Splunk hitting possible support

Application software firm Splunk Inc. (Nasdaq: SPLK) has seen incredible growth over the last few years. Earnings have grown at a rate of 108% per year over the last three years while sales have grown by 38% percent per year. The company reported earnings on August 21 and those results showed an earnings increase of 275% while sales increased by 33%. The EPS results beat estimates as did the revenue numbers. Unfortunately the company lowered its forecast as part of the earnings announcement.

Despite these impressive results for the most recent quarter and over the last few years, the stock is down over 20% since July 26. This sharp drop has brought the stock down to potential support in the $110 area. This area proved to be the low back at the end of May and beginning of June before the stock rallied almost 30% in just under two months.

Looking at the Tickeron Technical Analysis overview, we see that the RSI Indicator points to a transition from a Downtrend to an Uptrend -- in cases where Splunk's RSI indicator exited the oversold zone, 10 of 12 resulted in an increase in price. Tickeron's analysis proposes that the odds of a continued Uptrend are 83%.

The overview also shows that the lower Bollinger Band was broken -- a price increase is expected as the ticker heads toward the middle band, which indicates a buy or call consideration for traders. In 32 of 36 cases where Splunk's price broke its lower Bollinger Band, its price rose further in the following month. The odds of a continued uptrend are 89%.

The weekly chart shows that the stock just dropped below its 52-week moving average in the last few weeks, but it is still above its 104-week moving average. The stock hasn’t been below the 104-week since mid-2017.

The weekly overbought/oversold indicators aren’t in oversold territory yet, but they are levels not seen very often in the last few years. The 10-week RSI is under 40 at this time and that is about as low as the indicator got back in May. In fact, the only time the indicator has been lower in the last few years was in October of last year.

In addition to the earnings and sales growth mentioned above, the fundamental analysis shows a solid return on equity of 15.2% and a profit margin of 12%.

The Tickeron SMR rating for this company is 46, indicating strong sales and a profitable business model. SMR (Sales, Margin, Return on Equity) rating is based on comparative analysis of weighted Sales, Income Margin and Return on Equity values compared against S&P 500 index constituents.

The Tickeron Profit vs. Risk Rating for Splunk is 32, indicating low risk on high returns. The average Profit vs. Risk Rating for the industry is 73, placing this stock better than average.

The sentiment toward Splunk is mixed. There are 38 analysts following the stock at this time with 31 “buy” ratings, six “hold” ratings, and one “sell” rating. This puts the buy percentage at 81.6% and that is slightly above average and indicates an overly optimistic view from analysts.

Conversely, the short interest ratio is at 4.94 and that is higher than the average stock. This indicates overly pessimistic views from the short sellers. Looking at the range of readings for Splunk’s short interest ratio over the past year, the current reading is toward the higher end of the range and that suggests that pessimism is growing. Should the stock rally like I think it will, the short covering could add buying pressure and help accelerate the rally.

Contributor

Harry Richardson|Expert

Harry Richardson — Algorithmic Trader & Strategy Developer Harry is an algorithmic trader specializing in impulse and breakout trading strategies across cryptocurrency and equity markets. With more than 10 years of experience in developing automated trading systems, he focuses on building structured algorithms designed to capture momentum while maintaining strict risk control. His approach combines quantitative analysis, real-market execution, and continuous performance monitoring. Vitalii prioritizes risk management, drawdown control, and strategy stability over short-term optimization, ensuring algorithms are adaptable to changing market conditions. He has developed and tested hundreds of automated strategies, working extensively with live trading environments, forward testing, and portfolio-level algorithm management. His work centers on transforming trading ideas into fully operational, scalable automated systems.