{"id":69610,"date":"2018-03-01T06:40:40","date_gmt":"2018-03-01T05:40:40","guid":{"rendered":"http:\/\/beta.next-finance.net\/strategie\/does-a-liquidity-factor-premium-exist-in-the-stock-market\/"},"modified":"2018-03-01T06:40:40","modified_gmt":"2018-03-01T05:40:40","slug":"does-a-liquidity-factor-premium-exist-in-the-stock-market","status":"publish","type":"post","link":"http:\/\/beta.next-finance.net\/en\/strategie\/does-a-liquidity-factor-premium-exist-in-the-stock-market\/","title":{"rendered":"Does a liquidity factor premium exist in the stock market?"},"content":{"rendered":"<p><em> <strong>Academic studies present ample evidence in support of the existence of four<br \/>\nfactor premiums in stock markets: Low Risk, Value, Momentum, and Quality.<br \/>\nFactor investing puts these concepts into practice by enabling investors to<br \/>\nallocate their capital explicitly to these premiums in order to achieve higher<br \/>\nreturns and better risk management. Liquidity is another factor that some<br \/>\nacademics consider important to explain why some stocks earn higher returns<br \/>\nthan others, yet Liquidity has received much less attention than these other<br \/>\nestablished factors. In this note, we discuss the evidence for the Liquidity factor<br \/>\nand the role of liquidity in investment management.<\/strong> <\/em><\/p>\n<p>Liquidity can be defined as the ease of executing a transaction without creating excessive<br \/>\ncosts. When executing a transaction, investors pay explicitly for the prevailing bid-ask<br \/>\nspread, and implicitly for any adverse price swings due to the removal of liquidity from the<br \/>\nmarket. All else being equal, the more illiquid a stock, the more difficult and expensive it is<br \/>\nto trade it, and this property makes illiquid stocks less attractive than liquid stocks. For this reason, illiquid stocks should command a premium to be held, or conversely, liquid stocks<br \/>\nshould trade at a discount. This reasoning warrants the existence of a liquidity factor<br \/>\npremium in stock returns, and some academic studies indeed claim to observe such a<br \/>\npremium in the data.[[1<br \/>\nLiquidity has also been studied in bond markets. For instance, there is a yield difference between onthe-run<br \/>\nand off-the-run Treasury bonds, and the credit risk premium of corporate bonds seems much<br \/>\nlarger than justified by losses due to defaults, which has been interpreted as evidence that it maylargely<br \/>\nbe a liquidity-related premium. In this article, however, we focus on the question as to whether a<br \/>\nliquidity premium exists in public stock markets]]\n<p>However, unlike other established factors, liquidity has never received much attention from<br \/>\ninstitutional investors, at least not in equity markets. One possible reason for this is that<br \/>\ninvestment strategies based on the liquidity factor are difficult to implement in practice.<br \/>\nWhile other established factors can be exploited in broad, diversified portfolios that are<br \/>\nconsidered investable from an institutional investor\u2019s standpoint, these investors often face<br \/>\nliquidity constraints in their allocation decisions, that make an equity strategy that explicitly<br \/>\ninvests in illiquid stocks less appealing. Another possible explanation is that the evidence for<br \/>\nthe existence of a liquidity premium is not very solid. While liquidity undoubtedly matters<br \/>\nwhen it comes to portfolio construction and implementation, it is not clear whether stocks<br \/>\nearn higher returns simply because they are illiquid. In other words: are equity investors<br \/>\ncompensated for taking on extra illiquidity risk?<\/p>\n<p>In this article, we review the arguments for and against the existence of a liquidity premium<br \/>\nand conclude that the evidence for such a premium is, at best, weak. Our conclusion is<br \/>\nlargely based on the results of the academic studies that comprehensively address these<br \/>\nissues. Moreover, we note that these conclusions are consistent with what we ourselves<br \/>\nhave found when examining the liquidity factor. However, regardless of the existence of a<br \/>\nliquidity factor as an independent driver of expected stocks returns, liquidity itself is a very<br \/>\nimportant aspect in any sophisticated investment process. The last section of this article<br \/>\ndiscusses why liquidity matters, and how it can be used for efficient portfolio construction<br \/>\nand implementation.<\/p>\n<p><strong>Research on the Liquidity premium<\/strong><\/p>\n<p>Following the seminal paper of Amihud and Mendelson (1986) on liquidity and stock<br \/>\nreturns, a number of papers have documented patterns in the cross section of stock returns<br \/>\nthat they attribute to liquidity. Amihud and Mendelson (1986) show that in a world with<br \/>\nfrictions, which are assumed not to exist in the most influential asset pricing models, a<br \/>\nliquidity factor emerges naturally. The capital asset pricing model (CAPM) postulates that<br \/>\nexpected stock returns increase linearly with their market betas, but it is now widely<br \/>\nacknowledged that the CAPM does not hold in practice. Specifically, stocks with low market<br \/>\nbetas earn higher returns than predicted by the CAPM, and the converse is true for high beta<br \/>\nstocks. Amihud and Mendelson show that by including a liquidity factor, such differences in expected returns can be explained (at least partially). They conclude that these differences<br \/>\nare caused by differences in stock liquidity.