As a child, many of us have endured the arduous labor that is chores. While all of us have done the dishes and cleaned our rooms, let’s admit it… we were always a little envious of our friends who received pocket money. (Trading makes up for it though, so it’s all good :)) Imagine your friend, king of the playground and loaded with $100. Devastated, he loses $50 by accident. Now consider his dad, who likely earns a couple of thousands per month. If he lost the same $50 bill, it would certainly hurt his pride but not his wallet, as much as his young son’s. If you could choose to be in the son or father’s position, which one would you choose? To lose half of your earnings or a minute fraction? This form of decision making is analogous to that of a trader’s. In this post, we will be learning about trading size in your portfolio. One of the toughest things in trading or investing is figuring out the size of each trade. How much of your capital should be deployed in the markets and how much do you hold in cash? Let’s take a hypothetical portfolio of $10,000. If you have $10,000 in your trading account, how would you allocate this $10,000 in markets to make your money work for you? Well, you could pick your one favorite stock (selection made on criteria that suits you) and buy every stock possible for the entire $10,000 and hope that the stock goes up in value and you profit from it. Most traders who start fresh are inclined to do this. While this is an easy way to execute, it’s neither prudent nor profitable to put all of your eggs in one basket. What do you think would happen if the stock took a 20% hit and your portfolio value falls to $8000? You can of course wait for the stock to rally to get back to your initial investment of $10,000. But just how easy is it for the stock to move up in terms of it’s current value so that you can break even? If the stock falls 20% in value, it needs to gain 25% from that point for you to make a profit. If the stock falls 50% then it needs to gain 100% (or literally double it’s value) for you to breakeven. The table and chart listed below shows the nonlinear relationship between the gains need after a steep drop for your portfolio to recover: Alternatively, you could spread your risk and allocate this $10,000 across several different stocks in a manner that exposure of risk to your portfolio is spread across these different stocks. For example, we could allocate $250 per trade/investment and buy or sell 15 to 20 stocks/options. This would be a bit more diversified compared to the previous example but if all the stocks selected are highly correlated to each other, then the impact of the market move in a direction would have same impact as if the portfolio had just one stock as in previous example.
So, a better way of portfolio allocation would be is to add some diversification to your portfolio in terms of having some long positions, some short positions, having a mix of stocks, ETFs, commodities, options that have low correlation between each other. Advanced traders would add diversification in terms of strategies so at that one can benefit from the changes in volatility or direction or time. Personally, in this extremely low IV environment I'd like to keep 50% of my capital in cash and deploy the other 50% in the market, diversifying it through various underlyings and strategies. The trick is to diversify as much as possible and trade small, but trade often, as TastyTrade would say. Stay tuned for more posts from NishaTrades!
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In an earlier blog post, I discussed the ability to figure out the expected move based on volatility. Though there are extensive data science books written solely to extol the value of forecasting stock prices using statistical models and machine learnings, I prefer to rely upon trusty volatility as basis for my predictions. Why? 1. It’s tothepoint and saves me the hassle of looking at the output of probability based forecasts from different statistical models. 2. There’s actual market participation. Volatility is driven by fear or greed in the market and can provide me with a more realistic and tangible understanding of expected movement. While predicting the price of a stock in the future might be a good research exercise, it is also a great tool for finding trading opportunities. If a stock or underlying has moved quickly to an extreme, I can take the contrarian view and fade the move. Let’s work with an example. The figure below shows different underlyings and their associated volatility tickers created by CBOE. On Thursday evening Soybean (SOYB) had closed at $17.56. It’s volatility SIV had closed at 20.99. Based on this, the one day, one standard deviation move expected was $0.19. Here are the calculations: But around noon the next day SOYB was trading at $17.98 which was more than twice the expected $0.19; SOYB had moved more than two standard deviations from the prior day’s expected move. The degree of confidence for a 2 SD move is about 95%, that is to say there was only 5% chance that market was expecting this kind of move! Given such an extreme move, as a contrarian, I’d fade the move to short soybeans. Since SOYB is thinly traded with wide bidask spreads, I’d short the futures /ZS or maybe even short a put along with it to add a positive theta component. Here’s another exercise expected move with SPX and its volatility VIX. For now, let’s ignore the news surrounding the market: bulls vs. bears, political environment fueling the market, or how high it is. Let’s instead try to quantify it. I’m choosing the date of 2/11/16 which printed the lowest point in recent (short) memory – about 500+ days go. If we took the closing prices of 2/11/16, we can figure the expected move till today and see that SPX has been hovering pretty close to this number: Here are all the standard deviations using 507 days since 2/11/16 when SPX was at 1829.08 and VIX was at 28.14 as: If we see the large jump in the price of the stock beyond the given SDs, we can take the contrarian view to fade the move.
In this post, we explored another way to look for trading opportunities this time using the expected move! Stay tuned for more posts from NishaTrades! 
NishaNinteen yearold trader, future connoisseur of options. Follow me on Twitter!
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