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Was it the right conclusion? Is any losing trade necessarily a bad trade? The answer is no. No matter how well he executed his trade, there will be losing trades because we are playing a probability game. Trading is a business based on probability. And probability means that sometimes we get what we want, sometimes we don't. And that's the nature of the this business. The sooner we accept this, the better we can operate it as business. "There's a difference between knowing the path... and walking the path." - Morpheus The Certainty Trap I came across an excellent article by Ben Carlson from Seeitmarket.com. Ben discusses the differences between probability and certainty: "There are two arguments I see on a regular basis that show up as a result of data overload: …because that’s never happened before. …because that’s what’s always happened before. The problem with this line of thinking is that it can lead investors to fall into what I like to call the certainty trap. It’s this all-or-nothing line of thinking that causes so many to constantly attach extremes to every single market move or data point they see. The beginning of the recovery or the end of the world is always right around the corner. The assumption is that we’re always either at a top or a bottom when most of the time the markets are probably somewhere in the middle." If you are not a member yet, you can join our forum discussions for answers to all your options questions. Ben continues: The reason the investing certainty trap is so easy to fall for is because historical data can feel so safe and reassuring. Look here, my data says that this has never (always) happened in the past. Surely this trend will continue. I’ll just sit here and wait for my profits to start rolling in. ‘Never’ and ‘always’ have no place in the markets because no one really knows what’s going to happen next. ‘Most of the time’ is a much more reasonable goal, because nothing works forever and always in the markets. If it did everyone would simply invest that way. I think a much more levelheaded approach is to follow the Jason Zweig 10 word investment philosophy: Anything is possible, and the unexpected is inevitable. Proceed accordingly. To disregard the potential for the unexpected is the height of arrogance and arrogance is rarely rewarded for long in the ever-changing markets. Don’t get me wrong, I’m not saying you shouldn’t take on high probability bets based on the weight of historical evidence and your current views. That’s exactly what you should do. But probabilities take into account the fact that there’s always the possibility that we might see the minority situation occur. In fact the best you can hope for is a high probability for success because luck plays such a large role in shaping the outcomes in the markets. I’ve always liked the old adage that it’s better to be roughly right than precisely wrong. My feeling has always been that historical data is a great way to view the inherently risky nature of the markets, but that doesn’t mean the data always does a great job at predicting exactly what’s going to happen in the future. Investors have to remember that market data does a much better job of forecasting potential risk than it does potential return. There are no certainties in the markets. Otherwise there would be no such thing as risk. Nothing works all the time. Otherwise it would never work in the first place. There’s no room for ‘never’ or ‘always’ in the financial markets. Otherwise you’re sure to be surprised in the future." Excellent analysis. It is very important to understand the difference between probability and certainty. Nothing is certain in the trading. But if something happened 80% of the time, there is a good chance it will happened again. For example: if a stock moved after earnings less than the expected move in 8 out of 10 last cycles, there is a 80% chance that it might happen again the next cycle. Again, there is no certainty that it will happen, only probability. But this is the best we can do. Related articles: Why Retail Investors Lose Money In The Stock Market Are You Ready For The Learning Curve? Can you double your account every six months? Are You Following "Tharp Think" Rules? Adaptability And Discipline Want to learn how to reduce risk and put probabilities in your favor? Start Your Free Trial

These conclusions reveal how maximum profit and loss are calculated and aid in deciding which strategies are good fits for your risk tolerance. Does risk level justify the trade? That is where most options traders begin to address probability. But most often overlooked in selection of an underlying for options trading or for a strategy to be used, is the comparison between probability and risk. Too many traders consider these as the same thing. If probability of profitable outcome is high, it must mean risk is low, and vice versa. Most options traders make this assumption intuitively. Based on proximity of price to the money, time to expiration, and relative status of long or short positions, probability and risk are well known. But what about the correlation between the two, probability and risk? Probability often is the result of a calculation and may be viewed in isolation; the true risk level might not be considered at all, or simply assumed to be conclusive based on what probability reveals. This is a mistake. The correlation of probability and risk Once you decide