Why Watching the Screen Feels Worse: Myopic Loss Aversion and Checking Often

Every time you open the app your stomach drops, even when the month is still in profit. You’re not the only one, and it doesn’t mean the strategy is bad. It is the maths of volatility combined with a brain that feels losses more than gains. Behavioural economists call the combination myopic loss aversion. This article works out how much your checking frequency changes what you see, and how to set up your looking so that neither your mood nor your trading falls apart.

What is myopic loss aversion

It joins two ideas:

• Loss aversion: a loss feels heavier than a gain of the same size. Daniel Kahneman and Amos Tversky set this out in Prospect theory (1979). Tversky and Kahneman’s 1992 paper estimated it at about 2.25 times. Other experiments find different values, so this article uses a round “about twice” as an assumption, not a constant that holds for everyone. Read the full story in Trading biases
• Myopic means short-sighted: evaluating results often, in short slices, instead of looking at the whole.

Shlomo Benartzi and Richard Thaler used this idea in a 1995 paper in the Quarterly Journal of Economics to explain why many investors dislike holding stocks, even though over the long run stocks have paid much more than bonds. Their explanation: if people dislike losses about twice as much as they like gains, and they evaluate their portfolio about once a year, volatile stocks “feel” barely more attractive than bonds.

Uri Gneezy and Jan Potters tested this in the lab (Quarterly Journal of Economics, 1997). Participants placed repeated bets on a favourable gamble. The group that saw results and decided often bet less than the group that saw results bundled over several rounds at a time. Changing only how often people see results changed their decisions, even though the gamble was exactly the same.

The formula: the chance a check shows red

Say a strategy’s equity has an expected return μ per year and volatility σ per year. If you look every t years, the change between two looks has a mean of μt and a standard deviation of σ√t. Using the normal approximation:

P(see a loss) = Φ(−μt ÷ σ√t) = Φ(−Sharpe × √t)

Φ is the cumulative normal distribution, and Sharpe here means the annual μ ÷ σ (ignoring the risk-free rate to keep it simple; see what Sharpe means in Sharpe, Sortino, Calmar).

The key part is √t. The expected gain grows with time t, but the volatility only grows with √t. Over short spans the volatility completely drowns the gain. Over long spans the gain starts to show.

Worked example. A hypothetical Sharpe 1 strategy: μ = 15% a year, σ = 15% a year, checked once a day, using 1 year = 252 trading days.

1. t = 1 ÷ 252
2. Expected gain per day μt = 15% ÷ 252 = 0.0595%
3. Volatility per day σ√t = 15% ÷ √252 = 15% ÷ 15.87 = 0.945%
4. z = 0.0595 ÷ 0.945 = 0.063 (the same as Sharpe × √t = 1 × 0.063)
5. P(see a loss) = Φ(−0.063) = 47.5%

A strategy that counts as good by the usual yardstick, checked once a day, shows the account down on almost half of all days.

Doing the same for every interval: Forex trades 24 hours a day, 5 days a week, so 1 week = 5 trading days, 1 month = 21 days and 1 hour = 1/24 of a day.

Check every	Sharpe 0.5 strategy (μ 10%, σ 20%)	Sharpe 1 strategy (μ 15%, σ 15%)
1 minute	49.97%	49.93%
1 hour	49.74%	49.49%
1 day	48.74%	47.49%
1 week	47.19%	44.40%
1 month	44.26%	38.64%
1 year	30.85%	15.87%

Checked every minute, a good strategy and a thin one are almost impossible to tell apart. Both feel like coin flips. Only at monthly or yearly spacing does the difference become visible to the eye.

Counting painful checks against pleasant ones

Now add loss aversion. Give each check that shows a loss a weight of 2 and each check that shows a gain a weight of 1. This model counts checks only, not their size, to keep the picture simple.

Sharpe 1 strategy over 1 year:

Check every	Checks	Painful	Pleasant	Overall feeling (pleasant − 2 × painful)
Day	252	119.7	132.3	−107.0
Week	50.4	22.4	28.0	−16.7
Month	12	4.6	7.4	−1.9
Year	1	0.16	0.84	+0.5

Check the first row: 252 × 47.49% = 119.7 checks, leaving 132.3. Overall feeling = 132.3 − 2 × 119.7 = −107.0.

Same strategy, same money in every row. But the person who checks daily will “feel” it was a bad year, and the person who checks once a year will feel it was a good one.

The overall feeling turns positive when the chance of seeing a loss drops below 1 in 3, which means Sharpe × √t has to exceed 0.431 (because Φ(0.431) = 2/3). Solving gives t = (0.431 ÷ Sharpe)²:

• Sharpe 1: t = 0.186 years ≈ 47 trading days, about two months
• Sharpe 0.5: t = 0.742 years ≈ 187 trading days, about nine months

If you count size as well, so that losing 1 dollar weighs the same as gaining 2, the break-even spacing shortens to about 19 trading days (Sharpe 1) and about 77 trading days (Sharpe 0.5). Either way, checking daily or more often still comes out negative in feeling.

There is another angle that makes this worse. Most people don’t compare with their last look; they compare with the highest point they have seen. We simulated the Sharpe 1 strategy day by day over 5,000 random one-year paths: on about 88% of days in the first year, equity sat below its previous peak. For the Sharpe 0.5 strategy, about 91%. In other words, the account is in drawdown almost every day. To read drawdown correctly, see Equity DD vs Balance DD.

