Are Equity Markets Manipulated? Thursday, 13 December, 2018

We present two interesting research papers written by Bruce Knuteson. He contemplates why the cyclically adjusted price to earnings ratio of the S&P 500 index has been oddly high for the past two decades. He states that persistently strong equity market in US (and therefore also its high valuation in a comparison to history) and also in other developed countries can be an outcome of intraday trading pattern of several huge quant equity long-short funds which aggressively buy in the morning and sell in the afternoon to expand their trading book (or alternatively to simply manipulate market - push prices of stocks in their portfolio up).

Authors: Bruce Knuteson

Title: Information, Impact, Ignorance, Illegality, Investing, and Inequality

Link: https://arxiv.org/pdf/1612.06855.pdf

Abstract:

We note a simple mechanism that may at least partially resolve several outstanding economic puzzles, including why the cyclically adjusted price to ear nings ratio of the S&P 500 index has been oddly high for the past two decades, why gains to capital have outpaced gains to wages, and the persistence of the equity premium.

Notable quotations from the academic research paper:

"In United States equity markets, bid-ask spreads early in the trading day are typically larger than spreads later in the trading day. The price impact of aggressive trades early in the trading day is therefore typically larger than the price impact of equally sized aggressive trades later in the day.

Market makers make wider markets early in the trading day. The market, viewed as an information aggregator, respects the information content of aggressive orders early in the day more than the information content of equally sized aggressive orders later in the day. A repeated sequence of intraday round trips – e.g., buying in the morning and selling in the afternoon, repeated over many days – can therefore be expected to result in net price impact in the direction of the morning trade.

If some market participant (M) performs the same round trip each day – e.g., aggressively buying in the morning and selling in the afternoon – M’s trading will, on average, nudge the market’s midprice in the direction of his morning trading. If M has a large, slowly varying portfolio, the mark to market gains resulting from M’s daily intraday round trip trades can exceed the cost M incurs by crossing the spread twice each day.

In light of the above, it is striking that the returns to the S&P 500 index over the fifteen years spanning 1993 to 2007, inclusive, all came at the start of the trading day. Indeed, Figure 1 of Ref. [M. J. Cooper,  M. T. Cliff,  and H. Gulen (2008), Return Differences Between Trading and Non-trading Hours:  Like Night and Day, URL http://ssrn.com/abstract=1004081] is so striking it calls for a simple explanation. We propose such an explanation. We propose some market participant M, tending to trade in one direction early in the trading day and in the other direction later in the day, has had a much larger long-term effect on United States equity prices than has so far been widely appreciated.

day vs night return"

And:

Authors: Bruce Knuteson

Title: How to Increase Global Wealth Inequality for Fun and Profit

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3282845

Abstract:

We point out a simple equities trading strategy that allows a sufficiently large, market-neutral, quantitative hedge fund to achieve outsized returns while simultaneously contributing significantly to increasing global wealth inequality. Overnight and intraday return distributions in major equity indices in the United States, Canada, France, Germany, and Japan suggest a few such firms have been implementing this strategy successfully for more than twenty-five years.

Notable quotations from the academic research paper:

"The Strategy is very simple: construct a large, suitably leveraged, market-neutral equity portfolio and then systematically expand it in the morning and contract it in the afternoon, day after day. The Strategy works because your trading will, on average, move prices in a direction that nets you mark-to-market gains. Bid-ask spreads are wider and depths are thinner near market open than near market close, so aggressive trades early in the trading day move prices more than equally sized aggressive trades later in the day. An intraday round trip (e.g., aggressively buying in the morning and selling in the afternoon) thus nudges the market's midprice in the direction of your morning trading. A reasonable level of daily round-trip trading combined with a suciently large portfolio will therefore produce expected mark-to-market gains exceeding the expected cost of your daily round-trip trading.

