15/6/18: Italian High Yield Bonds and Markets Exuberance

Nothing illustrates the state of asset valuations today better than the junk bonds tale from Italy. Here is a prime example from the Fitch ratings note from June 7:

"...longstanding Italian HY issuer and mobile operator WindTre sequentially refinanced crisis-era unsecured notes at 12% coupons into 3% area coupons by January 2018, despite losing cumulative revenue and EBITDA of 30% and 25%, respectively, and re-leveraging from 4x to 6x."

Give this a thought, folks:

1. We expect rates to rise in the future on foot of ECB unwinding its QE, the Fed hiking rates and monetary conditions everywhere around the world getting 'gently' tighter;
2. Euro is set to weaken in the longer run on foot of Fed-ECB policies mismatch;
3. WindTre issues replacement debt, increasing its leverage risk by 50%, as its revenue falls almost by a thirds and its EBITDA falls by a quarter;
4. WindTre operates in the market that is highly exposed to political risks and in an economy that is posting downward revisions to growth forecasts.

And the investors are piling into the company bonds, cutting the cost of debt carry for the operator from 12 percent to 3 percent. 

Per FT (https://www.ft.com/content/31c635f4-64df-11e8-a39d-4df188287fff): "Lending to corporates rose 1.2 per cent in the year to February 2018, according to the Bank of Italy, and the average interest rate on new loans was 1.5 per cent — a historic low."

Say big, collective "Thanks!" to the folks at ECB, who worked hard to bring us this gem of a market, so skewed out of reality, one wonders what it will take for markets regulators to see build up of systemic risks.

19/12/16: Market Anomalies and Data Mining: Some Pretty Tough Love from Data

Investment anomalies (or in other words efficacy of exogenous factors in determining abnormal returns to investment) are a matter of puzzle for traditional investment analysis. In basic terms, we normally think about the investment as an undertaking that offers no ‘free lunch’ - if markets are liquid, deep and, once we control for risk factors, taxes and transaction costs, an average investor cannot expect to earn an above-market return. Put differently, there should be no ways to systematically (luck omitting) beat the market.

Anomalies represent the case where some factors do, in fact, generate such abnormal returns. There is a range of classic anomalies, most commonly known ones being Small Firms Outperform, January Effect, Low Book Value, Under-dogs or Discounted Assets or Dogs of the Dow, Reversals, Days of the Week, etc. In fact, there is an entire analytics industry built around markets that does one thing: mine for factors that can give investors a leg up on competition, or finding anomalies.

One recent paper have identified a list of some 314 factors that were found - in the literature - to generate abnormal returns. As noted by John Cochrane: “We thought 100% of the cross-sectional variation in expected returns came from the CAPM, now we think that’s about zero and a zoo of new factors describes the cross section.”

A recent paper published by NBER and authored by Juhani Linnainmaa and Michael Roberts (see link below) effectively tests this Cochrane’s proposition. To do this, the authors “examine cross-sectional anomalies in stock returns using hand-collected accounting data extending back to the start of the 20th century. Specifically, we investigate three potential explanations for these anomalies: unmodeled risk, mispricing, and data-snooping.” In other words, the authors look into three reasons as to why anomalies can exist:

1. Unmodeled risk reflects the view that some of risk premium paid out in the form of investment returns is not captured by traditional models of risk-return relations;
2. Mispricing reflects the view that markets’ participants routinely and over long run can misplace risk; and
3. Data-snooping view implies that anomalies generate returns in the historical data that do not replicate in forward-looking implementation because these anomalies basically arise from ad hoc empirical data mining.

The authors argue that “each of these explanations generate different testable implications across three eras encompassed by our data: (1) pre-sample data existing before the discovery of the anomaly, (2) in-sample data used to identify the anomaly, and (3) post-sample data accumulating after identification of the anomaly.”

In their first set of tests, the authors focus on profitability and investment factors, because prior literature shown that “these factors, in concert with the market and size factors, capture much of the cross-sectional variation in stock returns.”

Finding 1: the authors “find no statistically reliable premiums on the profitability and investment factors in the pre-1963 sample period… Between 1963 and 2014, these factors average” statistically and financially significant returns on average of “30 and 25 basis points per month, respectively.”

Finding 2: “The attenuations of the investment and profitability premiums in the pre-1963 data are representative of most of the other 33 anomalies that we examine. Just eight out of the 36 (investment, profitability, value, and 33 others) earn average returns that are positive and statistically significant at the 5% level in the pre-1963 period.

Finding 3: All of the measures of abnormal returns used in the study generate premiums that “decrease sharply and statistically significantly when we move out of the original study’s sample period by going either backward or forward in time.” In other words, anomalies tend to disappear or weaken every time the authors significantly broaden time horizon beyond that which corresponds to the time horizon used in the original study that uncovered such an anomaly.

