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Can India ETFs Continue To Soar After The Rate Cut?

The Indian stock market took giant strides last year gaining about 30% on optimism that the much-anticipated win of the pro-growth government would drive Asia’s third-largest economy higher. After all, the euphoria around one of the members of the BRIC nations was justified when investors saw robust corporate earnings, a drastic decline in inflation helped by the unbelievable decline in global oil prices and a stable currency despite the ascent of the greenback. All these tailwinds set the stage for the Indian central bank to go for the much-awaited rate cut on Thursday, Jan 15, by 25 basis points to 7.75%. The move, was prompted by consumer prices below the Reserve Bank of India’s ( RBI ) target for the third month in a row. RBI governor Raghuram Rajan also sounded hopeful that inflation might remain below 6% until January 2016. Though India’s inflation rate nudged up to 5% in December 2014 from a record-low of 4.38% in the earlier month, the number was way behind the average 8.98% noticed from 2012 until 2014 (per tradingeconomics). In fact, the number even touched the high of 11.16% in November 2013, compelling a cycle of rate hikes in the past one-and-half years that India had to undergo in order to contain rising prices. What’s Behind the Falling Inflation? India’s present economic background, created on both domestic and international parameters, is in one word – satisfactory. While a drastic slump in oil price caught the global stock market on the wrong foot, it came as a boon to the Indian economy. This was because India imports more than 75% of its oil requirements, thus being highly susceptible to oil prices. Per U.S. Energy Information Administration, India was the world’s fourth-biggest user of crude oil and petroleum products in 2013. Last June, Financial Times indicated that the Barclays’ projected $10 per barrel rise in crude oil price would cost India 0.5 percentage points in its growth rate. When the oil price moved in the opposite direction, the scene turned around as well. And now, with several research houses projecting oil prices to remain around $40 per barrel in the first half of 2015, Indian foreign reserves paired with policy makers have every reason to cheer. This in turn is easing pressure on India’s currency, that too at a crucial time like this when the greenback is soaring higher and might gain more strength after the inevitable Fed rate hike. Market Impact India’s benchmark stock index rose the most in eight months following the rate cut announcement. No doubt the latest rate cut and a host of transformational measures including coal reforms, opening up the road for 100% FDI in railway infrastructure and easing FDI policies in construction, defense and insurance will provide a good boost to investors’ sentiments. As a result, India ETFs are poised to surge in the coming days on this surprise rate cut. Investors should note that almost all India ETFs are Buy-rated at the current level. Below are three funds that could strengthen investors’ portfolio with enhanced returns. These products have generated excellent returns over the trailing one-year period and are off to a good start in 2015 as well. WisdomTree India Earnings ETF (NYSEARCA: EPI ) This product tracks the WisdomTree India Earnings Index, holding 230 securities in its basket. The fund measures the performance of the profitable Indian companies. As we all know, a rate cut should spur corporate India, EPI should make a wise investment proposition in the coming weeks. About one-fourth of the portfolio is dominated by financials, followed by energy (18.70%) and information technology (17.94%). EPI is the largest and most popular ETF targeting India with AUM of over $2.1 billion and average trading volume of around 4.5 million shares. The expense ratio comes in at 0.83% for this product. The fund was up 31.7% over the past one year and has added about 6.7% so far this year. The fund has a Zacks ETF Rank #1 (Strong Buy) with High risk outlook. iShares S&P India Nifty Fifty Index ETF (NASDAQ: INDY ) INDY is a large cap centric fund that follows the CNX Nifty Index, which seeks to track the performance of the largest 50 Indian stocks. As the fund tracks the key Indian stock market gauge, it should be an eye-catcher for foreign investors. The ETF has amassed $778 million in assets. The fund charges 94 bps in fees. Banks take the top spot in the portfolio. This Zacks ETF Rank #1 (Strong Buy) fund was up nearly 32% in the last one year and 7.6% in the year-to-date frame. EGShares India Consumer ETF (NYSEARCA: INCO ) This ETF targets the consumer industry of India and follows the Indxx India Consumer Index. It holds 30 stocks in its basket and has amassed $27.1 million in its asset base. The fund trades in a paltry volume of 20,000 shares in an average day, suggesting additional cost in the form of a wide bid/ask spread beyond the expense ratio of 0.89%. As the name suggests, the product is focused on the consumer sector. From an industry look, automobiles, which perform better in low rate environment, occupy the top position with 37.5% share while personal goods and industrial engineering round off the next two places at 27.1% and 15.4%, respectively. This Zacks ETF Rank #1 fund was up 56% in the one-year frame and 12% in the year-to-date frame.

