Predicting The Stock Market’s Next Move

Predicting The Stock Market’s Next Move

Hey, everyone. This is Kirk here again, at In this video, we’re going to talk about predicting
the market’s next move. Now, the first thing that most traders do
when entering a trade is try to predict where the market will go; it’s up, down, sideways,
you’re bullish, you’re bearish, you’re neutral, whatever the case is. We’re going to argue and prove in this video
that it doesn’t matter which way you trade the market with options because you’ll get
virtually the same probability of success and payout, regardless of the direction. Remember, the market is extremely efficient
so you can’t get some directional edge, necessarily, in every situation by trying to pick and assume
that the market’s going to turn or continue to move in one direction. As always, here at Option Alpha, we’re not
going to just tell you this is the case. We’re going to prove it to you. Let’s look at three different trades, or at
least three directionally different trades. We’re actually going to take a look here today
at GDX. This actually kind of fits the bill for us
anyway because at the time that we’re doing this video, GDX in gold has had a huge run-up
and is just now starting to move lower. It actually kind of gapped lower a couple
days ago and is now starting to move lower. Whatever you think might happen here in GDX
… I’m just going to want to scrunch this down so we can see further out strike prices. Take a second here, just real quick in this
video and make an assumption on where you think GDX is going to go. You can use some technicals or whatever you
want to do, but the reality is that after this move, some of you, maybe half, will say
it’s going to go lower. Some of you, maybe half, will say it’s going
to go higher. Maybe some small percentage says it goes sideways,
okay? Well, the reality is that doesn’t matter which
way you think GDX is going to go because we can build a trade that has a 70% chance of
success in any direction. Okay, this is something I go over a lot with
coaching students when I do one-on-one coaching is that if you think GDX is going to go lower,
you can build a trade that’s got a 70% chance of success based on the assumption that GDX
goes lower. You can build a trade that has a 70% chance
of success based on the assumption GDX goes higher, and you can build a trade that has
an assumption based on a 70% chance of success that GDX goes sideways or neutral or stays
range-bound. At the end of the day, you’re going to have
basically the same probability of success and basically the same payout across the board,
okay? Slight differences in one direction or another,
but you’ll see virtually the same payout, which really means that market is totally
efficient, totally normal distribution, and it really doesn’t matter which direction you
end up trading a stock, okay? Let’s go to the trade tap here. We’re in the May contracts, again, just based
at the time of this video, and the first thing that we’re going to do is we’re going to build
a bearish trade in GDX. Now, the way that we build bearish trades
is we basically sell call credit spreads. If GDX right now is trading for 1946, we’re
going to go out and we’re going to sell a call credit spread that basically gives us
about a 70% chance of success. Now, in this case, this is the probability
that the strike price that we’re looking at, the 21 strikes … This is our anchor strike
price. The 21 strikes have about a 30% chance of
being in the money at expiration. Again, there’s a 30% chance that GDX goes
from 1946 up to and above the 21 strike between now and May expiration. If we take the inverse of that, that’d be
about a 70% chance that it does not get that high. If we go ahead and we sell the 21, 22 credit
call spread in GDX, you can see that right now, that pays out a credit of about .26 cents
on this trade, okay? If we actually just hit confirm and send real
quick, you see that the risk in the trade is about $74, okay, because it’s just a $1
wide spread. In this case, we have got a 70% chance of
success, and we’ve got a trade that pays out about $26 and has risk of about $74, okay? That’s going to be the baseline for what we’re
doing here. Now, that’s if you’re directionally bearish
in GDX. Going back to the chart, that would mean that
you would basically make a trade where you’re selling a call credit spread here and basically
saying, “Look, if the market rallies higher, as long as it doesn’t go above 22, I make
money. If it stays sideways, I make money and if
it goes down, I make money.” Directionally, I’d love to see it go down. That’s the best case scenario, right? You can still make money if it goes sideways
or higher. Directionally, you’d love to see it go down. Let’s take the opposite side of the trade
here. Let’s assume that we are bullish on GDX, okay? Now let’s take the put credit spread and we’re
going to sell something with about the same probability, okay? This case, the closest probability to about
a 70% chance of success trade is selling the 17.5 puts and buying the 16.5 puts, okay? These basically give us about a 68%, 69% percent
probability of success, so very close to that 70 that we were targeting with the other trade
as well. We’re going to sell the 17.5, buy the 16.5,
and you can see this trade pays out $25, so about 2% probability of success but pays out
