Tag Archives: internet

Americans Spend Twice As Much Time on the Mobile Internet Than on Desktop

Image source Pabak Sarkar Flickr . Over the last several years, many pundits have talked about the “shift to mobile.” In fact, the shift was more about the rapid growth of

Trade Like A Chimp! Unleash Your Inner Primate

It is a long established fact that a reasonably well behaved chimp throwing darts at a list of stocks can outperform most professional asset managers. While there would be obvious advantages with hiring chimps over hedge fund traders, such as lower salaries and better manners, there are also a few practical obstacles to such hiring practices. For those asset management firms unable to retain the services of a cooperative primate, a random number generator may serve as a reasonable approximation of their skills. The fact of the matter is that even a random number generator can, and will, outperform practically all mutual funds. Such random strategies may seem like a joke, and perhaps they are, but if a joke can outperform industry professionals we have to stop and ask some hard questions. When designing investment strategies, it can be very useful to have an understanding of random strategies, how they work and what kind of results they are likely to yield. Given that random strategies perform quite well over time, they can act as a valid benchmark. After all, if your own investment approach fails to outperform a random strategy, you may as well outsource your quant modeling to the Bronx Zoo. Click to enlarge Meet your new boss. Portfolio Modelling Frequent readers of my articles (both of you) shouldn’t be surprised that we’re dealing with portfolio models here. A portfolio model is something very different from what most retail traders call a trading system. Oddly, the perception of trading system as a set of rules for timing buys and sells in a single market is still pervasive. That’s still what you tend to see if you ever pick up a trading magazine. That’s normally not how things look in reality of course. Not on the sharp end of the business. What we’re normally dealing with is portfolio models. In a portfolio model, the position level is of subordinate importance. The only thing that matters is how the portfolio as a whole performs. We’ll always have many positions on, and it’s the interaction of these positions that matter in the end. Portfolio modelling is a more productive way to spend your time. It would certainly be more useful in the asset management world. What may surprise some not in the industry is that often portfolio models don’t even bother to try any sort of entry and exit timing. Stop loss methodology is rare and concepts like position pyramiding would simply never be a topic. What we’re dealing with here are usually simple models, with mechanisms for selecting components, allocating to the components, rebalancing the components and of course benchmarking the result. Portfolio Model Benchmarking isn’t what it used to be Let’s start with that last point. Benchmarking. Every portfolio has to be measured against something. Very few professionals actually have the zero line as their benchmark. That’s what hedge funds are for. If you work in the industry, odds are that you have a specific index as your benchmark. We’ll go with one of the most common benchmarks here, at least for American equities; the S&P 500 Total Return Index. When you’ve got a benchmark index, you’re being measured against that. It doesn’t matter if you end the year +10% or -10%. It matters if you outperformed or underperformed the bench. At times it can be very comfortable to be measured relative to the index. It removes many difficult investment decisions. You gain and lose at the same time as everyone else. On the other hand, it can be frustrating when the markets are falling and you still have to be in. The index we’re using in this article, S&P 500 TR is different from the normal S&P index that you always see quoted. This is a total return index, meaning that all dividends are reinvested. The traditional S&P index is highly misleading over time, as the dividends appear as losses. So keep in mind that the S&P TR index will always show a better performance than the regular price index over time. In the long run, we’re all dead. Not too impressive, is it? Well, perhaps mutual funds can help. Mutual Funds Can’t Help The mutual fund industry is fundamentally flawed. There’s really no reason at all to ever, for any reason buy a mutual fund. If ever the internet memes about “You had one job…” fit any industry, this would be it. The mutual funds are tasked with tracking and outperforming an index. On average, around 85% of all mutual funds fail. How do I know that? The freaking SPIVA reports . A monkey would have a better chance. How can the Chimps Help? Professor Burton Malkiel once famously wrote in A Random Walk Down Wall Street that A blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts. Now I think that’s highly unfair. After all, why would we want to blindfold the monkey? In what way would that contribute? As we all know, academic research has to be confirmed by empirical observation to be of much use. Ladies and gentlemen, I give you Ola the Ape. Back in early 90 when I was in business school in Sweden, we