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How To Beat Goldman Sachs At The Prediction Game

“It’s tough to make predictions…especially about the future” The late Yogi Berra’s quip about predictions reminds us that we humans are a funny lot. In ancient times, the ancient Babylonians predicted the future using animal entrails. Today, millions of people still turn to astrology to get a glimpse of what’s to come. And we do the same when reading the financial media. Yet, for all of our relentless commitment to divining the market’s future by reading this morning’s Wall Street Journal , it’s hard to avoid feeling that financial predictions aren’t any more reliable than those we find in the astrology columns. Goldman Sachs’ Call on Oil Just consider the case of Goldman Sachs’ calls on the oil price over the past 12 months or so. In late 2014, Wall Street’s premier investment bank asserted that “downside risks” in the oil price were gaining momentum and it forecast a decline in the price of oil to $90 a barrel in the first quarter of 2015. Three weeks into 2015, and oil was trading below $50, confounding Goldman and nearly every other analyst on Wall Street. Fast forward to December 2015, and Goldman is standing by its latest prediction of a $20 per barrel bottom. To give Goldman its due, it was actually more bearish than its peers, lowering its forecast before other investment banks did. But Goldman has revised its predictions so many times that at this point the only thing certain is that Goldman’s predictions will change – rendering them essentially useless. Here’s what’s surprising. Although Goldman’s analysis moves the markets, no one ever calls Goldman Sachs on its bungled predictions. And it is highly unlikely that any Goldman Sachs oil analyst has ever been fired for making predictions about the oil price that have been wildly off the mark. Contrast that with the fate of any surgeon or airline pilot – all of whom would have been sued or put out of a job for showing similar levels of incompetence. The Achilles Heel of Wall Street’s Complex Models Most of us know deep down that astrological predictions are bunk. And we also realize that what Sam Goldwyn said about Hollywood also applies to Wall Street: “Nobody knows anything.” Yet, we still cling to the irrational hope that a sleep-deprived 26-year-old Goldman Sachs analyst, armed with her elaborate spreadsheet models, can tell us something about the future of oil prices. We are still wowed by a combination of the Goldman imprimatur and the apparent complexity of the firm’s financial modeling and its access to information. One of the myths of Wall Street high finance is that the more variables a financial model accounts for, the more accurate its predictions. Truth be told, any financial analyst worth his salt can construct a model that generates accurate predictions based on past data. But test the model on a different set of data and the predictive ability of the most elaborate model simply evaporates. Complex models are rarely robust. Goldman Sachs’ model to predict the oil price is no different. That’s why the “out of sample” data make Goldman Sachs’ oil price predictions essentially worthless. ‘Fast and Frugal’ Decision Making Prevails As psychologist Gerd Gigerenzer has argued, “fast and frugal decision making” trumps complicated predictive modeling almost every time. Goldman’s elaborate models for predicting the future are likely to be more wrong, more often, simply because they are so complicated. The more complicated the model, the larger the likely error. Gigerenzer cites an example from baseball. An outfielder doesn’t do calculus in his head when he estimates where to run to catch a fly ball. Yet the outfielder’s “fast and frugal decision making,” focusing on the one thing that really matters – that is, keeping the angle of the ball in relation to his line of sight constant – beats complicated models of optimization every time. That’s why simple Wall Street aphorisms such as “cut your losses and let your profits run” work better than overly complex statistical models based on normal distribution curves. In the outfield, you’d expect the Goldman Sachs analyst would try to do the calculus and end up dropping the ball. Of course, in “real life” they really wouldn’t. In fact, even Nobel Prize-winning economists don’t invest according to their own models. Gigerenzer recounts how Harry Markowitz, the economist who shared the Nobel Prize in economics in 1990 for developing the core insights of Modern Portfolio Theory, never used his own theory when investing his retirement funds. Instead, he used the “fast and frugal” heuristic (“rule of thumb”) to guide his investment decisions. Ironically, he actually made more money than he would have if he had stuck to his own Nobel Prize-winning theory. Manage Your Risks Instead With global financial markets off to their worst start of the year in history, clients have inundated me with questions about my views on the direction of global stock markets. My advice? Heed Vanguard founder Jack Bogle’s advice: “Don’t do something, just stand there!” Dozens of studies have shown that trying to time the market is a fool’s game. Miss out on just the 10 best days in the market, and your long-term returns in the S&P will halve. And those 10 days happen to come right after the worst 10 days, making trying to time the market that much more difficult. That picture changes only if you are a short-term trader. In that case, your focus should be on managing your risks. Prediction – whether complex or “fast and frugal” – matters little in investing, unless you have a plan to manage your downside risks. A “fast and frugal” plan to cut your losses, say, at 20% in all your investments in 2008 would have trumped the hundreds of gallons of virtual ink spilled on analyzing the causes and consequences of the global market meltdown. Chances are, that rule of thumb won’t be perfect. But as the economist John Maynard Keynes observed: “It is better to be approximately right than exactly wrong.” And the one thing that you can say with certainty about Wall Street’s complex models is: that they will be “exactly wrong.”