The Gone Fishin’ Portfolio: 14 Years Of Market-Beating Returns

I’ve always been fascinated by ‘Couch Potato’ portfolios, those where you invest in index mutual funds and rebalance only once per year. In the early 2000’s I read about the ‘Gone Fishin’ Portfolio. ‘Gone Fishin’ was intriguing because it is based on the work of Harry Markowitz, who won a Nobel Prize in 1990 for Modern Portfolio Theory – now known as asset allocation. Asset allocation is the process of developing the most effective – optimal – mix of investments. In this case, optimal means that there is not another combination of asset classes that is expected to generate a higher ratio of return to risk. Quite simply, it’s breaking down your portfolio into different baskets, or classes of investments, to maximize returns and minimize risk. Asset allocation is based upon the principal that non-correlated investments of varying risk, when astutely mixed together, will smooth out the volatility (or variability) of your returns. And the best part about it is that it also increases your returns. Alexander Green of The Oxford Club took the work of Markowitz and gave it a name – ‘The Gone Fishin’ Portfolio.’ The allocations are: So we have 30% U.S. Stocks, 30% International Stocks, 30% Bonds, then 5% REITs and 5% Gold/Precious metals. A nice well-balanced portfolio. Advantages of the ‘Gone Fishin’ Portfolio, and other Lazy portfolios, are that they help address four major investment risks: Being too conservative. Being too aggressive. Trying and failing to time the market. Using expensive fund managers (Recommend Vanguard funds) Back 14 years ago when I first heard of the Gone Fishin’ Portfolio, I thought that replacing the index funds with active managers (while keeping the same % allocation) might even be better than this passive index approach. I knew lots of great fund managers whom I thought would beat their index in the long term, so why not utilize their expertise within the Gone Fishin’ framework? I called this creation the ‘Masters Select’ Gone Fishin’ Portfolio. I then created an Excel spreadsheet to track both styles, and have updated it each January for the last 14 years. Here’s the ‘Masters Select’ Gone Fishin’ Portfolio I set up 14 years ago: So you can see in most cases I replaced the passive index fund with a favorite active manager. Fairholme Fund for the Total U.S. market, and Third Avenue Value for small caps. Matthews Asian Growth & Income for the Pacific stock index. And Third Avenue Real Estate replaced the REIT index. Back 14 years ago I could not identify a worthy active manager for the European or Emerging Markets indices, so I left these as the passive index. For Bonds, I took the combined 30% allocation and divvied it between two excellent bond managers – Bill Gross of Managers Fremont Bond, and John Hussman of Hussman Strategic Total Return. And I replaced the normal Gone Fishin’ precious metals fund with a broad commodity fund. I’ve been tracking the results of both the official Gone Fishin’ portfolio and my ‘Masters Select’ version. Here are the results. The standard Gone Fishin’ portfolio has performed as advertised, soundly beating both the S&P 500 and a 60/40 Stock/Bond portfolio. On a risk-adjusted basis the Gone Fishin’ portfolio does extremely well (the whole idea behind Markowitz theory), with the drawdowns in bad years less than the stock market. My ‘Masters Select’ Gone Fishin portfolio did even better than the Markowitz model. Half the time (7 of 14 years) it beat the other three portfolios, and had a total return better than the standard model. I should emphasize I did not replace an active manager during the entire 14 year period, so there’s no bias there. Of course perhaps I was lucky (or good) with the picks. Further, by using active managers, I realize I was introducing ‘tracking risk’ to the portfolio, where a manager strays away from the intended index. Nevertheless it paid off to combine these pros with a Nobel prize-winning framework. Of some concern – the Gone Fishin’ portfolios started off with a blast, beating the S&P 500 for 10 straight years (2001 – 2010). But since then they have underperformed. This also happened in the late 1990’s when the S&P 500 was in its strongest bull phase and international stocks were out of favor. We seem to be in a similar era right now and I expect the underperformance to be temporary. One other concern I have with the ‘Masters Select’ Gone Fishin’ concept is the fund size. Fairholme Fund and Third Avenue Value have grown incredibly in the past 14 years. Size is an anchor to performance, particular with small-cap funds. If I started this today I may choose a few different funds, or a combination of active funds.