had a highly prestigious national investment championship. This was normally won by the famous analysts at the big investment banks. This was quite a big deal and getting a high ranking in this competition was a big career move. Then in 1993, somehow a chimp from the local Stockholm zoo got entered into the competition. Ola the Ape threw actual darts at the actual stock listings of the newspaper to pick his stocks. And he won. Amateurs! Random Simulations Unfortunately, our office chimp Mr. Bubbles has just accepted a higher offer from a competing firm, so I will have to resort to random number generators to prove this point. The first strategy we’ll test is something you’ve probably seen elsewhere. But we have to start somewhere. Here are the rules: We only pick stocks from the S&P 500 index. Historical membership accounted for of course. At the start of each month, we liquidate the portfolio and buy random stocks. We buy 50 random stocks for each new month. Each position is given an equal cash weight. Monkeys 1 – Index 0 Not too bad, is it? Not a single monkey failed to beat the index. But what’s going on here? Surely there’s a trick here? Let’s push this concept a little further and see if it falls apart. Our next simulation is even randomer. Yes, I’m sure that’s a word. The previous simulation had equal weighted position allocation. Perhaps that’s the trick. But would a monkey really allocate an equal amount to each stock? Or would he pick that at random too? Here’s our next simulation: We only pick stocks from the S&P 500 index. Historical membership accounted for of course. At the start of each month, we liquidate the portfolio and buy random stocks. We buy 50 random stocks for each new month. Each position is given a totally random allocation . Yes, we’re allowing any position sizes here. Perhaps a position is 0.0001% or perhaps it’s 99.99%. Let’s go wild. Monkeys 2 – Index 0 Ok, this is getting ridiculous. We’re still clearly outperforming the market. Not a single monkey loses against the index. Sure, there’s a lot wider spread here and that’s to be expected. There’s quite a large difference between the best monkey and the worst one, but they’re all better than the index and certainly better than the mutual funds. So where’s the trick? Is it the 50 stocks? Could this whole thing have to do with the magical number 50? After all, isn’t this a Fibonacci number ? And why would a monkey pick this number of stocks anyhow? Fine, let’s relax this one as well. Let’s do another one. We only pick stocks from the S&P 500 index. Historical membership accounted for of course. At the start of each month, we liquidate the portfolio and buy random stocks. We buy a random number of random stocks for each new month. Each position is given a totally random allocation . A random number of random stocks at random allocations. Now that’s how a proper monkey trades. Will the monkeys finally lose this time? Game, set and match. No. The monkeys still win. Now we see some really wild swings, but in the end our primate friends persevere. But now it’s really getting silly, isn’t it. What are we doing here that’s clearly working? Actually, it’s the other way around. The single largest positive factor is that we avoid making a mistake. That mistake being market capitalization weights. Simply by avoiding market cap weighting, we outperform. The larger issue here is benchmarking against an equal weighted index, such as the S&P 500. We all know that there are (approximately) 500 stocks in the S&P 500. But is that really true? Did you know that the top 10 stocks in that index has an approximate weight of 18%? And that the bottom 300 stocks also have a combined weight of about 18%? We’re all pretending that the S&P 500 is a diversified index, but it’s really not. It’s tracking a handful of the largest companies in the world and the rest really don’t matter. There’s practically no diversification in the S&P 500 To be fair to the index, and the index providers, I’d have to point out that indexes were not originally meant to be investment strategies. They were meant to measure the health of a market. As such, they’re not all that bad. But that doesn’t mean that you should invest like the index. It’s easy to check out equal weighting performs against market cap weighting. Just compare the S&P 500 Equal Weighted Total Return Index with the S&P 500 Total Return Index. Same stocks, same index provider, same methodology. Easy. Some stocks are more equal than others. In the random simulations above, we’ve seen that both equal weights and random weights are better than market capitalization weights. Obviously only a chimp would use random weights. Equal weights are quite common, though in my own opinion it makes much more sense to use volatility parity weights. That’s nowhere near as complicated as it sounds. Vola parity just means that we size our positions according to inverse volatility. A more volatile stock gets a smaller allocation. Why? Because if you put an equal amount of cash in each stock, your portfolio will be driven by the most volatile stocks. If you buy a utility stock and a biotech, the biotech stock is likely to be the profit and loss driver of the portfolio. An equal weight in the two would mean that you put on more risk in