Copy err that.Gotcha. I'm slow on daze the markets are closed, and not too swift when they are open. Haha
Trading Basics
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Copy err that.Gotcha. I'm slow on daze the markets are closed, and not too swift when they are open. Haha
Typical Market Cycle
Any good trader worth his salt knows that it is the market cycle and real economics that ultimately drive prices. And the stock market is a big influence, since it is arguably the market that is most clearly affected by changes in economic fundamentals. Nonetheless, financial markets are always interconnected with one another, so no matter what asset you trade you must have a clear and sound understanding of a typical market cycle.
Signs at the end of the market cycle
During the latter stages of a bull market, such as experienced in 1999 and 2007, there is typically strong economic growth and high levels of optimism among market participants. It is during this time that the market cycle nears its peak. Consumer confidence is likely high, interest rates rise and cyclical stocks begin to outperform. It is not always the case, but these are the usual characteristics of a maturing market.
As markets move up, rising interest rates become an ever stronger threat to economic growth and markets begin to slow in momentum. Lending money becomes more expensive and thus risk taking begins to drop, leading to investors seeking more secure places to invest–such as utility and value stocks. This in turn leads to sharp falls in the growth stocks that were previously in charge and mounting pessimism begins to set in as the main driver. As pessimism increases, economic growth begins to fall and small cap stocks and growth stocks bear most of the pain.
In order to address the decline in growth, interest rates start to fall and the stock market most likely enters a prolonged bear market. Traders begin to sell higher yielding investments and move into safer investments such as bonds, utility stocks or safe havens.
Restarting the market cycle
As interest rates fall and pessimism takes hold, markets keep dropping until they reach a selling climax, where optimism is at its lowest. It is at this point when markets are washed out and at their lowest ebb that the cycle can begin again. Fuelled by the prospect of now low interest rates, and improving economic growth, small cap stocks and growth stocks take over and markets start to rise. Since the stock market looks ahead, this rise is often happening well in advance of any corresponding uptick in GDP or unemployment. In this way, the stock market is the ultimate leading indicator into future economic growth.
It is important to understand the market cycle. Although no cycle is ever the same, it is generally the case that riskier assets and small cap stocks do better in the beginning stages of bull markets. Whereas in bear markets, bonds and defensive stocks do better. As do safe-haven currencies such as the US dollar, the Swiss franc and the Japanese yen.
Understanding current market cycles aids in timing markets. But since cycles can go on for a number of years and each one is different, it is not always easy to ascertain what stage we are at. Fortunately, a closer examination of various economic indicators can help form a top down view of which stage of the market cycle we are currently in.
Money supply and the yield curve
Money supply, by and large, increases as an economy worsens and slows as a market cycle nears its peak. It is controlled by central banks and in the States the Fed can control the money supply by changing its reserve rate requirement. The principle way it does this is by changing the Fed target rate, which it is able to keep in place by buying and selling US government securities in the open market at the required rate. This process is known as an open market operation and, while there are several other methods the bank uses to control money supply, this is the main one.
Generally, traders use the economic indicators M2 and M3 to measure money supply in the economy and these numbers are released regularly by the Fed. M1 used to be used but these days it is subject to too many distortions for accurate analysis.
If you regularly keep track of money supply you should also be able to predict long-term shifts in interest rates. When money supply is shooting up, inflation is likely to follow which then has to be controlled by higher interest rates. For traders, this means currency yields in the US increase while the US dollar itself likely depreciates. Stocks, in theory, should benefit from an increase in money supply, as do commodities, particularly precious metals.
Knowing that money supply can affect interest rates, it's worth looking closely at interest rates themselves–in particular the difference between long-term rates and shorterterm T-Bills (known as the yield curve). The yield curve reacts to dynamics in cash markets and is an extremely useful guide to the future of the economy.
When long-term rates are much higher than short-term rates, the yield curve is steep and indicates an optimistic environment. The Fed is typically accommodative, stock prices typically do well and markets expect an improving economy.
Conversely, the yield curve can be said to be flat, or inverted, when short-term rates are close to or above longer-term rates. This occurs when markets expect economic weakness, and longer-term rates are deemed too risky. It is a scenario often accompanied by a restrictive Fed and a certain level of fear and pessimism in the financial markets.
It makes sense to follow the yield curve as it can be a useful leading indicator in the state of the market. By and large, a steep yield curve means economic growth which should benefit riskier assets such as stocks, while a flat or inverted yield curve means traders should be prepared to shift into safer haven markets. The yield curve is one of the best leading indicators available to traders and contains brilliant insight into the functioning of the money markets.
Central Banks
Good traders know that central banks have huge influence over the outcome of financial markets, be they stocks, bonds, commodities, forex or the global economy itself. Indeed central banks have such power that their actions can cause markets to plummet or soar with just a few carefully worded statements. And, when central banks follow through on their statements with real policies, the price changes can be even more profound.
One of the reasons for this, is their ability to control money markets that financial institutions use to fund their daily activities. By cutting interest rates central banks seek to make money cheaper to borrow, to increase consumption and, thus, to stimulate the economy, which they do if they think the economy is underperforming. By doing so, they increase the money supply and thereby also increase inflation. Cutting rates is usually bullish (positive) for the stock market and bearish (negative) for a country’s currency, since stocks benefit from the increase in consumption and currency depreciates as a fact of there being a greater supply of that currency. Similarly, raising rates is generally bearish for stocks and bullish for currency. By limiting the money supply, companies find it harder to borrow and the value of the currency is likely to go up.
In general, central banks aim to provide stability to markets and encourage lending between financial institutions. We saw in 2008 the problems that can occur when banks stop lending money to each other (for fear of losses) as the London Interbank Offered Rate (LIBOR) went up. LIBOR is the estimated interest rate offered between leading banks in London and is a key component of the TED spread; a name formed from the merging of T-Bill and ED, the ticker symbol for the Eurodollar futures contract.
Essentially, the TED spread is the difference between the three-month LIBOR and the threemonth risk-free T-Bill rate. This spread came to be a good indicator of fear during the financial crisis. An increase in the TED is therefore a sign that the risk of default on loans between banks is increasing. If banks are fearful of lending to each other it means they are worried about their solvency and is a worrying prospect for the financial system.
Money market funds
Investing in a money market fund is like putting your money in a big pool of secure, highly liquid short-term debt securities, the same pool of money as described above that the big institutions use to keep money overnight before deploying elsewhere. It’s important to note that money market funds differ from money market deposit accounts. Whereas money market funds are investment vehicles that the investor picks for his own investment, money market deposit accounts are investments where the bank receives the funds and can invest the cash at its own discretion.