a strategy is “low risk” what comes next? You might overlook a related next step, the possibility that risk changes once probability increases or decreases. A simplified example is how the probability of a profitable outcome changes when the underline moves unexpectedly close to the money. Does this mean risk is higher or lower? It might. But the point – comparing evolving probability and risk – could also be more subtle. Here is an example: A trader likes short puts because the market risk is identical to that of a covered call. Opening a series of short-term, slightly OTM short puts produces profits consistently and exercise is avoided by early close or rolling forward. But what if the company will be announcing earnings the day before last trading day? What if the company has a history of double-digit earnings surprises? Does this change the probability of profitable outcome? And does that make the risk picture entirely different as well? Yes. Of course, but the degree of risk in holding onto the open position with little or no “?buffer zone” point spread, also defines degrees of risk. The point is that neither probability or risk are absolute factors in judging options and their likely outcomes. There is a tendency among traders to want a binary answer. Is probability high or low? Is profitable income likely or unlikely? The answer depends on other factors like moneyness, time to expiration, historical volatility, earnings reports and likelihood of earnings surprises, and the unexpected announcement that often shows up at the worst possible time. These announcements include merger rumors, SEC investigations, accusations of financial wrongdoing by the top executive, product recalls, class action suits, strikes, natural disasters, and more – the list goes on and could be endless. Another way the correlation works addresses the problem of tied up capital and margin. If you are holding stock especially for use with covered calls, is your probability of loss low? Probably. But is the risk high? In some respects, it might be. For example, the lost opportunity risk of tying up capital for covered calls is rarely discussed by anyone. But it exists. Your capital is tied up in holding shares while you write covered calls. Probability of loss is low, but lost opportunity risk can be very high. Covered calls offer limited maximum profit equal to the premium of the covered call. Could that same amount of tied-up capital be used elsewhere to generate more attractive profits? That is a question with any number of answers, but the point is that in this situation, risk and probability coexist but may be influenced by different things. Anyone who tries to lower risk by keeping their money in low-yielding money market products has accomplished a low probability of loss, at or close to zero. At the same time, they accept a high risk of true net loss, at or close to 100%. This is so because the combination of inflation and taxes virtually guarantees that the low yield on savings cannot produce an after-inflation, after-tax profit. This paradox is real-world stuff, not just theory. It is possible to combine low probability of loss with high risk of loss, in the same product. The example of money placed in a guaranteed, low-yielding account demonstrates how this works. In the world of options, the variables are more complex, but the same point must be made. In options trades, you can lower the risk of loss while improving the probability of profits. For example, if you time trades for those rare moments when the Bollinger Bands exceeds upper or lower band by three standard deviations, the probability of an immediate reversal is close to 100% because the range never remains that far from the center line for long. If you time covered calls for the Friday one week before expiration, you drastically reduce risk because options lose one-third of remaining time value in the one trading day (but three calendar days) between Friday and Monday or expiration week. Does this mean it is possible to set up a foolproof system to make a profit consistently? No, that is never possible. But it does mean something almost as promising: By understanding the events and timing that affect probability, the risk of loss is drastically reduced. Most options traders will agree that this is good enough, given all those variables that tend to get in the way of every “perfect” system ever devised. Michael C. Thomsett is a widely published author with over 80 business and investing books, including the best-selling Getting Started in Options, coming out in its 10th edition later this year. He also wrote the recently released The Mathematics of Options. Thomsett is a frequent speaker at trade shows and blogs on his website at Thomsett Guide as well as on Seeking Alpha, LinkedIn, Twitter and Facebook. Related articles Is Your Risk Worth The Reward? Risk Reward Or Probability Of Success? Calculating The Probability Of Option Payoff Option Payoff Probability Probability And Option Risk