one stock that the other. Vola parity weighting means that you, in theory, put on equal amount of risk in both stocks. Yes, I deliberately used the word risk here so the comment field will be filled up with quants pointing out that I don’t understand risk. Go ahead. I’ll wait. Let’s do one more of these funny simulations before getting to the real stuff. We only pick stocks from the S&P 500 index. Historical membership accounted for of course. At the start of each month, we liquidate the portfolio and buy random stocks. We buy 50 random stocks for each new month. Each position is given a volatility parity allocation . Best monkeys so far. This looks pretty good, doesn’t it? Now we have better performance and more importantly, a narrower span of performance. The monkeys all do really well and there’s not all that much difference between them. If only we could figure out a way to be one of those better chimps. Let’s be the better primate! Why should the chimps get all the fun? Clearly these guys know how to trade, but perhaps we can figure out a way to beat them. We’ll have to take out the random factor and find a better way to pick our stocks. The volatility parity seems to work though, and so does the monthly rebalancing. We’ll keep those. There are several valid ways of picking stocks. You could use value factors, dividend yield, quality, momentum etc. I’m going to use momentum here, because clearly it’s the best one (not at all because I wrote a really neat book on that topic ). Besides, it’s the easiest one to quantify and model. The data is more readily available and so are the tools needed. Here’s our new, chimp free simulation: We only pick stocks from the S&P 500 index. Historical membership accounted for of course. Trading is done monthly only. Rank stocks based on Clenow Momentum™ . If cash is available at start of month, buy from top of ranking list until no more cash. Inverse vola position sizing, using ATR20. Sell at start of month if stock is no longer in top 20% of index or if Clenow Momentum ™ is lower than 30. Some may recognize this as a simplified version of the one presented in Stocks on the Move . It’s much simpler, but performs in a very similar manner. It has slightly deeper drawdowns and slightly higher return. Those of you who didn’t read Stocks on the Move, may wonder what a Clenow Momentum is, and whether or not I’m joking about that name. Step one, put my name on stuff. Step two, get a comb-over. The Clenow Momentum ™ is clearly a silly name for a pretty decent analytic. This is just an improved way of measuring momentum. First we take the exponential regression slope, instead of the linear, since it’s measured in percent and can therefore be compared across stocks. It will tell us the slope in percent per day, which will give you a number with too many decimals to keep track of. So we annualize it get a number that we can relate to. Now the number tells us how many percent per year the stock would do, should it continue the same trajectory. But the annualized exponential regression slope doesn’t say anything about how well the data fits the line. The coefficient of determination, R2, does. That’s a number between 0 and 1, where a higher value means a better fit. If we multiply the two, we essentially punish stocks with high volatility. And there you go. Clenow Momentum ™! Not too bad for a human! Now we’re seeing some interesting results! Even without the help of the chimps, we’re now clearly outperforming the bench. It’s a consistent outperformance too, during both up and down markets. The reason that we outperform in bear markets is that we don’t buy stocks with a low absolute momentum value. When there are no stocks moving up, we don’t buy any. This all seems good and well, but I’m sure you’re all wondering about the most important point. How did we do against the chimps? You can’t beat all the chimps. We may not be the best primate, but we’re certainly among the smarter ones! Being in the upper 5% of the chimps is pretty good. On the evolutionary scale, we have now moved beyond the mutual fund managers, beyond the index itself and we’re competing with the best of the chimps! So what’s the point here? There are several important learning lessons from all of this. Perhaps the best way to summarize it would be to paraphrase Gordon Gekko: The point, ladies and gentlemen, is that chimps are good. Chimps are right. Chimps work. Chimps clarify, cut through and capture the essence of the evolutionary spirit. Well, with all due respect to Gekko the Great, perhaps there are better ways to sum this up. Random models reveal the weakness of index construction. Benchmarking against random models help you put your own results into context. Does your portfolio model really add value, or is it just another chimp? It’s very easy to make a simulation that beats the index. Systematic momentum investing is likely to beat the index, and most of the chimps. You will never beat all the chimps. The recent book Stocks on the Move, incidentally written by yours truly, contains a more in depth analysis of how momentum strategies can be used to outperform the benchmark. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article. Additional disclosure: No chimps were harmed in the production of this article.

