Improving Your Trading Plan

IMPROVING YOUR TRADING PLAN 1
Improving Your Trading Plan: Making it Easier to Cut Your Losses, Let Your Profits Run
Benjamin S. Cheeks
International School of Management, Paris

Abstract
Studies within the field of Behavioural Finance have shown that due to human bias,
market participants do not always act rationally. This creates opportunities in the market for
traders to profit from the inefficiencies created by these biases. However, Behavioural Finance
also shows that these same biases cause traders to act in ways that could have negative impacts
on their personal trading results. The perception of fund managers does change when they have
been trained on Behavioural Finance (Nikiforow, 2010). Training sharpens the awareness of loss
aversion and limits the affinity for conformity. Nikiforow suggests that what is needed is to
incorporate these approaches into the investment process (Nikiforow, 2010). This paper reviews
some of the common behavioural biases and how they can impact trading. It then covers the
general content and structure of a trading plan and recommends areas where traders can
incorporate behavioural bias awareness into their plans using rules and checkpoints.
Keywords: Trading Plans, Behavioural Finance, Behavioural Economics, Behavioural Bias

Improving Your Trading Plan: Making it Easier to Cut Your Losses, Let Your Profits Run
This paper defines a trader as an investor that engages in the transfer of financial assets
with the purpose of profiting from short-term trends. It is the goal of every trader to beat the
market. If not, they would all invest in index funds. According to Modern Portfolio Theory an
investor should put together a portfolio of diversified stock. Modern Portfolio Theory states to
do exactly that and the Efficient Market Hypothesis states that efficient markets are
unpredictable and traders cannot consistently beat them.
Modern Portfolio Theory has had such an impact on the financial world that in 1990
Harry Markowitz received the Nobel Peace Prize in Economics in recognition of the Modern
Portfolio Theory. With such heavyweights against them, why is it that traders still try? Lo and
MacKinlay suggest a reason. They explain how $1 invested in US Treasury bills between
January 1926 and December 1996 would have been worth $14 at the end of the period. Had the
$1 been invested in the S&P500 during the same period, it would be worth $1370. However, an
investor with perfect foresight to switch their investment on a monthly basis to the higher
yielding asset for that month would have turned the $1 into $2,303,981,824 (Lo & MacKinlay,
1998). Even a modest ability to forecast trend could be handsomely rewarded.
While Modern Portfolio Theory and Efficient Markets Hypothesis are still hotly debated
topics, the most profound attaches focus on the assumption that all investors act rationally. If we
took a moment to reflect on the past investment decisions we have made, I'm sure we could think
of at least one example where we did not act in the most rational manner. The first noted study
regarding financial behaviour was published in 1979 by Kahneman and Tversky. They called
their theory Prospect Theory. With Prospect Theory, it was demonstrated empirically that when
deciding among alternatives that involve risk, people do not always act rationally. Prospect

Theory and similar studies inspired by it have more recently been branded Behavioural Finance.
In 2002, just 12 years after Markowitz, Kahneman won the Nobel Peace Prize in Economics for
his work in Behavioural Finance. Finally the traders had a heavyweight on their side. However,
in a twist of fate befitting a Greek tragedy, it is the same biases that create the opportunities in
the markets that prevent the traders from taking advantage of them. This paper reviews a brief
background of Modern Portfolio Theory, Efficient Markets Hypothesis, and Behavioural Finance
and proposes improvement that traders can make to their trading plan to attempt to overcome
their own behavioural biases.
Modern Portfolio Theory and Efficient Markets Hypothesis
Modern Portfolio Theory
Markowitz introduced the Modern Portfolio Theory (MPT) in a series of papers
published in the 1950s while working at the University of Chicago. The theory is an attempt to
quantify the concept of investment risk.
In short, the theory states that when considering the addition of an asset to a portfolio, the
investor should consider how the value of that asset changes in relationship to the other assets in
the portfolio under certain conditions. In order to do this, one must calculate the expected return
of an asset as well the standard deviation of that return. Simply put, expected return can be
calculated using the weighted average expected return of a series of outcomes. Variance is
defined as a measure of the dispersion of a set of data points around their mean value with
standard deviation being the square root of the variance.
As an example, assume that over a certain time frame, an investor has expectations about
how the value of the US dollar will change against a basket of currencies and how that change
will impact the price of gold. Historically speaking, there has been a negative correlation