The author explains, “I analyzed five major CME option markets – the S&P 500, eurodollars, Japanese yen, live cattle and Nasdaq 100 – and discovered that three out of every four options expired worthless.” The inaccuracy of this statement is explained easily. The actual statistic is that 75% of options held to expiration expire worthless. But up to 60% of all options are closed before expiration. So only than 30% of all options held to expiration will expire worthless. Those 60% of all options include positions closed or exercised. But instead of a 75% chance of a net loss, the true number is far lower, only 30% expiring worthless. This means that 70% of options are closed early or exercised. Of these 70%, many will be profitable or closed at breakeven, and some will lose money. But the 75% statistic is grossly misleading – and untrue. Why does the incorrect statistic endure? It is simply that the “hold to expiration” model is used for widespread comparisons, often representing the worst-case outcome. It does make sense to compare various outcomes based on what happens on the last trading day, despite the reality that 60% of all options no longer exist on last trading day sets up a distortion. This is widely misunderstood and misquoted to make options look like losers three-quarters of the time. In fact, they are certain to lose less than one-third of the time, which leaves the other two-thirds of early closing options to generate profits or breakeven outcomes (and some losses). Even calculation of breakeven outcomes is distorted because, invariably, it is based on what the picture looks like on last trading day. This is when remaining time value is miniscule and many positions are just left to expire. And there is another side to the distortion. The idea of “all options” includes both long and short positions. The claims that 75% of all options expire worthless assumes that 75% of all trades lose. But for short positions, options expiring worthless represent 100% profits. The author of the article cited above goes further to point out that 82.6% of all puts expired worthless, an even higher outcome than “all” options. The assumption underlying this alarming result is that puts are losers 82.6% of the time. This ignores the widespread popularity (and profitability) of opening uncovered short puts. These become profitable when they expire worthless but are included in that high outcome of 82.6%. The variables involved in developing an accurate understanding of outcomes, make this a complex matter. They include early profit-taking for either long or short positions (and for either calls or puts); the timing of profit-taking; the duration between opening a position and last trading day and the related rapidity of time decay; volatility in both the option premium and the underlying; and many other unknown or unanticipated variables. The acceptance or rejection of these variables may easily distort anyone’s understanding of what to expect: The hardest aspect about random variables to understand is that they are idealized models of reality. That is, we sacrifice a precise model of reality for the facility of dealing with precise mathematical objects. (Chriss, Neil A. (1997) Black-Scholes and Beyond. New York: McGraw-Hill, p. 70) This points out how complicated it is to accurately determine a likely result of any option trade. The random variables you pick to estimate a consistent profit or loss relies on the assumption that the field of variables applies equally in every case. But the field is different for every trade and even the best understood variables (type of option, time to expiration, long or short, and volatility, for example) are not the same in every case. But for the purpose of creating a model of probable outcomes, everyone (options traders as well as statisticians) makes certain assumptions to work up comparable models. But traders must recognize that these are idealized models and are not realistic. This is an obvious problem, given the possible range of variables. It is ore complex than the distortion found is the often-cited but erroneous belief that “75% of all options expire worthless.” That is an example of a statistic based on lack of understanding of the components in a study (that is, “all” options versus “all options held to expiration”). Some people are overly critical of options trading and may even cite the statistic intentionally to make their point. But to be generous, you may assume that it is not intentional, but due to a lack of understanding. Statistics are so often misquoted or poorly understood that they should be viewed with a healthy dose of skepticism. For example, consider the claim made in a Cold War-era report that “The Russian runner finished in second place, and the American came in second to last.” When the full story is exposed, that there were only two runners, the report takes on a completely different meaning. Options traders and all other types of market participants can learn a great deal simply by applying critical thinking to any reported statistic. Does it make sense? What proof is cited? In the article cited at the beginning of this report, the author claimed that his conclusion was based on a study of five major markets. That sounds comprehensive. But even so, the conclusion was still incorrect. If the underlying assumptions are false, it does not matter how much analysis is performed. Citing the use of five major markets adds credibility to the report … but it does not make the conclusion accurate. Michael C. Thomsett is a widely published author with over 80 business and investing books, including the best-selling Getting Started in Options, coming out in its 10th edition later this year. He also wrote the recently released The Mathematics of Options. Thomsett is a frequent speaker at trade shows and blogs on his websiteat Thomsett Guide as well as on Seeking Alpha, LinkedIn, Twitter and Facebook.