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Who Are The Market Makers? What Do They Do? WHY?

Summary We constantly talk about the market makers [MMs] and their activities. It is apparent from their comments, that many readers have varied, limited views about the function of MMs, their status, regulation, objectives, and their compensations. A late-August irregularity in securities markets functioning created knowledgeable analysis and comment discussing all that, much of which may help our perspective and understanding. The August 24 th Market Opening Problem The casual, intermittent user of US equities markets may not even be aware that there was a problem or the seriousness of its condition. By 10:30 am NYC time that Monday, things were pretty much back to near normal, and trading the rest of the day was being conducted about as usual. But the previous hour or two nearly shut down the ability of investors and speculators to carry out their planned transactions. Many unpublicized DK (don’t know) trades complicated the end of day settlement processes. Here is how one deeply involved observer firm described what happened: Recent Volatility in the US Equity Market In late August 2015, the US equity market experienced a rapid spike in volatility as global market sentiment weighed bearishly on stocks. During that period, the VIX volatility index doubled and equity-trading volumes surged as investors reassessed global growth prospects and inflation expectations. Market activity on August 24 was particularly extreme. Before the market opened, global equity markets were down 3% to 5% and the e-mini S&P 500 future was limit down 5% in pre-market trading before wider price curbs went into effect at 9:30 am. Due to these pre-opening factors, the morning began under selling pressure with substantial order imbalances at the open as investors reacting to global macro concerns flooded the marketplace with aggressive orders to sell (that is, orders to sell without any restrictions as to price or time frame such as market and stop-loss sell orders). According to the New York Stock Exchange (NYSE), the volume of market orders on August 24 was four times the number of market orders observed on an average trading day. Extensive use of market and stop-loss orders overwhelmed the immediate supply of liquidity, leading to severe price gaps that triggered numerous LULD (limit-up, limit-down) trading halts. The confluence of these factors contributed to aberrant price swings and volatility across the US equity market. For example, the S&P 500 index was at a low, down 5.3%, within the first five minutes of trading, then rallied 4.7% off the lows before selling off again late in the session to close down 3.9%. Bellwether stocks such as JPMorgan (NYSE: JPM ), Ford (NYSE: F ), and General Electric (NYSE: GE ) saw temporary price declines in excess of 20%. Individual stocks as well as ETPs (exchange traded products) and CEFs (closed end funds) experienced significant dislocations after the opening followed by unusual volatility. Transparency and Information Flow Price transparency and information flow in the US equity market were curtailed from the start, forming one of the key contributors to the day’s events. Anticipating widespread volatility, NYSE invoked Rule 48 prior to the open. NYSE Rule 48 suspends the requirements to make indications regarding a stock’s opening price and to seek approval from exchange floor officials prior to opening a stock. By suspending time-consuming manual procedures, this action should have permitted Designated Market Makers (DMMs) to open stocks more quickly and effectively. However, this rule had the unintended effect of limiting pre-open pricing information in securities, especially for any stocks experiencing delayed opens. Although DMMs actively worked to facilitate a prompt open for all securities, the opening auction was considerably delayed for an extensive number of stocks. At 9:40 am, nearly half of NYSE-listed equities had yet to begin normal trading. These delays, along with the absence of pre-open indications, impeded the normal flow of information, which market makers and other participants rely upon to perform their customary activities with respect to the market open. Without this information, and with many securities experiencing delayed openings, correlations snapped between prices for securities in the same industry or ETPs tracking identical benchmarks deviating significantly from one another. In financials, for example, JPMorgan experienced a sharp decline, while Morgan Stanley (NYSE: MS ) did not. The basis between futures and cash prices for the S&P 500 index also widened considerably – futures traded at a 1.66% discount to the corresponding equity basket. These dislocations heightened uncertainty in the market because the validity of automated pricing models becomes challenged when there are meaningful disparities between the prices of normally correlated securities. Additionally, since many of the computerized processes, which support market making, rely on futures as a reference asset, the ability of market makers to efficiently allocate capital and price risk was inhibited. Market makers faced further uncertainty on the cancellation of potentially “erroneous trades,” adding to their reluctance to trade. The lack of price transparency impaired the ETP “arbitrage mechanism” because market makers were unable to rely upon price information for individual stocks to determine when arbitrage opportunities exist between the ETP and its underlying basket, and to hedge their positions. In the absence of the necessary data, many market makers