Unitil’s (UTL) CEO Robert Schoenberger on Q1 2016 Results – Earnings Call Transcript

Unitil Corporation (NYSE: UTL ) Q1 2016 Earnings Conference Call April 21, 2016, 02:00 PM ET Executives David Chong – Director of Finance Robert Schoenberger – Chairman, President and Chief Executive Officer Mark Collin – Senior Vice President, Chief Financial Officer and Treasurer Thomas Meissner – Senior Vice President and Chief Operating Officer Laurence Brock – Chief Accounting Officer and Controller. Analysts Peter Wernau – Wernau Asset Management Insoo Kim – RBC Capital Markets Operator Good day, ladies and gentlemen, and welcome to the Unitil Q1 2016 earnings conference call. [Operator Instructions] I’d now like to introduce your host for today’s conference Mr. David Chong, Director of Finance. Sir, please go ahead. David Chong Good afternoon, and thank you for joining us to discuss Unitil Corporation’s first quarter 2016 financial results. With me today are Bob Schoenberger, Chairman, President and Chief Executive Officer; Mark Collin, Senior Vice President, Chief Financial Officer and Treasurer; Tom Meissner, Senior Vice President and Chief Operating Officer; and Larry Brock, Chief Accounting Officer and Controller. We will discuss financial and other information about our first quarter on this call. As we mentioned in the press release announcing the call, we have posted that information including a presentation to the Investors section of our website at www.unitil.com. We’ll refer to that information during this call. Before we start, please note that comments made on this conference call may contain statements that are commonly referred to as forward-looking statements, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. These forward-looking statements include statements regarding company’s financial condition, results of operations, capital expenditures and other expenses, regulatory environment and strategy, market opportunities, and other plans and objectives. In some cases, forward-looking statements can be identified by terminologies such as may, will, should, estimate, expect or believe, the negative of such terms or other comparable terminology. These forward-looking statements are neither promises nor guarantees, but involve risks and uncertainties, and company’s actual results could differ materially. Those risks and uncertainties include those listed or referred to on Slide 1 of the presentation and those detailed in the company’s filings with the Securities and Exchange Commission, including the company’s Form 10-K for the year ended December 31, 2015. Forward-looking statements speak only as of the date they are made. The company undertakes no obligation to update any forward-looking statements. With that said, I’ll now turn the call over to Bob. Robert Schoenberger Thanks, David. I’ll begin by discussing the highlights of our past quarter. Beginning on Slide 5 of the presentation. Today we announced net income of $10.9 million or $0.78 per share for the first quarter of 2016, a decrease of $2.7 million or $0.20 per share over the first quarter of 2015. This decrease in earnings for the first three months of 2016 was driven by lower natural gas and electric sales and margins, reflecting significantly warmer winter weather compared to the same period last year. As Mark will discuss later, we estimate that weather impacted our earnings per share negatively by $0.25 in the first quarter. Turning to Slide 6. The graph shows that our financial results have increased over the past three years, while maintaining a strong rate of return on our utility investments. Our financial results go hand-in-hand with our strong operating performance. We have met or exceeded all service quality metrics for safety, reliability and customer service, and our customers have seen an almost 50% reduction in outages since 2010. The continuing investment of both our gas and electric utility distribution systems and the successful execution of our regulatory strategy, and our attention to customer service is providing a platform for sustained growth. Moving on to Slide 7. Our utility rate base continues to grow, as we had new customers and improved both the gas and electric distribution systems. Over the past four years, our combined gas and electric rate base has grown at an annual rate of 7%, driven by customer additions and our infrastructure replacement and improvement programs. On the gas side of our business, our rate base has doubled and our gas segment profit has nearly quadrupled since acquiring our New Hampshire and Maine gas business. Looking forward, we believe we have ample investment opportunities that would allow us to continue to grow around these levels for the foreseeable future. Slide 8, highlights the growth we have achieved on our natural gas business. Our gas customer