between gold and the value of the US dollar. In other words, when the US dollar decreases in
value, gold tends to increase in value.
Let’s assume that the investor has the following expected outcome for gold based upon
the US dollar.
Scenario Probability Effect on Gold Weighted Average
US$ up 5% 25% -0.5% -0.1%
US$ flat 25% 0.0% 0.0%
US$ down 5% 25% 15.0% 3.8%
US$ down 10% 25% 20.5% 5.3%
Expected Return 8.9%
Standard Deviation 9.2%
In this example, the investor believes there is a 25% probability of any of the four
scenarios taking place with each having a different effect on gold prices. Using the formula
proposed by Markowitz (Markowitz, 1952, p. 83), we can determine that the expected return is
8.9% and the standard deviation is 9.2%.
When considering a second investment to add to the portfolio, Markowitz suggests
finding an investment that is not correlated to the others. Continuing this example, with
Markowitz words in mind, the investor chooses an S&P500 index fund believing that the overall
US Market is positively correlated to changes in the value of the US dollar. The investor has the
following expected outcome for the S&P500 based upon changes in the value of the US dollar.
Scenario Probability Effect on S&P500 Weight Average
US$ up 5% 25% 27.5% 6.9%
US$ flat 25% 15.5% 3.9%
US$ down 5% 25% -2.0% -0.5%
US$ down 10% 25% -5.5% -1.4%
Both investments have the same expected return of 8.9%, but the S&P500 index has a
much greater standard deviation. Common sense might suggest to some investors that this is a

more risky investment and by adding to the portfolio, it will increase the overall portfolio risk.
However, the following table shows that this is not true.
Gold Effect S&P500 Effect
50% 50% Combined Weighted Average
-0.3% 13.8% 13.5% 3.4%
0.0% 7.8% 7.8% 1.9%
7.5% -1.0% 6.5% 1.6%
10.3% -2.8% 7.5% 1.9%
A portfolio equally weighted between gold and the S&P500 will yield the same return
with a much lower standard deviation of 2.7%. This according to Markowitz will have a much
higher appeal to an investor as investors do desire expected return with the lowest variance.
Note that assigning different weights to the investment rather than a 50/50 split will also
have an impact on the standard deviation. One should not assume that an equal weighting of
each asset is preferred.
Using this very basic example, one can see how it is possible through trial and error using
MPT to theoretically put together a portfolio that would provide maximum expected return with
the lowest variance. Studies reveal that with as few as 40 well-chosen stocks, an investor can
eliminate all but market risk; also called systematic risk (Statman, 1987). This risk cannot be
diversified away.
Assumptions of MPT.
There are several assumptions inherent in MPT. At least two of these are explicit. The
first is that investors wish to maximize their return with the least variance. As discussed later in
the paper, this assumption does not always hold true for a variety of reasons. The second
explicitly stated assumption is that risk can be defined by variance or standard deviation.

An implicit assumption of the MPT is that investors have an accurate conception of
possible returns. This assumption leads one to the Efficient Markets Hypothesis.
Efficient Markets Hypothesis
The work of Markowitz was furthered by Eugene Fama. Fama is known for developing
the Efficient Markets Hypothesis (EMH) (Fama, 1965). He showed that price movements were
consistent with an “efficient” market and furthered defined an efficient market as one in which
prices “fully reflect” available information (Fama, 1970).
One cannot describe the EMH without mentioning the old economic joke of the $100 bill.
It describes an economist and his colleague strolling down the street when they spot a $100 bill
lying on the ground. As his colleague reaches to pick it, the economist says, “Don’t bother, if it
were real someone else would have already grabbed it” (Lo, 2004, p. 16). In other words, an
efficient market reacts almost instantaneously to new data; therefore are no arbitrage
opportunities.
The theory was later refined to include weak from, semi-strong form, and strong form
efficiency (Fama, 1970). The three versions of the hypothesis broke the available data into three
different subsets. The weak form tests the hypothesis where the information set is historical
prices and volume information. The semi-strong includes other publically available information
such as earning announcements, stock splits, dividend payments, etc. Finally the strong form
tests include data not available to the public such as insider information or monopolistic
information.

Behavioural Finance
Behavioural Finance is the primary competing doctrine to the MPT and EMH. The
modern day roots of Behavioural Finance can be traced back to the work of Kahneman and
Tversky (Kahneman & Tversky, 1979).
Advocates of Behavioural Finance argue that investor behaviour is not always rational as
assumed by the EMH. Specific behavioural biases lead investors to make decisions that could be
undesirable to their economic outcome. Some of the most studied are loss aversion,
overconfidence, regret, overreactions, and herding.
Loss Aversion
People weigh losses twice as much as gains of similar magnitude (Tversky and
Kahneman, 1992). Loss aversion and related theories (i.e. disposition effect) have been used to
explain why investors engage in irrational behaviour. The relationship between Prospect Theory
and investor behaviour is consistent with selling winners too early and holding onto losers for
too long (Shefrin & Statman, 1985). Many assert that not only do investors avoid risk in order to
minimize losses, but they may assume additional risk in order to avoid sure losses (De Bondt,
Muradoglu, Shefrin, & Staikouras. 2008) and Neilson (2002). One could easily speculate that it
is this propensity towards more risk that causes some investors to double down or invest more in
their loser in hopes that they will more quickly return to profitability through dollar cost
averaging. Extreme and well-known examples of this behaviour are Nick Leeson the trader that
brought down Barings Bank and Jérôme Kerviel who nearly did the same to Societe General.
Loss aversion is reference or frame dependent. Koszegi & Rabin state “How a person assesses

