Non-Performing Loans and the Texas Ratio: the political economy of the Italian banking sector

Mattia Corritore, BSc Financial Management, year 3

The fallout from the Great Recession left European banks severely exposed. Most have improved the fundamentals of their balance sheets, such as reducing their leverage or their amount of non-performing loans, but Italian banks are still, according to Steve Eisman, looking vulnerable to potential new shocks. I am going to use Bloomberg to construct one commonly used metric, the Texas Ratio, to evidence the criticality of the current situation. I am also going to investigate the underlying reasons why economic and financial stagnation in Italy is still enduring compared to elsewhere in Europe.

Southern European countries were particularly hard hit by the 2008 financial crisis.  The precise way each country was affected depends on the structure of its banking sector.  In the case of Italy, the crisis acted to expose the contradictions within the regulatory framework of the Italian banking system.  This was reflected in the abnormal increase of non-performing loans on the banks’ balance sheets, increasing from 94 to 360 billion Euros in the space of 8 years.

Numerous Italian banks tailor their business plans according to regional logics, as their stated mission focuses on the support of local entrepreneurial projects through lending activities. On top of that, several banking institutions are owned by public foundations, whose board members are elected by local public authorities. The resulting conflicts of interest are, according to many exponents, a major cause for the structural problems of non-performing loans affecting Italian banks. It is undeniable, in fact, that the exertion of political influence pursued through unconventional methods such as the concession of easy credit, has been heavily burdensome for the sustainability of many Italian banks. As Professor Casella of Bocconi university argues, there is a clear correlation between the presence of board directors with political bonds and the amount of non-performing loans.

Famous is the case of Monte dei Paschi di Siena, the world oldest bank founded in 1472. Initially conceived to provide economic support to the lower classes of Siena, the narrative and mission of this Italian bank have shifted with the tightening of close ties between its board members and influential Italian entrepreneurs. From 2007, the bank has been involved in many political scandals and criticized for its inaccuracy in estimating the real value of each loan’s collateral. The list of the major insolvent debtors is somehow comic, such is the loan provided to Siena football club’s owner, whose degree of risk was incredibly distant to the type of lending activities a bank like Monte dei Paschi should pursue. Striking is also Monte dei Paschi’s incautious acquisition of Antonveneta bank in 2007 for 9 billion euros, which only 6 months earlier was acquired for 6 billion euros by Santander. Someone is perhaps wondering why the bank was paid an additional 3 billion, but the absurdity does not end here. The complete absence of due diligence among Monte dei Paschi’s board members also resulted in a miscalculation of the acquisition, not “noticing” the debt restructuring plan of 8 billion euros previously imposed by Santander. The disbursement of 17 billion euros for a bank barely worth 6 billion, represents the starting point of Monte dei Paschi’s decline.

Unfortunately, Monte dei Paschi di Siena is not an isolated case. The so called Banche Popolari, small local banks, reproduce similar dynamics resulting in major losses due to non-performing loans or reckless acquisitions. Such suboptimal allocation of credit is hence liable to offset the monetary stimulus in the form of “Quantitative Easing” promoted by the European Central Bank governor, Mario Draghi. Quantitative Easing is thought to benefit the real economy through the injection of liquidity into banks’ balance sheets, hoping these latter will lend this money to enterprises. Unfortunately, to date the cumbersome persistence of non-performing loans has not enabled them to benefit from this monetary policy, leaving Italian enterprises without their primary source of financing.

The impact of a vulnerable banking sector with excessive NPLs has been particularly harmful to the Italian economy because of the reliance on the large number of small and medium enterprises (SMEs) which are reliant on the banking sector for financing. The economic slowdown suffered from Italy in the past decade has been rendered harsher by a profound lack of alternative lending channels for the Italian SMEs. Only lately have innovative lending strategies, such as private debt funds, taken place in the Italian capital markets, but the path to counterbalance the destabilizing effects of NPLs still presents many challenges ahead.