The whole point of a money market fund is to be a secure place to store money so that the value of the fund never drops below the net asset value (NAV) of $1. As such, money market funds generally offer safe returns at rates a little bit higher than checking accounts. Nevertheless, money market funds can lose money on rare occasions and are not insured by the Federal Deposit Insurance Company (FDIC).
When money market funds do lose investors’ money, it is referred to as breaking the buck, as the fund drops below the NAV of $1. Prior to 2008, there was only one instance of a money market fund breaking the buck and that was an institutional fund that paid out at 96 cents for every dollar invested.
In 2008, after Lehman Brothers went bankrupt, another money market fund broke the buck with its value falling to 97 cents per share. The amount of money invested in money market funds at the time meant that a single fund breaking the buck could have led to ripple effects and cause a potential run on banks. Because of this, the Fed stepped in and guaranteed funding to protect public money market funds from slipping below $1.
On the whole, however, money market funds are secure and there are several factors that make them safe places to store money:
- Money market funds typically only invest in high quality AAA grade debt;
- They’re not allowed to invest more than 5% of the fund in one issuer (except the government), which means the risk is spread across several firms;
- Money market funds have an average weighted portfolio maturity of less than 90 days. This means managers have a lot of room to manage the risks of certain securities.
Particular attention should be paid to the current macro-economic environment and the health of the banking sector. In general, money market funds from the bigger institutions provide lower risk as they are more heavily capitalized and better able to ride out extreme market volatility.
Bond market
Like money market funds, bonds also react directly to changes in money supply. Although not as secure as money market funds, they are among the safest investments in the financial community. Because of their perceived security, bonds take up a huge role in the financial system.
Bonds are essentially IOUs. You lend money to a company or institution for a certain amount of time; in return, you receive interest and the money you lent in full at the end of the loan, called the maturity date. You can also sell your bond early. If the value of the bond goes up, you can bank the capital gained on your investment.
The key thing to remember is that bonds move in the opposite direction of interest rates. When rates fall, bonds rise and vice versa. All bonds have a par price of 100 and go up or down depending on interest rates. So if you buy a bond at 100 with a yield of 3% and rates go down enough so that your bond goes up in value to 105, you earn 3% per year in interest as well as bank 5% capital gain if you sell it. If rates go up, you still get the 3% but receive your money back in full at the end of the term without any gain.
How the Trade Balance Affects Currency Prices
Predicting the direction of forex markets is notoriously difficult. In fact, there are many people in the academic world who believe it to be almost impossible–at least on a short-term basis. On longer time frames, there is more agreement. Many of those who study macroeconomics believe that the trick to predicting where a currency might go is to look at a country’s current account balance.
The current account balance is the sum of the balance of trade, factor income and cash transfers–where factor income means earnings on foreign investments minus payments to foreign investors. It basically describes the flow of money between two countries.
Macroeconomic theory dictates that a country’s currency is subject to the same laws of supply and demand as any other asset. If a currency is in greater demand it becomes worth more, whereas if it is greater in supply, it depreciates. To understand this, consider a country whose inhabitants develop a strong urge to buy products from a foreign country. In this situation, the act of buying foreign goods increases the demand for the foreign currency, since that is what the goods are priced in. At the same time, the current account balance of the country deteriorates, as more imports are coming in compared to exports. The result is a trade deficit and a depreciation of the native currency since it is in much less demand.
However, there are other scenarios too. Consider for example, a country whose financial products, such as bonds, become wanted by foreign investors. In this case, the current account balance also deteriorates (as a result of negative cash transfers), but the currency goes up in value instead of down. Since the increase in demand for the country’s financial assets means an increase in demand for its currency.
Economists tend to disagree on just how easily the current account balance can be used to predict foreign exchange prices. However, it is generally thought that sustained current account deficits lead to a depreciation in a country’s currency, while prolonged current account surpluses lead to appreciation. It is also thought that a current account deficit larger than 5 (as a percentage of GDP) is unsustainable and a potential warning sign for a country. While it would be extremely difficult to use such data to trade forex markets in the short-term the current account balance can provide some use for predicting markets over longer term 31 horizons.
Interest rates and forex
Interest rates, or more importantly–the expectations of future interest rates–are probably the most significant factors that influence forex markets in the short to medium term. The reason for this, is that market participants naturally move their money towards those currencies with the highest yields, as those yields give the best return for their money. Therefore, those countries with the higher interest rates see much larger inflows of money coming into their currencies.
As a general rule then, countries with higher rates should see their currency appreciate while those countries with lower rates should see their currency weaken. However, as noted above, the real key to predicting forex markets is identifying where interest rates are headed before they go there. Since, by the time a country has raised interest rates, that decision is likely already priced into the markets.
It’s important therefore, to recognize market expectations as a key driver for all forex pairs. For example, if interest rates drop for a while and traders suddenly decide that they have hit bottom, they begin to buy up the currency, well in advance of interest rates going up. In fact, sometimes the very act of traders changing their market views can alter the economic environment enough to influence future outcomes.
Blind Spots
A driving blind spot is an area on the road that a driver cannot see while looking through their windshield, side-view mirrors, or rear-view mirror. Blind spots are caused by the structure of the vehicle, such as pillars and its body, and other obstacles like other cars or structures on the road. The most common blind spots are the rear quarter blind spots, which are areas towards the rear of the vehicle on both sides. On average, a car has two main blind spots, generally on the back left and the back right side of the car. Blind spots can hide objects such as other cars, pedestrians, and barriers from the driver's normal field of vision. Checking your blind spots often requires the driver to break out of their normal field of vision, which makes them dangerous.
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(Source: https://money.usnews.com/money/blog...f-these-blind-spots-in-your-portfolio?onepage)
Here are seven behavioral biases that could potentially have a negative impact on your portfolio.
Anchoring
This bias occurs when you rely heavily on the first piece of information you get about an investment. You’ve anchored your beliefs about an investment to what you think is its worth or potential – and it’s hard to let go of that. As long as you act on that first bit of information, rather than taking into account new realities, you could wind up making poor decisions based on outdated facts.
Confirmation
When you fall prey to this behavioral bias, you seek out only information that supports your perception. So, if you see something that contradicts your supposition, or your belief about an investment, you immediately disregard it while cherry-picking what supports your belief. This can be especially dangerous when combined with anchoring, because you might be searching only for information to hold up your anchored belief – and make poor trading decisions as a result.
