MachineGhost's Research Resort

[Kbg]

Nice.

I've been messing around with HV/momentum and PP and leveraged PP variants...let's just say I'm getting WAY interested in going this route. I had no idea HV could be such an amazing throttle with regard to risk and returns. Read about it many times, just never really internalized it because I couldn't figure out an easy way to set position size in Amibroker...now that that's done (thanks FF9900 by the way for the breakthrough), it's a pretty easy thing to test.

[MachineGhost]

As my final post before the world-as-we-know-it ends, I thought it was fitting to announce that I was finally able to crack the rebalancing bands issue and backtest for the optimal thresholds to use. <drum roll>

The optimal rebalancing bands are 12%-13% on a end of year basis. That means rebalancing when any asset reaches 37%-38%/13%-12% in weight, checking after the end of December to beginning of January.

HB was indeed a genius.

Finis.

[bedraggled]

MG,

Clarify, please.

Are you suggesting 37%-38&/12%-13% in the traditional HBPP4x25 portfolio?

Thanks.

[Kbg]

As my final post before the world-as-we-know-it ends, I thought it was fitting to announce that I was finally able to crack the rebalancing bands issue and backtest for the optimal thresholds to use. <drum roll>

The optimal rebalancing bands are 12%-13% on a end of year basis. That means rebalancing when any asset reaches 37%-38%/13%-12% in weight, checking after the end of December to beginning of January.

HB was indeed a genius.

Finis.

Real friends share their code.

[MachineGhost]

[dragoncar]

As my final post before the world-as-we-know-it ends, I thought it was fitting to announce that I was finally able to crack the rebalancing bands issue and backtest for the optimal thresholds to use. <drum roll>

The optimal rebalancing bands are 12%-13% on a end of year basis. That means rebalancing when any asset reaches 37%-38%/13%-12% in weight, checking after the end of December to beginning of January.

HB was indeed a genius.

Finis.

Real friends share their code.

What's the best place to learn this stuff? I mean, I can hack away at perl with the crappiest of them, but I understand there are better languages used for backtesting. Interested in screwing around as a hobby in retirement.

[Kbg]

Serious quants appear to use Python, R and Matlab(?). Semi-serious quants/non-pros seem to use Amibroker. I think both MG and I use Amibroker...though I'm personally just a hack. There are several other options though. TradeStation probably being the biggest one.

AmiBroker is relatively cheap, support is excellent, there's a pretty good users group on Yahoo. AB is a Cish kind of language for programming, though without the requirement to define the data types of all your variables.

[MachineGhost]

Serious quants appear to use Python, R and Matlab(?). Semi-serious quants/non-pros seem to use Amibroker. I think both MG and I use Amibroker...though I'm personally just a hack. There are several other options though. TradeStation probably being the biggest one.

AmiBroker is relatively cheap, support is excellent, there's a pretty good users group on Yahoo. AB is a Cish kind of language for programming, though without the requirement to define the data types of all your variables.

AmiBroker is most excellent for backtesting and optimizing; beyond that it gets rapidly skimpy though there is an open-source broker interface to Interactive Brokers available which you can call within the array-processing AFL (which runs one sequential pass per ticker). I use it for automated trading but I have to patch up the skimpyness with JScript, Python and R to get other things done. Python is the de facto standard for quants, but it is a royal pain to program in because it is anal about proper spacing and the errors can be cryptic. I actually hate it, but its not that dissimilar to R or AFL, just more obtuse. AFL doesn't have libraries you can call in to use like R or Python, though (other than DLL plugins of which there aren't very many or useful).

If you want to mess around, I suggest just using https://www.quantopian.com/ which is a web front-end to the Python zipline library. It supports both Interactive Brokers and RobinHood for live trading. There's also https://www.quantconnect.com/ which live trades via Tradier or Interactive Brokers.

KBG did you notice how the all-weather sectors is virtually the RSP? So there's your explanation. It might be a short-term anomaly since the sectors are specifically weighted to deal with the complete business cycle based on 50-years. I like that A LOT better for my core plank than fiddling with different equity subclasses which have terribly underperformed over the past decade.