the outcome of a choice is often determined as much by its contrast with a reference point as by
intrinsic taste for the outcome itself” They go on to add:
Equating the reference point with expectations rather than the status quo is also important
for understanding financial risk: while an unexpected monetary windfall in the lab may
be assessed as a gain, a salary of $50,000 to an employee who expected $60,000 will not
be assessed as a large gain relative to status-quo wealth, but rather as a loss relative to
expectations of wealth (Koszegi & Rabin, 2006, p. 1134).
This can be applied in the financial world to the loss a fund manager feels when his fund
returns 20% on a year where the benchmark index gained 25%.
This point has also been supported in small scale classroom projects. In one such
example, a paper trading account is established for each student. The paper trading account
allows the student to simulate trading while keeping track of paper profits and losses. The
students have two months to trade in a variety of markets to see who can generate the highest
return at the end. Early in the allotted trading time, one of the students established a sizeable
lead over his classmates. This had the effect of establishing a new reference point to other
students. In class discussions following the trading exercise, the majority of students
acknowledge the big lead and admitted that it caused them to make more risky trading decisions
or invest in more speculative instruments such as futures in order to catch the winner. In
addition to the paper trading account the experiment also included in-class trading where the
students were assigned to groups and had to make a group decision as to how to invest. Due to
the limited time trading, examples in this exercise are minimal, but anecdotal data showed that
regardless of the indicators and trend, groups that were trailing by a large margin took more risk.
Examples of such risk taking were allocating large amounts of capital to one trade or placing

contrarian bets in the hopes that the other teams guessed incorrectly. In contract to this, the
group that was leading or within striking distance of the lead stuck closer to the trading rules and
asset allocation (personal account, April, 2011).
Psychological Accounting and Decisions Framing
Tversky and Kahneman provide another example of how behaviour can be influenced by
changing how the outcome is framed. They provide an example of a gambler at a racetrack to
explain:
For another example, consider a person who has spent an afternoon at the race track, has
already lost $140, and is considering a $10 bet on a 15:1 long shot in the last race. This
decision can be framed in two ways, which corresponds to two natural reference points.
If the status quo is the reference point, the outcomes of the bet are framed as a gain of
$140 and a loss of $10. On the other hand, it may be more natural to view the present
state as a loss of $140, for the betting day, and accordingly frame the last bet as a chance
to return to the reference point or to increase the loss to $150. Prospect theory implies
that the latter frame will produce more risk seeking than the former. Hence, people who
do not adjust their reference point as they lose are expected to take bets that they would
normally find unacceptable. This analysis is supported by the observation that bets on
long shots are most popular on the last race of the day (Tversky and Kahneman, 1981, p.
456).
Read the example above again mentally substituting stock market for race track, trade for
bet, and high risk investment for 15:1 long shot and you have an investing scenario to which
many traders can relate.

Overconfidence.
In order to overcome the anxiety that such a situation creates, people tend to ignore the
uncertainty; resulting in overconfidence in their decision. Overconfidence keeps us from
realizing how little we know about a certain situation and how much and what additional
information we need to understand the risks associated with our actions. From an investment
point of view it has been shown that overconfidence leads investors to attribute too much of their
success to ability and their failures to bad luck and will trade more frequently than “rational”
investors and by doing so lower their returns (Gervais & Odean, 2001).
Regret and Regret Aversion.
As previously stated, people have anxiety related to decision of risk. This is because if
their decision proves to be worse than it may otherwise have been, the person experiences regret
(Bell, 1982). Regret aversion is also used to explain why traders hold losing positions longer
than they should (Shefrin & Statman, 1985). Traders are prone to letting their losses ride in
hopes that they will eventually break-even. They call this the “disposition effect”. To the trader,
realising the loss is proof that their judgement was incorrect thus regretting it was made.
Holding the stock gives the hope that the decision will eventually prove to be correct.
Overreaction.
There is data consistent with the hypothesis that market participants have the tendency to
overreact (De Bondt & Thaler, 1987). Investors tend to overweight recent information and
underweight older data. To demonstrate this phenomenon, De Bondt & Thaler used monthly
data from the NYSE from 1926 to 1982 to construct two portfolios that consisted of stocks with
high abnormal positive and negative returns. Analysing this data, they find that extreme changes
in stock price are followed by significant stock price changes in the opposite direction. Similar