ceased arbitraging US equity ETPs. Exchange-Traded Products The market forces discussed above led to a temporary breakdown in the arbitrage mechanism of many ETPs. 327 ETPs experienced LULD halts on August 24. Many ETPs also experienced brief periods where they traded at significant discounts to the value of their underlying portfolio holdings. As a result, the events of August 24 left many investors dissatisfied with the prices at which trades were executed and raised concerns about the functioning of markets and ETPs. Further, like individual stocks, the confluence of order imbalances, lack of information flow, and opening issues contributed to differing experiences, even for comparable ETPs. Retail investors who had standing stop-loss orders were especially impacted – once the stop price was reached, the orders were converted into market orders, which were often executed at prices that were markedly lower than the stop price. As stop-loss orders are typically intended to be used to mitigate losses, investor education about the risks of stop-loss orders should be significantly increased. To that end, Figure 1 may be helpful. Figure 1 (click to enlarge) Now You Probably Know More Than You Want And there is even more complexity involved. But the necessary message is that in a trillion dollar a day market complex, lots of actions need to be coordinated. Computer programs that expedite actions have rigidities that need to be softened in some circumstances by human judgment. Often that is where market makers [MMs] get involved. Several of the key MM functions and responsibilities are outlined in Figure 2 Figure 2 (click to enlarge) Source: BlackRock Capital Management Figure 3 identifies the principal roles of MMs as providers of liquidity, the usual MM function thought of when the subject of market makers comes up. Figure 3 (click to enlarge) Source: BlackRock Capital Management Key to understanding these roles are the impact they have on prices and price trends. The size of capital involved in typical transactions is a principal determinant. That makes the first listed category of Liquidity Provider, the block trade facilitating broker-dealer, the most significant stock price impactors of MMs by far. These are irregular but frequently occurring, multimillion-dollar trades. Each one typically has the price impact potential to step away from the posted last trade and the current bid~offer quote by a full percent or more. Skillful execution may prevent such a change, or encourage it. Trade and market savvy are important resources, along with arbitrage experience. Firms engaging in the block trade business are often vertically integrated or diversified in their MM activities into several other or all of the roles listed. Exchange-registered market makers tend to be the traffic cops of the current day exchange world and have procedural influence that affords stature in the internal community. Their exposure to the public is usually quite limited, but their day-in, day-out functions may be essential. The remains of the exchange floor specialist system are here. Wholesale MMs serving regional brokers are essentially an internal function of the MM community and are among the least influential as to procedure or securities prices. Technology dominates the electronic MMs, earning them frequency and pervasiveness of presence in number of trades. The billions of shares regularly traded could not be exchanged without this support. But the typical price changes involved from last trade tends to be tiny and highly mechanistic. Their principal contribution is immediacy of executions at low cost. The high-frequency arbitrageurs or HFT players are the intellectual and market savvy step-outs of the electronic MM organizations. Their influence is in the bid~offer realm more than in the trade volume arena. They are constantly sniffing quotes to find risk-free arb opportunities, and individual investors rarely are aware of their presence. But their reach is extensive and they are a liquidity-providing influence. Competition hones their honesty, as a group. Their accomplishments financially tend to be a basis point at a time, just a million times over. They are expert exploiters of the leverage of time. For those interested in the full complexities of the market making process here is the complete BlackRock discussion and their recommendations for market operating revisions. Some of the underlying problems go back to the 1987 “portfolio insurance” market failure debacle. Conclusion Market makers come in a variety of flavors and perform many functions essential to the power and value of today’s equity markets. Where their influence to the advantage of individual investors is the greatest is in their service to those investment organizations that must trade in market-disrupting units because of their size. That limitation of size is unavoidable since the economic basis for their investing businesses is in the amount of capital under their management. They are active investors in order to utilize their info-gathering intelligence resources. But the advantage for us is that they use the arbitrage skills of trusted market making firms to provide the other side of those big trades and the temporary financial liquidity to acquire or dispose of the thousands of shares regularly involved. In the process of MMs hedging the risk to their capital, what is revealed is the extent of the risk believed to be present. Those self-protective actions and the implicit price-range forecasts prove to be useful guides as to future specific price moves, on a very comparable base among equity investments of wide diversity.

