Liquidity effect

Novel Market Structure Insights From Intraday Data

19.November 2020

In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.

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ETF Liquidity

3.September 2020

Exchange-traded funds (ETFs) have become popular and important investment vehicles in the financial markets. However, that is not a shock given the numerous benefits connected with ETFs. Naturally, they have caught the interest of academics, and there is plenty of literature about ETFs. While the profits and trading strategies are probably the most important research topics for practitioners, liquidity in the financial markets is almost equally important. Concerning liquidity in the ETFs, novel research by Pham et al. shows when exactly are ETFs the most liquid. Looking on the spreads, they are the lowest at market close. Such a finding can be an essential part of an optimal trading position making, where the aim is to minimize the trading costs.

Authors: Pham, Son Duy and Marshall, Ben R. and Nguyen, Nhut (Nick) Hoang and Visaltanachoti, Nuttawat 

Title: Predicting ETF Liquidity

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Equity Factor Strategies In Frontier Markets

12.July 2019

A new research paper related to all equity factor strategies …

Authors: Zaremba, Maydybura, Czapkiewicz, Arnaut

Title: Explaining Equity Anomalies In Frontier Markets: A Horserace of Factor Pricing Models

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3378785

Abstract:

We are the first to compare the explanatory power of the major empirical asset pricing models over equity anomalies in the frontier markets. We replicate over 160 stock market anomalies in 23 frontier countries for years 1996–2017, and evaluate their performance with the factor models. The Carhart’s (1997) four-factor model outperforms both the recent Fama and French (2015) five-factor model and the q-model by Hou, Xue, and Zhang (2015). Its superiority is driven by the ability to explain the momentum-related anomalies. Inclusion of additional profitability and investment factors lead to no further major improvement in the performance. Nonetheless, none of the models is able to fully explain the abnormal returns on all of the anomaly portfolios.

Notable quotations from the academic research paper:

"In times of soaring correlations among global stocks and increasing controversies on anomaly performance in emerging stock markets, one specific asset class may offer a remedy: frontier equities. Deemed the least developed emerging markets, the frontier countries are scattered around the globe, with presence in Africa, Asia, Europe, and Latin America. Being very diverse both economically and geographically, they range from the wealthy oil-producing kingdoms in the Gulf to some of the poorest countries in Africa. While the current size of the frontier stock markets is still fairly small – the total capitalization of the MSCI Frontier Market Index constituents equaled $134 billion in May 2018 (MSCI, 2018), accounting for less than 0.4% of developed markets – yet, the interest in them is growing quickly.

Considering the future potential, along with the soaring interest of the international community, and the investment opportunities, it is surprising how underresearched – if not ignored – the frontier equities are. The number of academic studies on this stock market class seems astonishingly modest. This leaves numerous important questions, which may be of huge importance for global investors, unanswered. Which equity anomalies – discovered originally in developed countries – work also in the frontier stock markets? Could they be translated into profitable strategies using easily-to-implement quantitative methods? Finally, which asset pricing models and factors best summarize the cross-sectional return patterns and the equity anomalies in frontier countries? Could the recent five-factor framework by Fama and French (2015) or the q-model by Hou, Xue, and Zhang (2015) be also applied in this growing asset class? The principal target of this study is to close this gap in the existing body of literature at least partially.

Research sample

The elevated liquidity constraints, higher trading costs, short sale unavailability accompanied by less sophisticated investors may potentially result in larger mispricing and more pronounced stock market anomalies.

Our research aims to contribute in three primary ways. Our first goal is to conduct the most comprehensive test on which equity anomalies, discovered originally in the developed countries, are also present in the frontier equities. Thus, we examine the performance of 167 anomalies from the finance literature, encompassing different classes of patterns related to value, trend following, investment, profitability, risk, and many others. The large-scale analysis available in broadly-accessible journals was either limited to the few most prominent strategies, such as size, value, and momentum (Blackburn and Cakici 2017, De Groot, Pang, and Swinkels 2012). Our study aims to take a substantial leap forward in understanding the multidimensionality of equity returns in the frontier markets.