As authors note, “these findings are consistent with data-snooping as the anomalies are clearly sensitive to the choice of sample period."

How? "...If the anomalies are a consequence of multidimensional risk that is not accurately accounted for by the empirical model (i.e., unmodeled risk), then we would have expected them to be similar across periods, absent structural breaks in the risks that matter to investors. Similarly, if the anomalies are a consequence of mispricing, then we would have expected them to be larger during the pre-discovery sample period when limits to arbitrage, such as transaction costs, were greater.”

But there is a note of caution due. “Our results do not suggest that all return anomalies are spurious. The average in-sample anomaly earns a CAPM alpha of 32 basis points per month (t-value = 10.87). The average alpha is 13 basis points (t-value = 4.42) per month for the pre-discovery sample and 14 basis points (t-value = 4.06) for the post-discovery sample. Although these estimates lie far below the in-sample numbers, they are highly statistically significant.”

The kicker is that “investors, however, face the uncertainty of not knowing which anomalies are real and which are spurious [or due to data mining], and so they need to treat them with caution. …because data-mining bias affects many facets of returns—averages, volatilities, and correlations—it is best to test asset pricing models out of sample," or absent such opportunity (perhaps due to tight data) - by selecting a model / factor that "is able to explain half of the in-sample alpha".




Full paper: Linnainmaa, Juhani T. and Roberts, Michael R., The History of the Cross Section of Stock Returns (December 2016). NBER Working Paper No. w22894. https://ssrn.com/abstract=2880332

10/1/16: Tsallis Entropy: Do the Market Size and Liquidity Matter?

Updated version of our paper:
Gurdgiev, Constantin and Harte, Gerard, Tsallis Entropy: Do the Market Size and Liquidity Matter? (January 10, 2016), is now available at SSRN: http://ssrn.com/abstract=2507977.

Abstract:      
One of the key assumptions in financial markets analysis is that of normally distributed returns and market efficiency. Both of these assumptions have been extensively challenged in the literature. In the present paper, we examine returns for a number of FTSE 100 and AIM stocks and indices based on maximising the Tsallis entropy. This framework allows us to show how the distributions evolve and scale over time. Classical theory dictates that if markets are efficient then the time variant parameter of the Tsallis distribution should scale with a power equal to 1, or normal diffusion. We find that for the majority of securities and indices examined, the Tsallis time variant parameter is scaled with super diffusion of greater than 1. We further evaluated the fractal dimensions and Hurst exponents and found that a fractal relationship exists between main equity indices and their components.

18/2/2013: Short-selling and Markets Volatility

A large number of analysts and policy makers tend to believe that highly leveraged trading activity, especially that linked to HFT, is a significant, even if only partial, driver of markets volatility. The channel through this logic usually works is that in the presence of leverage, speed of positions unwinding in response to unforeseen events increases, thus amplifying volatility.

An interesting study by Harrison Hong, Jeffrey D. Kubik and Tal Fishman, titled "Do arbitrageurs amplify economic shocks?" (Journal of Financial Economics, vol 103 number 3, March 2012, pages 454-470) examined the impact of arbitrageurs' activity on stock performance. Based on quarterly data from 1994 through 2007 for NYSE, Amex, and Nasdaq, share prices were examined over two distinct sub-periods: one day before earnings announcement and one day after the announcement. Medium-term performance was analysed for two days before earnings announcement and 126 days after earnings announcement.

The authors find that:

1. Stock price reaction to earnings news is more severe in heavily shorted stocks than in stock with fewer short positions;
2. Changes in the short ratio and earnings surprises counter-move;
3. Share turnover as a result of large earnings surprises is higher for heavily shorted stocks as consistent with (1) above;
4. Positive earnings surprises push up the valu of heavily shorted shares (as consistent with (1) and (2) above)
5. Following positive earnings announcement, returns are higher (in general) for stocks with heavy shorting positions prior to the announcement since price appreciation post-announcement forces covering of short positions and triggers more demand for shares;
6. Consistent with (5) above, post-positive earnings announcement, previously heavily shorted stocks become better targets for further shorting;

Overall, the study finds that:

• Any earnings surprise in any direction (either positive or negative) leads to a corrective action by (either long or short) investors;
• The above increases price sensitivity to newsflow and thus volatility;
• Trading volume and stock price increase abnormally for heavily shorted stocks;
• The abnormal volatility and volume & price effects are temporary and in the medium terms, prices revert to the mean.

5/11/2012: Academic research and market efficiency

Fascinating article on both the issue of markets efficiency (pricing-in of newsflows) and the impact of herding via learning (triggered by academic research) in finance: here.

A nice addition to our discussions both in TCD and UCD courses.