2 Better Ways To Play Chinese Growth

The WSJ recently featured an article about the silver lining in Chinese growth. Even though the GDP growth rate had fallen below 7% to 6.9%, there was evidence of rebalancing away from the same old, same old lending-based model of infrastructure spending to the household sector (emphasis added) : There is robust growth in China if you know where to look, some contrarian investors believe. Monday’s gross domestic product report offered the latest sign that the world’s second-largest economy is slowing. But the gloom is overdone, said some portfolio managers who are focusing on the nation’s booming service sector: Their purchases amount to a bet on Beijing’s efforts to engineer an economic rebalancing, toward a consumer-led, service-driven economy from one dominated by manufacturing and trade. While slowing Chinese economic growth and declines in the country’s use of materials such as copper, nickel and cement have rippled through financial markets, some traders say some less-publicized metrics paint a more upbeat picture. To name a few, box-office sales are up more than 50% this year, Internet traffic through mobile devices has nearly doubled and railway passenger traffic and civil aviation are increasing steadily, government data show. The most recent numbers highlighted the Chinese economy’s increasingly dual nature. China reported its economy expanded at a 6.9% annual rate in the third quarter, its slowest pace since the global financial crisis. At the same time, the services sector expanded 8.4%, accounting for more than half of China’s GDP growth for the first time , according to official statistics. For years, US investors have either bought FXI or commodity-related vehicles as a way to play Chinese growth. Now that there is growing evidence of growth rebalancing, those vehicles may not be the most appropriate anymore. Consider this chart of two “New China” versus “Old China” pairs. (click to enlarge) The first is a long position in PGJ (NASDAQ Golden Dragon Index) versus a short position in FXI (FTSE China 50), which is depicted in black. The Golden Dragon Index is far more heavily weighted in consumer services and technology, which are also consumer e-commerce oriented (think Baidu (NASDAQ: BIDU ), etc.), while the FTSE China 50 Index is tilted towards financials, which represent “Old China” finance and infrastructure plays. The second pair is a long position in the Global X China Consumer ETF (NYSEARCA: CHIQ ) and a short position in the Global X China Financials ETF (NYSEARCA: CHIX ), depicted in green. In both cases, these pairs tell the story of progress of growth rebalancing towards the consumer sector of the economy. Even if you don’t want exposure to China, monitoring these pairs is a useful way of seeing how rebalancing is progressing in real time. Disclaimer: The opinions and any recommendations expressed in this blog are solely those of the author. None of the information or opinions expressed in this blog constitutes a solicitation for the purchase or sale of any security or other instrument. Nothing in this article constitutes investment advice and any recommendations that may be contained herein have not been based upon a consideration of the investment objectives, financial situation or particular needs of any specific recipient. Any purchase or sale activity in any securities or other instrument should be based upon your own analysis and conclusions. Past performance is not indicative of future results. Mr. Hui may hold or control long or short positions in the securities or instruments mentioned.

4 Portfolio Recipes That Consistently Beat The ‘Lazy Portfolios’