Cold Snap Sparks Sudden Rally In Oil Price: ETFs Surge

After crashing to below the 12-year low in Wednesday’s trading session, oil price spiked nearly 21% over the past two days, representing the biggest two-day rally since September 2008. It has also extended its gains in the early trading session today with both U.S. crude and Brent trading above $32 per barrel (read: Oil Hits 12-Year Low: Short Energy Stocks with ETFs ). The steep increase came on the back of short covering, bargain hunting as well as freezing conditions and snowstorms in parts of the U.S. and Europe that boosted the short-term demand for heating oil. Notably, speculators’ short position in WTI dropped 8.4% for the week ended January 19, as per the data from U.S. Commodity Futures Trading Commission. In addition, weekly data from oil services firm Baker Hughes (NYSE: BHI ) showed that the number of rigs fell for the fifth consecutive week by 5 last week to 510, the lowest level since April 2010. Further, hopes of additional stimulus in Europe and Japan, and China comments on no plans to devalue the yuan boosted the confidence in the overall economy, thereby bolstering the case for global oil demand. ETF Impact The tremendous trading in oil sent the oil ETFs space into deep green in Friday’s trading session. In particular, the United States Diesel-Heating Oil ETF (NYSEARCA: UHN ) surged 10% followed by gains of 9.5% for the United States Brent Oil ETF (NYSEARCA: BNO ) , 8.6% for the PowerShares DB Oil ETF (NYSEARCA: DBO ) and 8.3% for the United States Oil ETF (NYSEARCA: USO ) . While the returns of these funds are tied to the oil price, they are different in some way or the other. This is especially true as UHN tracks the movement of oil prices while BNO provides direct exposure to the spot price of Brent crude oil on a daily basis through future contracts. DBO provides exposure to crude oil through WTI futures contracts and follows the DBIQ Optimum Yield Crude Oil Index Excess Return while USO seeks to match the performance of the spot price of light sweet crude oil WTI. Out of the four, USO is the most popular and liquid ETF in the oil space with AUM of $2.3 billion and average daily volume of 34 million. UHN is unpopular and illiquid with AUM of $2.5 million and average daily volume of just 3,000 shares. Further, USO is the least expensive, charging just 45 bps in fees per year from investors. Meanwhile, leveraged oil ETFs also shot up with the VelocityShares 3x Long Crude Oil ETN (NYSEARCA: UWTI ) and the ProShares Ultra Bloomberg Crude Oil ETF (NYSEARCA: UCO ) surging 24.6% and 16.8%, respectively. The former seeks to deliver thrice the returns of the daily performance of WTI crude oil while the latter tracks the two times daily performance of futures contracts on WTI crude oil. What Lies Ahead? Despite the steep gains, oil price is down 13% so far this year and the long-term fundamentals remain bearish (read: If the Oil Crash Continues, Buy These 5 ETFs to Outperform ). This is because oil production has risen worldwide with the the Organization of the Petroleum Exporting Countries (OPEC) continuing to pump near-record levels, and higher output from the U.S., Iran and Libya. The lift in oil sanctions in Iran would add a fresh stock of oil to the already oversupplied global market as the country is expected to increase its crude oil exports by half a million barrels a day immediately and a million barrels a day within a year of lifting the ban. On the other hand, demand for oil across the globe looks tepid given slower growth in most developed and developing economies. In particular, persistent weakness in the world’s biggest consumer of energy – China – will continue to weigh on the demand outlook. The negative demand/supply imbalance would push oil prices and the related ETFs further down at least in the short term. Link to the original post on Zacks.com

Using Active Share To Evaluate High-Yield Bond Portfolios

There are two chief ways of measuring a portfolio’s deviation from its benchmark: tracking error and active share. The first, tracking error , is the older and more traditional. It gauges a portfolio’s performance deviation from a benchmark return over time – essentially telling an investor how different the returns are from the benchmark. The second, active share , is newer but steadily gaining steam. It specifically measures how unique a portfolio is, at the holdings level, relative to the benchmark. Tracking Error vs Active Share Of the two, which is best? That’s the question MFS Fixed Income Portfolio Manager David Cole, Chief Risk Officer Joseph Flaherty, and Quantitative Research Analyst Sean Cameron set out to answer in an October 2015 white paper Active Share: A valuable risk measure for high-yield portfolios . As evident from the title, the trio values active share – but not exclusively. While active share can be an alternative to tracking error, one can complement the other, particularly in measuring the relative risk of a high-yield bond portfolio, which is the subject of the paper. Their findings: Active managers are increasingly being asked to demonstrate just how active they really are. Active share is the best measure for making this determination, since it looks at portfolios on the holdings level, whereas tracking error merely shows deviation of performance. Both can be useful, but tracking error is more a proxy for “systematic factor exposure,” whereas active share provides “valuable information on the degree of conviction,” according to the paper’s authors. As stated earlier, active share and tracking error can be used together, and this is especially useful in classifying high-yield bond portfolio managers. Using both measures allows investors to gauge a manager’s “activeness” and determine the sources of that activeness. According to the authors, “relatively high active share in combination with relatively low tracking error would be consistent with an active, diversified, high-yield credit manager.” Portfolio Insights Active share has typically been used in evaluating equity portfolios, but Cole, Flaherty, and Cameron show its usefulness-sometimes in conjunction with tracking error-in assessing high-yield bond portfolios, too. Active share in particular can give investors insight into the drivers of risk and return in credit-oriented fixed-income portfolios, which may have low tracking error but are actually quite active. “This,” according to the authors, “is consistent with a high-yield manager’s investment process, which frequently entails minimizing systematic risk while seeking to maximize returns from the security selection process.”