Second, we research which of the broadly-acknowledged asset pricing models serve best in explaining the cross-section of anomaly returns in the frontier markets. In particular, we consider seven factor pricing models: the capital asset pricing model (Sharpe 1964), abbreviated CAPM, the three-factor model (Fama and French 1993), abbreviated FF3, the four-factor model (Carhart 1997), abbreviated C4, the five-factor model (Fama and French 2015), abbreviated FF5, the q-model by Hou, Xue, and Zhang (2015), the six-factor model by Fama and French (2018), abbreviated FF6, and the six-factor model by Barillas and Shanken (2018), abbreviated BS6.

Last but not least, our research may be regarded as a large out-of-sample test of equity anomalies.

To answer our research questions, we replicate the 167 equity anomalies from Zaremba et al. (2018) in an extensive sample of over 3,600 companies from frontier markets from all over the world for years 1996 – 2017. We form the long-short anomaly portfolios and evaluate their returns using the seven considered factor pricing models: CAPM, FF3, C4, FF5, Q4, FF6, and BS6. We compare the models’ performance by employing a range of tools and statistics that assess their ability to explain the risk and mean returns jointly.

The principal findings of this study could be summarized as follows. First, out of the 167 anomaly portfolios, only 38% (19%) of the equal-weighted (value-weighted) long-short strategies produce profits significantly departing from zero at the 5% level. The successful return patterns are usually linked to the “value vs. growth” or trend following effects, verifying positively the arguments of Asness, Moskowitz, and Pedersen (2013) that value and momentum are everywhere.

Second, we demonstrate that Carhart’s (1997) four-factor model best explains the anomaly returns in frontier markets, outperforming other models in many ways. It displays lower average absolute intercepts and largest number of explained anomalies. Its cross-sectional and time-series R2 is higher CAPM, FF3, FF5, or Q4, and only marginally lower than in the case of FF6 and BS6.

Returns of long short portfolios

"


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Transaction Costs of Factor Strategies

25.May 2019

A very important research papers related to all equity factor strategies …

Authors: Li, Chow, Pickard, Garg

Title: Transaction Costs of Factor Investing Strategies

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3359947

Abstract:

Although hidden, implicit market impact costs of factor investing strategies may substantially erode the strategies' expected excess returns. The authors explain these market impacts costs and model them using rebalancing data of a suite of large and longstanding factor investing indices. They introduce a framework to assess the costs of rebalancing activities, and attribute these costs to characteristics such as rate of turnover and the concentration of turnover, which intuitively describe the strategies' demands on liquidity. The authors evaluate a number of popular factor-investing strategy implementations and identify how index construction methods, when thoughtfully designed, can reduce market impact costs.

Notable quotations from the academic research paper:

"Factor investing strategies have become increasingly popular. According to data from Morningstar Direct, assets under management (AUM) in factor investing ETFs and mutual funds across global markets increased from just below US$75 billion in 2005 to more than US$800 billion by the end of 2016.

In practice, when a provider rebalances an index, most managers tracking it execute the necessary transactions near the close of the rebalancing day in order to minimize their portfolio’s tracking error. The fund managers may appear to be perfectly tracking the index; in another words, minimizing implementation shortfall, which is the aggregate difference between the average traded price and the closing price of each of the index's underlying securities on the rebalancing day. Thus, the total implementation cost of an index fund could be perceived as merely the sum of the explicit costs associated with trading, such as commissions, taxes, ticker charges, and so forth. This notion misses the propagating market impact that trading has on the index’s value. The large volume of buy and sell orders for the same securities, executed at the same time, can result in securities prices moving against the managers, producing losses for both the index and the fund investors. This implicit cost is often overlooked because it is not visible when comparing a fund’s net asset value (NAV) and the index’s value; it can, however, be overwhelmingly large relative to the explicit costs for strategies with massive AUM. This article focuses on unmasking the market impact costs that arise from synchronous buying and selling.

We analyze the behavior of stocks that were traded during the rebalancing of 49 FTSE RAFI™ Indices (henceforth, “the indices”). We find significant evidence of market impact on the rebalancing day and a subsequent price reversal over the next four days. We find that the magnitude of price impact is predictable, because it is directly related to the security’s liquidity and the size of the trade.