Hindsight
The saying goes, “hindsight is 20/20,” and nowhere is this truer than with trading. Looking back, it’s easy to see the signs that pointed to a certain outcome. Rather than acknowledging that they might know everything or missed something, people like to pretend that they would have seen the signs because of their obviousness. Then, they look for the same signs in the future – even though there’s no way to completely predict what’s next. They take historical data and try to apply it to the future, potentially making devastating decisions as a result.
Familiarity
People like the comforting and familiar. Sometimes that means investors don’t take some of the small, calculated risks that are needed to create a successful portfolio. Other times, they stick with an investment, refusing to sell long after the fundamentals have changed. That familiarity can cause problems down the road. Related to this problem is the sunk-cost fallacy. You feel like you’ve put too much into an asset already and you’re reluctant to cut bait and move on. If you stick with something that is dragging you down, it could negatively impact your portfolio in the long run, when you’d have been better off accepting the loss, deducting it on your taxes and moving on to something better.
Self-attribution
One of the biggest biases is self-attribution – the idea that they’re geniuses when things go right. The market is rising and the portfolio is doing well, and investors figure it’s all to their own efforts and smart trading. On the other hand, when things go poorly, people tend to attribute that to external factors. Generally, folks like to take credit for the good and blame others for the bad. When that happens, they’re blind to their own mistakes and become overconfident. Once that overconfidence sets in, it’s easy to think you have all the answers and make poor decisions, sure that it has to work out. When it doesn’t work out, your portfolio could suffer.
Loss aversion
The biggest issue associated with loss aversion is the concern that you’ll lose out. So, rather than taking a calculated risk, you avoid most risk. When this happens, you actually do lose out. If you get so worried about stocks that you overweight your portfolio with assets that have lower returns, you end up missing out on a lot of potential gains down the road.
Recency
Many tend to think that things that are happening now will keep on in the same vein. This also manifests as a bias toward trends. Recent trends are projected into the future, when there’s a good chance that things could change – and change quickly. Getting too caught up in recent trends and failing to take heed of potential warnings, or even acknowledging that the trend just might not continue, can lead you to make changes to your portfolio that aren’t in line with your long-term plan.
How to overcome your behavioral biases
When overcoming a bias, the first step is acknowledging the issue. At its core, trading is about self-awareness. Understanding who you are, your goals and your weaknesses are all important if you want to be successful as an investor. Take an honest look at yourself and your biases. Then, take steps to protect yourself. Consider putting together a plan (with the help of a professional) for your investments. Avoid heading off course. Once you have a handle on your potential weaknesses, you’re far more likely to adjust for them – and your portfolio will be the better for it.
It is sometimes difficult to distinguish the degree to which skill and luck play in trader's performance. Trading is often compared to gambling and most often to poker, and for good reason. There is no doubt that chance, or the cards drawn, plays a role in poker outcomes. Recent research also shows, contrary to what the authorities say, that poker on the whole is a game of skill. Unfortunately, to identify skilled traders is much more difficult than identifying skilled poker players.
Confirmation Bias
Confirmation bias is a persistent feature of the financial markets. Simply put, the confirmation bias is the tendency to seek information that confirms prior conclusions and to ignore evidence to the contrary. When it comes to our own skills, we are more than happy to delude ourselves by ignoring the hard truths. It’s not hard to see how our desire to focus on information that confirms our already existing beliefs could be a liability when it comes to trading. For example, underperforming traders will usually point to the performance figures that put themselves in the best light. Traders who believe they can pick winning stocks are regularly oblivious to their losing record, recording wins as evidence confirming their stock-picking skills but neglecting to record losses as disconfirming evidence.
The fact is that even the very best traders are wrong a lot of the time, except that with right money management it is possible to be wrong most of the time but still be profitable. Successful traders recognize that it is not a sin to be wrong, but it is a sin to stay wrong. It doesn’t matter why you are wrong, but recognizing you are wrong will save you from blowing up your account.
The question then is why are we stuck with this mental model that makes us so willing to overlook a broader reality? Confirmation bias is really all about keeping the external world consistent with our own version of the world. Constantly questioning every idea we hold with new data and information would not only be time consuming but also exhausting. We humans simply don’t have enough mental bandwidth to be constantly testing our core beliefs. Confirmation bias allows us to stop thinking about an issue and thereby relieves us from the need to act or react.
Not only is confirmation bias a flaw in the way we process information; it is also present in the way we take in information. With confirmation bias at work, we perceive information in a selective fashion. On one level, we make conscious decisions on what information to take in: we watch CNN or CNBC; we read the Wall Street Journal or the New York Times. On a subconscious level, we simply never perceive information that may not fit with our worldview. Consuming information that conforms to our ideas of the world is easy and pleasurable; consuming information that challenges us to think in new ways or question our assumptions is frustrating and difficult.
Given all the internal and external forces working against us as traders, it is imperative that we work to try and offset the effects of confirmation bias. In a sense, we need to constantly be asking, “What is it that I don’t already know?”—because there is a good chance the markets already know it.
There are three ways in which we can try and work against confirmation bias:
- Try to get outside our comfort zone and visit sites that stretch our thinking. A conscious effort to expand our influences can go a long way in opening up different ways of thinking.
- Actively search out discomfiting evidence. If we have a strong belief about something, we need to consciously seek out evidence that is at odds with our view. This is admittedly hard work, but it is a necessary antidote to confirmation bias.
- Expose our ideas to others. On the face of it, confirmation bias shouldn’t exist. It is not a helpful adaptation. However, the argumentative theory posits that we have confirmation bias because it makes us more effective advocates for a position. When people are able to discuss their ideas with other people who disagree with them, then the confirmation biases between different participants will balance each other out, and the group as a whole will be able to focus on the best solution.
Rats get tripped up by uncertainty in a way that should appear very familiar to us. Classical stimulus-response experiments have shown that the introduction of uncertainty drastically slows learning. When rats are trained on a fixed reward schedule (for example, a pellet for every tenth press of a lever), they learn pretty fast to press that lever for food. If you withdraw the reward, the lever-pressing behavior is quickly extinguished. The rats figure out that no more food is on its way.
But when you reward the rats on a variable or intermittent reinforcement schedule (a pellet that comes on average every tenth lever press), that introduces uncertainty. The average number of lever presses for the reward is the same, but the rat could get a reward on the next press or not for thirty presses. In other words, the rats are rewarded the way humans usually are: having no way to know with certainty what will happen on the next try. When you withdraw the reward from those rats, the lever-pressing behavior extinguishes only after a very long time of fruitless lever pushing, sometimes thousands of tries.