One trade is enough: most winners were red first

The same thing happens inside a single trade. We simulated 20,000 trades with price moving randomly minute by minute plus a small edge, Stop loss −1R and TP +2R (R is the risk distance per trade), with 1R set to roughly 100 minutes of typical movement. The results:

• Wins (reaching TP) 41.4%, above the 33.3% break-even for RR 1:2, an expected profit of about +0.24R per trade
• Of the winning trades, 90.6% were in the red for at least one minute
• 61.9% went down to −0.25R and 37.2% went down to −0.5R before running to TP
• The median winning trade lasted 232 minutes and spent about 15% of that time in the red

Now let a screen-watcher who “can’t take it” close every trade that reaches −0.5R. In the same model, using the same random price paths, the win rate falls to 26.2% and the expected profit drops from +0.24R to +0.155R per trade. About a third of the edge is gone, purely from seeing red and feeling the pain. Add spread and every trade starts in the red from the first second, which only increases how often you see red. The deepest point each trade reaches against you is called MAE; read more in MAE and MFE.

The strategy doesn’t change because you look more often. What changes is how many times you see red, and how many chances you get to make a decision out of that pain.

Why seeing red often makes you trade worse

• Closing good trades early. As the simulation above shows, escaping when in the red cuts winners too.
• Moving the Stop loss away. Pain repeated every minute pushes you to widen the SL to “give price room to breathe”, and the loss per trade ends up bigger than planned.
• Trading to get it back. Red piling up all day creates an urge to do something, which easily turns into revenge trading.
• Cutting risk at the wrong time. Just like the Gneezy and Potters experiment, people who see results often tend to take less risk on things that have an edge, for example dropping a good strategy during a normal losing patch.
• Accumulated fatigue. Being on alert every time price moves drains the mental energy you need for the decisions that matter.

Nassim Taleb tells the same idea in his book Fooled by Randomness, with the example of an investor who checks so often that he sees only the noise and never the return.

What to do: design how you look, don’t force yourself

The pain of seeing red is a normal response. Trying not to feel it works poorly. What works better is reducing how often you have to see it, and deciding in advance while you are calm.

1. Check on a schedule, not on a feeling. Write into your plan when you will look. For trades held overnight, twice a day, for example at the European open and before bed. For day trades, when a candle on your timeframe closes, not in between. Look at the overall account result weekly or monthly. As the tables above show, the wider the spacing, the closer what you see is to the strategy’s real edge.

2. Hide floating P&L, or think in R. The money figure triggers the strongest feelings. Many platforms let you hide the column or show points instead of money (check your platform’s menus). Then read it in R, for example “this trade is at −0.4R”, which tells you plainly it is still within plan, unlike “−35 dollars”, which invites alarm.

3. Let alerts watch for you. Set price alerts at the levels where you actually need to decide, such as where your plan moves the SL, or the level that invalidates the plan. Don’t look in between. If no alert has fired, there is nothing to do.

4. Set and forget with SL and TP in the order. Put the Stop loss and Take profit in from the moment you open the trade. The position closes by itself when price touches either one first, working like OCO (One-Cancels-the-Other): one fills and the other is cancelled. Some platforms offer a true OCO order for two pending orders, and some don’t, so check yours. See all the order types in Order types explained.

5. A lot size you can watch without flinching. If watching a trade at −0.5R makes your hands go cold, your risk per trade may be too big. Reduce it until losing the full SL is still acceptable, for example around 1% of the account.

6. Phone hygiene. Move the trading app off the home screen. Turn off P&L notifications and keep only the price alerts you set yourself. Log out after checking, so the next look has to be deliberate rather than your thumb taking you there. And don’t keep the phone by your pillow.

7. Notice the urge to look. Notice when you want to open the app. It is usually when you’re bored or anxious, not when there is a decision to make. Take three slow breaths and ask, “If I open it, what will I do differently from the plan?” If the answer is nothing, you don’t need to open it. Practices are in Mindfulness for traders and Urge surfing.

Before every look at your account, ask: “If I see red, what will I do?” If the answer is “follow the plan as before”, you don’t need to look right now.

If worry about your account is affecting your sleep, eating or relationships to the point where you can’t manage it yourself, talk to a mental health professional. In Thailand, the Department of Mental Health hotline is 1323.

Limits of the numbers in this article

• They assume a normal distribution and constant volatility. Real markets have fat tails and volatility clustering; see Fat tails and Volatility clustering. Figures for very short intervals may be off, but the conclusion that the more often you look, the closer you get to 50%, still holds.
• The Sharpe 0.5 and Sharpe 1 strategies are hypothetical. They are not returns you should expect, and they say nothing about what any real strategy can achieve.
• The “twice” weight is a round number. It differs between people, and for the same person on different days.
• The single-trade model is a random price path with an edge we put in ourselves, to show the mechanism. It is not a test result of any strategy.

Common mistakes

• Judging the strategy by what you see today. A red day for a Sharpe 1 strategy happens on about 47.5% of days. It is not new information about the strategy. Judge it on a large sample of trades instead; see Process over outcome.
• Watching the money instead of the plan. Opening the app and looking only at the P&L total, instead of whether price has reached a level where you need to decide.
• Checking more often when losing. A losing stretch is exactly when your schedule matters most, because that is when decisions made out of pain come easiest.
• Using screen-watching in place of a Stop loss. “I’ll keep an eye on it” is not risk control. If your connection drops or you fall asleep, nothing protects the account.
• Turning on P&L notifications for every trade. That is choosing to check every minute without realising it.