virtually the same amount of money, $25, has risk of $75, and has virtually the same probability
of success as the last trade, about 70%. Okay, so I’m rounding up one or two percent
here and with break-even points, it might be very close, okay? You can see that no matter which direction
you trade GDX, you’re making the same amount of money. You can build a strategy that profits the
same amount of times and pays out the same amount of money, and regardless of whatever
you choose. If you choose to be bearish or choose to be
bullish, it really doesn’t matter as long as you’re trading high-probability options. Again, in this case, if we went ahead and
sold the 17.5 puts, we’d basically be drawing a line in the sand here at 17.5 and saying
to the market, “Look, if you want to go down, that’s fine, just don’t go down below 17.5.” You can trade sideways, or the better option
here is to see the market go much higher from here. That would be the ideal situation by selling
the credit put spread. Now let’s take the third example here and
let’s assume that you actually want to trade GDX completely neutral, so you don’t really
have a directional assumption. You don’t care if it goes up or down. You want to trade at directionally neutral,
but what we have to do is we have to build a strategy that wins 70% of the time inside
of our strike prices, okay? Again, we have to build a strategy that wins
70% of the time inside of our strike prices. When we were doing one-directional trades,
we were looking for the 30% probability range on either side, our probability level on either
side. Now, if we want 70 on the inside, we basically
have to look for something that loses about 15% on either end, okay? In this case, it would be these two strikes,
the 23 calls and the 15.5 puts because together, the likelihood that GDX is above 23 or below
15.5 is about a 30% probability, right? You can see we’re just adding these two numbers
together. About a 30% probability that GDX is below
15.5 at expiration or above $23 at expiration, that leaves us an inside range between these
strike prices, 15.5 and 23, about a 70% chance that the stock ends inside of this range,
okay? Now, you can see we’re now building a strategy
that has a little bit wider of a wing or base on either end to hopefully capture a wider
range, still has the same probability of success so let’s go ahead and build out this strategy
here. We’ll sell the 15.5 puts, buy the 14.5 puts,
so doing a $1 wide spread on that end of the market. On the top side, we’ll do the same thing. We’ll sell the 23s and buy the 24s, and that
basically gives us an iron condor around them market and the credit we receive on this trade
is about $22. Okay, so a couple dollars less, not a huge
different obviously. It’s not a $10 or a $50 difference in pricing. It’s basically a $3 or $4 difference in pricing,
but you basically have a trade that pays out; 70% of the time is going to be a winner. When it is a winner, it pays out $22 and has
risk basically of about $78, okay? A very, very similar trade to everything else
that we’ve done. When we go back to the chart here, we can
now draw the lines and see that our new iron condor pays out if we were at 15.5, which
is further down below the market, and 33 is much further above the market so now this
is our new kind of win window here for this iron condor, much wider on each end. We need the stock to stay range-bound within
here. It can still move a lot, but we need the stock
to stay range-bound within here. Again, the key here is that you didn’t have
to have necessarily a good directional assumption to make a decent trade. In fact, the market is so efficient that it
basically prices in the same probability of success on any end or any way that you want
to shape it, and gives you about the same payout for that probability of success because
the market’s fair and efficient. It’s not going to give you something without
basically paying you enough money to make it worth your while. They’re paying out the risk that’s involved
in the trade. All right, now as we move on, we want to talk
a little bit about overbought and oversold. Now, we’ve previously proved that markets
move in a normalized distribution, and we did this a couple of videos before here in
track two, also in track one when we tracked DIA over the last basically 26 years, or going
back to 1990, and looked at all the daily moves that it had percentage-wise and there
was a complete, normal distribution pattern of percentage up and down. Now, that also means that we can assume there
are times when the markets will be either oversold or overbought and should revert back
to the mean or reverse, which means that if the market moves up 10% over the course of
three days, that is likely an outstretched move and the market should either stop that
move, which is the most important part, so not continue on at that pace, or possibly
reverse. Not that it would reverse all the time, but
at least stop that move higher. Now, the beauty of options trading is that
we can choose to play stocks directionally with a greater margin for error to win, even
if we are wrong in our assumption. The reason I say 50% there is because the
reality is that as stock traders and stock pickers, we’re actually really bad, not just
us; me, you, everybody. I mean even the investing community. Even Goldman Sachs, one of the biggest investment