Also, the cluster risk parity portfolio is where you equalize the correlated risk WITHIN assest classes, but also equalize the correlated risk BETWEEN asset classes as determined by principal component analysis (PCA). If you just equal weight WITHIN and BETWEEN the asset classes found via PCA, then you wind up with the 25%x4 (see below). UUP is a proxy for cash but with the volatility vs other currencies present. LT TIPS could also be used as a proxy.



So yes, Browne was a genius in figuring out the 25x4 back in 1987, but times have evolved. The main advantage to cluster correlated risk parity is a lot less risk.

[MachineGhost]

Last night, I backtested the All Weather Sectors and other core portfolio potentialities. What wasn't immediately obvious beforehand is the sectors rely on monthly rebalancing to achieve those superior returns (the risk is about the same as the S&P 500) and by using Fidelity's active management sector funds. Overall, the Fidelity sector funds outperformed the Select Sector SPDR's, the Gone Fishin' portfolio, the S&P 500 Total Return and the Rydex Equal Weight S&P 500. The Equal Weight is consistently last over various time frames, but the top winner for being an absolute stinker is the Gone Fishin' portfolio... it just sucks beyond belief in the short-term (2010+) and the medium-term (1998+).

Just for fun I also ran the portfolios with trend following and/or naive risk parity added and the performance improved as expected, but again the exception was the Gone Fishin' portfolio that actually had a worse CAGR and a 2x bigger MaxDD! I've never seen an instance before where using naive risk parity resulted in inferior performance. It is quite probable that many of the Gone Fishin' assets are too highly correlated when trend following.

In closing, you can expect about a 2.54% to 1.64% outperformance vs the S&P 500 since 1999 using the All Weather Fidelity sectors on a buy and hold basis depending on the rebalancing frequency.

[Kbg]

One thing I'm seriously considering is changing the way GLD and TLT are handled...if one buys into the four economic climates then these two are always mutually exclusive in all four quadrants. (Stocks and cash are for that matter as well.) Don't know why this didn't occur to me but given this is the case why not consider segregating GLD and TLT into one bucket and cash and stock into another bucket. The second bucket would assume one was disinterested in cash's portfolio volatility dampening qualities and exclude considerations for cash as an emergency fund. In other words, the above conception would be limited to a long term investment program vice what we would consider as the PP approach.

Edit/Update:

I did a quick check on this to see if the idea had any merit...and I think it does, though the quick and dirty version leaves me not knowing what max DD is, but I would guess it is greater than a standard PP.

Test period, 1/1/06 - yesterday, using (SHY,SPY) and (GLD,TLT) 50% each. Rank by annual return for selection, check quarterly. PP was rebalanced annually.

Total Returns / Avg Annual (not CAGR)
PP 80.00% / 7.27%
GLD/TLT 105.70% / 9.61%
SPY/SHY 88.00% / 8.00%
Buckets 96.85% / 8.80%

Eyeballing the annual returns...the worst PP year was -2.7%. The worst bucket year was -3.75%. 2015 for both. PP has had 2 down years, bucket has had 3 though the two down years were less when combined than PPs other down year. Summing up the total for all down years you get -5.1% for PP and- 6.25% for bucket. There are some pretty extreme differences in annual returns between the two however (+/- 11 and 12%). Terminal wealth starting at 10K was 24552 for bucket and 21300 for PP. If I disadvantage bucket by starting it on the worst discrepancy years where there is a big delta favoring PP in all cases bucket still has a higher terminal wealth.

So as is often the case, a little more return for a little more volatility but nothing remotely near stock market only volatility.

[MachineGhost]

In other news, I still haven't settled on a portfolio composition for my core plank. Diversity has been such a terrible performer since 2009 that it would have been way too painful to actually have done anything but the S&P 500. I'm currently leaning towards the Golden Butterfly's dual allocation, but first I'm gonna revisit Swedroe's bulbuous volumes of writings since he's an excellent data miner of the literature.