Analysts are showing many signs of concern for Italian banks, warning investors of the relatively high probability of default due to non-performing loans. As recently highlighted by Steve Eisman, a commonly used metric to assess their stability and resilience in this regard is the Texas Ratio. To compute this metric, the total amount of non-performing loans, including owned real estate, are divided by the tangible common equity plus the reserves for loan losses. In a nutshell, the numerator deploys all the bad loans whereas the denominator provides an estimate of the capital available to banks to cover their potential losses from lending activities, or as Mr Eisman has famously described it, “all the bad stuff divided by the money you have to pay for all the bad stuff”.

When the Texas Ratio is above 100%, a bank is generally considered at serious risk, since the shortage of its available capital leaves it with little flexibility when facing the downward trend of an economic cycle. Below, I have computed the Texas Ratio of the biggest Italian banks in term of capitalization and I compared it with some important German, French and English banking institutions. The data provided are taken from the fiscal year 2010 and 2016 in order to demonstrate the persistent vulnerable state of Italian banks compared to European ones.

italy table2

The table is a good indicator to grasp the state of the Italian banking system, especially when compared with other important European banking institutions. Most of the Italian banks have a Texas Ratio above 100% and are deemed extremely vulnerable to shocks, as the quantity of NPLs in their balance sheets exceeds the capital at their disposal. Intesa Sanpaolo and Unicredit, the two biggest banking groups in Italy, look healthier than their Italian peers, but two facts must be pointed out. Firstly, these two banks were created as a result of maxi fusions, and since then, their governance assumed a more institutional approach than the other Italian banks. Secondly, whether compared with Deutsche Bank, Commerzbank, BNP Paribas, Credite Agricole, Societe Generale or HSBC, their capital structure, analyzed through the lens of the Texas Ratio, still does not provide signals of strong stability. In fact, none of the European banks present a Texas Ratio above 50%, meaning that their lending practices are way more meticulous than those of healthy Italian banks.

The Texas Ratio is a meaningful metric to assess the exposure of a bank, but it must be kept in mind that banks can remain solvent even though their Texas Ratio is above 100%. The financial evaluation of banks is a thorough procedure and one single ratio cannot provide a comprehensive overview. For example, the table shows that Deutsche Bank Texas Ratio is only 13% but obviously such a ratio alone cannot encapsulate it as a good bank. Whilst this bank did not appear to have NPL issues, their efforts to render their assets profitable have failed in the past decade. Deutsche Bank’s return per dollar employed, also defined as Return On Asset (ROA), is currently negative, -0.09, while a positive ratio indicating the effectiveness of a business model should be around 1%. Furthermore, several of Deutsche Bank’s assets are derivative securities categorized as “Level 3 Asset”, whose fair value is not estimable using classic parameters due to their illiquid nature. This data does not necessarily imply a negative outlook for Deutsche Bank but it suggests the need of a more subtle analysis to understand the true quality of its assets. Investors should therefore interpret a range of different ratios to assess the fundamentals of a bank.

All being said, Italian banks have challenging times ahead. The persistence of non-performing loans has slowed down the engine of the Italian economy, the banking system, impeding SMEs from being able to borrow capital necessary to finance their entrepreneurial objectives. It is extremely urgent for banks to hasten restructuring plans which involve the sell-off of non-performing loans to private securitization vehicles. The sale of a large quantity of NPLs as packages could quickly improve Italian banks’ balance sheets. Until then, investors must hold on.

Submitted: 14/12/17

Appendix

To obtain the Texas Ratio: Type and click on the name of the security you are interested in; On the main menu of the security, click on 7) Financial Analysis; on the horizontal bar, click on 7) ADDL; on the light gray horizontal bar select the option Banking; in Banking page, pay attention to the following estimates: Non-Performing Loans; Tangible Common Equity;  Reserves for Loan Losses. Finally, compute the Texas Ratio: NPLs / (Tangible common equity + reserves for loan losses)*100

Return on assets: Financial Analysis – Addl – Banking – Return on Asset ]

Level 3 assets: Financial Analysis – Addl – Banking – Fair Value Analysis – Level 3 Assets/Total Assets]

Advertisements

EQS: Screening stocks and back testing strategies using the Bloomberg Terminal

Jamie Flanagan, BSc Banking and Finance, year 3

Generating new stock ideas can be tough. To assist with this issue one can use Bloomberg’s Equity-screening tool, EQS, to filter for stocks based on user-defined metrics. A stock screener can be an excellent way to generate investment ideas using fundamental ratios.