<\/p>\n<p>This paper was followed by a number of other articles confirming the relationship between<br \/>\nliquidity and stock returns. Datar, Naik, and Radcliffe (1998) find that a measure of share<br \/>\nturnover, defined as the number of shares traded over a certain period divided by the<br \/>\nnumber of shares outstanding, is strongly related to future stock performance. In particular,<br \/>\nthey show that stocks with low share turnover, arguably illiquid and neglected, generate<br \/>\nhigh risk-adjusted returns. Chordia, Subrahmanyam, and Anshuman (2001) document<br \/>\nsimilar patterns using the dollar trading volume and its coefficient of variation. Liu (2006)<br \/>\nlooks at the number of days with zero trading volumes and identifies as illiquid those stocks<br \/>\nwith a large number of days on which they were not traded. Further, he shows that these<br \/>\nstocks outperformed their most liquid counterparts. Acharya and Pedersen (2005) develop<br \/>\ntheir own measure of stock level liquidity, which is based on sensitivities to an underlying<br \/>\nilliquidity factor, and also show that it matters for stock returns. Perhaps the most influential<br \/>\nempirical paper in this area is by Pastor and Stambaugh (2003), who show that a marketwide<br \/>\nilliquidity factor is important for explaining the cross section of stock returns, as well<br \/>\nas a big part of the momentum premium.<\/p>\n<p>Given all this evidence, one may wonder why the liquidity factor is not more broadly<br \/>\naccepted. The reason is simply because the robustness of these findings has been called into<br \/>\nquestion. In particular, concerns have been raised in multiple studies about the claims made<br \/>\nin the abovementioned papers. Those concerns can be divided into two categories:<\/p>\n<ul>\n<li> The effect is not robust across different time periods, and can only be found<br \/>\nduring the in-sample period, if at all.<\/li>\n<li> The liquidity effect is largely driven by microcaps \u2013 stocks that according to Fama<br \/>\nand French (2008) represent around 3% of total market cap of the US stock<br \/>\nmarket, but account for around 60% of the total number of stocks<\/li>\n<\/ul>\n<p>Once these elements are taken into account, the liquidity factor simply cannot be found.<\/p>\n<p>For example, Drienko, Smith, and Von Reibnitz (2017) take a comprehensive look at results<br \/>\nof Amihud (2002) who finds a strong relationship between liquidity and stocks returns. They<br \/>\nconclude that results found in Amihud\u2019s paper only hold in-sample. Using data from the last<br \/>\ntwo decades, the authors find that the liquidity risk is not rewarded, possibly due to the<br \/>\ntechnological innovations that have increased average stock-level liquidity. All these<br \/>\nfindings cast major doubts on whether liquidity is an independent driver of stock returns.<\/p>\n<p>In a more recent paper, Hou, Xue, and Zhang (2017) find that 95 out 102 documented<br \/>\nliquidity-related measures are completely insignificant if one gives less weight to microcaps<br \/>\nin portfolio construction. In fact, all of the prominent anomalies mentioned above, except<br \/>\nthat discussed by Pastor and Stambaugh (2003) which was not considered in this paper,<br \/>\nare insignificant. Thus, Hou, Xue, and Zhang (2017) essentially dismiss decades of academic<br \/>\nresearch on liquidity premiums with their fresh look at the data and their use of a<br \/>\nconservative methodology. <\/p>\n<p>In another recent study, Li, Novy-Marx, and Velikov (2017) find that the performance of the<br \/>\nPastor and Stambaugh (2003) liquidity factor is very sensitive to how it is constructed, and<br \/>\nthat a factor constructed following the standard Fama and French (1993) methodology,<br \/>\nthat gives less weight to microcaps due to the use of capitalization-weights, as opposed to<br \/>\nequal-weights, generates a statistically insignificant return. Also, they find no relationship<br \/>\nbetween liquidity risk and momentum, as opposed to what was documented in the original<br \/>\npaper. An additional challenge with this liquidity measure is that it can only be tested in<br \/>\nrespect of the U.S. market, as the employed liquidity factor is derived from data from U.S.<br \/>\nexchanges. It is therefore unclear if and how the results carry over to international markets.<\/p>\n<p><strong>Liquidity and other factor premiums<\/strong><\/p>\n<p>The relationship between size and liquidity is an intrinsic one, as small stocks also tend to be<br \/>\nless liquid. For this reason, for some liquidity definitions that produce significant dispersions<br \/>\nin raw returns between illiquid and liquid stock portfolios, the Fama-French three-factor<br \/>\nmodel that includes a size factor is usually able to completely explain this differential. This<br \/>\nis, to a large extent, due to a significant loading on the size factor. Nevertheless, the<br \/>\nevidence for size as an independent factor that goes beyond liquidity effects is much<br \/>\nstronger, causing the size factor to be more widely accepted. Size also has the advantage of<br \/>\nbeing much easier to measure than liquidity.