Implications of the upstate NY gaming expansion
- Voters approved Proposal 1
- The proposed amendment to section 9 of article 1 of the Constitution would allow the Legislature to authorize up to seven casinos in New York State for the legislated purposes of promoting job growth, increasing aid to schools, and permitting local governments to lower property taxes through revenues generated.
- 4 casinos in 3 upstate locations: Catskills (2 casinos likely), Albany/Saratoga Springs, Southern Tier near Binghamton
- The location for the other three casino licenses has not yet been determined, but upcoming legislation prohibits the award of a license in New York City for at least 7 years
- 2 slots parlours on Long Island (up to 1,000 VLTs each)
- Legislation allows for two struggling Long Island offtrack betting companies in Nassau and Suffolk to each open slot parlors with as many as 1,000 machines
- Catskills: CZR, Empire Resorts, Mohegan Sun, Foxwoods, Navegante, Louis Capelli, Genting partnership
- Albany/Saratoga Springs: CZR, Saratoga ((VLT expansion into full-scale casinos
- Southern Tier near Binghamton: Tioga Downs (VLT expansion into full-scale casino)
What’s the opportunity?
- 1,000-1,500 slots at 4 casino locations plus up to 1,000 slots at 2 slot parlors, translates into roughly 6,000-8,000 new slots between mid 2015 through 2017
- Slot parlors will be regulated by the NY Lottery and should come online first in mid-2015
- Slot operators will benefit from this expansion, especially Bally’s who will also benefit from the table placements due to the SHFL acquisition. An incremental 1,000 units has about 14 cents of impact on BYI’s EPS. Currently, BYI has north of 50% share in New York and it's likely that the new parlors will be tacked onto the existing agreement for ship share.
What are the obstacles?
- Given that the legislation has passed the NY legislature and the voters have approved the legislation, it will be implemented.
- 5 current NY full-scale casinos operated by Native American tribes
- 9 slot-machine parlors operated by racetracks.
- Atlantic City/Pennsylvania/Connecticut markets
- The next step in the process is for NY State Gaming Commission to form a five-member Facilities Location Board that will decide where casinos go.
- The board cannot be legally seated until the proposal above becomes law on Jan 1, 2014. Once a three-person majority has been named, the Location Board has 90 days to issue a request for applications from potential casino operators.
Takeaway: Hedgeye TREND resistance for US Dollar Index is $81.29
The new Public Enemy #1 to the purchasing power of Americans (Janet Yellen enter stage right) will be front and center in Washington this Thursday. Will she eliminate economic gravity expectations for the U.S. to ever taper?
Will the foreign exchange market reverse all of last week’s US Dollar's gains? Hedgeye TREND resistance for US Dollar Index is $81.29
Trade :: Trend :: Tail Process - These are three durations over which we analyze investment ideas and themes. Hedgeye has created a process as a way of characterizing our investment ideas and their risk profiles, to fit the investing strategies and preferences of our subscribers.
- "Trade" is a duration of 3 weeks or less
- "Trend" is a duration of 3 months or more
- "Tail" is a duration of 3 years or less
Anything longer than 3 years is unpredictable
Risk Managed Long Term Investing for Pros
Hedgeye CEO Keith McCullough handpicks the “best of the best” long and short ideas delivered to him by our team of over 30 research analysts across myriad sectors.
Takeaway: Our [new] innovative quantitative tools can help you consistently generate alpha on both the long and short side of the US equity market.
A few months back, we published a note tilted, “MACRO MEETS MICRO: ARE YOU HUNTING WHERE THE FISH ARE IN THE US EQUITY MARKET?” in which we introduced our US equity market screening application.
The model is designed to triangulate sell-side, buy-side and insider sentiment on a particular stock, recording deviations in those metrics (from the sample) in order to systematically produce a list of names that may be under or over-owned; we consider that to be a good starting point to dig into the long or short side of a particular ticker(s) from a bottom-up perspective.
In the months since publishing that note, we’ve greatly expanded upon the model, sourcing valuable feedback from our institutional client base. Noteworthy changes include:
- Expanding the sample of tickers from the S&P 500 to the Russell 3000 Index, with the latter representing ~98% of publically-traded equity market cap;
- Tracking MoM deltas to see which tickers moved the most with respect to the scores assigned to it from the model, as big moves in sentiment over a reasonably short time frame may signal a meaningful change in the underlying fundamental trends; and
- Adding in-depth summary statistics to the backtest results.
Please CLICK HERE to download the Excel file containing the latest refresh of the model and its summary results. For more information on how the model works, the associated backtest statistics or strategies for how best to apply it to your specific investment process or mandate, please review the following three sections.
***Email us if this is something you’d like to see more often or to have the sample customized for a specific coverage universe.***
1. HOW THE MODEL WORKS
The model is setup as a quantitative screen that assigns +/-1 point for a specific ticker's reading being in excess of [1x] standard deviations from the sample mean, for each of the following three metrics:
- Sell-side Sentiment: Bloomberg Consensus Ratings (1-5 point scale): -1pt if the figure is in excess of +1x standard deviations relative to the mean of the broader sample; +1pt if the figure is less than -1x standard deviations relative to the mean of the broader sample; 0pts if within [1x] standard deviations of the mean.
- Buy-side Sentiment: Short Interest as a % of Float: +1pt if the figure is in excess of +1x standard deviations relative to the mean of the broader sample; -1pt if the figure is less than -1x standard deviations relative to the mean of the broader sample; 0pts if within [1x] standard deviations of the mean. We cap the readings at 15% to dampen the variance of the sample in order to produce a more robust collection of deviations.