We might imagine the rats thinking, “I bet the next lever press will get me a pellet. . . . I’ve just been getting unlucky . . . I’m due.” Actually, we don’t even have to imagine this. We can hear it if we listen to what people say while they play slot machines. Slot machines operate on a variable-payoff system. It’s no wonder that, despite those machines being among the worst bets in the casino, the banks of slots in a casino are packed. In the end, our rat brains dominate.
Outcomes don’t tell us what’s our fault and what isn’t, what we should take credit for and what we shouldn’t. Unlike in chess, we can’t simply work backward from the quality of the outcome to determine the quality of our beliefs or decisions. This makes learning from outcomes a pretty haphazard process. A negative outcome could be a signal to go in and examine our decision-making. That outcome could also be due to bad luck, unrelated to our decision, in which case treating that outcome as a signal to change future decisions would be a mistake. A good outcome could signal that we made a good decision. It could also mean that we got lucky, in which case we would be making a mistake to use that outcome as a signal to repeat that decision in the future.
If this all doesn’t seem difficult enough, outcomes are rarely all skill or all luck. Even when we make the most egregious mistakes and get appropriately negative outcomes, luck plays a role. For every drunk driver who swerves into a ditch and flips his car, there are several who swerve harmlessly across multilane highways. It might feel like the drunk driver in the ditch deserved that outcome, but the luck of the road conditions and presence or absence of other drivers also played a role. When we do everything right, like drive through a green light perfectly sober and live to tell the tale, there is also an element of luck. No one else simultaneously ran a red light and hit us. There wasn’t a patch of ice on the road to make us lose control of our vehicle. We didn’t run over a piece of debris and blow a tire.
When we field our outcomes as the future unfolds, we always run into this problem: the way things turn out could be the result of our decisions, luck, or some combination of the two. Just as we are almost never 100% wrong or right, outcomes are almost never 100% due to luck or skill. Learning from experience doesn’t offer us the orderliness of chess or, for that matter, folding and sorting laundry. Getting insight into the way uncertainty trips us up, whether the errors we make are patterned (hint: they are) and what motivates those errors, should give us clues for figuring out achievable strategies to calibrate the bets we make on our outcomes.
Outcomes don’t tell us what’s our fault and what isn’t (or what we should take credit for and what we shouldn’t).
But when you reward the rats on a variable or intermittent reinforcement schedule (a pellet that comes on average every tenth lever press), that introduces uncertainty. The average number of lever presses for the reward is the same, but the rat could get a reward on the next press or not for thirty presses. In other words, the rats are rewarded the way humans usually are: having no way to know with certainty what will happen on the next try. When you withdraw the reward from those rats, the lever-pressing behavior extinguishes only after a very long time of fruitless lever pushing, sometimes thousands of tries.
We might imagine the rats thinking, “I bet the next lever press will get me a pellet. . . . I’ve just been getting unlucky . . . I’m due.” Actually, we don’t even have to imagine this. We can hear it if we listen to what people say while they play slot machines. Slot machines operate on a variable-payoff system. It’s no wonder that, despite those machines being among the worst bets in the casino, the banks of slots in a casino are packed. In the end, our rat brains dominate.
Outcomes don’t tell us what’s our fault and what isn’t, what we should take credit for and what we shouldn’t. Unlike in chess, we can’t simply work backward from the quality of the outcome to determine the quality of our beliefs or decisions. This makes learning from outcomes a pretty haphazard process. A negative outcome could be a signal to go in and examine our decision-making. That outcome could also be due to bad luck, unrelated to our decision, in which case treating that outcome as a signal to change future decisions would be a mistake. A good outcome could signal that we made a good decision. It could also mean that we got lucky, in which case we would be making a mistake to use that outcome as a signal to repeat that decision in the future.
If this all doesn’t seem difficult enough, outcomes are rarely all skill or all luck. Even when we make the most egregious mistakes and get appropriately negative outcomes, luck plays a role. For every drunk driver who swerves into a ditch and flips his car, there are several who swerve harmlessly across multilane highways. It might feel like the drunk driver in the ditch deserved that outcome, but the luck of the road conditions and presence or absence of other drivers also played a role. When we do everything right, like drive through a green light perfectly sober and live to tell the tale, there is also an element of luck. No one else simultaneously ran a red light and hit us. There wasn’t a patch of ice on the road to make us lose control of our vehicle. We didn’t run over a piece of debris and blow a tire.
When we field our outcomes as the future unfolds, we always run into this problem: the way things turn out could be the result of our decisions, luck, or some combination of the two. Just as we are almost never 100% wrong or right, outcomes are almost never 100% due to luck or skill. Learning from experience doesn’t offer us the orderliness of chess or, for that matter, folding and sorting laundry. Getting insight into the way uncertainty trips us up, whether the errors we make are patterned (hint: they are) and what motivates those errors, should give us clues for figuring out achievable strategies to calibrate the bets we make on our outcomes.
Yes, I am that rat practicing trend following: buy when the price goes up and sell when the price goes down.
But it works only some of the time and like the rat, I have no clue why sometimes it works and sometimes it doesn't.
A brief outline of the average man's behavior in a strongly advancing market is something like this: he buys timidly at first, very little, if any, at low prices, but gains confidence as advance continues and buys more. He takes small profits but, noting that stocks still advance, he is sorry he sold and buys the same stocks back higher up. This time he determines to get more profit, but waits too long to sell and sees prices decline. Then he mistakenly sees each lower price as a bargain and buys more. Later, when the media pundits are full of doom and gloom and stocks have finally touched bottom, he gets scared and sells his entire holdings for a great loss.
A person who seriously studies market trends, business cycles, reactions and recoveries will discover that it is of practically no value to know that a stock is cheap, unless one also knows whether it is cheap on the way up, or the way down. Stocks are actually a little like the weather. If you are experiencing the hottest day in several years, you may cheer up over the thought that it will surely be cooler tomorrow. Likewise, when stocks are unusually high, they are almost certain to react.
Before the Big Crash of October, 1929, the public had ample warning that the big fellows were selling and the little fellows buying. Week after week, published reports of the Federal Reserve Banks indicated that brokers' loans were going up, even though average stock prices were declining. In other words, the growth in loans could not be explained by greater value of stocks, for the price trend was downward. The figures could only indicate that the number of margin accounts—stocks held by brokers for customers with loans against them—were increasing, while wiser folk, able to hold their stocks outright, were selling. The only reason they could be selling was because, from their superior vantage point, they foresaw a decline and expected to repurchase stocks at lower levels. Nobody could have asked for a better hint to step out of the market. The danger signal was adequate and unmistakable. But how many heeded it?