studies in support of the overreaction hypothesis include strong evidence demonstrating the
effect of overreaction to negative events are stronger than to those of positive events (Brown &
Harlow, 1988), and overreaction to be more prevalent for smaller firms than larger firms and are
more prevalent in the short window around quarterly earnings announcements (Chopra,
Lakonishok, & Ritter, 1992).
Herding.
Human herding behaviour results from impulsive mental activity in individuals
responding to signals from the behaviour of others. They are driven to follow the herd because
they do not have firsthand knowledge adequate to form an independent conviction, which makes
them seek wisdom in numbers (Pretchner, 2001). Relating this back to the finding of Shefrin &
Statman (1984) would suggest that herding is also related to regret aversion. If the investor does
not follow the crowd and the crowd is right, regret could ensue. Herding is different from
overreaction in that herding can happen slowly and smoothly over a relatively extended amount
of time. Continuing the theme from the animal world, it is overreaction that causes the herd to
stampede and trample the weaker and slower in the herd.
Adaptive Markets Hypothesis.
A review of the schools of thought related to the scientific practice of financial
management would not be complete without a mention of the Adaptive Markets Hypothesis
(AMH) (Lo, 2004). The AMH is an effort to bridge the gap between the two opposing views.
Lo calls the hypothesis an “evolutionary approach to economic interactions”. He says it is
heavily influenced by “evolutionary psychology” and the need for humans to maximize the
survival of their genetic material rather than their expected utility. The AMH embraces the

biases shown in Behavioural Economics and puts an evolutionary spin on them. One example is
that of risk preference.
Until recently, U.S. markets were populated by a significant group of investors who had
never experienced a genuine bear market—this fact has undoubtedly shaped the
aggregate risk preferences of the U.S. economy, just as the experience of the last four
years, since the bursting of the technology bubble has affected the risk preferences of the
current population of investors. In this context, natural selection determines who
participates in market interactions; those investors who experienced substantial losses in
the technology bubble are more likely to have exited the market, leaving a different
population of investors today than four years ago (Lo, 1984 p. 24).
As this article was written before the Global Financial Crisis, the same logic would suggest that
the investing population today has once shifted from that of three years ago. The theory suggests
that as the investor population changes, they begin to exploit different profit making
opportunities. It is this exploitation and the resulting scarcity that eventually destroys these
opportunities but in turn creates new ones. This is witnessed in booms and busts, bubbles and
crashes. This in turn implies that different trading strategies will perform well in some market
environments and not in others. Therefore, in order to achieve a consistently high level of
investment returns, one much adapt to the current market conditions.
The Trading Plan
Both Behavioural Economics and Adaptive Markets Hypothesis allow for the possibility
that markets do not always exhibit rational behaviour and are therefore not efficient. This creates
opportunities for traders to make money by taking advantage of these inefficiencies. In order to
take advantage of the opportunities that Behavioural Finance present, the investor should be

familiar with the concepts. Psychologists argue that behavioural biases are difficult to overcome
even with the knowledge of their existence (Pronin & Kugler, 2007). The perception of fund
managers does change when they have been trained on Behavioural Finance (Nikiforow, 2010).
Training sharpens the awareness of loss aversion. Training also limits the affinity for
conformity. Nikiforow suggests that what is needed is to incorporate these approaches in the
investment process. One way to do this is to establish a trading plan that has a mechanism that
helps to temper those behaviours that can have a negative impact on trading. Much as every
good business needs a business plan, it is also important that the investor setup a plan for trading.
As the aphorism goes, “plan your trade and trade your plan”.
Understandably, there is little academic research on trading plans and their make-up.
Successful traders are unwilling to discuss how they execute their trades under the belief that the
more people who know the strategy, the less successful it will be. In order to demonstrate how
an understanding of Behavioural Finance can be incorporated into a trading plan, it is important
to discuss the general content structure of a good trading plan. The elements of the trading plan
that follows draws heavily from Wilson 2003, class notes from Horn (personal account,
February-April, 2011) and the author’s personal trading experience.
For the sake of this paper, a trading plan is defined as a systematic method for describing
the trader’s goals and objectives for trading, markets to trade, system or systems to be used to
select trades, money management strategy, and trading routine. All of these elements will be
described in more detail shortly.

Goals and objectives.
In this section, the trader should define the following: What am I aiming to achieve? Is
it to replace full-time income or earn a specific amount of money or return? Is it a hobby?
• How many hours per day will I allocate to trading?
• What will my trading hours be? As highlighted in Fortune Magazine, night trading is
becoming the new “day trading”. Many brokers are offering extended trading hours
as late as 8pm. For those interested in stocks, the Asian markets open around 7.30pm
US Eastern Time. There are also commodities and futures market that trade 20 to 24
hours per day.
Markets to trade.
What market or markets will you trade? Will you focus on equities or futures? Will you
combine equities with options? Will you trade futures options? Will you trade the US markets,
the Australian market, one of the European markets or a combination of several of the above?
Trading system.
The trading system is the central part of the trading plan. The investopedia definition of
trading system is as follows:
A trading system is simply a group of specific rules, or parameters, that determine entry
and exit points for a given equity. These points, known as signals, are often marked on a
chart in real time and prompt the immediate execution of a trade
(http://www.investopedia.com).
I would add that it should also include how trading orders will be placed. The trading system is
the heart beat of the trading plan. Entire books have been written as to how to develop the best
trading system. While the design of the trading system is one of personal preference and