brokerage houses on Wall Street with the most amount of money, the most amount of influence,
is actually really bad at picking directional assumptions for their stocks. We don’t want to assume that all the time
that we can pick the directional assumption, but with trading and trading options, it’s
like having an unfair advantage because you get a margin for error to assume that a stock
is going to go one way and if it’s a little bit wrong, you actually still could make some
money. Going back to our GDX example that we talked
about before, for example, if we sold those puts down below the market and we basically
draw the line in the sand and say, “Okay, look, we’ve got the 17 sure puts here on GDX. We don’t want GDX to go below this level,”
this is actually a bullish trade. This is a bullish trade because you want GDX
to go higher, but you actually have a little bit of margin for error because if GDX goes
sideways or actually GDX falls a couple dollars, you still make some money, and that’s the
beauty of trading options over stock. If you just trade stock, if you want GDX to
go higher, you basically draw the line in the sand. This is your break-even point. GDX has to go higher for you to make money. There’s nothing else that you can do in the
market where you can be wrong in your assumption and still have a very high probability of
success and making money. Again, trading with options, it’s like having
an unfair advantage and we can use some of these overbought and oversold ranges to give
us a little bit more of an edge in how we choose which direction that we go after. Again, one of the more popular ways to predict
the market’s next move is with technical analysis. Over the years, I’ve evolved as a trader in
that I would much rather focus on the numbers-based approach to technical analysis versus a chart
pattern approach, what you’ll often see, whether you’re a new trader or not, is you’re eventually
going to see things like support and resistance. This is a really big one for new traders,
very easy to understand, but the assumption that there are support and resistance levels
out there, chart patterns like candlestick patterns that basically build these patterns
over the course of a couple days based on the open, high, close, low, etc. Then there will also be traditional chart
pattern like head and shoulders tops and cup and handles and pennants and flags, things
like that. In my honest opinion, a lot of those are subjective
in nature. What might look like a head and shoulders
top to you, might also look like an up-trending pennant to another person. The reality is that since I’m generally more
of a math and numbers trader and I base a lot of my trades on probabilities and statistics,
I would prefer to use technical indicators that give me clear buy and sell signals on
the charts, not these assumptions about chart patterns, not that you can’t do very well
with those and some guys do, but I do not think that they are … I think that they
are the exception to the rule, not the rule. If I even just look at GDX here, I can basically
draw a bunch of different chart patterns and say, “Okay, this is basically the trend line
that GDX followed. No, wait. This is the trend line that GDX followed. No, wait. This is the support and resistance zone that
GDX follows. No, wait. This is the support and resistance zone that
GDX follow,” right? There are so many different things that I
can do here to basically assume all of these things I want GDX to be. I want it to be a bullish move, or I want
it to be a break out. I can draw trend lines and chart patterns,
and I can look at candlesticks to basically get that. That’s why I don’t usually rely on them because
I want more of a math-based approach. I want something that’s been proven to work,
not something that’s more subjective in nature. The real question then becomes which indicators
really work? That’s what we want to get into towards the
last half of this video. Now, which ones actually are reliable enough
to trade long term? Again, the question you might ask yourself
is should I be using simple moving averages, SMAs, or stochastics or MACD or RSI? If you’ve been trading for any amount of time,
you probably use one of these or maybe a handful of these, but the reality is that you don’t
know which ones actually really work, which ones are proven to work long term. Again, which ones actually generate profitable
trades that outperform the market? Even if XYZ indicator works, meaning it generates
good signals, reliable signals, does it generate profits on those stock signals? That’s a good question to ask. Back in 2014, we actually purchased our own
historical data and developed an in-house program to back test technical analysis indicators. Once we had everything in place, we basically
started bask testing hundreds of stocks and indicator variations, covering the last 20
years of market data. After 12 months of non-stop back testing and
analysis, we basically packaged up everything that we had discovered and put it into one
single comprehensive report. Now, this report is Signals. You can purchase a copy of this report on
our website. It is something that we do sell, one of the
very few things that we sell here, but it is something that I think is extremely valuable
and I am not giving away for free because it’s basically 20 years of data; 223 different