Surely this gigantic Halloween festival, plus the September-October drop of 1937, the August-September setback in 1946, and the late tape liquidation in the spring of 1962, provide overwhelming proof that most people are generally wrong in the stock market. Otherwise, the majority would not have placed themselves in a position where they would be anxious, or compelled to dump stocks at scared-price levels.
Wise men—wise, that is, as far as the market is concerned—are usually selling their stocks at the very time when the general public is most eager to buy. Indeed, the readiness of the public to buy is what gives the resourceful ones an opportunity to sell. When the first warning break comes at the peak of a long bull uptrend, the public invariably considers this an ideal opportunity for bargain hunting and uses further available buying reserves to acquire more stocks. Naturally, this adds to the burden of protecting their holdings and increases their susceptibility to fright; and with money suddenly becoming more scarce and pessimism spreading everywhere, the volume and speed of sales pick up, until the market finally touches bottom and a period of dullness and stagnation sets in.
Bargains that you never would believe in if you didn't see them are now suddenly available all through the list. But how many, except for the select few who anticipated the downtrend and now have money locked safely away, are courageous enough to buy even at these incredibly low levels? The badly frightened majority is no longer interested in bargains even if it had any money left, because it reasons: Stocks are in a violent downtrend, therefore, they'll go still lower tomorrow! Whatever is will always continue.
The most logical thing the average individual can do, indeed, wnat he is most likely to do, is to buy when prices are high and sell when they are low, thus suffering a loss. Unwise as this is, it is nevertheless logical, because when stock prices are at, or near, their peak the majority of news and information one hears, or reads, is favorable, suggesting that soon they will be higher still. But when prices are at their lowest ebb, all that one learns from newspapers, or from conversation with knowing friends, is discouraging. To a mind that works logically, it appears obvious that the worst is still to come; that the end of the downward swing is not yet in sight. No wonder that most people tend to buy near the top and sell near the bottom.
Under such circumstances, not only will you buy toward the top, but you are likely to buy at the exact top. Well, why not? Never does the market for a stock appear so bullish as on the day it reaches a record high. It was favorable news that made it go there, and it was the same alluring tidings that prompted others to buy. Every evening you discover that if you had only bought certain issues when the market opened that day and sold them at the close at 4:00 P.M., you would have had a nice profit. Naturally, your hindsight vision is 20/20, and, since you also have a logical mind, you say to yourself: "The thing to do is buy these stocks tomorrow and catch the rise from now on."
Anybody knows that when a thing has happened several times over, the presumption is in favor of its continuing to happen in the same way. (Stage magicians recognize this trait of human nature and capitalize upon it. You have seen a magician toss balls into the air one after another, until finally the last ball he throws mysteriously disappears—only, he didn't throw the last ball, but just made the motion of throwing it. Most of the audience is positive he really threw it—because that was what he had been doing.) It is this disposition to expect a stock to continue in the same direction that it has been going, which leads people to buy at top levels after several days' rise, or to sell near the bottom after several days of decline.
Looking back, you now realize that it could not have kept advancing without a normal correction. The setback, you imagine, is only temporary. Yet each day thereafter, let us say, it reacts further. After it does this for a few days, you repeat the logical reasoning you followed when it was moving up. You now decide that it will probably continue to trend lower. But the day you sell is reasonably certain to mark the end of the decline, because you were not the only one who was finally scared into selling. Being an average man, you were merely representative of many other people who have also sold. Since the immediate selling pressure has now been lifted, the stock suddenly stops going down.
To be too logical in one's thinking toward the stock market can be dangerous and perhaps fatal. If you are logical, you are merely doing what nearly everyone else is doing. Profits in the market depend largely on your being smarter than the majority of people. But you can't do that if you make the identical errors that they consistently make. This same margin seems to separate those who own their own swimming pools from those who can't even keep their heads above water.
(This post is longer than usual, but those new to options should find it useful.)
OPTION RISK
The single most important measure of any group of data is the average or mean. Adding up all the data and dividing by the number of data points gives you the average or mean. After finding the average or mean, the next important question is, How far do all the data points stray from this central point (the mean)? Measuring the amount that data varies from the mean gives us the second most important fact about a distribution. This is called variance, or deviation from the mean. Variance is an important statistical term, and it measures deviation from the center. There is another closely related formula that calculates another version of the variance called the standard deviation.
The mean and variance are the two most critical facts about each and every normal distribution. In fact, if you know those two facts, you have properly identified the whole normal distribution. You don’t need to hunt around for other information because you have it all. The mean tells us where the center of the distribution is, and the variance tells us how wide the distribution is. Change the mean or the variance and you have an entirely different normal distribution. Mean and variance are the fingerprints of each normal distribution.
If you know the variance, then you also know the standard deviation (it’s the square root of the variance). So, knowing one means you know the other. You just have to translate by using the square root. This is very important since option formulas demand that we know the standard deviations of the stocks we want to analyze. In fact, the standard deviation has another name in option analysis: volatility. Without exaggeration it is the most important part of every option model. It’s a measure of how far the stock price can be expected to move, based on experience. A pretty important measure if you are going to buy an option on that stock.
Historical volatility is simply a measure of the past, a statistical analysis of what has already happened. To calculate it we take recent data, say 30 days worth, apply the formula that computes standard deviation, and then annualize it. Traders always look at this data to get a feel and make up their minds as to what should be happening today and what might be happening tomorrow or next week.
Implied volatility is what the market is implying right this minute, the present. To calculate it, we take today’s call premium (C) and apply the Black-Scholes formula backwards. If we include C as an unknown, there are then six unknowns in the formula. If we know any five of these, we can readily compute the sixth. Normally we input the five we call SKIT-V* and receive C (the call premium) as our answer. Alternatively, we can input SKIT-C and derive the value for V instead. This will tell us the volatility that the market is presently trading at. That is, the market is presently implying this volatility, which is why we call it implied volatility.
The Holy Grail of option trading is the Black-Scholes formula and a knowledge of future volatility. If you have these two, you can become the world’s greatest option wizard. In the search for this Holy Grail many very clever analysts and traders massage the historical and implied volatility information trying desperately to get insight and a glimpse of the future and of future volatility. However, none can agree on the best way to predict future volatility. A general consensus of the research would be that the market’s implied volatility (the opinions of the smartest players) is as good as it gets. And it has been shown again and again that implied volatility is a far better predictor of the future than any simple historical volatility measure.