experience, one thing that most traders will agree upon, it is critical to thoroughly backtest the
trading system before trading with real money.
Selecting trades - technical and fundamental analysis.
In order to define the rules, the trader must first determine how to analyse trading
opportunities. There are two analytical models used to determine which investment to buy and at
what price. These are fundamental and technical analysis. Each method has its own underlying
philosophy and methods, both have the same ultimate goals; to determine market direction,
anticipate future direction, and provide price targets for entry and exit.
Technical analysis relies on historical price and volume information. By charting these
points and matching patterns, technical traders attempt to predict future price changes.
Functional analysis relies on information from such sources as financial statements and company
reports. Fundamental analysis may also consider macroeconomic data such as interest rates,
inflation, and GDP. Functional traders attempt to profit by buying investments that are
underpriced under the assumption that the right price will eventually be realised.
For those investors that cannot decide between the two, research shows that while each of
the analytical models can work well, models that integrate the two have superior explanatory
power (Bettman, Sault, Schultz, 2009). Others suggest selecting stocks with sound fundamentals
while using technical trading rules to determine when to enter and exit trades using observed
signals (Coe & Laosethakul, 2010).
Entry and exit points - buy and sell signals.
Entry points occur when a buy signal or set of buy signals have been triggered. For a
technical trader, a buy signal may be that the price of equity has crossed its 20 day moving
average or the combination of the aforementioned crossover with volume for that day that

exceeds the average of the last 15 days. For fundamental traders, an entry point may be when a
price per earning target has been reached along with a current ratio on balance sheet of 2.5.
Regardless of the type of analysis being used or the nature of the buy signals, they all have the
same purpose: to alert the trader that a change in trend has occurred and the stock price is
moving up. The opposite is true for sell signals and exit points.
Order placement.
Order placement is made up of the rules that a trader follows before, during, and
immediately after entering the trading order. For examples, many traders will always enter
market orders, while others will enter limit orders a few cents above the current bid price to see
if a seller will accept. Also, some traders will not place orders before midday on a Monday
morning under the belief that the market needs a few hours to set direction.
Stop-loss orders.
Many traders also enter a stop-loss order immediately following a buy order. This stop-
loss order will instruct the broker to sell the security when it drops to a certain price. Some
brokers have software that will allow the stop-loss order to be entered simultaneously to the
initial buy order. When entering stop-loss orders, the trader must determine the rule for setting
the loss limit. The rules will vary with investor and risk tolerance. Some traders set the stop-loss
order at 10% for all purchases. Other traders vary the stop-loss dependent upon the stock’s
average trading range for a set period. Regardless of the method, a stop-loss order is one of the
most important tools that a trader has for managing losses.

Trading timeframe.
Your trading timeframe determines how long you will hold an investment. Some buy
and hold investors will hold onto investments for years while some day traders may only hold an
investment for a few minutes.
Other considerations.
Other considerations when developing a trading plan include choosing the right broker.
When choosing a broker, some key things to consider are commissions, level of service, and
speed of trade. All of these items have a cost. Commissions are the easiest to quantify and
naturally get the most attention. However if your low cost broker cannot provide access to the
data you need, either real time or historical, the cost you pay to access this data elsewhere could
outweigh the savings in commission. The importance of speed will vary dependent upon how
quickly the trader needs their ideas implemented. A day trader measures speed in fractions of a
second. On the other hand, a fundamental trader making weekly trades may find several seconds
acceptable. For some traders, speed may not be as important as the ability to place limit orders
rather than market orders. Those limited by their broker to market orders absorb the full bid-ask
spread. This can add up to several dollars per trade and several thousand dollars per year
dependent upon your trading.
Money management.
In this section, the trader should define the following:
• How much capital will I trade with?
• How much capital will be allocated to any one trade?
• How will position size be determined?
• How will I control the downside? How will I set stop-loss values?

• How will I measure my performance?
• How will I draw an income or pay myself?
Trading routine.
It is important to establish daily, weekly, quarterly, and yearly routines. Routines are
beneficial to help bring order and discipline to the trading plan. The daily routine would include
tasks such as reviewing open positions and researching new stocks. Weekly reviews of trade
performance are also important. In addition to trading performance, the quarterly and yearly
reviews should include a "review" of the entire trading plan. Required adjustments should be
made at this time.
Measurement and feedback.
Key measures for system evaluation consist of the total number of trades, the average
profit per trade, largest winning trade, profit factor (gross profit/gross losses), maximum
drawdown, percent profitable trades, and maximum consecutive losers. Professor Horn refers to
the last three as personal evaluations as they have the ability to challenge the resolve of any
trader (personal account, February-April, 2011).
Incorporating Behavioural Bias into the Trading Plan
How can we incorporate this new found knowledge of behavioural biases in the trading
plan? The first recommendation is to add a section to the trading plan called the Belief System.
Belief system.
People have a difficult time recognizing bias within themselves (Ehrlinger, Gilovich, &
Ross, 2005). However, introspection can help us to understand the causes of our behaviour and
help predict future actions (Jones & Nisbet, 1972, as cited in Pronin et al, 2007). Then again,
unconscious cues such as behaviour biases can also mislead (Pronin et al, 2007).