Figure 1, which shows cumulative overnight and intraday returns over the past 25 years in six major stock market indices: the S&P 500 index and the NASDAQ Composite index in the United States, Canada's TSX 60, France's CAC 40, Germany's DAX, and Japan's Nikkei 225 [20]. The return pattern in the S&P 500 index was rst pointed out over a decade ago. Similar return patterns have been identi ed in major indices of other developed countries. The only plausible explanation so far advanced for the highly suspicious return patterns in Figure 1 is someone using the Strategy.

day vs. night in other countries

"


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A Portfolio of Leveraged Exchange Traded Funds vs. Benchmark Asset Allocation Wednesday, 5 December, 2018

A new interesting financial research paper gives an idea to build a diversified portfolio of leveraged ETFs (scaled down to have the same risk as a benchmark asset allocation built from a non-leveraged ETFs) to beat benchmark asset allocation. However, caution is needed as the most of the outperformance is due to inherent leveraged position in bonds because excess ratio of cash in portfolio (which is the result of using leveraged ETFs instead of non-leveraged ETFs) is invested in a short to medium term bonds:

Authors: Trainor, Chhachhi, Brown

Title: A Portfolio of Leveraged Exchange Traded Funds

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3272486

Abstract:

Leveraged exchange traded funds (LETFs) are marketed as short-term trading vehicles that magnify the daily returns of an underlying index. With the proliferation of LETFs over the last 10 years, a diversified portfolio that mimics the returns of a 100% investment can be created using only a fraction of the investor’s wealth. Results suggest a portfolio created with LETFs outperforms a portfolio using traditional ETFs by approximately 0.6% to 1.4% annually by investing the excess wealth in a diversified or short to mid-duration bond portfolio. Downside risk is reduced using LETFs because the majority of the LETF portfolio is invested in a relatively safe bond fund.

Notable quotations from the academic research paper:

"Leveraged exchange traded funds (LETFs) were first listed in 2006 by Proshares, although leveraged mutual funds have been around since 1993. Although Proshares introduced +/- 2x products, Direxion upped the leverage ante with +/- 3x funds in late 2008. Because LETFs are designed to return a daily multiple, the constant daily leverage results in uncertain realized leverage over longer periods of time. In general, realized leverage tends to fall over time due to the volatility of returns.

With the expanded scope of the LETF market, it is now possible to create a diversified portfolio of LETFs that mimic a typical investor’s portfolio. By using 2x or even 3x funds, only a fraction of the investor’s portfolio is needed to attain the same exposure an investor has using standard ETFs and/or mutual funds. The downside is the higher expense ratios of LETFs, their internal financing costs, general leverage decay, and trading costs due to needed rebalancing to maintain the correct exposure. The upside is the excess wealth available that can be invested in relatively safe assets, and if the return to the invested excess wealth exceeds the higher cost of LETFs, returns should be enhanced.

This study shows a portfolio using 2x or 3x LETFs outperforms a portfolio using standard ETFs based on the same underlying indexes. This is possible since a 2x needs only 50% while a 3x needs only 33% of the wealth to create the same exposure to the underlying indexes. Even in the low interest environment from 2010-17, a portfolio of 2x LETFs outperforms a portfolio of standard ETFs by 0.9% annually. A portfolio of 3x LETFs outperforms by 1.8% annually.

Simulated LETF returns since 1946 show a portfolio of 2x LETFs can be expected to outperform a standard portfolio by 0.6% while a 3x LETFs outperforms by 1.4% after expenses. The caveat to this strategy is LETF portfolios must be rebalanced as their initial positions deviate from “optimal” asset allocations even faster than standard portfolios. A 10% barrier threshold keeps the risk exposure within reasonable bounds while keeping 2x rebalancing requirements to roughly each quarter and 3x to approximately monthly.

performance table

A critical determinant of the success of this strategy is the magnitude of returns to the capital not invested in the LETFs. This study assumes excess capital is invested in a short-term bond ladder and if the return to this ladder exceeds the borrowing cost from the implied leverage of LETFs and their higher expense ratios, LETF portfolios outperform. Historically, this occurs the majority of the time with 0.6% to 1.4% average annual outperformance with a simultaneous reduction in downside risk."