growth has contributed significantly to our operating results, with customer additions in the range of 2% to 3% annually over the last three years. In addition to customer growth of weather normalized unit sales have grown in the range of 4% to 6% annually over the past few years. And weather normalized unit sales for commercial and industrial customers were up about 7.4% year-over-year. And while it’s still early in the year we are up, we are ahead of our schedule over the last year in terms of customer additions. Turning to Slide 9. We continued to look for opportunities to expand our gas distribution system. For example, a recently approved rate surcharge mechanism in Maine allows us to economically extend our gas mains to new targeted service areas. This rate surcharge mechanism is being piloted in Saco, Maine. It allows customers in targeted area of Saco, the ability to pay a rate surcharge instead of a large upfront payment or capital contribution to connect to our system. This pilot has a potential to add a thousand new customers to our system with roughly $1 million in annual distribution revenue. We believe that the successful implementation of programs like this will continue to allow us to reach new service areas beyond the current reach of our distribution system in a cost effective and efficient manner. In fact, we have had surrounding towns ask us to be able to participate in this program in the years ahead. Slide 10 provides an update of our current electric system investment initiatives. Construction is continuing on schedule for our two new substation projects in New Hampshire, with the first coming online in the second quarter of 2016. These electric distribution substations will provide the capacity needed for continued load growth on our New Hampshire systems, while addressing constraints of existing substations and improving reliability. Another electric initiative we are pursuing is grid modernization in both our Massachusetts and New Hampshire electric subsidiaries. At a high-level, this program is an effort to improve the reliability, resiliency and operational efficiency of the electric grid, while empowering customers to use the electricity more efficiently and facilitating the integration of distributed energy resources. So before I turn it over to Mark to go into more detail, I want to put the first quarter results in proper context. If you look at the factors that had contributed to the results we reported over the last five years, controlling O&M spending, our capital investment program, our regulatory agenda and our gas growth program, they all remain intact going forward. And we’re confident that those factors will help us achieve similar growth in the years ahead. So, Mark? Mark Collin Thanks, Bob. I will begin by discussing the weather impact on our gas and electric sales margin for the first quarter shown on Slide 11. This winter, including the key heating months of January and February of 2016, was one of the warmest on record throughout New England. In contrast, last winter was one of the coldest on record in New England. The combination of these two winters, extreme cold last year and extreme warm this year create a accumulative estimated impact to earnings per share of $0.25 year-over-year due to the lower gas and electric margins. Now turning to Slide 12. Natural gas margin was $35.9 million in the quarter, a decrease of $2.9 million or 7.5% compared to the first quarter of 2015. Gas sales margin was negatively impacted by lower therm unit sales due to the warmer weather, partially offset by the positive impacts of higher natural gas distribution rates and the growth in the number of customers. There were 23% less heating degree days in the first quarter of 2016 compared to 2015, which we estimate negatively impacted earnings per share by about $0.22, due to the lower gas margins. Excluding the effect of the weather on sales, weather normalized gas therm sales were up 2% in the first quarter of 2016 compared to the same period in ’15. This weather normalized growth was led by a quarter-over-quarter increase in estimated gas terms sales of 7.4% to large commercial and industrial customers. Slide 13 highlights our electric business sales and margin. Electric sales margin was $20.1 million in the first quarter of 2016, a decrease of $1.1 million or 5.2% compared to the same period in 2015. As on the gas side, electric sales margin decreases reflect the impact of weather, albeit electric sales are clearly less sensitive to weather than gas. We estimated that the weather impacted electric sales by about $0.03 in the first quarter of 2016 compared to the first quarter of 2015. Excluding the effect of weather on sales, weather normalized electric sales were led by 2.9% increase in sales to large commercial and industrial customers. Now, turning