For the Blue Chip plank, I've all but settled on dividend growth stocks to earn double digit yields over a decade. In theory it is a "set it and forget it" portfolio even at this ridiculous level of stock valuations, but I don't know about that. Buying more and more shares on the way down in a bear market needs to be contrasted with avoiding the capital gains losses alltogether. If you never sell, do you have a loss?

[Kbg]

The Low Volatility Factor

http://www.etf.com/sections/index-inves ... nopaging=1

The Small Cap Factor

http://econompicdata.blogspot.com/2016/ ... atter.html

[MachineGhost]

[MachineGhost]

[MachineGhost]

Huzzah! Finally a concentrated momentum fund with downside risk protection. http://www.aptusfund.com/index.html



Wonder what happend in 2011, that's pretty bad underperformance.

[bedraggled]

MG,

How is the yearly tax burden?

[MachineGhost]

[ILoveMoney]

Huzzah! Finally a concentrated momentum fund with downside risk protection. http://www.aptusfund.com/index.html



Wonder what happend in 2011, that's pretty bad underperformance.

Why would you add a fund like this to your portfolio MG and how much of it would you add?

When I saw it, it reminded me of PHDG (PowerShares S&P 500 Downside Hedged Portfolio ).

Someone talked on here a few years ago about using that in a XIV barbell.

I forgot if he was demoing it or if he had done it on a live account.

[MachineGhost]

Why would you add a fund like this to your portfolio MG and how much of it would you add?

When I saw it, it reminded me of PHDG (PowerShares S&P 500 Downside Hedged Portfolio ).

Someone talked on here a few years ago about using that in a XIV barbell.

I forgot if he was demoing it or if he had done it on a live account.

Because downside risk managed products have superior risk/reward performance if they're implemented correctly. Relying on perpetual positions in T-Bonds or Gold is inefficient at best, portfolio damaging at worse. They're for certain economic climates, not hedging Prosperity per se.

I'm keen on a 50%/50% split between momentum and value; the only question is where to put it? I don't think either qualifies as a core holding but are they specific enough to qualify as targeted sector? For now, I've tentatively assigned it to the latter. Just need to find a value fund that's downside risk managed. There is hybrid VAMO but given Mebane Faber's horrible track record with his overly simplistic approaches, I cast a wary eye on it.

[MachineGhost]

This could be an interesting investment strategy (or not):

[MachineGhost]

The two takeaways from the below paper is that the best downside risk management technique is not being offered to retail (except via Hedgeable) but has a flaw in being stuck when the risk capital portion is wiped out and also that there is an optimal leverage-to-use formula based on market volatility. I also suspect using a discounted zero coupon bond might even be superior to using cash as it might allow more risk capital to be used compared to posting margin on futures.

CPPI has become a well-known investment concept that combines downside risk protection with participation on the upside in a risky asset. This strategy is typically implemented by investing an amount into the money market that accrues interest over time and ensures a certain capital protection level at a future point in time. To ensure upside participation, the remaining investment amount that is not needed to ensure the capital protection level is invested in the risky asset, typically allowing for a leveraged investment with the leverage multiple M. The use of a multiple greater than 1 implies the risk that the investment in the risky assets is wiped out completely and only the risk-free money market investment survives till maturity.

The derivation of the optimal leverage factor within a Black-Scholes model framework goes back to the seminal article of Merton (1971) and shows that (taking equities as a risky asset) the expected return maximizing multiple M (see formula (1)) is given by the premium of the expected equity-drift rate over the risk-free rate divided by the variance of the returns of the equity market. The optimal leverage factor ensures the optimal balance between leveraging the upside potential of the risky asset on one hand and the well-known adverse “buy high/sell low” effect due to the frequent rebalancing of the leveraged investment scheme on the other hand (for details, see Giese 2010).

Furthermore, Estep and Kritzmann (1988) have extended the basic concept of the CPPI to investment strategies that are not limited to a fixed maturity date. Instead, they consider a so-called time invariant proportional portfolio (TIPP) strategy with value It, where, analogous to the CPPI, the portfolio value exceeding the protection level Ft is invested in the risky asset (using a leverage multiplier M), whereas the amount Ft is invested in the risk-free money market. The crucial part of the methodology is the definition of a maximum drawdown limit 1-f, which the strategy can never exceed, where f denotes the protection level on the current portfolio value It. We assume that the floor is ratcheted using a fixed capital protection floor f<1, i.e., Ft is assumed to grow with the risk-free rate in time unless the investment scheme grows such that f times It reaches or surpasses the current floor Ft, in which case the floor is ratcheted to Ft = f It.