Heeding this warning I recommend that the trader reflect on their trading in terms of past
actions or behaviours rather than solely as introspection. For example, instead of asking yourself
how you would respond to a 20% drawdown of trading capital, ask how in the past a
considerable loss of capital affected your behaviour and emotions. It is important to look at
specific situations.
Another good tool to overcome bias blind site is the use of analogy. Analogy allows us
to by-pass conscious processes. Asking what analogy you would use to describe the stock
market can provide some interesting insight.
Some key questions to answer in the belief section are:
What analogy would I use to describe the stock market? The answer to this question can provide
key unconscious thoughts about the market. For example, someone describes the stock market
as a casino in Las Vegas implies that they feel the stock market is a gamble and controlled by
chance. Someone defining the stock market as a game implies there are winners and losers and a
set of rules by which to play.
Key questions to ask include:
• What are my strengths and weaknesses?
• How have I used my strengths in the past during trading?
• How have my weaknesses hurt my trading and how can I overcome them?
• How have I demonstrated the self-discipline required to follow my plan? In what
circumstances do I not show discipline? How can I prevent it from impacting my
trading?
• How have I reacted to a large loss of capital in the past? What impact did this have
on my friends and family?

Trading diary.
The second section that I recommend adding to your plan is a Trading Diary. The
measures discussed previously will show the trader what did occur, not what failed to occur. For
this you will become dependent upon your trading diary. In the diary, the trader will detail their
thoughts and emotions when making trades. Litvak and Lerner (2009) describe four ways in
which emotional bias is demonstrated. Reviewing these when making decisions is instrumental
in determining if there is bias in the decision. The first is whether or not the judgment lacks
correspondence with your criteria. In this context as the criteria has been defined in the trading
plan the question to answer is – does the decision fit your trading plan? These second to detect
behavioural bias is to determine if the judgement is based upon bad information. The third
question to answer is whether or not the decision is influenced by the failure to use good
information. Is the trader looking only at data that supports the decision while missing good
information to the contrary? The last method of detecting bias is to ask if the decision
corresponds to the judgement of others. A trend trader would need to be able to show a trend has
formed that supports the decision. However, a contrarian trader would want to confirm that the
trader does not follow the prevailing pattern. By recording this information and reviewing it as
part of the trading routine, a trader can gain a deeper insight into their trading and more easily
detect patterns of bias that are having an impact on trades.
Including behavioural bias considerations in the trading plan.
This section of the paper highlights tools and behaviours that an investor can incorporate
into a trading plan in an attempt to overcome or to raise awareness of behavioural biases that
may have negative impacts. Some of these tools will be rules that are easily defined and easy to
implement within the strategy; assuming the trader can recognize and overcome the bias. Others

will be structured as decision points that force the trader to consider the situation through
different eyes, or a different frame of reference. The following tables highlight how the various
behavioural biases can manifest themselves during trading and suggests rules or guidelines as for
mitigation.
Loss Aversion.
Table 1
Rules and Guidelines for Loss Aversion
Behavioural Bias – Loss Aversion Rule / Guideline
Holding losers for too long (Also regret
aversion)
1. Set stop-loss orders. These will
automatically trigger a sale when a specific
price is reached. Do not lower the stop
once it has been set.
2. Accept that losses will happen. You will
not be right on every decision.
Selling winners too soon Adjust the stop-loss order up as the stock price
moves up. This will allow the trader to realise
most of the gain.
Buying riskier investments in order to make up
a loss faster (also decision framing). The loss
can be defined by a loss of capital or falling
short of a reference point such as a benchmark
index or personal investing goal.
1. Only trade in those investments in your
trading plan.
2. Use the status quo as the reference point
and frame the investment as a high risk
investment rather than framing it as a
chance to return to even.

Doubling down to lower the average unit price Only place trade when your entry/exit criteria
have been met.
Setting stop-loss orders too close to the buy
price. Setting the stop price within the average
trading range of a stock could trigger a sale
during the day yet the stock end the day with a
gain
1. Stop-loss rules should be tested as a part of
the trading system. Once these have been
set, do not change them.
2. Review the average trading range for the
investment to understand the risk of an
early stop.
Overconfidence.
Table 2
Rules and Guidelines for Overconfidence
Behavioural Bias – Overconfidence Rule / Guideline
Trading too often Review of your trading metrics will show this.
Buying high in hopes of selling higher. A good trader always has and exit plan when
making a trader. An overconfident trader
believes that they can always find someone to
buy the investment at a higher price. Consider
how you will do this before making the trade.