to Slide 14. We have outlined the major expense variances for the quarter. Operation maintenance expenses increased $0.5 million or 3% in the quarter compared to the same period of ’15. Depreciation and amortization increased $0.4 million or 3.5%, primarily reflecting higher depreciation on normal utility plant additions. Taxes, other than income taxes, increased $0.1 million or 2%, primarily reflecting higher local property tax expense. Net interest decreased $0.3 million, reflecting lower levels of long-term debt. Finally, income taxes were down $1.9 million, reflecting lower pre-tax earnings for the period. On Slide 15, we have provided an update of our financial results at the utility operating company level. The chart shows the trailing 12 months actual earned return on equity in each of our regulatory jurisdictions. Our total return on equity is lower in the last 12 month period ending March 31, 2016, reflecting the unseasonably warm weather in the first quarter that we have been talking about. Also, as we’ve discussed in the past and as shown on the table to right, we have long-term capital cost trackers in place to recover a significant portion of current and future capital spending. We have some other rate case activity underway, which I will summarize shortly. We expect these rate cases will help us to improve our realized rate of return as the year progresses. Slide 16 highlights our electric and gas rate case fillings in Massachusetts. Combined, both fillings reflect a revenue deficiency of approximately $6.8 million. We expect a decision in these two rate proceedings by May 1, 2016. In addition, we recently filed a notice of intent to file a base rate case for our New Hampshire electric subsidiary. We expect to file this rate case later next week, with a revenue deficiency of approximately $6 million. Now, this concludes our summary of our financial performance for the period. I’ll turn the call over to the operator who will coordinate questions. Thank you. Question-and-Answer Session Operator [Operator Instructions] Our first question comes from the line of Peter Wernau with Wernau Asset Management. Peter Wernau I had a quick question. We look at the business as sort of the underlying growth of volume versus the weather impacts. It’s nice that we had a nice and warm season, but if this doesn’t really impact our investment thesis, one thing I was hoping that you might provide some color on. I noticed you showed the compounded annual growth rate of the gas business and we’ve been modeling that. Is there a comparable metric for electric? Mark Collin In terms of the growth rate, as you said, if you get away from the volumetric kilowatt-hour sales the one thing that the weather doesn’t impact is our customer growth or our investment growth. And relative to our customer growth on the electric side of the business, we have been then continued to add customers on that side. It’s a little slower than gas. It doesn’t have the same high growth rate we’re seeing on gas primarily, because electric is just about served everywhere, so it grows along with households. We’re growing about 0.5% a year in terms of customers. On the investment side, we’ve also continued to have investment in rate base on electric. And that’s intended to grow between 3% and 4% per year in terms of our rate base. So in contrast, the gas business is growing more in the 8% to 10% range whereas the electric is down in the 3% to 4% range on rate base. But they’re both growing and they’re both continuing to contribute. And as I indicated earlier, our planned rate case for our largest electric division here in New Hampshire, we’ll be filling that next week. And we hope that that will get us on a path, so that we can earn our authorized rate of return on that division and make sure these investments are returning for us. Robert Schoenberger I mean just from an anecdotal point of view, the amount of actual and planned construction both in Maine and the seacoast area of New Hemisphere is really robust and growing. So hopefully that will contribute to the growth rate in the gas as well as the electric business. Operator Our next question comes from the line of Insoo Kim with RBC Capital Markets. Insoo Kim First of all, in terms of weather for 4Q ’15, which was, I guess last quarter, how much of the EPS was impacted by weather compared to normal? Robert Schoenberger In the fourth quarter? Insoo Kim In the previous quarter and the fourth quarter of last year? Robert Schoenberger I’ve got to make sure I understand the periods of comparing. This last quarter compared to the same quarter, a year ago? Insoo Kim No, just versus normal, I’m just trying to see –? Robert Schoenberger Versus normal, we’re down about $0.09. $0.09 in EPS due to versus what normal weather would have been. Insoo Kim But that’s for