Consequently, (1-f) is the maximal drawdown that can occur at any given point in time, and therefore the factor f can also be interpreted as the minimum capital protection level at any given point in time.

It is interesting to note that Grossman and Zhou (1993) show that the expected return maximizing leverage factor M on the risky asset portion It -Ft for the TIPP strategy is (analogous to the CPPI strategy) given by the expected equity-drift rate over the risk-free rate divided by the variance of the returns of the equity market.

Both CCPI and TIPP models have led to a broad stream of academic research that we briefly summarize in the following without claiming completeness. To start with, the properties of continuous-time CPPI models are studied in Black and Perold (1992), Perold and Sharpe (1995), and Bookstaber and Langsam (2000). The development path from a standard CPPI to so-called optimized PPI and a comparison of the models involved can be found in Bertrand and Prigent (2002a). A theoretical extension related to models involving stochastic volatility and extreme value approaches is developed in Bertrand and Prigent (2002b, 2003), with more advanced downside-risk measures developed in Bertrand and Prigent (2011). Within the broad stream of research analyzing the risk and return profile of optimized PPI models (OPPI), we would like to mention the works of Cox and Huang (1989), Brennan and Schwartz (1989), Grossman and Villa (1989), Black and Perold (1992), Grossman and Zhou (1993, 1996), Basak (1995, 2002), Browne (1999), Tepla (2000, 2001) and El Karoui et al. (2005). Furthermore, more empirical simulation-based studies can be found in Cesari and Cremonini (2003) and Do and Faff (2004).

Although CPPI and TIPP strategies have become the catalyst of extensive academic research due to the fact that they allow for closed-form solutions as well as the application of optimization techniques, their practical importance is quite limited.

To be precise, apart from the CPPI—which has become a well-known model in the insurance industry—these models have had little success in the wider asset management community, particularly in the market for funds, ETFs and financial products in general.

At a first glance, this is quite surprising, since risk-reduction methodologies such as target-volatility strategies, low-beta strategies or minimum-variance strategies have become standard investment schemes in the asset management space, and are even available in the form of certificates or exchange-traded funds.

In fact, the TIPP strategy offers a more attractive risk-reduction mechanism in the form of an absolute drawdown protection level, whereas target-volatility, low-beta or minimum-variance strategies simply limit or reduce the volatility of the investment portfolio without necessarily limiting the drawdown in a serious market crash. In principle, this makes the TIPP strategy more appealing to investors in related financial products than volatility-reducing investment strategies.

http://www.etf.com/publications/journal ... nopaging=1

[InsuranceGuy]

[deleted]

[MachineGhost]

The answer leads us to the third reason why I believe in this framework: It addresses a major question facing many investors today — “how do I put it all together?”

Investors today have access to more market data and strategic information than at any other time in history. Yet from the perspective of the average investor, this huge volume of fragmented information presents a challenge — how should one actually implement everything?

So the third reason I’m advocating this framework is because it’s holistic. On one hand, the approach is broad and sturdy, rooted in respected, wealth-building investment principles. On the other hand, it’s strategic and intuitive, able to adapt to all sorts of market conditions. The result is a unified, complementary framework that can relieve investors of the handwringing and anxiety of “what’s the right strategy right now?”

If you’re an investor who’s struggled with generating long-term returns that make a real difference in your wealth, I believe this portfolio can help. If you want less anxiety during periods of heightened market volatility and drawdowns, I believe this portfolio can help. And if you’re unsure how to balance the simplicity of buy-and-hold with the various benefits of an active portfolio, I think the investing framework in this paper can help.

If that sounds like your type of investing, I hope you’ll read on.

http://www.cambriainvestments.com/wp-co ... _final.pdf

[MachineGhost]

[Kbg]

Where did you get these?