<\/p>\n<p>While the existence of a stand-alone liquidity factor is questionable, it is also interesting to<br \/>\nconsider if there are interactions between liquidity and other established factors. Assuming<br \/>\nthat illiquid segments of the market are less efficient at determining the fair value of assets,<br \/>\none may argue that some factors are stronger amongst illiquid stocks. Small, illiquid stocks<br \/>\ncan thus be seen as a catalyst for other factor premiums, as opposed to an independent<br \/>\nsource of return. Provided there are interaction effects between established factors and<br \/>\nstock-level liquidity, it is the job of an active manager to identify and model these effects in<br \/>\nthe stock selection and portfolio construction phases. Given that these stocks, by definition,<br \/>\ntend to be more difficult and expensive to trade, smart portfolio implementation can<br \/>\ntherefore add substantial value for investors.<\/p>\n<p><strong>Portfolio construction and implementation<\/strong><\/p>\n<p>Liquidity is a critical element to take into account when translating theoretical investment<br \/>\nstrategies to live portfolios. In the words of Perold (1988), \u201cThere are crucial differences<br \/>\nbetween transacting on paper and transacting in real markets\u201d. This gap is better known as<br \/>\nthe implementation shortfall. When constructing investment portfolios, trading costs, which<br \/>\nare a direct function of the liquidity level of a stock, erode the expected alpha. Although<br \/>\nexpected alphas are driven by exposures to proven factor premiums and liquidity does not<br \/>\nqualify as an independent alpha factor, it is nonetheless a key driver of transaction costs,<br \/>\nand therefore net returns. As a result, a sophisticated portfolio construction process and<br \/>\nsmart portfolio implementation can add a lot of value to the investment process.<\/p>\n<p>Estimates of liquidity are needed to optimize portfolio turnover and ensure that the<br \/>\nportfolio\u2019s investments remain within liquidity risk boundaries. There is widespread<br \/>\nevidence in the literature that the performance of mutual funds is highly dependent on their<br \/>\nturnover and transaction costs. For example, Carhart (1997) demonstrates that expenses<br \/>\nand turnover are negatively related to fund performance and that changes in execution<br \/>\ncosts per transaction partly explain why certain funds perform consistently better than<br \/>\nothers. Keim and Madhavan (1998) find that stock trading costs vary with the sophistication<br \/>\nof order-placement strategies and the skill of the trader. Wermers (2000) determines that<br \/>\napproximately 70% of the average difference between the gross (before costs) and net<br \/>\n(after costs) outperformance of stocks held by mutual funds is due to implementation<br \/>\ninefficiencies. Naturally, any sophisticated portfolio construction process should include<br \/>\nliquidity estimates to minimize the \u2018slippage\u2019 between investment decisions and actual<br \/>\ntransactions.<\/p>\n<p>If the usage of liquidity estimates within portfolio construction is successful, portfolio<br \/>\nmanagers can take a prudent approach to investing in assets that take a long time to trade<br \/>\nor that are expensive to buy or sell. Additionally, liquidity can be used to adjust position sizes<br \/>\nof investments, to reduce the impact on after-cost investment performance.<\/p>\n<p>In the portfolio implementation phase of the investment cycle, liquidity is also of utmost<br \/>\nimportance. After portfolio construction, the order is sent to traders. The moment an order<br \/>\nhits the trading desk, minimizing the implementation shortfall of that investment becomes<br \/>\nthe trader\u2019s top priority. At this point, estimates of liquidity and knowledge about expected<br \/>\ntransaction costs help the trader to get the best price for the asset.<\/p>\n<p><strong>Concluding remarks<\/strong><\/p>\n<p>The idea of a \u2018liquidity premium\u2019, an expected return on a stock that is higher simply because<br \/>\nit is illiquid, has been challenged and debunked in various studies. Given the state of the<br \/>\nevidence, we conclude that there is currently no reason to implement a strategy that is<br \/>\nspecifically aimed at investing in illiquid stocks. However, small and illiquid stocks may serve<br \/>\nas a catalyst for other factor premiums, and interactions with these premiums should be<br \/>\nconsidered. Liquidity is also a key aspect to ensure the efficient and optimal implementation<br \/>\nof investment strategies. As such, it should explicitly be taken into account when managing<br \/>\nequity portfolios.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Academic studies present ample evidence in support of the existence of four<br \/>\nfactor premiums in stock markets: Low Risk, Value, Momentum, and Quality.<br \/>\nFactor investing puts these concepts into practice by enabling investors to<br \/>\nallocate their capital explicitly to these premiums in order to achieve higher<br \/>\nreturns and better risk management.<\/p>\n","protected":false},"author":1,"featured_media":69608,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1483],"tags":[1663,1655,1813,1651,1807,1814,2068,2020,2118],"_links":{"self":[{"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/posts\/69610"}],"collection":[{"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/comments?post=69610"}],"version-history":[{"count":0,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/posts\/69610\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/media\/69608"}],"wp:attachment":[{"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/media?parent=69610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/categories?post=69610"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/beta.next-finance.net\/en\/wp-json\/wp\/v2\/tags?post=69610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}