Option risk is defined as any unhedged change in an option’s value. The change in option value is always caused by changes in the components. Change is the only thing that matters. If everything remains constant, then there is no risk, no exposure. But, of course, things do change. There are three key questions in our analysis: What risks show up when things change? What is their rate of change? How can we hedge this?
Let’s use a familiar example: driving your car down the road at 45 miles per hour. To change your speed you must either accelerate or decelerate. In the calculus of motion, speed is the first derivative while the acceleration is the second derivative. For traders, the only second derivative normally used is called gamma. Traders say the option’s speed is its delta and the acceleration is its gamma. Just as a car’s power is measured by its ability to accelerate, so too, is an option’s. Traders who don’t pay close attention to gamma are often in shock when they get whiplashed right out of their seats.
Delta is the single most discussed options tool. When people trade IBM options as a substitute for owning IBM stock, the delta puts a simple number on the relative speed of the two. For example, if the delta is 0.45, it means the option moves 45 percent as fast as IBM stock. Deltas always run between 0.00 and 1.00. Then the analysts took it a step further and wanted to know how fast the delta was changing. They used calculus to take the second derivative and named it gamma. You can see the relationships clearly in the following table.
Delta and Gamma
The previous table is an oversimplified example of how deltas and gammas are computed. Notice the column titled Option Premium C (in the Black-Scholes formula, C stands for call premium). Each pair of premium values generates only one delta value. Now look at the Delta column. Each pair of deltas generates only one gamma value.
Notice that when the stock price is trading at $101 the option premium is $3.10. However, we expect this $3.10 to become $3.30 if the stock pops up a buck to $102. This $.20 change per $1 spot stock move is the delta. A delta of 0.20 means the option moves 20 percent as fast as the stock between those two points.
That’s about all there is to the way the average person understands and uses delta. Traders, however, spend all their waking hours trying to control and hedge their ever-changing delta exposures. The expected up move or down move of any option premium should be almost identical over a small stock price move and not as represented in our table. Also, then, the deltas and gammas should be changing more gradually than in our table. We wanted larger changes than reality usually gives for purposes of the example.
From $102 to $103 there is a bigger move (0.30) in option value. This change in delta, from 0.20 to 0.30, must be protected against. Note the gamma between those two deltas. It is 0.10 which represents the delta move we just identified. That’s why we need gamma, to point out the risk of potential changes in delta.
The measurements of all the other multidimensional types of option risk require similar analysis. Whether we wish to measure volatility risk* (vega = ∂C/∂V) time risk (theta = ∂C/∂T), or interest rate risk (rho = ∂C/∂I), we are always measuring the option premium change, which is ∂C, per small move of the component.
So, we’ve introduced the standard measurements of option risk. Now, how do we eliminate the risk after we’ve identified and measured it? The answer is that, we must offset the risk with an appropriate hedging vehicle.
We define hedging vehicles as any and all tradable assets in the spot, forward, futures, or options markets. It is up to us to find the best asset to neutralize our risk. The hedging vehicle must offset the driving force of the risk you have. Clearly, all option risks are not driven solely by the spot price. Traders worry not just about S, but about the big three, STV. Hedges must be selected for their ability to offset T (time) and V (volatility) and the lesser risks (rhos), too. If we fail to hedge all the multidimensional risks, there is some market movement that will ambush us in the end.
Let’s start by talking about delta exposure. Imagine you have bought 1000 call options on IBM. This represents the right to call 100,000 shares of IBM stock. The option delta happens to be 0.15. Your exposure is calculated as delta underlying shares, which is, 0.15 x 100,000 = +15,000. This means your exposure is identical to owning 15,000 shares of IBM right now. Said another way, your daily P&L will move up and down in value as if you were the proud owner of 15,000 shares of IBM. To offset this risk you must sell short 15,000 shares of IBM against it. If you did this, your portfolio would show a position of long 1000 IBM options and short 15,000 shares IBM stock. For the time being your risk would be neutralized,
vis-à-vis IBM’s spot price.
This is called being delta neutral. That’s how traders hedge their delta risk. They buy or sell IBM shares in spot, forward, or futures markets trying to stay flat. But as various SKIT-V components change, the delta of 0.15 will shift relentlessly up and down, say to 0.20, then to 0.17, and so forth. This makes the portfolio longer or shorter IBM without the traders doing any tinkering at all. If the traders run a new exposure report, it will indicate the equivalent shares of IBM that they are long or short as of that moment. To stay delta neutral the traders must sell excess long positions or buy back excess short positions. This is known as dynamic hedging (also called rebalancing or rehedging). The traders hope everything stays quiet and they won’t have to rehedge very often. But the various components of SKIT-V never seem to keep still very long. For a fleeting second you think you understand exactly what must be hedged, but then minutes later the whole thing has slithered off into an entirely different risk profile.
Once we’ve neutralized the delta, what do we do about gamma? The purpose of gamma is to foretell potential moves in delta. That is, it helps you anticipate the increases and decreases in delta as IBM spot prices move. There are two trading approaches to gamma.
The first approach is simple and passive: You ignore the gamma and simply rebalance the delta hedge as needed. Any cost to your P&L is chalked up to the contrariness of options. The second approach is more aggressive: You try to neutralize the gamma to make it go away. But you cannot make gamma go away by buying or selling shares of IBM. This is a perfect example of an inappropriate hedge. You must hedge with other options to lower your exposure to gamma.
Here are a few quick hints about hedging gamma. At-themoney options have the most gamma. As you move away from being at-the-money, in either direction, the gamma falls toward zero. There is very little gamma to deep in-the-money or deep out-of-the-money options. So, at-the-money options are best hedged with other at-the-money options, and so on.
Typically, a portfolio that is long options has positive gamma risk. So you would need to sell other options to lower this risk. Conversely, a portfolio that is net short options tends to have negative gamma risk and you must buy other options to reduce the risk. Hedging gamma is a relatively advanced concept which we can’t do justice to here. Suffice it to say that only the more experienced option people are knowledgeable on the topic. The others avoid it completely using the first approach (benign neglect), but their ostrich-like tendencies do not make the risk disappear. They try to hedge their gamma risk by constantly readjusting their delta neutral positions and are surprised when they can’t ever keep up.