Summary We analyzed several Lazy Portfolios (e.g., static asset allocation portfolios) by running a full set of risk and return metrics. We compared these Lazy Portfolios to over 250 other asset allocation portfolio recipes, both tactical (with dynamic reallocation) and strategic (with a fixed allocation). Four portfolio recipes emerged as winners that consistently beat the Lazy Portfolios. These winners have lower risk and higher return over both the 1-year and 10-year time periods. We recently received a question about the performance of the 8 “Lazy Portfolios” tracked by investment columnist Paul B. Farrell. The term “Lazy Portfolio” refers to a fixed asset allocation that is periodically rebalanced. We like to call this a “strategic portfolio recipe,” but a fixed asset allocation like this can also go by several other names, such as buy-and-hold portfolio, static portfolio, or passive allocation. A strategic portfolio with a fixed allocation can be contrasted with a tactical/dynamic portfolio that changes its allocation over time. Each of the Lazy Portfolios has a backstory or underlying theme, such as modeling the Yale endowment’s asset allocation or copying Ted Aronson’s family portfolio. This article will focus on the overall performance of the 8 portfolios, not their origin stories. Since VizMetrics already tracks over 250 portfolio recipes , we decided to add these 8 Lazy Portfolio recipes to the list of portfolios that we analyze monthly. We were eager to see how these compared to our entire set of strategic and tactical portfolio recipes. The Analysis Process We followed four steps to analyze the risk and return of the Lazy Portfolios and then search for portfolios that outperform the 8 Lazy Portfolios. Create the Lazy Portfolios . We used the exact Vanguard mutual funds and allocations specified for each Lazy Portfolio, and then we backtested their performance using monthly total returns and monthly rebalancing. Our data covered the 10 years ending September 30, 2015. Run the analysis . Then we compared risk and return over the past 1 year and over the past 10 years. We like using the 1-year period since we’ve seen some market turbulence recently, and we like looking at the last 10 years since that period includes the downturn of 2008-2009. If you look at risk vs. return for only a short, upward period, then you can overlook the true risk of the portfolio since the evaluation period doesn’t include much downside variation. Create scatterplots. We plotted risk vs. return for the Lazy Portfolios and all the other portfolios that we track. Filter the results . We found portfolios that beat the Lazy Portfolios, based on both risk and return. Identify the winners . We identified 4 portfolios that beat every Lazy Portfolio over both the 1-year and 10-year periods. The winners included two mutual funds, and two tactical portfolio recipes. Step 1: Create the Lazy Portfolios We created the Lazy Portfolios using Vanguard mutual funds, matching Farrell’s allocations. ETFs could be used instead of mutual funds, but we wanted to remain true to the original portfolio recipes. The Lazy Portfolios are constructed as follows: Lazy Portfolio Name Lazy Portfolio Recipe (ingredients and percentages) Lazy Portfolio: Aronson Family Taxable VEURX =5%, VIPSX =15%, VPACX =15%, VWEHX =5%, VISGX =5%, VISVX =5%, VTSMX =5%, VEIEX =10%, VEXMX =10%, VUSTX =10%, VFINX =15% Lazy Portfolio: Fundadvice Ultimate Buy & Hold VFINX=6%, VFISX =12%, VFITX =20%, VEIEX=6%, VGSIX =6%, NAESX =6%, VISVX =6%, VIVAX =6%, VIPSX=8%, VTMGX =12%, VTRIX =12% Lazy Portfolio: Coffeehouse VFINX=10%, VGSIX=10%, NAESX=10%, VISVX=10%, VIVAX=10%, VGTSX =10%, VBMFX =40% Lazy Portfolio: Margaritaville VIPSX=33%, VGTSX=33%, VTSMX=34% Lazy Portfolio: Dr. Bernstein’s No Brainer VFINX=25%, VEURX=25%, NAESX=25%, VBMFX=25% Lazy Portfolio: Second Grader’s Starter VBMFX=10%, VGTSX=30%, VTSMX=60% Lazy Portfolio: Dr. Bernstein’s Smart Money VEIEX=5%, VEURX=5%, VPACX=5%, VGSIX=5%, NAESX=5%, VISVX=10%, VIVAX=10%, VTSMX=15%, VFSTX =40% Lazy Portfolio: Yale U’s Unconventional VEIEX=5%, VTMGX=15%, VIPSX=15%, VUSTX=15%, VGSIX=20%, VTSMX=30% Step 2: Run the analysis Next we calculated the risk and return metrics for each of the 8 Lazy Portfolios. For the risk measure, we used Maximum Drawdown, which is the maximum percentage that each portfolio lost in value during the period, as measured at the end of each month. We like Maximum Drawdown for measuring risk since it captures quantitatively the “ouch!” that we feel when our portfolio hits the bottom. For the return measure, we used total annual return, which assumes that distributions are reinvested during the period. The Lazy Portfolios showed the following risk and return characteristics, for the period ending September 30, 2015: Lazy Portfolio Name 1-year annual return (%) 1-year maximum drawdown (%) 10-year annual return (%) 10-year maximum drawdown (%) Lazy Portfolio: Aronson Family Taxable -2.8 -9.2 5.9 -41.1 Lazy Portfolio: Fundadvice Ultimate Buy & Hold -2.4 -7.2 5.3 -35.7 Lazy Portfolio: Coffeehouse 0.7 -5.6 6.1 -36.0 Lazy Portfolio: Margaritaville -4.0 -8.5 5.0 -40.5 Lazy Portfolio: Dr. Bernstein’s No Brainer -1.6 -7.8 6.0 -43.3 Lazy Portfolio: Second Grader’s Starter -3.4 -9.6 5.7 -49.2 Lazy Portfolio: Dr. Bernstein’s Smart Money -1.0 -6.3 5.5 -37.6 Lazy Portfolio: Yale U’s Unconventional 0.9 -7.0 6.5 -42.2 Benchmark: The SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) -0.8 -8.5 6.7 -50.8 Note that all the Lazy Portfolios had a maximum drawdown exceeding -35% over the past 10 years, with most worse than -40%. By comparison, the SPDR S&P 500 Trust ETF had a maximum drawdown of -50.8% with a 10-year return of 6.7%. Step 3: Create the scatterplots Now let’s separate the wheat from the chaff using a risk vs. return scatterplot. We plotted the performance of all the Lazy Portfolios along with all the other portfolio recipes in one view. This allows us to visualize two important metrics (risk and return) at the same time. With risk and return shown on the scatterplots below, the best portfolios (with the highest return and lowest risk) appear at the top left. In the plots below, the orange diamonds are the Lazy Portfolios. The blue squares are the portfolio recipes that showed both higher return and lower risk compared to the Lazy Portfolios. The yellow triangles are the additional portfolio recipes tracked by VizMetrics . For a benchmark comparison, we’ve also added SPY, shown as the purple circle. (click to enlarge) The 10-year scatterplot covers the period October 2004 to September 2015. The 1-year scatterplot covers the period October 2014 to September 2015. Step 4: Filter the results You can see that several blue squares are “northwest” (above and to the left) of all the orange Lazy Portfolios. Each blue square represents a portfolio with higher return and lower risk than every one of the Lazy Portfolios. In the 1-year scatterplot, there are 36 blue squares that beat all the Lazy Portfolios. In the 10-year scatterplot, there are 38 blue squares that beat all the Lazy Portfolios. Over the past 10 years, the broad U.S. equity market (represented by an exchange-traded fund, SPY) has generated a higher return than each of the Lazy Portfolios. But this higher return is accompanied by higher volatility. The Lazy Portfolios each have some fixed income exposure and this offers a lower-risk alternative to SPY that some investors may prefer. If we consider both the 1-year and 10-year time period, we find that the following four portfolios beat every Lazy Portfolio based on risk and return: The Four Winners (that outperform all of the Lazy Portfolios) 1-year annual return (%) 1-year maximum drawdown (%) 10-year annual return (%) 10-year maximum drawdown (%) Vanguard Wellesley ( VWINX ) 0.9 -3.2 6.8 -18.8 Vanguard Balanced ( VBIAX ) 1.0 -5.2 6.6 -32.5 Minimum Conditional Value-at-Risk Portfolio ( t.cvar ) 4.1 -4.0 10.0 -11.0 Minimum Drawdown Portfolio ( t.loss ) 8.0 -4.6 9.8 -13.4 Benchmark: S&P 500 ETF -0.8 -8.5 6.7 -50.8 Another portfolio, the “Strategic 60-40 Portfolio” ( s.6040 ) nearly beats all of the Lazy Portfolios, too. This portfolio beats 7 of the 8 Lazy Portfolios (all except “Yale U’s Unconventional”) over the 1-year and 10 year periods. The Strategic 60-40 Portfolio returned 6.4% over 10 years, and “Yale U’s Unconventional” returned 6.5%. Conclusions The 8 Lazy Portfolios do provide some diversification and have shown middle-of-the-pack performance. But there are better choices for investors. If you want a lazy, easy-to-maintain portfolio then either of the Vanguard funds, VWINX or VBIAX, are a better choice. These funds are even lazier than the 8 Lazy Portfolios, since you don’t have to buy and rebalance the ingredients yourself. Importantly, these Vanguard funds also provide better performance with lower risk. That’s a true “no brainer.” Or if you seek higher returns, and if you’re willing to rebalance monthly, you can look at tactical portfolio recipes, such as t.cvar and t.loss . To view the full set of risk and return scatterplots for over 250 portfolio recipes, sign up for a free trial of the VizMetrics Investor subscription. This also includes risk and return analytics for the 1-, 3-, 5-, 7-, and 10-year periods.