Explaining Why The Portfolio-Barbell Works

One classic (liability-driven) portfolio strategy, known for obvious reasons as the “barbell,” entails a lot of very defensive low-beta assets on the one side, and a lot of aggressive high-beta assets on the other. Practitioners follow the advice of Mr. Bing Crosby: they don’t “mess with Mr. In-between.” For the most part, this is a practitioners’ strategy, not a theorists’ strategy. There’s been little reason in theory to think that it should work. After all. if you want diversification, why not include some assets in between those extremes? And if you don’t care for diversification, why not go whole hog with a “bullet” strategy? Despite its lack of conceptual foundations, practitioners continue to use it. Theory in Pursuit of Practice Donald Geman, a Fellow at the Institute of Mathematical Statistics and a professor of Applied Mathematics at Johns Hopkins, with expertise in machine learning, joins with two other scholars in writing a paper, now a preprint at arXiv, which seeks to put a foundation under this practice. The other authors are: Hélyette Geman of the University of London and… Nassim Nicholas Taleb, of black swans and anti-fragility renown. The gist of the paper, expressed non-mathematically, is that managers work with the facts they know. What they know is that they have to constrain the tails of their portfolio-return bell curve to satisfy various regulatory or institutional demands, Value at Risk, Conditional Value at Risk, and stress testing. The “operators,” as the authors call the decision makers in portfolio management, aren’t “concerned with portfolio variations” except insofar as they have “a vague notion of association and hedges.” They set out on the one hand to limit the maximum drawdown with investments at the conservative side of the scale in response to the sort of pressures and mandates just listed, then they move to the other end of the scale to seek to get the upside benefits of the same market uncertainties against which they’ve just protected themselves. In the course of making these points, the authors get in the by-now customary jabs at Modern Portfolio Theory. One footnote for example explains that MPT’s aim of lowering variance, thus its habit of treating the left-hand tail and the right-hand tail as equally undesirable, is rational only if there is certainty about the future mean return, or if “the investor can only invest in variables having a symmetric probability distribution.” And the authors consider neither premise plausible. The latter they find especially “farfetched.” From MDH to Entropy To get a bit more technical, their discussion elaborates on an existing literature on the “mixture” of two or more normals, the “mixture of distributions hypothesis.” It has been part of the finance literature for at least twenty years, since Matthew Richardson and Thomas Smith wrote a paper of the “daily flow of information” for the Journal of Financial and Quantitative Analysis in 1994. The underlying idea of the MDH is that information is moving into markets at uneven rates, and that this unevenness renders asymmetric distribution curves inevitable. In 2002, Damiano Brigo and Fabio Mercurio used MDH to calibrate the skew in equity options. What Geman et al. add is a model that makes “estimates and predictions under the most unpredictable circumstances consistent with the constraints.” They also, somewhat confusingly, call this a “maximum entropy” model. Entropy of course is a concept taken from the physical sciences, and the maximum entropic state for any system is one in which all useful energy has been converted into heat. Not a good thing. The idea has long been adopted into information theory, re-conceiving useful energy as signal and heat as noise. Thus, unsurprisingly, early efforts to introduce entropy into finance have seen entropy as something to be minimized. The Question in Unanswered Indeed, Geman et al are aware that their invocation of “maximum entropy” will seem an odd innovation to many of their readers. Most papers that have invoked entropy “in the mathematical finance literature have used minimization … as an optimization criterion” they say. Their use of a “maximum entropy” model (not as a “utility criterion” of course but as a way of recognizing “the uncertainty of asset distributions”) is itself not entirely novel though. They seem to have imported it from the world of developmental economics. In 2002 Channing Arndt, of the UN’s World Institute for Development Economics Research, witrh two associates, published an article announcing a ” maximum entropy approach” to modeling general equilibrium in developing economies, illustrating it with specific reference to Mozambique. Geman at al deserve some credit for their syncretism, their willingness to look in a variety of different places for the solution to the puzzle they’ve set themselves. Still, it seems to this layperson expert-on-none-of-it that the resulting construction is a ramshackle hut rather than a model. The simple question of why barbells work remains (so far as I can tell) unanswered.