refers to the point where you cannot find a buyer at your limit and end up with a lower price than expected.

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Determination of the stop loss order

Determining the placement of stop-loss orders is all about targeting an allowable risk threshold. This price must be derived strategically with the intention of limiting the loss. For example, if a stock is bought at $ 30 and the stop-loss is placed at $ 24, the stop-loss is limiting the downside catch to 20% of the original position. If the 20% threshold is where you feel comfortable, place a trailing stop loss.



Know where you will place your stop before you start trading a specific value.

There are many theories about stop loss placement. Technical traders are always looking for ways to time the market, and different stop or limit orders have different uses depending on the type of timing techniques that are implemented. Some theories use universal placements, such as a 6% trailing stop on all values, and some theories use pattern or safety specific placements, including average true range percentage stops.

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Stop Loss placement methods

Common methods include the percentage method described above. There is also the support method that involves sharp stops at a fixed price. This method can be a bit more difficult to practice. You will need to find out the latest level of support for the stock. As soon as you have discovered it, you can place your stop loss order just below that level.

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The other method is the moving average method. Using this form, stop losses are placed just below a long-term moving average price rather than short-term prices.



Swing traders often employ a multi-day high / low method, in which stops are placed at the low price of a predetermined trading day. For example, lows can be constantly relocated to the two-day low. More patient traders can use indicator stops based on a broader trend analysis. Indicator stops are often combined with other technical indicators, such as the Relative Strength Index (RSI).



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What to consider with Stop Loss orders

As an investor, there are a few things to keep in mind when it comes to stop-loss orders:



Stop loss orders are not for active traders.

Stop-loss orders don't work well for large blocks of stocks, as you can lose more in the long run.

Brokers charge different fees for different orders, so keep an eye on how much you are paying.

And never assume that your stop-loss order has been executed. Always wait for the order confirmation.



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Understanding the standard deviation



The standard deviation can show the consistency of an investment's performance over time.

A fund with a high standard deviation shows price volatility.

A fund with a low standard deviation tends to be more predictable.

The standard deviation is calculated by taking the square root of the variance, which is itself the average of the squared differences from the mean.



Understanding the standard deviation

One reason for the great popularity of the standard deviation measurement is its consistency.



The standard deviation of the mean represents the same, whether you look at gross domestic product (GDP), crop yield, or height of various dog breeds. Also, it is always calculated in the same units as the data set. You do not need to interpret an additional unit of measure resulting from the formula.



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Standard deviation measurement example

For example, suppose a mutual fund achieves the following annual rates of return over the course of five years: 4%, 6%, 8.5%, 2%, and 4%. The mean, or average, value is 4.9%. The standard deviation is 2.46%. That means each individual annual value is on average 2.46% of the mean.



Each value is expressed as a percentage, making it easy to compare the relative volatility of various mutual funds.


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Due to its consistent mathematical properties, 68% of the values in any data set are within one standard deviation of the mean and 95% are within two standard deviations of the mean. Alternatively, you can estimate with 95% certainty that annual returns do not exceed the range created within two standard deviations of the mean.1



Bollinger Bands

When reversing, standard deviations are generally demonstrated with the use of Bollinger bands. Developed by technical trader John Bollinger in the 1980s, Bollinger bands are a series of lines that can help identify trends in a given value.2


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In the center is the exponential moving average (EMA), which reflects the average price of the security over a set period of time. On both sides of this line are bands established one to three standard deviations from the mean. These outer bands oscillate with the moving average according to changes in price.



In addition to their many useful applications, Bollinger Bands are used as an indicator of market volatility. When a security has experienced a period of high volatility, the bands are widely separated. As volatility decreases, the bands narrow, hugging the EMA.

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Standard deviation measures consistency. Consistency is good, but it is not the only measure of the quality of a fund.

Even the most limited range charts experience short bouts of volatility from time to time, often after earnings reports or product announcements. In these charts, the normally narrow Bollinger bands appear suddenly to accommodate increased activity. Once things settle down again, the bands tighten.


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Since many investment techniques depend on changing trends, being able to identify highly volatile stocks at a glance can be especially helpful.

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Other data to consider

While important, standard deviations should not be taken as a final measure of the value of an individual investment or portfolio. For example, a mutual fund that returns between 5% and 7% each year has a lower standard deviation than a competitor fund that returns between 6% and 16% each year, but that does not make it a Better option.



It is important to note that the standard deviation can only show the dispersion of a mutual fund's annual returns, which does not necessarily imply future consistency with this measurement. Economic factors, such as changes in interest rates, can always affect the performance of a mutual fund.

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Disadvantages of measuring standard deviation

Even as an assessment of the risks associated with a mutual fund, the standard deviation is not an independent answer. For example, the standard deviation only shows the consistency (or inconsistency) of the fund's returns. It does not show the fund's performance against its benchmark, which is measured as beta.



Another potential drawback to relying on the standard deviation is that it assumes a bell-shaped distribution of data values.1 This means that the equation indicates that there is an equal probability of achieving values above or below the mean. Many portfolios do not show this trend, and hedge funds especially tend to lean one way or the other.

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