There is a cost for constantly rebalancing the hedge when you are gamma negative. You must buy some IBM every time it rallies and sell when the price goes lower. You are at risk in choppy markets. But you are being paid to render this service to the market, if you think about it. The reason you are gamma negative is that you have sold options to the marketplace. Therefore, you have already been paid the premiums for taking on this risk.
Think of it like this: you are wearing a new fashion creation for traders, the option hedging poncho. It’s one of those hooded pullovers with a single-pocket pouch in front to keep your hands warm (it’s continuous and goes all the way through). When you sell options, you immediately earn money and you stuff this cash into the pouch. But you must pull it out later in dribs and drabs as you gradually lose money from delta neutral hedging because you are always buying high and selling low. On average the sum of the dribs and drabs will exactly offset any money you earn from selling options—if you sell at fair prices. After thousands of cases, the poncho will be empty. Any interest earned on the money is included in this calculation.
The reverse is true if you are gamma positive. Because you start by buying options you must pay the money up front, so you will have to borrow it at first. You will regain this money in dribs and drabs as you sell high and buy low while delta neutral hedging. The sum of all the dribs and drabs you are able to stuff into the poncho will allow you to pay back your borrowings and any interest due. On average, after many cases, you will break even and the poncho will once again be empty.
This break-even philosophy requires, as the Black-Scholes formula does, that we can hedge without fees and bid/ask spreads, and so forth. In the real world, however, we will lose any such expenses when we try to dynamically delta hedge to stay neutral (flat).
OPTION RISK
The single most important measure of any group of data is the average or mean. Adding up all the data and dividing by the number of data points gives you the average or mean. After finding the average or mean, the next important question is, How far do all the data points stray from this central point (the mean)? Measuring the amount that data varies from the mean gives us the second most important fact about a distribution. This is called variance, or deviation from the mean. Variance is an important statistical term, and it measures deviation from the center. There is another closely related formula that calculates another version of the variance called the standard deviation.
The mean and variance are the two most critical facts about each and every normal distribution. In fact, if you know those two facts, you have properly identified the whole normal distribution. You don’t need to hunt around for other information because you have it all. The mean tells us where the center of the distribution is, and the variance tells us how wide the distribution is. Change the mean or the variance and you have an entirely different normal distribution. Mean and variance are the fingerprints of each normal distribution.
If you know the variance, then you also know the standard deviation (it’s the square root of the variance). So, knowing one means you know the other. You just have to translate by using the square root. This is very important since option formulas demand that we know the standard deviations of the stocks we want to analyze. In fact, the standard deviation has another name in option analysis: volatility. Without exaggeration it is the most important part of every option model. It’s a measure of how far the stock price can be expected to move, based on experience. A pretty important measure if you are going to buy an option on that stock.
Volatility
Traders talk about three different categories of volatility: historical, implied, and future. We can best explain them by categorizing them into their time frames: past, present, and future.
Historical volatility is simply a measure of the past, a statistical analysis of what has already happened. To calculate it we take recent data, say 30 days worth, apply the formula that computes standard deviation, and then annualize it. Traders always look at this data to get a feel and make up their minds as to what should be happening today and what might be happening tomorrow or next week.
Implied volatility is what the market is implying right this minute, the present. To calculate it, we take today’s call premium (C) and apply the Black-Scholes formula backwards. If we include C as an unknown, there are then six unknowns in the formula. If we know any five of these, we can readily compute the sixth. Normally we input the five we call SKIT-V* and receive C (the call premium) as our answer. Alternatively, we can input SKIT-C and derive the value for V instead. This will tell us the volatility that the market is presently trading at. That is, the market is presently implying this volatility, which is why we call it implied volatility.
* SKIT-V is the five inputs required by the Black-Scholes formula:
Future volatility is the volatility that will occur over the coming weeks and months. It is the volatility required by the Black-Scholes formula in order to give the correct call premium C. To calculate it you need a crystal ball. Nobody knows how to calculate future volatility (or if they do, at least they are not sharing the knowledge). Anyone knowing a future volatility for certain could make some awesome trading coups. In fact, the best option traders are those who understand the ways of volatility movements. They exert their edge by being right about certain volatility patterns of the public. They aren’t even close to always being right, but they have a tradeable advantage that nets a profit at year end over many plays. No one knows the future for sure, but knowing a coin is biased 52 percent for heads can make a big difference—casinos have grown rich on similar edges.- S = spot price of stock
- K = strike price of option
- I = interest rate
- T = time to expiry (in years)
- V = volatility of stock, defined as 1 SD of annual price moves
The Holy Grail of option trading is the Black-Scholes formula and a knowledge of future volatility. If you have these two, you can become the world’s greatest option wizard. In the search for this Holy Grail many very clever analysts and traders massage the historical and implied volatility information trying desperately to get insight and a glimpse of the future and of future volatility. However, none can agree on the best way to predict future volatility. A general consensus of the research would be that the market’s implied volatility (the opinions of the smartest players) is as good as it gets. And it has been shown again and again that implied volatility is a far better predictor of the future than any simple historical volatility measure.
How to Measure Option Risk
Option risk is defined as any unhedged change in an option’s value. The change in option value is always caused by changes in the components. Change is the only thing that matters. If everything remains constant, then there is no risk, no exposure. But, of course, things do change. There are three key questions in our analysis: What risks show up when things change? What is their rate of change? How can we hedge this?
Let’s use a familiar example: driving your car down the road at 45 miles per hour. To change your speed you must either accelerate or decelerate. In the calculus of motion, speed is the first derivative while the acceleration is the second derivative. For traders, the only second derivative normally used is called gamma. Traders say the option’s speed is its delta and the acceleration is its gamma. Just as a car’s power is measured by its ability to accelerate, so too, is an option’s. Traders who don’t pay close attention to gamma are often in shock when they get whiplashed right out of their seats.
Delta is the single most discussed options tool. When people trade IBM options as a substitute for owning IBM stock, the delta puts a simple number on the relative speed of the two. For example, if the delta is 0.45, it means the option moves 45 percent as fast as IBM stock. Deltas always run between 0.00 and 1.00. Then the analysts took it a step further and wanted to know how fast the delta was changing. They used calculus to take the second derivative and named it gamma. You can see the relationships clearly in the following table.
Delta and Gamma
The previous table is an oversimplified example of how deltas and gammas are computed. Notice the column titled Option Premium C (in the Black-Scholes formula, C stands for call premium). Each pair of premium values generates only one delta value. Now look at the Delta column. Each pair of deltas generates only one gamma value.