Once an investor defines their trading strategy they may want to back test using historical data to see how it performed in the past. To assist with this, Bloomberg provides a backtesting function within EQS which enables the testing of whether a portfolio strategy would have outperformed a benchmark during previous years. The Bloomberg terminal also gives a list of risk statistics so you can check how much risk your strategy has and check if your returns satisfy the amount of risk being taken.

EQS can also be useful for students undertaking a capstone dissertation since it enables students to sort securities by metrics and then back test against a suitable benchmark. This type of methodology is widely used in the literature, from tests of momentum to tests for returns to different metrics such as price-earnings, book-to-market, dividend yield, size, volume, etc.

The aim of this article is first to give a step by step process on how to use the Bloomberg stock screener and secondly provide a guide on how to perform a back test using the Bloomberg Terminal.   In this guide, we are going to screen for stocks using a similar method to the one used by Basu who tested to see the performance of stocks with low price to earnings ratios on the New York Stock Exchange (NYSE) between 1957 and 1971 (Basu 1977).

Please note that the example criteria we are using to screen stocks in this article is just showing how the process to screen and back test stocks is done. The purpose of this article is not to give anybody any induvial stock recommendations.

  1. How to Screen for Equities

Step 1: Type EQS into the search bar and press enter. The screenshot in Figure 1 shows the Bloomberg Equity screening homepage.

eqs1

Figure 1: The EQS screen

Step 2: To choose what country or countries you want included in your screen, click on EXCHANGES and this will give you a wide range of countries to select, when finished click update. For the purposes of this article we use the US.

eqs2

Figure 2: Choosing an exchange or country

Step 3: To choose which market indexes you would like to include, click on indices, select the required index then click update. We have decided to select the S&P 500 for this example.

eqs3

Figure 3: choosing an index

Step 4: if you wish, you can narrow this down even further to a given sector. To do this just click on sector and choose from the list below. If you can’t find the sector you are looking for you can just search it in the search bar.

eqs4

Figure 4: choosing a sector

Step 5: Now on to what characteristics you want the companies to meet. There is a wide range of metrics and financial ratios you can you use such as Price/Earnings ratio, Price to Book, Price to sales and so on. You can search for companies who have a certain level of debt or who generate good returns e.g. return on assets, return on equity and return on capital. If you are unsure exactly what you are looking for, just type in a letter or word in the search bar and it will give you suggestions based on what you have typed.  In our example, typing “price e” was sufficient for the predictive text to provide the required metric, price-to-earnings.

Multiple criteria are possible.  Each time you add a criterion another bar will appear below your existing criteria which says no display. Click on this and it will give you a list of options on how you want to apply the new criterion. For example, you can click on LESS THAN and then type a number in and it will screen for stocks with a Price-to-earnings less than the number you entered. Perhaps more useful is the facility to according to percentiles, for example quartiles, deciles or quintiles. The screenshots below put this in into practice. In this instance, we are screening stocks based on their Price to earnings (P/E). In our example, we are going to screen for stocks that are in the bottom decile of stocks based on their P/E (lowest 10%). To implement the filter, once you have typed in “10” you need must press return (Figure 5).   Then click on SEERESULTS and this gives us the current 10% of stocks in the S&P 500 with the lowest Price to earnings ratios, as seen in Figure 6.

eqs5

Figure 5: Selecting decile with lowest P/E ratio

 

eqs6

Figure 6: The lowest decile stocks by P/E

For the purpose of this guide, we have used current P/E ratios.  For a more realistic forward looking scenario, and one that better fits with methodologies such as that used by Basu (1977) and others, we should choose a period preceding the current.  This is easily done by clicking on CURRENT in Figure 5 and choosing an alternative window which will in part depend on the frequency of your data.

  1. How to backtest a strategy based on historical data

An excellent feature of Bloomberg is that it can allow us to back test performance for any criteria we have chosen using data back to 1993, rebalancing according to your criteria.