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A Turn of the Month Strategy in Asset Allocation Monday, 26 November, 2018

A new research paper related mainly to:

#41 - Turn of the Month in Equity Indexes

Authors: McGroarty, Platanakis, Sakkas, Urquhart

Title: A Seasonality Factor in Asset Allocation

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3266285

Abstract:

Motivated by the seasonality found in equity returns, we create a Turn-of-the-Month (ToM) allocation strategy in the U.S. equity market and investigate its value in asset allocation. By using a wide variety of portfolio construction techniques in an attempt to address the impact of estimation risk in the input parameters, we show significant out-of-sample benefits from investing in the ToM factor along with a traditional stock-bond portfolio. The out-of-sample benefits remain significant after taking into account transaction costs and by using different rolling estimation windows indicating that a market timing strategy based on the ToM offers substantial benefits to investors when determining the allocation of assets.

Notable quotations from the academic research paper:

"Seasonality is a well-known characteristic of financial markets with much empirical literature noting various types of seasonality in stock returns. Simple seasonality-driven investment strategies have attracted significant interest from academics and investors over the last forty years.

Amongst the calendar effects, the turn-of-the-month (ToM hereafter) has been acknowledged as one of the strongest and persistent seasonality found in stock returns. The ToM effect is the tendency of the stock market returns to display particularly high returns on the last trading day of the month and the first three trading days of the next month.

This study contributes to the literature on calendar anomalies in several dimensions. We examine the out-of-sample portfolio benefits resulting from adding the ToM portfolio to (i) a traditional equity-bond mix, (ii) a market portfolio, (iii) a portfolio which consists of the market portfolio, the small size portfolio and the value portfolio, and (iv) a portfolio, which consists of the market, the small size, the value portfolio and the winner portfolio.

performance of selected strategies

We employ a wide variety of sophisticated and popular asset allocation techniques to provide robustness to our results. Specifically, we employ the mean-variance (Markowitz) portfolio optimization, portfolio optimization with higher moments, Bayes-Stein shrinkage, Bayesian diffuse-prior portfolio, Black-Litterman and another portfolio construction method that combines individual portfolio techniques, to ensure that our results are not just a peculiar artefact on one particular asset allocation technique. Finally, we assess the ToM for low, medium and high-risk averse investors, as its effectiveness in the portfolio might depend on the investor’s level of risk aversion.

Our empirical evidence suggests that the ToM portfolio adds value when included in different portfolios. Our results hold for different levels of risk aversion, portfolio techniques and estimation windows. Finally, our results are not eliminated by the including realistic transaction cost estimates, indicating that the creation and implementation of a ToM factor should be of great interest and potential value to investors.

allocation to turn of the month strategy"


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Does High Market Liquidity Cause Vanishing Equity Anomaly Returns ? Wednesday, 21 November, 2018

Short answer? - Probably no ... A new academic research paper concludes that "the recent worldwide regime of increased liquidity, apart from some exceptions, is not accompanied by robustly significant decreases of anomalous returns in the US and the majority of other markets." Research paper related mainly to:

#14 - Momentum Effect in Stocks
#25 - Size Premium
#26 - Value (Book-to-Market) Anomaly
#77 - Beta Factor in Stocks

Authors: Auer, Rottmann

Title: Have Capital Market Anomalies Worldwide Attenuated in the Recent Era of High Liquidity and Trading Activity?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275377

Abstract:

We revisit and extend the study by Chordia et al. (2014) which documents that, in recent years, increased liquidity has significantly decreased exploitable returns of capital market anomalies in the US. Using a novel international dataset of arbitrage portfolio returns for four well-known anomalies (size, value, momentum and beta) in 21 developed stock markets and more advanced statistical methodology (quantile regressions, Markov regime-switching models, panel estimation procedures), we arrive at two important findings. First, the US evidence in the above study is not fully robust. Second, while markets worldwide are characterised by positive trends in liquidity, there is no persuasive time-series and cross-sectional evidence for a negative link between anomalies in market returns and liquidity. Thus, this proxy of arbitrage activity does not appear to be a key factor in explaining the dynamics of anomalous returns.

