Seminar: Strategies in Financial Markets, 55868
School of Business Administration
Hebrew University of Jerusalem
Name: Doron E. Avramov, Ph.D, Professor of Finance
Office: 5132 Mount Scopus, Jerusalem
Web: http://pluto.huji.ac.il/~davramov/
Class Hours: Wednesday 15:30-17:15
Room: ‫חדר עסקאות – מנהל עסקים‬
‫דורון אברמוב; פרופסור למימון‬
Course Description:
From its inception the finance profession featured markets to be informationally efficient. That is to say
that asset prices correctly, and at all times, reflect information available to investors. If markets are really
efficient then the role of active investment management becomes virtually meaningless. In particular, one
cannot beat the market on a risk adjusted basis– expect by luck. In efficient markets higher expected
investment return is attributable to higher risk undertaken as well as often to pure luck, but not to
managerial skill in market timing and stock picking. Thus, the preferred investment choice would be
purchasing index mutual funds or market-wide ETFs which charge relatively low fees.
During the past few decades the market efficiency concept has been challenged. Empirical studies have
been demonstrating that prices of stocks, bonds, and commodities, as well as exchange rates often deviate
from traditional economic theory in predictable ways, deviations that are termed "market anomalies." The
major anomalies in the equity markets include the time-series and cross-sectional predictability of future
stock returns by past stock returns, market capitalization, the book-to-market ratio, trading volume,
accruals, volatility, credit risk, among many others, as well as the time-series predictability by macro
variables such as the short-term interest rate, the term spread, the dividend yield, the credit spread, and
most relevant to this seminar – by technical indicators such as trends and oscillators.
Analyzing investment strategies that capitalize on asset return predictability is at the frontier of research in
financial economics among practitioners and academics alike. Top academic journals in economics,
finance, accounting, and statistics have hosted a plethora of papers analyzing anomalies in financial
markets. Market anomalies have been thoroughly scrutinized by economists and quantitative analysts at
worldwide investment companies, central banks, and market regulators. Nevertheless, there are still
considerable theoretical and empirical debates on the nature of market anomalies. Whether or not markets
are efficient is an open question that will never get resolved (Never say never.) Moreover, blindly investing
in market anomalies could be harmful. For instance, the previously robust momentum trading strategy
delivered a negative 80% return during 2009. Moreover, over 2011 the small cap value stocks returned
15.15% while the big cap growth stocks returned 17.89%. Over the past years, small cap value stocks
considerably outperformed.
In this seminar, you are required to answer all the three questions below. The due date is Sunday during the
last week of the semester, i.e. 17/1/16. Submit your seminar through a power point file. Send the file to my
email address davramov at huji.ac.il during the due date prior to class time. Notice that the first two
questions are about timing while the last one is about stock picking.
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Section 1 – Volatility
VIX is the implied volatility index, also known as the fear index. You cannot directly buy long or sell short
the VIX. However, you can bet on market volatility through ETNs or Exchange Traded Notes which are
investment funds. VXX and VIXY, for one, are ETNs that seek to match the performance of the S&P 500
VIX Short-Term Futures Index. You may consider buying VXX or VIXY if you think that volatility is
heading up. On the other hand, XIV and SVXY are ETNs seeking to match the inverse of the daily
performance of the S&P 500 VIX Short-Term Futures index. You would consider buying such funds if you
think that volatility is about to fall.
a. Describe the empirical evidence about the relation between VIX and subsequent realized volatility.
Three steps are required. First, describe the empirical evidence on that relation based on published
work. Second, run time series regressions of actual volatility on lagged actual volatility and lagged
VIX and analyze the slope coefficient of VIX and its significance. Use several lags, including one
month, three months, six months, and one year. Third, compare average, volatility, and
autocorrelation of each of those annualized measures. The VIX is already annualized. You compute
actual volatility based on daily returns and then multiply the quantity by the square root of the
number of trading days per year.
b. Examine the performance of VXX and VIXY over the past five years. Use plots available at
yahoo.finance or any other source you prefer. Could you explain such performance based on the
VIX level? Could you explain it based on any other economic mechanism? (hint: try contango).
c. Looking forward, does it make sense to invest in VXX or VIXY for the long run? Who, if anyone,
should purchase volatility ETNs?
d. Common sense suggests that volatility and inverse volatility fund returns are perfectly negatively
correlated. Is it really the case?
e. Suppose that five years back you invested $1000 in one of the volatility ETNs and you also invested
the same amount in one of the inverse volatility ETNs. Plot the evolution of the $2000 invested
wealth over time. What do you learn?
f. Would it be correct to say that just like VXX and VIXY destroy value, in the same vein XIV and
SVXY provide positive performance for the long run (at least, as long as the VIX does not hit
extreme levels)?
g. Design a trading strategy in which you use the VIX level to invest in XIV.
h. Same as above but using the time series of XIV closing price.
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Section 2 – Technical trading rules
This question is based on daily returns on the S&P index. Focus on the last 20 years.
a. Use both simple moving average as well as exponential moving average (EMA) to implement
trading strategies, switching between the S&P index and cash. You can make your own decision
about the length of the moving average.
b. Same as above but using the MACD histogram which is the difference between the MACD line and
the Signal line. The MACD line is constructed as follows. Calculate a 12-day EMA and a 26-day
EMA. The difference between the 12-day EMA and the 26-day EMA establishes the MACD line.
The signal line is the 9-day EMA.
c. While the moving average as well as the MACD-histogram attempt to identify a trend, oscillators
aim to identify a change in trend. There are different sorts of oscillators. Pick one and implement a
market timing strategy based on that one.
Section 3 – Momentum in US Sectors
Momentum is the tendency of past winner (loser) stocks to outperform (underperform) in the future. In
addition, there are various sectors in the US economy – such as real estate, consumer goods, energy, among
others. Identify at least 12 US sectors and find an ETF implementing momentum using stocks belonging to
that particular sector. Compare the performance of that ETF to two benchmarks:
(i) The sector itself as represented through a sector ETF (e.g., XLF and XLK are both ETFs investing
in financial and technology stocks, respectively).
(ii) The market index.
Does momentum work? Explain in great detail.
Grade Components:
Class Participation (30%): There will be class sessions from time to time. I will communicate with
you through email announcements. It is mandatory to attend all those scheduled sessions. In general, you
are responsible for class lectures as well as any announcements, discussions, or remarks.
Project (70%): As described above.
On-line resources and databases: Below you can find a partial list of main international and local
databases which are available at HUJI.
Yahoo.finance
Reuters Data Stream – Financial data for all companies traded at the major stock exchanges. The access to
this data base is at the trading room or at the social sciences library.
Reuters 3000 – Real time data, news, and recent developments for all companies traded at the major stock
exchanges. Can be found only at the trading room.
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Predicta – Historical data for stocks, bonds, mutual funds, beneficial owners and others (mainly local
firms). Can be found at the trading room or at the social sciences library.
A-Online (Super Analyst) – Financial data and real time trading data for Israeli companies (Traded at the
TASE or in the US) which enables technical analysis. Can be found at the trading room or at the social
sciences library.
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