Step 1: Click on actions at the top of the screen (Figure 6), then click on back test. Give the system a few moments to load up the screen and then enter the period you want to use for the back test.  Click on analysis period and you can either test a certain amount of years of data or you can do it manually between 2 set dates of your choice. The last option, REBALANCE FREQUENCY, is how often you want the portfolio to be rebalanced during the backtest. The options range from daily to annually. Click update.

eqs7

Figure 7: Equity backtesting model builder

Step 2: If you wish you can click on ANALYTICAL PARAMETERS. This allows you to weight your portfolio either equally or by market cap. Lastly you can also compare your portfolio to a benchmark i.e. a market index. Click update, then click run to back test the criteria. You then have the option to name your test. Click save and run.

eqs8

Figure 8: Choice of benchmark for backtesting

The time it takes to complete the back test depends on how many years of data you are back testing.  A rule of thumb is, the more years you are back testing the longer it will take to complete. No back test should take longer than ten minutes to complete. When your back test is ready you will get a Bloomberg message in the top right corner of your screen, this will be flashing purple. Open the message and then click on the link

Step 6: The Screenshot below shows the results of the back test. On the right of the screen, are a selection of statistics for return, risk and return compared to risk during the performance period.  In the top left corner, the total return of both the portfolio and the index are reported. The top graph represents visually the performance of both. The graph below presents the spread between the portfolio and the index i.e. portfolio – index. If the spread return is green this means the portfolio is outperforming the index and vice versa for a red line.

The bars at the bottom of the screenshot each represent a single period return; if you hover the mouse over each bar you can see the return for the period. You can also click on the bars and this will bring you to which stocks met your criteria during the period. If a stock says out on it this means it will drop out of the portfolio at the end of the period as it no longer meets your criteria.

eqs9

Figure 9: Results from the backtest

This demonstration shows how simple it is to: 1) screen for stocks and 2) back test strategies using the Bloomberg terminal. Having access to such a fast and reliable tool to perform such a task is invaluable for those with an interest in developing  an investment strategy since it allows you to stay more focused and have an independent though process from the rest of the market, ignoring the hype from other market participants. On the other hand, if you are doing a dissertation, using the EQS function can save you a lot of time and effort when analysing performance according to a range of metrics.

References:

Basu, S. (1977). Investment performance of common stocks in relation to their Price to their Earnings ratios: A test of the efficient market hypothesis. The Journal of Finance, 32(3), pp.663-682.

Submitted 2/12/17

Venezuela update: has Goldman’s gamble backfired?

Iman Binti Zuki, BSc Accounting & Finance, year 3

Since the publication of my article “Venezuelan Hunger Bonds”, Standard & Poor’s credit rating agency has declared that Venezuela is in default due to the country’s inability to pay coupons for bonds maturing in 2019 and 2024. The coupon payments that it has missed totaled up to $200 million. As shown in Figure 1, on 13th November 2017 the S&P Rating Agency issued an SD rating for Venezuela’s Foreign Currency Long Term Debt. According to the definitions of S&P Long Term Issuer Credit Ratings, SD stands for Selective Default, which means that the rating agency believes that Venezuela has selectively “defaulted on a specific issue or class obligation but it will continue to meet its payment obligations on other issues in a timely manner”.

update1

Figure 1: S&P foreign currency long term debt rating for Venezuela

S&P declared the revised rating following a bondholder meeting on 13 November where Venezuela’s official representative failed to provide a concrete plan to address the country’s massive $60 billion bond debt to the investors/creditors. The announcement of the intention to restructure has caused the bond price to plummet. As shown in Figure 2, the Venezuelan government 10 year bond price has dropped to as low as 20 cents on the dollar. Hence, the bond yield has rocketed as it has an inverse relationship with the bond price.

update2

Figure 2: Venezuela’s government and PDVSA bond price and bond yield

In my previous article I noted that the US bank Goldman Sachs is a major bondholder of the state oil company, PDVSA, following a controversial purchase of PDVSA bonds discounted to 31 cents on the dollar. Although Goldman has subsequently sold some of these bond to other investors, it is still the biggest single holder of PDVSA bonds with a face value of $1.3billion at the end of the third quarter meaning that Goldman is facing a large loss as their gamble appears to have backfired.

What of the future for Venezuela?  With the US financial sanctions preventing US institutions from being involved in new bonds issued by the Venezuelan government, a conventional debt restructuring is not possible. There looks to be no easy or quick resolution to this situation.

