Three years ago the information technology sector was priced at a 50% discount to energy (see graph below). Since then, the discount has collapsed and the ratio of IT over energy (represented by XLK/XLE) is approximately 1.

Given the expectation of a "leaner" Fed and rates going up, energy might outperform technology and the ratio could pull back going forward. Over the last 30 years the energy sector has outperformed the overall market by almost 2.5% when rates are between 2.75% and 4%. That outperformance is even more pronounced when rates are higher. The main reason for this outperformance is that energy costs are included in the consumer price index and the energy sector is positively correlated to an expectation of higher rates and inflation. Thus, going forward, the above ratio may experience some head winds.

It is critical for investors to measure the potential impact to their portfolios if the ratio were to start a downtrend from here.

One simple way to check how exposed your portfolio is to these sectors is to aggregate the net exposure from stocks that are classified as 'IT' and 'energy'. However, there are 3 main shortcomings with this approach:

1. These classifications are mere labelings and no correlation effects are taken into account.
2. Stocks that are not classified as information technology and energy are excluded from the tally. For example: Tesla is not an IT or energy company but we know it will react to an IT correction.
3. Other non-equity assets cannot be included in the aggregation.

Instead of simply looking at exposures, we prefer to look at measures that help diagnose the explanatory power of technology in "normal" and "extreme" times. This way we can gauge how much of IT factor might creep into the portfolio when/if technology begins to underperform energy. Let's make it more concrete:

Step 1: MCTR

In normal times we decompose the risk of a portfolio (or a single security) into an on-the-fly factor model comprised of: technology sector represented by the XLK ETF, energy sector represented by the XLE ETF and residual. Residual is the amount of risk that cannot be explained by XLK and XLE.

We use a measure for risk called Marginal Contribution of Total Risk (MCTR), which takes into account the position sizes, the volatility of each position on a standalone basis, and the correlation of that position with other securities in the portfolio.

The table below shows various securities and their amount of technology risk in normal times.

(*) XLK included as a "control" security

It can be seen from the above table that the methodology allows us to rank securities that are not classified as "information technology" or "energy". For example, the technology component in Tesla's risk is similar in magnitude to that of Netflix. We can also classify non-equity securities, such as Tesla's corporate bond.

Given that correlations tend to increase in extreme market conditions, we also need to calculate the influence of the factor model: [XLK, XLE, residual] in scenarios whereby technology is under pressure. This is not a trivial calculation as we are not simply interested in measuring the expected profit and loss (P&L) when XLK is down 5% (for example), but we are interested in decomposing that expected P&L to determine what portion can be explained by XLK, XLE and what portion would remain unexplained. Decomposing the P&L under stressed scenarios is akin to a "stressed" factor model, or more precisely, a factor decomposition that is conditional on a set of posterior probabilities representing XLK's expected drop to be 5%. The following table summarizes the results:

Step 2: Expected P&L when XLK down 5%

The table shows how the component of expected P&L explained by technology is more pronounced during scenarios of a technology decline. Investors should closely monitor this "contagion" effect.

The tables above can be easily replicated with a few lines of our API code (available upon request).

Please contact us if you would like to calculate the above measures for your investment program(s) or individual securities.

An article on Bloomberg called our attention recently. The authors quote a research from Goldman Sachs contending that traditional safe havens (Gold, bonds, Yen) might not be appropriate diversifiers in a market environment such as the one we seem to be embarking: with volatility, rates and inflation all going up.

They called this lack of positive beta from traditional safe havens with the VIX and rates, "diversification desperation". Goldman's research also identifies Consumer Discretionary as a sector that has positive correlation with rates and might work well as a diversifier.

We decided to fact check the behavior of these traditional safe havens in times of market stress. Using our PortEngine API, and a few lines of code, we quickly created the table below:

Each row illustrates a different unlevered portfolio with one security. For example: the first row contains 1 XAUUSD with a NAV of $1326.4 (spot price of an ounce of gold). Each column is a different shock.

Our results corroborate the findings from the article, but our numbers are all forward looking and not the result of a regression beta.

Going forward, traditional safe havens seem pretty ineffective in times of stress. Consumer discretionary seems to possess positive correlation with interest rates.

These results were obtained with our "fast" model, i.e. a correlation half-life of 6 months and a volatility half-life of 2 months.

Please let us know if you would like us to run your own portfolio with these scenarios to make sure your expected PL will be within acceptable bounds.

We are proud to have been nominated for the 2018 Benzinga Global Fintech Awards in the "Best Proprietary Platform or API" category. The BZ Awards is a competition to showcase the companies with the most impressive technology, who are paving the future in financial services and capital markets.

Industry leaders will determine the finalists and winners, but there is a social voting as well. It is important to be well placed in both and therefore we kindly ask for your vote. Go to our our official Benzinga Fintech Awards profile page and click on any "Like" or "Tweet" buttons:

Thank you for your vote.

The Everysk Team

Last week the FT published an article titled: Rising interest rates punish US power sector.

It is well known that utilities tend to underperform when interest rates are expected to rise. These companies rely on regulated assets to generate their income, making their earnings and dividends more predictable: they tend to behave more like bonds.

The article also cites various companies that might get affected, such as Duke, FirstEnergy, Exelon, NextEra and American Electric Power.

We showed in our last blog how Everysk can calculate and propagate the impact of a rate hike to any security (not just fixed income). We essentially shocked the 10 year rate and calculated the expected profit and loss (P&L) of a $1 invested in various securities. The ability to perform these type of calculations, i.e. to propagate the risk of a shock to securities that do not depend directly on the exogenous shock for their pricing, is called transitive risk.