- Insider Sentiment: 6M % Change of Insider Ownership: +1pt if the figure is in excess of +1x standard deviations relative to the mean of the broader sample; -1pt if the figure is less than -1x standard deviations relative to the mean of the broader sample; 0pts if within [1x] standard deviations of the mean. We cap the readings at +100% to dampen the variance of the sample in order to produce a more robust collection of deviations.
The model produces scores for each ticker ranging from -3 to +3 on an integer scale. For example, a reading of +3 signals extreme negative sentiment amongst both the sell-side (+1) and buy-side (+1) amid extreme insider buying (+1), while a reading of -3 would signal extreme positive sentiment amongst both the sell-side (-1) and buy-side (-1) amid extreme insider selling (-1).
2. BACKTEST RESULTS
Interestingly enough, the model was initially setup as a contrarian screening tool and the -3 to +3 scale was setup with the assumption that names the model signaled as under or over-owned would subsequently mean revert from a performance perspective.
What we learned in the backtesting phase, however, is that names which “the market” loved the most were typically rewarded with subsequent outperformance of considerable magnitude over the intermediate-to-long term (as defined by 1Y forward returns). Moreover, names that the market disliked the most were typically punished with subsequent substantial underperformance. Furthermore, the hit rate of positive absolute performance is substantially higher for names which "the market" loved the most that it is for names in which "the market" greatly disliked.
Additionally, names that the market “learned to love” in a short time (as defined by its score dropping -2pts over the span of one month) generally outperformed those that the marked “learned to hate” (as defined by its score growing +2 over the span of one month).
Using these results, we have decided to assign any ticker that scores a -3 or -2 as a compelling LONG idea and any ticker that scores as a +2 as a compelling SHORT idea going forward.
In summary, there appears to be some element of efficient markets present in these backtesting results. While this is certainly not the proper forum to debate the merits of the Efficient Market Hypothesis, we can reasonably conclude that “the market” itself can be a reliable tool for identifying good opportunities on both the long and short side.
It also lends support to our belief that momentum exists in the market place over the immediate term, intermediate term and long term durations, which is precisely why we use a three-factor quantitative risk management model to identify key levels of breakout/breakdown risk in specific securities and/or asset classes.
Lastly, the only potential draw-back to interpreting these results at face value is the lack of historical data beyond MAR ’10 (that’s as far as Bloomberg’s insider ownership change data goes back). While the mini-crises of summer 2010 and 2011 are certainly included in the sample, we’d ideally like to see how the model would perform across multiple market cycles (i.e. 2007-09 would be a good place to start).
All that being said, using end-of-month observations for each ticker in the Russell 3000 Index, we were able to produce 86,976 subsequent 1Y forward return data points to analyze spanning MAR ’10 to OCT ‘12. We’d argue that’s plenty robust enough of a sample to work with.
3. USING THE TOOL EFFECTIVELY
Offhand, we think there are two types of primary users for this tool:
- Fundamental Analysts: Sift through our list of long and short ideas to find names that you think are either cheap or expensive on your preferred metric(s), or those that have some industry-specific tailwinds or headwinds that you think will either benefit or hurt a particular name(s). We’re giving you names we think will work anyway, so marrying your own bottom-up analysis with our top-down analysis may yield superior performance results.
- Quants: Use our list of long and short ideas to run your own proprietary factoring on; again, we’re giving you names we think will work anyway, so layering your own proprietary factoring on top of our analysis may yield superior performance results. Moreover, we’re including our US Equity Market Style Factor Divergence Monitor to the extent you see a particular factor outperforming on a trending basis and you (or we) feel it is appropriate to chase or fade it. In the Excel download above, we’ve included all of the relevant style factor characteristics for each of the individual tickers; please note that the data is presented on a percentile basis to make it easier to determine how much a specific ticker is weighted to a given factor.
Lastly, all interested parties should feel free to ping us for our proprietary risk management levels on any given ticker(s). As you know, we are hyper-focused on timing, so allowing us to flag names that are breaking out or breaking down from an immediate-term TRADE or intermediate-term TREND perspective should only enhance the probability of generating alpha from this process.
Associate: Macro Team
Below are key European banking risk monitors, which are included as part of Josh Steiner and the Financial team's "Monday Morning Risk Monitor". If you'd like to receive the work of the Financials team or request a trial please email .
European Financial CDS - Swaps tightened broadly across European Financials last week. #EuroBulls remains alive and well in the banking sector as the EU Financials posted a mean and median tightening of 5 and 14 bps, respectively. The biggest improvements came from Greece, Spain, Italy and Portugal. The only negative divergence was again Sberbank of Russia, where swaps widened 7 bps to 219 bps on further commodity deflation.
Sovereign CDS – Sovereign swaps were tighter across the board except in the US, where swaps widened a modest 1 bp to 31 bps. The biggest improvements came from Portugal (-31 bps) and Spain (-13 bps).
Euribor-OIS Spread – The Euribor-OIS spread was unchanged at 11 bps. The Euribor-OIS spread (the difference between the euro interbank lending rate and overnight indexed swaps) measures bank counterparty risk in the Eurozone. The OIS is analogous to the effective Fed Funds rate in the United States. Banks lending at the OIS do not swap principal, so counterparty risk in the OIS is minimal. By contrast, the Euribor rate is the rate offered for unsecured interbank lending. Thus, the spread between the two isolates counterparty risk.
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