Overreaction.
Table 3
Rules and Guidelines for Overreaction
Behavioural Bias – Overreaction Rule / Guideline
Buying a stock based upon recent news Only place trade unless your entry/exit criteria
have been met.
Buying a stock that has moved up due to
overreaction
It is possible that due to market overreaction a
stock has met all entry criteria. Before buying,
scan the news to determine if this is the case.
If so, be aware of the fact that within a short
time frame, the effect of overreaction is usually
out of the price. Is your trading system
sensitive enough to monitor this?

Conclusion
Contrary to the assumptions in Modern Portfolio Theory and the Efficient Markets
Hypothesis, studies in Behavioural Finance show that due to human bias, market participants do
not always act rationally. This creates opportunities in the market for traders to profit from the
inefficiencies created by these biases. However, the same “irrationality” that affects the market
in general can also affect the individual traders. By training on Behavioural Finance, traders can
increase their awareness of times when bias may be affecting their trading. In order to limit the
negative impacts, this paper has recommended that traders train in behavioural bias and update
their trading plan with new rules and behavioural checkpoints.
Measuring the impact of behavioural biases on traders is difficult, because researchers
rarely have access to individual investor decisions. It is relatively easy to determine what a
trader did. It is not as easy to determine why they did it, nor is it easy to determine what the
investor did not do or why they did not do it. In addition, this paper only mentions a few of
those behavioural biases that have been shown to impact traders and investors. There are many
other biases that should also be reviewed and considered.
Additional research is needed with individual traders to determine how their behaviour
changes after implement behavioural bias into their trading plan. I would suggest an experiment
where two groups were assembled and one of the groups was trained on behavioural biases and
how the manifest themselves in trading. The other group would receive no such training.
Provide them basic trading rules with clear entry and exit criteria. Then during a trading
simulation determine if the group with the behavioural bias training does a better job of
following the trading rules.

References
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common
stock investment. Quarterly Journal of Economics, 116(1), 261-292. Retrieved from
http://www.ebscohost.com
Beechey, M., Gruen, D., & Vickery, J. (2000). The efficient market hypothesis: A survey, RBA
research discussion papers. Reserve Bank of Australia. Retrieved from
http://www.rba.gov.au/publications/rdp/2000/pdf/rdp2000-01.pdf
Bell, D. (1982). Risk premiums for decision regret. Management Science, 29, 1156–66.
Retrieved from
http://www.people.hbs.edu/dbell/risk%20premiums%20for%20decision%20regret.pdf
Bernstein, P. L. (2005). Capital ideas: From the past to the future. Financial Analysts Journal,
61(6), 55-59. Retrieved from http://www.ebscohost.com
Bettman, J. L., Sault, S. J. & Schultz, E. L. (2009). Fundamental and technical analysis:
Substitutes or complements?. Accounting & Finance, 49, 21–36. doi:10.1111/j.1467-
629X.2008.00277.x. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1467-
629X.2008.00277.x/full
Brown, K.C., & Van Harlow, W. (1988). Market overreaction: Magnitude and intensity. Journal
of Portfolio Management, 14, 6-13. Retrieved from
http://www.mccombs.utexas.edu/faculty/keith.brown/Research/JPM-12.88.pdf

Buttell, A. E. (2010). Harry M. Markowitz on modern portfolio theory, the efficient frontier, and
his life's work. Journal of Financial Planning, 23(5), 18-23. Retrieved from
Chopra, N., Lakonishok, J., & Ritter, J.R. (1992). Measuring abnormal performances: Do stocks
overreact?. Journal of Financial Economics, 31, 235-268. Retrieved from
Coe, T.S. & Laosethakul, K. (2010). Should individual investors use technical trading rules to
attempt to beat the market? American Journal of Economics and Business
Administration, 2, 201-209. doi:10.3844/ajebasp.2010.201.209
De Bondt, W., Muradoglu, G., Shefrin, H., & Staikouras, S. K. (2008). Behavioral finance: Quo
vadis?. Journal of Applied Finance, 18(2), 7-21. Retrieved from
De Bondt, W. M., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock
market seasonality. Journal of Finance, 42(3), 557-581. Retrieved from
Ehrlinger, J., Gilovich, T., & Ross, L. (2005). Peering into the bias blind spot: People’s
assessments of bias in themselves and others. Personality and Social Psychology
Bulletin, 31(5), 1-13. doi:10.1177/0146167204271570
Fama, E. F. (1965). The behavior of stock market prices. Journal of Business, 38(1), 34-105.
Retrieved from http://www.ebscohost.com