the first quarter, just this past quarter, right? Robert Schoenberger Yes. Insoo Kim What about further quarter before that on the fourth quarter? Robert Schoenberger Fourth quarter, I’d have to check that. I don’t have the fourth quarter normalized results in front of me, right now. Insoo Kim Because, I mean, obviously the first and the fourth quarter being the largest quarters and with the [ph] 20s year-over-year decline in the first quarter, I’m just trying to have a base level of earnings to compare for the fourth quarter that’s going to be coming up in a few quarters, so I guess I’ll check with that offline. Robert Schoenberger Okay. Insoo Kim In terms of the gas penetration rates, do you still see given where the oil prices are, the gas sales to grow at a lower end of that 46% range that you guys were talking about on weather normalize basis? Robert Schoenberger As I was telling you before, again, it’s early in the year, so it’s still early, but we’re about 25%, 30% ahead in terms of gross meter adds over the last year. So the oil price obviously has had some impact, but anecdotal evidence, for example, on in Saco, Maine, there is an industrial park there with 36 businesses, every one of them has indicated their interest into converting to natural gas. So to be conservative, I’d say on the low side, but we have hopes that it might be better than that. Insoo Kim And from a commission standpoint. Have there been conversations recently or in the past about whether decoupling mechanism that, and whether they’re interested in or you may be interested in implementing something like that in the future, to mitigate some of this follow-through the Maine earnings? Robert Schoenberger As you know, in our Massachusetts jurisdictions, our subsidiary in Massachusetts, we do have both decoupling on the electric and the gas side of the business. And that is complemented by, on the gas side we have a cost tracker for cast iron replacement. And we’ve requested a capital tracker for electric as well. In New Hampshire, there is a lot of activity now, particularly around energy efficiency program planning and such and the decoupling concept has come up as a potential rate making concept to help encourage or support increased energy efficiencies spanning and basically make the utility indifferent to lost sales from that. One partial decoupling mechanism that is getting a lot of discussion now as a lost base revenue calculation that essentially decouples the energy sales losses due to energy efficiency from the utilities revenue, that’s got a lot of attention. And then, in Maine, where we have the gas business up there, the biggest thing that we moved towards is more of a rate design. It allows us to recover a larger percentage of our delivery cost based on fixed charges or charges that are not subject to weather or are not as volatile to weather. In fact, even this quarter, it was dampened by fact that we’ve been able to move our rates towards higher fixed charges and so that the approach there has generally been to move towards higher fixed charges. We haven’t had much discussion around decoupling, but it wouldn’t surprise me if that comes back up. End of Q&A Operator Thank you. And that concludes today’s question-and-answer session. Ladies and gentlemen, thank you for your participation in today’s conference. This concludes the program and you may now disconnect. Everyone, have a great day. Copyright policy: All transcripts on this site are the copyright of Seeking Alpha. However, we view them as an important resource for bloggers and journalists, and are excited to contribute to the democratization of financial information on the Internet. (Until now investors have had to pay thousands of dollars in subscription fees for transcripts.) So our reproduction policy is as follows: You may quote up to 400 words of any transcript on the condition that you attribute the transcript to Seeking Alpha and either link to the original transcript or to www.SeekingAlpha.com. All other use is prohibited. THE INFORMATION CONTAINED HERE IS A TEXTUAL REPRESENTATION OF THE APPLICABLE COMPANY’S CONFERENCE CALL, CONFERENCE PRESENTATION OR OTHER AUDIO PRESENTATION, AND WHILE EFFORTS ARE MADE TO PROVIDE AN ACCURATE TRANSCRIPTION, THERE MAY BE MATERIAL ERRORS, OMISSIONS, OR INACCURACIES IN THE REPORTING OF THE SUBSTANCE OF THE AUDIO PRESENTATIONS. IN NO WAY DOES SEEKING ALPHA ASSUME ANY RESPONSIBILITY FOR ANY INVESTMENT OR OTHER DECISIONS MADE BASED UPON THE INFORMATION PROVIDED ON THIS WEB SITE OR IN ANY TRANSCRIPT. USERS ARE ADVISED TO REVIEW THE APPLICABLE COMPANY’S AUDIO PRESENTATION ITSELF AND THE APPLICABLE COMPANY’S SEC FILINGS BEFORE MAKING ANY INVESTMENT OR OTHER DECISIONS. If you have any additional questions about our online transcripts, please contact us at: transcripts@seekingalpha.com . Thank you!