Submitted 14/11/17

Suggested further reading

1) Edward White and Hudson Lockett (2017) Financial Times: S&P says Venezuela is in Default on Sovereign Debt. https://www.ft.com/content/88bc3246-c8f4-11e7-ab18-7a9fb7d6163e .

2) Edward White (2017) Financial Times: S&P Declares Venezuela in Default After Missed Payments. https://www.ft.com/content/062f6250-9695-30fc-acb5-0478c96b3b6e .

3) John Paul Rathbone and Robin Wigglesworth (2017) Financial Times: Venezuela slips deeper into crisis after default. https://www.ft.com/content/7f066afc-c8cf-11e7-ab18-7a9fb7d6163e .

4) Robin Wigglesworth (2017) Financial Times: Goldman Sachs Asset Arm Faces Large Paper Loss on Venezuelan Bond. https://www.ft.com/content/94a6cbc0-c101-11e7-b8a3-38a6e068f464?segmentId=080b04f5-af92-ae6f-0513-095d44fb3577 .

5) Tracy Rucinki (2017) Reuters: Venezuela Creditors Recoil at Proposed Caracas Bondholder Meeting. https://www.reuters.com/article/us-venezuela-bonds-advisers/venezuela-creditors-recoil-at-proposed-caracas-bondholder-meeting-idUSKBN1D8343 .

6) Standard & Poor’s (2017) S&P Global Rating: Ratings Definitions. http://www.standardandpoors.com/en_EU/web/guest/article/-/view/sourceId/504352.

7) Robin Wigglesworth (2017) Financial Times: What Happens Now After Official Default. https://www.ft.com/content/5f07e298-c326-11e7-a1d2-6786f39ef675 .

Appendix

Figure 1: WCDM – Change to Emerging Markets – Click Venezuela – Ratings – CRPR – S&P Foreign Currency LT Debt

Figure 2: WB – Americas – Venezuela – Click Venezuela 10Y – GY

Click Chart Content- Add a security, field, or study event: Click Browse – Field – Last Price

Venezuelan “Hunger Bonds”

Iman Binti Zuki

BSc Accounting & Finance, year 3

Behind the international headlines relating to economic hardship and protests in Venezuela is the intensifying sovereign debt crisis which is widely predicted to end with an inevitable default.  The link between the severity of the hardship and the government’s debt has led some to describe the instruments that finance the debt as “hunger bonds”.  In this case study, I explain why these hunger bonds have been the cause of much controversy.

Venezuela’s current plight has its roots in the debt which built up under the late President Hugo Chavez and continued under the current President, Nicholas Maduro.  While oil prices were high, the debt was easily serviced, but the reduction in oil prices alongside long term economic mismanagement has left the country in a dire state.  The details of how Venezuela got into this mess is beyond the scope of this note, but some of the consequences are illustrated by the economic indicators in Figure 1.  Inflation, as measured by the year on year growth of the Consumer Price Index has been increasing at an alarming rate from 400% in 2016 to 680% in 2017 and is expected to rise to as much as 954% in 2018. On top of that, the growth of real GDP has also remained negative since 2014 and is forecast to remain negative until at least 2018.

venfig1

Figure 1: Venezuela’s economic indicators.

The credit rating for Venezuela has been concerning. As shown in Figure 2, the credit rating provided by Fitch regarding the Venezuelan government has been declining since 2005.According to Fitch’s International long-term issuer credit ratings and definitions, a ‘CC’ rating means very high levels of credit risk and a default of some kind appears probable. When the credit rating is getting lower, the government has a higher default risk and the bonds require a higher yield which corresponds to a lower price.

venfig2

Figure 2: Fitch’s Long term Venezuela’s Default Rating

Despite Venezuela having serviced the overseas notes for quite some time, the issue only caught the world’s attention when the US bank Goldman Sachs bought the bonds issued by Venezuela’s state oil company, Petroleos de Venezuela SA (PDVSA) in May 2017. Goldman Sachs’s decision to buy the bonds at a large discount has been deemed controversial because the purchase would serve as effectively funding to the authoritarian Venezuelan government and prolonging the economic mismanagement. This leads to the emergence of the term “Hunger Bonds”.