23/10/2012: HFT restrictions and market efficiency

In my class on Investment Theory (MSc in Finance, TCD) we've discussed the issues relating to markets efficiency, HFT and relative speeds in newsflow and trading. We are going to talk more about this subject in my course on HFT in early 2013.

Here is the latest report on the effects of the EU regulatory interference in HFT.

Quote: "European Union plans to clamp down on trading shares faster than the blink of an eye could damage market efficiency and reduce liquidity, a UK government-sponsored paper said… A report by the Foresight Project, which was sponsored by the British government and gathered evidence from 150 academics and experts from 20 countries, said plans to force minimum resting times on orders could reduce liquidity."

The Project (led by John Beddington, the UK's chief scientific advisor) has found that:

• "...some of the commonly held negative perceptions surrounding HFT are not supported by the available evidence and, indeed, that HFT may have modestly improved the functioning of markets in some respects"
• "However it is believed that policymakers are justified in being concerned about the possible effects of HFT."
• "The report found no direct evidence that HFT increased volatility, nor evidence to suggest it has led to an increase in market abuse."
• "It said that computer-based trading could have adverse side effects in some circumstances and that these risks should be addressed."

As my students would know, I am of two views on HFT:

1. HFT is a necessary activity with inherent risks (as any other activity in the market) 
2. HFT can act in contradiction to the direct real-activity nature of the financial markets, but so can other financial instruments and strategies (e.g. hedging across non-asset-related risks, e.g. using Forex markets).

13/4/2012: Short-selling - more evidence that restriction hurt, not help financial stability

Keeping up with some old topics of interest, here is another paper studying markets efficiency within the context of short-selling bans of 2007-present. The study, titled “Price Efficiency and Short Selling” by Pedro A. C. Saffi and Kari Sigurdsson, forthcoming in Review of Financial Studies covers a unique, large set of stocks across a number of countries for the period of January 2005 - December 2008. Data is daily, covering lending and borrowing transactions in 12,621 stocks in 26 countries. The study covers more than 90% of global stocks in terms of market capitalization.

The core questions the authors attempted to answer are:

• What is the impact of short-selling constraints on financial markets?
• Do they make markets more or less efficient?

After Lehman Brothers’ bankruptcy in September 2008, in the US, SEC and the UK FSA restricted the short selling of particular stocks. The emergency order enacting the short-selling restrictions in 2008 by the SEC recognized the usefulness of short-selling for market liquidity and price efficiency, but it also claimed that: “In these unusual and extraordinary circumstances, we have concluded that, to prevent substantial disruption in the securities markets, temporarily prohibiting any person from effecting a short-sale in the publicly traded securities of certain financial firms, (...), is in the public interest and for the protection of investors to maintain or restore fair and orderly securities markets. This emergency action should prevent short selling from being used to drive down the share prices of issuers even where there is no fundamental basis for a price decline other than general market conditions.” Securities Exchange Act Release No. 34-58952 (September 18th, 2008). Following the US and UK, Germany banned short-selling in June 2010 for eurozone sovereign bonds and credit default swaps, claiming that short-selling “had led to excessive price shifts, which could have led to significant disadvantages for financial markets and have threatened the stability of the entire financial system.”

The study considers whether short-sale constraints affect price efficiency and characteristics of the distribution of stock returns of firms around the world. The study defines price efficiency “as the degree to which prices reflect all the available information, both in terms of speed and accuracy.”

The study finds that:

• Lending supply influences price efficiency so that “stocks with limited lending supply are associated with lower efficiency.”
• Higher level of lending supply is “associated with a greater degree of negative skewness and fewer occurrences of extreme price increases, but is not linked with extreme price decreases.” In other words, absence of restrictions on short-selling is not associated with significant presence of extreme downward pressures on stocks – something the bans on short-selling were designed to reduce.
• In the presence of short-selling restrictions, the decrease in skewness is “due to less frequent extreme positive returns, in line with the view that arbitrageurs cannot correct overvaluation as easily when short selling constraints are tighter.” Or put differently, presence of a short-selling ban reduces volatility – if at all – via reducing upward movements in the stocks, not the downward ones.
• Limited lending supply – consistent with short-selling restrictions – “does not affect downside risk and total volatility. We actually find that less lending supply and higher loan fees are associated with greater downside risk and total volatility.” In other words, the short-selling restrictions act in exactly the opposite direction to their intended objectives.

“These findings do not support the view expressed by regulators that unrestricted shorting can destabilize prices, while simultaneously supporting the academic findings that short-sale restrictions generally make market less efficient.”   

“The negative relationship between short-sale constraints and stock price efficiency is found at a stock level all over the world, and equity lending supply is an important driver of differences in price efficiency.”

Interestingly, the findings are robust to membership in the Organization for Economic Cooperation and Development (OECD) countries, and to endogeneity concerns.