Specifically, we identify that a fund incurs approximately 30 basis points (bps) of trading costs due to market impact for every 10% of a stock’s average daily volume (ADV) traded in aggregate by the factor investing index–tracking funds.

Market Impact

Our simple relationship of market impact versus the security’s liquidity and the size of the trade can be used to estimate the implicit transaction costs of rebalancing trades. We apply our model and evaluate the costs of an extended list of popular strategies with various turnover rates, trade sizes, levels of security liquidity, and number of rebalances. We find that, at a modest level of AUM, and assuming all rebalancing trades occur near the end of
the rebalancing date, the expected transaction costs can significantly erode the expected alpha as indicated by long-term historical backtests. Specifically, with as little as $10 billion in AUM, momentum indexing strategies can have trading costs of 200 bps or more. At the same level of assets, income strategies’ costs are in the 60–80 bps range, and quality strategies’ costs fall below 40 bps. We report the capacities, defined as AUM when expected costs reach a high and fixed level (50 bps a year), of these strategies. We also present an attribution model to relate costs to strategy characteristics and explain in detail how certain styles of investing—for instance, those that trade frequently and those that trade completely in and out of a few illiquid positions—require higher costs than others.

Liquidity characteristics

"


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Three Insights from Academic Research Related to Momentum Strategy

4.April 2019

What are the main insights?

– momentum is not an anomaly in a risk-based asset pricing framework. Riskier assets tend to be in the loser portfolios after (large) increases in the price of risk. The risk of momentum portfolios usually decreases with the prevailing price of risk, and their risk premiums are approximately negative quadratic functions of the price of risk (and the market premium) theoretically truncated at zero.

– changes to market liquidity adds to the explanation of momentum crashes along with the market rebounds, this relationship is driven by the asymmetric large return sensitivity of short-leg of momentum portfolio to changes in market liquidity that flares the tail risk of momentum strategy in panic states

– momentum returns are highly related to market risk arising from return dispersion (RD) as momentum risk loadings and RD risk loadings are similarly priced in momentum portfolios

1/

Author: Souza

Title: A Critique of Momentum Anomalies

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3341275

Abstract:

This paper offers theoretical, empirical, and simulated evidence that momentum regularities in asset prices are not anomalies. Within a general, frictionless, rational expectations, risk-based asset pricing framework, riskier assets tend to be in the loser portfolios after (large) increases in the price of risk. Hence, the risk of momentum portfolios usually decreases with the prevailing price of risk, and their risk premiums are approximately negative quadratic functions of the price of risk (and the market premium) theoretically truncated at zero. The best linear (CAPM) function describing this relation unconditionally has exactly the negative slope and positive intercept documented empirically.

2/

Authors: Butt, Virk

Title: Momentum Crashes and Variations to Market Liquidity

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3314095

Abstract:

We document that the variation in market liquidity is an important determinant of momentum crashes that is independent of other known explanations surfaced on this topic. This relationship is driven by the asymmetric large return sensitivity of short-leg of momentum portfolio to changes in market liquidity that flares the tail risk of momentum strategy in panic states. This identification explains the forecasting ability of known predictors of tail risk of momentum strategy such that the contemporaneous increase in market liquidity predominantly sums up the trademark negative relationship between predictors and future momentum returns. Our results are robust using a different momentum portfolio and alternative measures of market liquidity that make a substantial part of the common source of variation in aggregate liquidity.

3/

Authors: Kolari, Liu

Title: Market Risk and the Momentum Mystery

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3280559

Abstract:

This paper employs the ZCAPM asset pricing model of Liu, Kolari, and Huang (2018) to show that momentum returns are highly related to market risk arising from return dispersion (RD). Cross-sectional tests show that momentum risk loadings and RD risk loadings are similarly priced in momentum portfolios. Comparative analyses find that zero-investment momentum portfolios and zero-investment return dispersion portfolios earn high returns relative to other risk factors. Further regression tests indicate that zero-investment momentum returns are very significantly related to zero-investment return dispersion returns. We conclude that the momentum mystery is explained by market risk associated with return dispersion for the most part.


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