MarketTamer | by Gregg Harris
Posted on April 6th, 2015
Being consistently successful in trading typically requires a lot of work. Sometimes you get lucky, and in extreme bull markets we all look like geniuses. But most of the time it takes a lot of work. While I’ve written a lot of software over the years to generate high probability trade candidates, I still often end up with 5 to 10 good candidates and I have to narrow the list down further. I can spend up to an hour per trade candidate to finally come up with a trade setup I’ll publish in my newsletter.
For example, over the November 22nd, 2014 weekend, after spending about 8 hours looking over my seasonal scans and checking charts and fundamentals, I came up with the stock that had the most going for it – Brinker International (EAT), the owner of restaurant chains like Chili’s Grill & Bar. The stock looked ready to break above early 2014 highs.
What led me to consider Brinker’s stock to begin with was the impressive seasonal pattern. I stated“The seasonal pattern of EAT for this time of year shows a strong track record of gains over the next 4 to 6 months. Notice that over the past 17 years, during the next 22 weeks EAT has had only one losing year.”
So in Monday’s newsletter (11/24/2014), I gave this trade setup:
The stock opened at 54.38 but quickly rose above 55.50, so the trade was triggered.
I covered how the trade initially worked out in the January 5th, 2015 MarketTamer blog posting titled Another Seasonal Trade Comes Through. But up to this point, the trade was successful only on paper. If not managed properly, it could still end up with a loss.
The stock position quickly gained 13% over 3 months. But since then, EAT has settled into a sideways range.
This may only be a consolidation phase. EAT may eventually break out on the upside for further gains. But there were 3 things that bothered me. First was the mere fact the stock had changed character. A brief consolidation would be normal. But this was now going into 3 months.
The next thing that bothered me was news came out that their same-store sales increases were below many competitors. Their polish was starting to wear off.
Finally, Brinker is due to announce earnings on April 21st, before the open. Brinker is one of those stocks that often reacts sharply to earnings announcements.
These may just be minor factors, and with a strong stock they may be meaningless. But with the first quarter earnings seasonal about to start, and rising speculation that there may be many disappointments in the results, I felt it wasn’t worth giving back any profit we had already made. I can always re-enter a position in the stock once the earnings-season smoke clears.
So in last Monday’s newsletter I told readers that I was closing the EAT position at the open. The stock gave us a nice 9.4% return (actually 10.5% return because we received two dividend payments while in the position).
Now I can consider it a successful trade because the profit is booked. But until I closed the trade, I had to continually re-evaluate the trade to see if the original conditions had changed, or if there is an upcoming event that could change the reward-to-risk setup of the trade.
So it’s not just doing the work to get into a trade, it’s doing the work to stay in or exit it with good cause to make sure it gets booked as ‘another success’.
Of course, there’s much more you need to know and many more stocks you can capitalize upon each and every day. To find out more, please click on the following link:www.markettamer.com/seasonal
Copyright (C) 2015 Stock & Options Training LLC
Inventors Can Predict the Cost and Demand for Their Products by Using This Model
Although it takes a lot of time and effort, in the beginning, stages to create a product you imagined, the work is often well worth it when it sells well to make up for the expenses used to make it and then some. Financial success for entrepreneurs varies, depending on their marketing skills and how useful their product actually is, but most wonder how retailers actually distribute their products.
It is the same question Kris Ferreira, an assistant professor of business administration asked at a presentation at Future Assembly. Every retailer much faces tricky and tactical choices related to product placement, assortment, pricing, and inventory. Ferreira mentions that every single one of those choices would have been simple to make if retailers were made aware of consumer demands we.
She added that the primary issue for her was that she had a lot of uncertainty when it came to the demand for her product. Nevertheless, it’s a less than complicated riddle that can get solved. Ferreira believes that the trick one should use for tactical design making such as this lies in quantitative analysis. She stated that the business world uses a few interesting analytics for products. One of them is called descriptive analytics. It analyzes what has already happened. Another is called predictive analytics, which analyzes data to figure out what to do next. The third is prescriptive analytics, which uses data to choose what to do next.
These forms of analysis involve events, analytics, and products. Events include the date length, and the type of event. Analyicits consists of discount percentage, cost, relative cost of competing styles, amount of styles bought in the same subclass and event, the number of branded events in the last year, and the number of concurrent events in the department. Products include the class, color popularity, size popularity, type of brand (A or B), the popularity of the brand, and department.
Ferriera believes to be able to combine predictive analytics to predict demand with prescriptive analytics so they make tactical choices, lies in the data. She showed field work that she and her colleagues worked on along with Rue La La, an online retailer located in Boston. The Boston-based company is known to be a flash sales business that offers highly discounted and limited time offers on accessories and designer clothing. Most of the “limited time” offers that they sell in the store include items the retailer never sold at their store, and a couple of them would sell out quickly. In that event, the retailer takes it as a sign that they could have charged more for the product.
On the other side of the fence, there were products that didn’t sell well, which would signify that the products were priced too high. The primary challenge to those conducting the research was figuring out how to predict demand and create prices to maximize the revenue of the new products with no prior sales data. They decided to make a pricing decision tool that had the ability to use current data to maximize revenue on new products. The researchers took advantage of machine learning techniques that could estimate lost sales in the past (the products that sold out), and predict the demand for a new product the company would sell in the future.
While they were working on the research, they discovered that the demand for a certain product also depended on the cost of other products in the same category. From that finding, the researchers made a highly efficient multi-product price optimization algorithm to suggest a cost for all the items listed on Rue La La’s website on a given day. In January of 2015, the researchers worked alongside Rue La La during a field experiment to assist the retailer in creating optimal prices for new products they will add. The researchers were able to show how Rue La La was able to increase their revenue of products in the experiment by nearly 10% via price recommendations from the algorithm. It also had a low impact on gross sales quantity.
Ten percent may not seem like much, but it’s a big difference, especially in preventing experiencing a loss in sales. Even though the research gave its undivided attention to a flash sales setting, they are certain their techniques would also be extremely useful for just about any other retailer. It doesn’t matter if they operate fully online, at physical locations or a blend of the two. It would be beneficial to them anytime they had to create unique costs for new products they would sell before they make them available for consumers.
In a broader aspect, the work the researchers conducted positively illustrated how both prescriptive analytics and predictive analytics can get mixed to create a tactical decision-making tool that can largely impact how well sales can go for a product.
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