stocks that were in this back testing report, 17 different indicators that we looked at,
1,476 different variations. What I mean by variations is we tested everything
from a one-day moving average to a 10-day to 11-day or 12-day. I mean we tested everything that we could
possibly test to see what really worked. We tweaked the system as much as humanly possible
and we’ll actually go through some of those examples here in this video. At the end of the day, we ended up tracking
and testing and analyzing 17.34 million trades, so probably what I found to be the most comprehensive
report on technical analysis out there. I do not believe anybody else has covered
this as much as we have. We were basically looking for the answer to
three questions, three questions that I had gotten time and time again from our members
here over the last eight years, questions that I had myself. The number one question is: is technical analysis
more reliable than randomly picking a stock’s direction? Number two, if it is, can you generate more
money using technical analysis than holding the SPY on a consistent basis? The SPY is considered the market portfolio,
or the [fission 00:16:56] portfolio. Number three, if you can generate that excess
return, what are the specific indicators and settings that work best long term? Of course, I’m not going to show you all of
the results in this video today because you do have to purchase a copy of the report to
see what works, but I want to quickly cover three case studies and insights that we learned
from the report which will help you with technical analysis, regardless of if you purchase a
copy of Signals or not. The first one that we’re actually going to
go over is just a simple moving average. Now, most of you should know what a simple
moving average is but if you don’t, you basically buy the stock whenever the stock crosses above
this orange line here which is the simple moving average. In our case, on the chart here was a 30-day
simple moving average of whatever stock we were looking at. When we actually gave or did all the back
testing for simple moving averages, and we did 15 different variations within this for
one set of these indicators, we basically tested a simple moving average from 15 days,
20, 25, 30, 40, 50, etc., etc., etc. Then what we also did, and this is where the
change really started to shape in how we figured out what indicators really worked and what
didn’t, is we looked at a simple moving average crossover. We looked at basically a signal of if the
20-day moving average crosses above or below the 50, if the 20 crosses above or below the
100, if the 20 crosses above or below the 200, etc., etc., etc. what you’ll actually
notice in here is that the win rate in these trades in these trades went up dramatically
when we actually started to use the simple moving average crossover. You see, the simple moving average by itself
was not actually that reliable. It had a very low win rate, had very low overall
net profitability, but when we started to look at the crossover, we saw that the profitability
jumped and the win rate in the trading jumped as well. We really didn’t see anything in the simple
moving average that was above a 50% win rate. Okay, so I’m not going to show you obviously
the best indicators that we found, but this is just one example of something that we started
to uncover, and that’s as you start to use a couple more indicators, indicators that
intersect and cross and start to show momentum, that ended up being a little bit more reliable. Another example of this is stochastics. Stochastics is one of the most popular trading
indicators out there. I probably would say probably one of the top
five most popular talked about trading indicators out there. Again, you can see how it works here on this
chart. We basically would buy with a cross and sell
with a cross, or you could buy in these oversold and overbought regions and virtually the same
thing happened when we started testing stochastics. We tested it on its full basis, meaning we
tested the lines crossing above and below each other, and then we also, basically on
the back half of our testing for stochastics, also tested to see if those lines crossing
in an overbought or oversold range, meaning in a reading of around 20 versus crossing
at a reading of around 70, 20 versus 80, 30 versus 70, etc., etc. … Did that produce better results? Basically, did overbought and oversold regions
produce better results overall? Again, the answer was yes. As far as overall profitability, it was stochastics. It produced a much better result. Using stochastics in its raw, full variation
and term really did not produce profitability on any scale or shape, in any variation of
the many different signals and moving averages that we tested. Once we threw in this additional component
here of basically requiring that the signal was produced in an overbought or oversold
region, it really kind of led the signal to profitability. Now, as an overall win rate, it also increased
the win rate, again, across the board for stochastics. It actually was fairly a high probability
of winning on this trade, meaning it had a high win rate, but generally speaking, profitability
over the 20-year term of 1% or 7% or 3% was really not that high. It dramatically underperformed the benchmark. In fact, there were many other indicators