Goldman Sachs purchased the notes through a broker called the Dinosaur Group, indirectly channeling money to the Venezuelan central bank. As illustrated in Figure 3, Venezuela’s foreign reserves have been declining rapidly from 2015. According to Bloomberg News, during the period of Goldman Sachs’s bond purchase, there is a two day jump of $749 million in the Venezuela International Reserves which can be seen in Figure 3 below. Despite the increase in the foreign reserves, this loan only serves as a quick and momentary inflow of funds, and is only delaying the future collapse.

venfig3

Figure 3: The movement of Venezuela International Reserves

The bonds that were purchased by Goldman Sachs that led to the controversy are coupon bonds issued by the state oil company, Petroleos de Venezuela SA (PDVSA). A coupon bond is a bond paying fixed interest payments (coupon payments) until the maturity date when the face value is repaid. Face value is a specified value of the bond that the issuer is obligated to pay at maturity date. It is also known as the par value and the principle value. The bonds were issued in 2014 with the coupon rate of 6% and a maturity date of 28/10/2022 at the face value of $3 billion as shown in Figure 3 below. Goldman Sachs Asset Management (GSAM) bought $2.8 billion out of the $3 billion bond at a discounted price of 31 cents on the dollar, having to pay only $865 million in total. This means that GSAM pays $1.935 billion less than the bonds’ original worth.

venfig4

Figure 4: Description of the PDVSA bonds bought by Goldman Sachs

This has been condemned by critics, stating that the US bank has the intention of making quick money at the expense of the Venezuelan people. A bond’s current yield can be defined as the coupon payment divided by bond price. According to this formula, a lower bond price with a constant coupon payment will result in a higher bond yield. Hence, referring to this case, the heavily discounted bond price has resulted in a high bond yield which is different to the coupon rate.  Looking beyond the current yield, Ricardo Hausmann, a director of the Center for International Development at Harvard University, has stated that the PDVSA bonds “can expect a yield of 48 per cent”. To put this in context, according to the JP Morgan Emerging Market Bond Index, although Venezuelan bonds account for only 5% of the index, it represents 20% of the index yield.  Hausmann has also stated that “Venezuela’s yield is 5 times larger than any other countries in the index”.

As a result, this circumstance has spurred the discussion of moral issues related to the buying of “Hunger Bonds” and profiting from the suffering of the Venezuelan people. Part of the moral issues discussed is that the loans issued to PDVSA were not used to expand its capacity or improve productivity, but were used for political purposes instead. It has also raised the question as to whether it is correct for JP Morgan to include Venezuela and PDVSA bonds in its indices, which implicitly required investors to hold those bonds. Moreover, Hausmann also argued that debt taken out by autocratic leaders cannot represent a moral obligation on future generations: this is the debt of the regime, not the people.

Submitted 27/10/17

Suggested further reading

Suggested listening

  • FT Alphachat podcasts on the Venezuelan crisis: “Ricardo Hausmann on the tragedy in Venezuela” and “Alphachat podcast: The making of the crisis in Venezueal” (8/9/17)
  • A series of FT Alphachat podcasts on sovereign debt: “When a country goes bankrupt”; “When a country defaults”; “How Greece restructured its debt”; and “How Jamaica turned its debts around”.

Appendix

Figure 1: ECFC – Change country

Figure 2: WCDM – Change to Emerging Markets – Click Venezuela – Ratings – CRPR – Fitch LT Issuer Default Rating

Figure 3 :VNRS Index – GP – Change date

Figure 4 : PDVSA Corp – Click PDVSA 6.000 10/28/2022 SINKABLE 10/28/2014 – DES –

The power of a mere ticker – the case of CUBA

Dorota Hotova, BSc Banking and Finance, year 2

The case of CUBA is one of the most interesting examples of how a stock market is rather an unpredictable environment and how investors may not always behave in a completely rational way, contrary to the efficient market hypothesis. To illustrate this, the example of CUBA has recently been used by Nobel prize winning behavioural economist Richard Thaler.