Notable quotations from the academic research paper:

"Recent years have been characterised by unprecedented changes in trading technology and transaction costs. A lot of authors document signi ficant advances in algorithmic trading and increased utilisation of online brokerage accounts and observe drastic declines in, for example, standardised aggregate costs of trading for NYSE, NASDAQ and AMEX stocks of on average 97% from 146 basis points in 1980 to 11 basis points in 2006. Standard measures of liquidity have increased substantially because this new market environment of reduced trading frictions stimulates trading activity. For example, they present evidence that:

(i) the average monthly share turnover on the NYSE rose from about 5% in 1993 to about 26% in 2008 (and the average daily number of transactions increased about ninetyfold in the same period), whereas it was almost unchanged in the decades before,

(ii) mainly institutional trading volume accounts for this increase and

(iii) increased volume is associated with higher market quality (i.e., closer conformity to random walk behaviour).

Motivated by these observations, several studies have analysed whether increased liquidity has triggered greater anomaly-based arbitrage and thus attenuated capital market anomalies. In their conclusion, Chordia et al. (2014, p. 57) argue that  "return predictability would diminish to a greater extent in countries that have experienced greater enhancements in trading technologies and larger increases in trading activity and liquidity" and that this "hypothesis awaits rigorous testing in an international context". This is where we step into the picture. We extend the hedge portfolio evidence of Chordia et al. (2014) to an international setting.

Figure 1, reporting trends and growth rates for the number of traded shares in di fferent regions of the world, shows positive tendencies worldwide and that there are indeed di fferences in the timely development of trading activity. For example, trading activity appears to have increased more significantly in European markets than in the US. Thus, we would not only expect to find evidence on vanishing anomaly returns in other markets as well but also that the magnitudes of the changes in anomaly returns are quite di fferent across individual markets.

Using a novel dataset containing arbitrage portfolio returns for the four well-known anomalies of size, value, momentum and beta for a wide range of developed stock markets, we start our analysis by testing whether these anomalies still exist and whether the corresponding arbitrage portfolio returns exhibit trending behaviour. We also investigate trends in market liquidity in a more detailed fashion than in our previous illustration.

Using a novel dataset covering three decades, we find that the recent worldwide regime of increased liquidity, apart from some exceptions, is not accompanied by robustly signifi cant decreases of anomalous returns in the US and the majority of other markets. We cannot establish a persistent negative link between arbitrage portfolio returns and share turnover in both the time-series and the cross-sectional dimension. These results suggest that aggregate liquidity may be a measure too coarse for our purposes or that liquidity in general may not be the key driver of the dynamics of international anomaly portfolios."


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Capital Allocation Across a Range of Cross-Asset Alternative Risk Premia Wednesday, 14 November, 2018

A new financial research paper gives an ideas of how to allocate capital across several well known factor strategies:

Authors: Blin, Ielpo, Lee, Teiletche

Title: Factor Timing Revisited: Alternative Risk Premia Allocation Based on Nowcasting and Valuation Signals

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3247010

Abstract:

Alternative risk premia are encountering growing interest from investors. The vast majority of the academic literature has been focusing on describing the alternative risk premia (typically, momentum, carry and value strategies) individually. In this article, we investigate the question of allocation across a diversified range of cross-asset alternative risk premia over the period 1990-2018. For this, we design an active (macro risk-based) allocation framework that notably aims to exploit alternative risk premia’s varying behavior in different macro regimes and their valuations over time. We perform backtests of the allocation strategy in an out-of-sample setting, shedding light on the significance of both sources of information.

Notable quotations from the academic research paper:

"Alternative risk premia investing has grown rapidly in popularity in the investment community in recent years. They provide systematic exposures to risk factors and market anomalies that have frequently been widely analyzed in academic research. The vast majority of the academic literature solely focuses on the identification and the analysis of individual alternative risk premia strategies. On the contrary, this article addresses the question of allocation among alternative risk premia.

