@jack … You take notes, @LinkedIn

A few days ago Twitter CEO Jack Dorsey, using the search term #Twitter2017, asked users for suggestions on additions and improvements to the networking service. The leading replies—all single words, naturally, owing to space limitations—were edits, topics, conversations, and abuse.

These suggestions may make for an improved product; nevertheless, none are the correct answer… at least for a CEO (who should be) focused on this company’s seemingly troubled future.

Twitter’s woes—whether statistics on number of new accounts, engagement, etc.—have been documented and discussed throughout 2016. Can anyone seriously claim improvements or features along the lines of edits, topics, conversations, or abuse are the ‘fix’? Abuse exists in all social media. Twitter’s direct messaging enables conversations. Topics are followed easily with the hashtag search facility. As for editing a tweet with a typo, is that the BIG PROBLEM? (No.)

… mission creep towards Old Media?

The answer lies in Twitter’s place in the Media Spectrum, ranging from old to new. ‘Old Media’ is a one-way street on which consumers may choose content to be consumed from a number of channels provided by corporations. ‘New Media’ is a round table at which users may be consumers and/or providers, and at which those who are consumers curate their own channels.

We’ve all heard variations on this before, but the question for Twitter: has there been mission creep towards Old Media? When platforms deign to make decisions on which “quality” content users will “care about most” or find “relevant”—these words should seem familiar—the overall experience tends towards Old Media. When tweets do not show up in hashtag searches, even after selecting “View all,” then the perception tends to that of Old Media masquerading as New Media. (The account, since closed, had associated with it a verified email address.)

@jack, don’t be misled by self-satisfied computer ‘scientists’; their benchmark for an intelligent algorithm is simply a finite set of examples that seem sensible (“It works!”). Once a platform provides exaggerated support to users based on some simplistic metrics, the ensuing snowball effect yields one result: Old Media.

@LinkedIn, the new newsfeed is worthless. I repeatedly see posts that I have chosen repeatedly to hide. I see multiple posts in languages not listed in my profile. There is no choice to see posts from connections in chronological order. It is not clear that users will see all of their connections’ posts. I don’t get the feeling that I’ve curated my own ‘channel’.

@jack & @LinkedIn, follow the example set by @facebook. Their “Most Recent” newsfeed choice allows its algorithm to be judged by users, showing their confidence in the platform’s ability to assist users as we curate our content. It has a certain air about it… that of New Media.

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Observing Market Risk

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Why does every decade produce a once-in-twenty-year event? Because Statistics support this result.

Owing to a series of actions taken in the public and private sectors beginning in the early 1980s, Value-at-Risk (VaR) became and has remained the archetypal metric for analyzing Market Risk, the risk of loss due to changes in market prices. Because of the highly uncertain price dynamics in financial markets, a statistical approach is a natural choice. Despite its longevity, it is hardly perfect.

Each VaR method has its own characteristics, proponents often citing simplicity for Analytical, realness for Historical, and adaptability for Monte Carlo. Conventional wisdom however faults all implementations for their dependence on the future resembling the past. Many also criticize widespread reliance on the Normal distribution (although popular, such an assumption is not necessary).

Missing from the ongoing discussion is a universal understanding of statistical implications. What is the true meaning of a conventional VaR statement regarding an x% probability of a loss greater than r over some period t? Do “state-of-the-art” techniques employ simplifying assumptions of which Risk Management professionals are not even aware? The question posed at the beginning suggests a failing of VaR. Given the drastic difference between 10- and 20-year timeframes, non-Normality and historical dependency make for obvious (and easy) choices as culprits.

Even if the distribution employed by VaR analysis is accurate AND future market moves perfectly resemble the past, such supposed once-in-twenty-year events should be expected every decade. Many finance professionals are not aware of this reality.

Sand Key Research’s patent-pending method provides a view of Market Risk consistent with the experience of investors.


 

 

 

 

 

Leveraged ETF 2.0

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Multifactorial LETF slide presentation.

Ever since leveraged ETFs were first made available to US investors in 2006, their long-term return distributions have caused widespread misunderstanding and frustration.  The daily return objectives seemed simple enough, but real-world results over periods such as 3 months and 1 year confounded investors. Although many finance professionals had dealt with ETFs for decades, such experience was exclusive to the special case of leverage equal to +1. LETFs represent a generalization that was not well-understood.

Fund sponsors want to offer products long-term investors find attractive. Recently introduced alternatives to daily-rebalanced, constant-leverage LETFs have been failures due to R&D efforts based on individual examples and general rules of thumb.

  • Monthly Rebalancing: Despite 95% fewer rebalancing actions, these funds provide to investors an annual return distribution almost exactly the same as daily rebalancing.
  • Lifetime Funds: These lack fungibility (eliminating the advantageous market-pricing mechanism of LETFs) and ask investors to risk a complete loss of equity.

The basis for these funds is the misguided notion that the frequency of rebalancing actions completely specifies their long-term returns. Introducing such funds has accomplished nothing more than expose a prevailing lack of facility with the mathematics of leverage.

Sand Key Research has responded to the challenge of long-term LETF returns by employing a mathematically tractable paradigm for LETF specification. Our patent-pending method for deriving LETF portfolio management protocols makes it possible for ETP sponsors realize any mathematically possible investor utility profile, including those satisfying the utility profiles of long-term investors.