Notice that when the stock price is trading at $101 the option premium is $3.10. However, we expect this $3.10 to become $3.30 if the stock pops up a buck to $102. This $.20 change per $1 spot stock move is the delta. A delta of 0.20 means the option moves 20 percent as fast as the stock between those two points.
That’s about all there is to the way the average person understands and uses delta. Traders, however, spend all their waking hours trying to control and hedge their ever-changing delta exposures. The expected up move or down move of any option premium should be almost identical over a small stock price move and not as represented in our table. Also, then, the deltas and gammas should be changing more gradually than in our table. We wanted larger changes than reality usually gives for purposes of the example.
From $102 to $103 there is a bigger move (0.30) in option value. This change in delta, from 0.20 to 0.30, must be protected against. Note the gamma between those two deltas. It is 0.10 which represents the delta move we just identified. That’s why we need gamma, to point out the risk of potential changes in delta.
The measurements of all the other multidimensional types of option risk require similar analysis. Whether we wish to measure volatility risk* (vega = ∂C/∂V) time risk (theta = ∂C/∂T), or interest rate risk (rho = ∂C/∂I), we are always measuring the option premium change, which is ∂C, per small move of the component.
Delta and Gamma Hedging
So, we’ve introduced the standard measurements of option risk. Now, how do we eliminate the risk after we’ve identified and measured it? The answer is that, we must offset the risk with an appropriate hedging vehicle.
We define hedging vehicles as any and all tradable assets in the spot, forward, futures, or options markets. It is up to us to find the best asset to neutralize our risk. The hedging vehicle must offset the driving force of the risk you have. Clearly, all option risks are not driven solely by the spot price. Traders worry not just about S, but about the big three, STV. Hedges must be selected for their ability to offset T (time) and V (volatility) and the lesser risks (rhos), too. If we fail to hedge all the multidimensional risks, there is some market movement that will ambush us in the end.
Let’s start by talking about delta exposure. Imagine you have bought 1000 call options on IBM. This represents the right to call 100,000 shares of IBM stock. The option delta happens to be 0.15. Your exposure is calculated as delta underlying shares, which is, 0.15 x 100,000 = +15,000. This means your exposure is identical to owning 15,000 shares of IBM right now. Said another way, your daily P&L will move up and down in value as if you were the proud owner of 15,000 shares of IBM. To offset this risk you must sell short 15,000 shares of IBM against it. If you did this, your portfolio would show a position of long 1000 IBM options and short 15,000 shares IBM stock. For the time being your risk would be neutralized,
vis-à-vis IBM’s spot price.
This is called being delta neutral. That’s how traders hedge their delta risk. They buy or sell IBM shares in spot, forward, or futures markets trying to stay flat. But as various SKIT-V components change, the delta of 0.15 will shift relentlessly up and down, say to 0.20, then to 0.17, and so forth. This makes the portfolio longer or shorter IBM without the traders doing any tinkering at all. If the traders run a new exposure report, it will indicate the equivalent shares of IBM that they are long or short as of that moment. To stay delta neutral the traders must sell excess long positions or buy back excess short positions. This is known as dynamic hedging (also called rebalancing or rehedging). The traders hope everything stays quiet and they won’t have to rehedge very often. But the various components of SKIT-V never seem to keep still very long. For a fleeting second you think you understand exactly what must be hedged, but then minutes later the whole thing has slithered off into an entirely different risk profile.
Once we’ve neutralized the delta, what do we do about gamma? The purpose of gamma is to foretell potential moves in delta. That is, it helps you anticipate the increases and decreases in delta as IBM spot prices move. There are two trading approaches to gamma.
The first approach is simple and passive: You ignore the gamma and simply rebalance the delta hedge as needed. Any cost to your P&L is chalked up to the contrariness of options. The second approach is more aggressive: You try to neutralize the gamma to make it go away. But you cannot make gamma go away by buying or selling shares of IBM. This is a perfect example of an inappropriate hedge. You must hedge with other options to lower your exposure to gamma.
Here are a few quick hints about hedging gamma. At-themoney options have the most gamma. As you move away from being at-the-money, in either direction, the gamma falls toward zero. There is very little gamma to deep in-the-money or deep out-of-the-money options. So, at-the-money options are best hedged with other at-the-money options, and so on.
Typically, a portfolio that is long options has positive gamma risk. So you would need to sell other options to lower this risk. Conversely, a portfolio that is net short options tends to have negative gamma risk and you must buy other options to reduce the risk. Hedging gamma is a relatively advanced concept which we can’t do justice to here. Suffice it to say that only the more experienced option people are knowledgeable on the topic. The others avoid it completely using the first approach (benign neglect), but their ostrich-like tendencies do not make the risk disappear. They try to hedge their gamma risk by constantly readjusting their delta neutral positions and are surprised when they can’t ever keep up.
There is a cost for constantly rebalancing the hedge when you are gamma negative. You must buy some IBM every time it rallies and sell when the price goes lower. You are at risk in choppy markets. But you are being paid to render this service to the market, if you think about it. The reason you are gamma negative is that you have sold options to the marketplace. Therefore, you have already been paid the premiums for taking on this risk.
Think of it like this: you are wearing a new fashion creation for traders, the option hedging poncho. It’s one of those hooded pullovers with a single-pocket pouch in front to keep your hands warm (it’s continuous and goes all the way through). When you sell options, you immediately earn money and you stuff this cash into the pouch. But you must pull it out later in dribs and drabs as you gradually lose money from delta neutral hedging because you are always buying high and selling low. On average the sum of the dribs and drabs will exactly offset any money you earn from selling options—if you sell at fair prices. After thousands of cases, the poncho will be empty. Any interest earned on the money is included in this calculation.
The reverse is true if you are gamma positive. Because you start by buying options you must pay the money up front, so you will have to borrow it at first. You will regain this money in dribs and drabs as you sell high and buy low while delta neutral hedging. The sum of all the dribs and drabs you are able to stuff into the poncho will allow you to pay back your borrowings and any interest due. On average, after many cases, you will break even and the poncho will once again be empty.
This break-even philosophy requires, as the Black-Scholes formula does, that we can hedge without fees and bid/ask spreads, and so forth. In the real world, however, we will lose any such expenses when we try to dynamically delta hedge to stay neutral (flat).
Yes, I am that rat practicing trend following: buy when the price goes up and sell when the price goes down.
But it works only some of the time and like the rat, I have no clue why sometimes it works and sometimes it doesn't.
50% time trend following and 50% time reversal, if you can tell the difference.