that were much better than stochastics. We actually kind of wrote up stochastics as
one of the lowest performing indicators out there. Again, you can see the concept here of using
multiple ways that you can look at technical analysis. Final example we want to go through here is
the APO. Okay, so APO is the absolute price oscillator,
and this is a very simple indicator. Most people like this because you basically
have this purple line right here in the middle of your indicator chart. Whenever the line is above that middle indicator
line or that zero barrier, you basically buy. Whenever it crosses below, you basically sell. It crosses above you buy, etc., etc., etc.,
okay? This is a price oscillator. Now again, what we found here with this price
oscillator is that the longer-term price oscillators, basically the first one that we tested was
a 10-day versus a 26-day, and the last one that we tested was a 20-day versus a 26-day. Well, what we found is that the win rate is
dramatically higher when we start testing a little bit longer-term indicators, meaning
you can’t really pick a very good indicator as far as high win rate when you have a very
short window of opportunity, meaning the timeline between indicators changing over is very short. In this case, the shorter indicator, although
it had a lower win rate overall of 31.06%, did produce a higher profit. In this case, it was more profitable even
though it lost, meaning that the times that it won, it won really big; the times that
it lost, it didn’t lose that much. The reality is that the win rate with the
higher, longer-term moving averages was much more attractive on a long-term scale, especially
as an options trader. I’m more concerned with the win rate in this
column here being able to appropriately assume or pick the next direction 68% of the time. I can build a strategy that profits from that
with that margin for error that we talked about. I don’t necessarily care that it was marginally
profitable on the stock basis because I would rather see it be very, very profitable directionally
so that I can build a strategy around that, okay? Again, these are three really cool examples
that kind of help give you a little bit of insight into how we built out this technical
analysis report in this research report at Signals. Again, these were not the best indicators. We obviously saved those for those of you
who do buy a copy of the report. I think the key here that I want to get across
is that some of the basic indicators that are out there right now are actually not that
profitable and you have to really dig deep to find the ones that give you great profitability
and a great win rate. Again, the point here is that the right technical
analysis indicators can help you make more profitable trades. In our view, the top five indicators that
we found generated winning trades 82% of the time and outperformed the market by 2,602%
on average. Now, that’s just on the stock basis. That’s not including any options that might
have been on top of that. When it comes to options trading, selling
options based on the high-probability strategies with defined risk always outperforms even
the best technical indicators. Even the best, best, best signal that we found
still did not produce a potential gain that was better than trading options. Again, for us, technical analysis can be a
supporting factor in your decision to go long or short a stock directionally, but it should
never be the only factor. As we talked about in the beginning of this
video, what’s more important than picking the directional assumption of a stock is making
sure that your portfolio has good balance because you can trade any stock in any direction
and basically get the same probability of success and the same payout with options. Yes, you can use technical analysis if you
want to, you know, just get a little bit better understanding of where the stock may or may
not go, based on the signals that we provide, but it’s not required to be successful trading
options, and that’s the key point that I want to get across in this video. As always, hope you guys enjoyed this video. Thank you so much for watching. If you have any comments or feedback, please
let me know. Any questions about the Signals report, please
ask them in the comment box right below. If you love this video, thought it was really
helpful, please help us spread the word about what we’re trying to do here at Option Alpha. Share this video online, send it to a friend,
a co-worker or a family member and until next time, happy trading.

5 thoughts on “Predicting The Stock Market’s Next Move”

  1. 0.33 odd with 70% chances to success is very close to a negative zone! You must either to increase the chances to 80% or to wait for odd at least 0.5

  2. Hi Kirk, if we have the probability of 0.7 to win $ 26, and the risk is $ 74. This means that the probability of loosing $ 74 is 0.3 right? Now, if we make large number of trades then on average we would loose $4. (0.7*26-0.3*74=-4) how is that profitable? Also returns are not exactly normally distributed. The distribution has "fat tails", meaning that extreme values are more frequent than in normal distribution.

  3. are there stratiegies that allow you to put an ATM trade where you risk as much as you could possibly make that have a 70% winnong ratio?

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