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of
Finance, 25(2), 383-417. Retrieved from http://www.ebscohost.com
Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial Studies,
14(1). Retrieved from http://www.ebscohost.com
Herstatt, C., & Kalogerakis, K. (2005). How to use analogies for breakthrough innovations.
International Journal of Innovation & Technology Management, 2(3), 331-347.
Jones, E. E., & Nisbett, R. E. (1972). The actor and the observer: Divergent perceptions of the
cause of behavior. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. E. Nisbett, S. Valins,
& B. Weiner (Eds.), Attribution: Perceiving the causes of behavior (79–94). Morristown,
NJ: General Learning Press.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An analysis of decision under risk.
Econometrica, 47(2), 263-291. Retrieved from
http://www.princeton.edu/~kahneman/docs/Publications/prospect_theory.pdf
Kahneman, D. & Tversky, A. (1981). The framing of decisions and the psychology of choice.
Science, New Series, 211(4481), 453-458. Retrieved from
http://links.jstor.org/sici?sici=0036-
8075%2819810130%293%3A211%3A4481%3C453%3ATFODAT%3E2.0.CO%3B2-3
Kahneman, D. (2002). Autobiography of Daniel Kahneman. Retrieved from
http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman.html

Koppel, R. (1996). The intuitive trader: Developing Your Inner Trading Wisdom for More
Successful Trades. Hoboken: John Wiley and Sons.
Koszegi, B., & Rabin, M. (2006). A model of referece-dependent preferences. Quarterly Journal
of Economics, 121(4), 1133-1165. Retrieved from http://www.ebscohost.com
Litvak, P. & Lerner, J. S. (2009). Cognitive bias. In D. Sander & K. Scherer (Eds.), The Oxford
Companion to the Affective Sciences. New York: Oxford University Press. Retrieved
from http://content.ksg.harvard.edu/lernerlab/papers/
Lo, A. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary
perspective. Journal of Portfolio Management, 30, 15–29. Retrieved from
http://web.mit.edu/alo/www/
Lo, A., & MacKinlay, C. (1998). Stumbling block for the random walk. Financial Planning,
28(7), 120. Retrieved from http://www.ebscohost.com
Lo, A. W. & Repin, D.V. (2002). The psychophysiology of real-time financial risk processing.
Journal of Cognitive Neuroscience 14(3), 323-339 Retrieved from
http://web.mit.edu/alo/www/
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Retrieved from
Menkhoff, L., & Nikiforow, M. (2009). Professionals’ endorsement of behavioral finance: Does
it impact their perception of markets and themselves?. Journal of Economic Behavior &
Organization, 71(2), 318-329. doi:10.1016/j.jebo.2009.04.004

Neilson, W. S. (2002). Comparative risk sensitivity with reference-dependent preferences.
Journal of Risk & Uncertainty, 24(2), 131-142. Retrieved from
Nicolosi, G.,Peng, L., & Zhu, Ning. (2009). Do individual investors learn from their trading
experience?. Journal of Financial Markets, 12(2), 317-336. Retrieved from
http://www.elsevier.com
Nikiforow, M. (2010). Does training on behavioural finance influence fund managers' perception
and behaviour?. Applied Financial Economics, 20(7), 515-528.
doi:10.1080/09603100903459832
Odean, T. (1998). Volume, Volatility, Price, and Profit When All Traders Are Above Average.
Journal of Finance, 53(6), 1887-1934. Retrieved from http://www.ebscohost.com
Prechter, J. R. (2001). Unconscious herding behavior as the psychological basis of financial
market trends and patterns. Journal of Psychology & Financial Markets, 2(3), 120-125.
Pronin, E., & Kugler, M. B. (2007). Valuing thoughts, ignoring behavior: The introspection
illusion as a source of the bias blind spot. Journal of Experimental Social Psychology, 43,
565-578. Retrieved from
http://psych.princeton.edu/psychology/research/pronin/publications.php
Resnik, B. L. (2010). Did modern portfolio theory fail investors in the credit crisis?. CPA
Journal, 80(10), 10-12. Retrieved from http://www.ebscohost.com

Seyhun, H.N. (1994). Stock market extremes and portfolio performance 1926 – 2004. Retrieved
from http://www.towneley.com/pdf/MT%20Study%2004.pdf
Schwager, J. D. (1995). The new market wizards: conversations with America's top traders. New
York: Harper Paperbacks.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too
long: Theory and evidence. Journal of Finance, 40(3), 777-790. Retrieved from
Statman, M. (1987). How many stocks make a diversified portfolio?. Journal of Financial &
Quantitative Analysis, 22(3), 353-363. Retrieved from http://www.ebscohost.com
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation
of uncertainty. Journal of Risk & Uncertainty, 5(4), 297-323. Retrieved from
Wilson, L. (2003). The business of share trading: From starting out to cashing in on trading the
Australian market. Milton: Wrightbooks.
Yulong, M., Tang, A. P., & Hasan, T. (2005). The stock price overreaction effect: Evidence on
Nasdaq stocks. Quarterly Journal of Business & Economics, 44(3/4), 113-127. Retrieved
from EBSCOhost.

Improving Your Trading Plan

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (14)

Similar a Improving Your Trading Plan

Similar a Improving Your Trading Plan (20)

Más de Benjamin Cheeks

Más de Benjamin Cheeks (7)

Último

Último (20)

Improving Your Trading Plan