The standard approach in the industry is to apply a risk-based allocation mechanism, particularly equal risk contribution (ERC) in which one allocates the same risk budget to all components in the portfolio. One of the perceived key benefits of this approach is that it does not require expected returns as input but solely risk measures, hence the name “risk-based”. This no-views/agnostic feature alleviates the pitfalls of forecasting, which is already a challenge for traditional assets but even more so for alternative risk premia that are newer or are perceived as more complex strategies.

Despite this, recent research lends more support to the idea of some predictability of factor returns. In this article, we extend those results by focusing on the relationship between alternative risk premia and macro regimes that we define through nowcaster indicators. We consider that three major macroeconomic risks that affect risk premia: growth, inflation and market stress/volatility.

To model the influence of these macro risks, a regime approach has proven effective. To characterize macro regimes for growth, inflation and market stress, we build our own nowcasting (contraction of “now” and “forecasting”) indicators since 1990. Seeking for simplicity, our nowcasters are simple averages of z-scores of a large cross section of indicators across a large set of countries. For instance, the “growth” nowcaster contains close to 500 economic times-series across major developed and emerging countries accounting for 85% of world GDP. In Appendix A, we provide more details on the construction of the nowcasters.

To characterize economic regimes in a more precise way, we use both the average of all properly scaled economic indicators (the “nowcaster” per se) but also take advantage of the information in the cross-section by accounting for the proportion of indicators that are improving or deteriorating for every period (called “diffusion” index below). In practice, the diffusion index gives some further indication about whether the economy is improving (when diffusion index rises) or deteriorating (when diffusion index declines). On the basis of the nowcaster and diffusion indices, we define four regimes:

- Low-Up: negative nowcaster (Low) and diffusion index above 50% (Up)
- Low-Down: negative nowcaster (Low) and diffusion index below 50% (Down)
- High-Up: positive nowcaster (High) and diffusion index above 50% (Up)
- High-Down: positive nowcaster (High) and diffusion index below 50% (Down)

For the growth factor, this is similar to the usual Recession (Low-Down) / Recovery (Low-Up) / Expansion (High-Up) / Slowdown (High-Down) classification.

In Figure 1, we represent the different macro factor nowcasters and highlight different regimes by using a distinctive color scheme.

Nowcasters

To give a first sense of the sensitivity of alternative risk premia to macroeconomic regimes, we represent in Figure 2 growth regime-conditional excess Sharpe ratios, i.e. the difference between Sharpe ratios in each growth regime and the long-term (unconditional) Sharpe ratio. Some strategies can be seen as being “defensive”, as they tend to do relatively well in either slowdowns (High-Down regime) or recessions (Low-Down regime), such as equity quality, equity low-risk, trend-following, or bonds carry. Conversely, some strategies benefits from better economic conditions such as equity size, FX carry or volatility carry.

Factors in different regimes

In next section, we define and implement a process to allocate among alternative risk premia which incorporates, along other dimensions, each risk premium’s sensitivities to the macro regimes. The approach is based on the active risk-based methodology derived in Jurczenko and Teiletche (2018) which adapts Black and Litterman (1992) framework to the risk-based world. In practice, the model ends up combining a risk-based strategic portfolio with a set of dynamic allocation active views.

As our focus is on dynamic signals, we do not seek to improve the strategic portfolio. We adopt a simple ERC portfolio, which consists of equal contributions to portfolio volatility across all alternative risk premia. The strategic portfolio is then modified to incorporate dynamic deviations in two steps.

In a first step, we compute z-score reflecting dynamic allocation based on an equal-weight of two z-scores, for macro factors and valuation respectively. Regarding macro factors (growth, inflation, market stress), z-scores are computed as the excess return in the current (“nowcasted”) regime vs full sample return scaled by historical volatility.

In the second step, these dynamic z-scores are transformed into active portfolio deviations calibrated to deliver 1% tracking-error relative to the strategic portfolio. The sum of active deviations is set to zero, so that the portfolio is fully invested, similar to the strategic allocation.

Table 4 summarizes the performance statistics of the portfolio. The first column shows the strategic ERC portfolio. The second to fourth columns show the “dynamic” portfolios that incorporate active tilts, based on nowcasters and valuation signals individually and in combination.

 

Result of allocaton

"


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