CUBA is the ticker symbol for the closed-end fund called Herzfeld Caribbean Basin Fund Inc. which invests in various companies that are usually US based and have some connections with the Caribbean region or with Mexico. The top stock holdings of the fund can be seen in Figure 1. None of these companies are Cuban as there has yet to be any Cuban company whose stock can be traded by investors. Even if there were any such companies, the sanctions that the US imposes on make it illegal for a US-based company like Herzfeld to trade Cuban shares. However, after the president of the United States, Barack Obama, announced his plans to lift restrictions on trade with Cuba on 14th December 2014, the fund’s stock price jumped 70% in one day!

CUBA fig1

Figure 1: Holdings of the CUBA closed-end fund

One of the ways to determine a fund’s fair price is to look at the prices of the assets that are in its portfolio. An ordinary tool used to assess the fairness of the price of the fund is called Net Asset Value (NAV) which reflects the proportionate price of the portfolio’s underlying assets. In theory, if the markets were perfectly efficient, the net asset value of any stock should be equal to the price at which stocks of the company holding the assets trades. Nevertheless, in the real world this is not always the case. As can be seen from Figure 2, CUBA traded at a 10% discount to its NAV just before the announcement, this in itself being contrary to the law of one price. Closed-end funds trading at discount is nothing unusual in the real world and was first described by Lee, Shleifer and Thaler in 1991 as a closed-end fund puzzle. The reason why closed-end funds often trade at discounts is that they have a fixed number of shares and therefore their price is determined by their demand. Similarly, the investors do not always act rationally and a huge role is played by sentiment. Another reason being the cost of arbitrage or illiquidity of the underlying assets in the portfolio. Therefore, when the demand is low, the price of the fund’s stocks is lower that its NAV.

cuba fig2

Figure 2: The comparison of net asset value of the fund to it stock price

The jump in the stock price of the fund is a paradox because on that day, the price of the underlying assets of the fund went down a little. Figure 3 shows the movements in the price of the stocks of the company that has the highest weight in the fund’s portfolio (MasTec Inc.). The price actually dropped on that day, although the price of the fund went up drastically.

cuba fig3

Figure 3: The performance of MasTec Inc. during 2014

This can be understood as an example of a market anomaly when the investors act irrationally. It seems that the ticker is what confused some investors. This would be very unusual if the majority of the holders of the fund’s stock were institutional investors but looking at the proportion of the owners (Figure 4), on the day of the announcement, the percentage of institutional owners dropped significantly which means that many individuals bought this fund’s stocks on that day. That is one of the causes of anomalies and many experienced investors are concerned when they see a lot of individual investors buying a company’s stocks as this might be a sign of irrational exuberance which causes bubbles which are inevitably doomed to crash, as in the case of CUBA.

cuba fig4

Figure 4: Institutional holdings of CUBA

When looking at Figure 2, however, the stock price did not correct itself immediately as the efficient market hypothesis would predict. Although, there is a partial correction a day after the announcement, the stocks traded at a premium of about 20% which is quite unusual for a closed-end fund which traded at 10% discount before the announcement. It took almost a year for the stock to return to its previous position. That is the reason why some investors might question the relevance of the efficient market hypothesis.

Suggested further reading

Thaler, R.H. (2015) Misbehaving: the making of behavioural economics, Allen Lane.  See especially chapters 1 and 25.

Lee, C.M.C., A. Shleifer, R.H. Thaler (1991) Investor sentiment and the closed-end fund puzzle, Journal of Finance, vol. XLVI, No. 1, pp. 75-109. Also available in Thaler (ed.) (1993) Advances in Behavioural Finance, Russell Sage Foundation.

Appendix

Information on the Herzfeld Caribbean Basin Fund can be obtained either from typing the full name in or the ticker, CUBA.  Once CUBA has been selected the individual graphs are created as follows:

Figure 1: DES – Holdings – Top holdings

Figure 2: GP – Chart Content – Add NAV – change dates

Figure 3: MTZ US Equity – GP – change dates

Figure 4: CUBA – OWN (Ownership summary) – change dates

Welcome to EBJ

This blog provides early views of articles accepted for publication in the Essex Bloomberg Journal, a new journal, run by students for students, with the mission of illustrating and encouraging the use of Bloomberg.  We hope you find the articles interesting and we welcome your contributions.