In this post, we use another important feature of our API: factor modeling. We go one step further from our last post, and not only quantify the P&L impact of a rate rise on stocks from the utility sector, but we also qualify that impact, decomposing how much is explained by rates (we use the 10- Year Treasury Constant Maturity rate published by the Fed) and how much is residual.

We wrote a simple code with our API (available upon request) to rank the top 100 dividend paying stocks according to their forward looking "rate sensitivity", conditional on rates rising. We used an instantaneous shock on 10 year rates of +50bps. The top 5 stocks are all from the Utilities sector:

So, if rates were to rise 50 basis points, Duke Energy would have an expected shortfall (CVaR) of -9.36%. This is a "worst case" expected P&L. Of that impact, -5.94% would be explained by the 10 year rates (63.5%) and -3.42% by residual, idiosyncratic risk. Despite Duke not having the largest P&L correction, it has the largest rate risk when rates rise.

Contact us to explore how our API can be used to reliably identify securities in your portfolio that are vulnerable to a rate rise.

The Fed is starting its massive balance sheet unwind. Given the current economic recovery and inflationary pressures, higher yields need to be paid on those treasuries in order to entice new holders. Last week the bond auction was already met with lukewarm demand, triggering the 10-year yields to reach a 4 year high.

Many market players had anticipated this moment, but the speed and magnitude of rate rises might surprise. Volatility is back.

Therefore, measuring the impact of a rate rise in your portfolio is critical at this juncture.

Most risk systems approach this somewhat simplistically: a rate rise propagates only to fixed income securities that depend on rates for their pricing. Equities would not show any PL impact.

Conversely, more sophisticated systems have the capability to propagate the rate risk to all securities in the portfolio. These systems are called transitive.

Everysk is transitive. It computes how an exogenous (or endogenous) shock propagates to ALL securities in the portfolio. The math and calculations to do so are not trivial. The computational power to quickly crunch these numbers is also significant.

Below, we show how a 1% rate hike in the 10 year treasury would instantaneously impact $1 invested in each security:

Some remarks on the table above:

As expected, the utilities ETF (XLU) has a larger negative PL impact from a rate hike, compared to the industrials ETF (XLI)

Banks (GS) tend to benefit from higher rates

The swap above pays fixed, receives floating and therefore benefits from higher rates, which is captured via Libor vs. Treasury correlation

Nestle, traded in Europe has a higher risk for a USD investor. The USD tends to appreciate against other currencies when treasuries sell-off. The investor with USD as base currency is effectively short USD as her/his money is parked in a Swiss Franc asset. Everysk captures all the currency effects in the results.

To understand our symbology, please refer to: Symbology.

To review our supported global exchanges please refer to: Supported Exchanges.

It is trivial to check the impact of a rate hike in your own portfolio using our REST API. Please visit us at: www.everysk.com/api.

We are pleased to announce the launch of our REST API V2. V2 is a powerful upgrade in terms of functionality and performance. Its most salient features are:

Efficiently performs risk aggregation (bottom-up) and factor analysis (top-down) in a single API call. Ability to supply forecasts for certain positions: these forecasts can represent outcomes that are difficult to be captured by market information, such as: a takeover situation, a pharmaceutical company clearing a drug trial or even a large correction not observed in the recent past. Ability to supply your own volatility surface for accurate option simulations. Ability to specify absolute (not percent) shocks on rates and spreads. More powerful exception handling of securities that cannot be mapped to our symbology. New documentation system and landing page.

Please contact us if you are interested on learning more about the API and its use cases.

Now when you sign in to your Dashboards account and navigate either to the description of plans or to the support page you will have access to a chat icon in the lower right corner:

Type any question and an Everysk representative will provide answers right away.

This blog demonstrates some properties from our fixed income asset classes. Specifically we will show how Everysk can capture intricate correlation effects between a corporate bond and a credit default swap.

First, let's build a portfolio in Dashboards containing $1M face of a hypothetical 5 year bond issued by Chesapeake and a hypothetical 5 year CDS.

The corporate bond above matures on june 2022, has a 6.77% coupon and is priced at par. The CDS has the same maturity and buyer of credit protection pays 500 basis points annually. See all supported symbology here.

By stress testing this portfolio against a credit index, such as the Merrill Lynch US Corporate Bond BB Total Return Index, we get the following expected PL (for a 2% drop, which is in the range of weekly movements that this index can experience):

We can see that the risk of the portfolio is largely hedged.

A more interesting stress test that captures some correlated effects is to shock the SP500. Below, we include a dynamic visualization whereby SP is being shocked from -1.2% (leftmost grey bar) to +1.2% (rightmost grey bar). This range is automatically calculated by Everysk and reflects the expected range of SP moves in a week:

The leftmost bar is highlighted to show it is the "active" bar. Move your mouse over the bar and you will be able to see the expected PL for the portfolio in that scenario, 0.22%. We also show low probability, high impact positive profit and loss (PL) of +1.09% and negative of -0.67%. These extreme PLs, specially the negative one, are generally called Conditional Value at Risk (CVaR) or expected shortfall. Then move the mouse over the 2 securities on the right: in a scenario of SP falling 1.2%, the corporate bond tends to lose money and the CDS tends to make. The contributions from each security to the overall portfolio properties are shown when you move the mouse over the securities. The summary is provided below:

To visualize what happens when SP is up, just unselect the leftmost bar by clicking on it and then click on the rightmost bar. As you move your mouse over the other scenarios, you will realize that the right visualization changes accordingly. The summary for a +1.2% SP shock:

So why is that the the CDS is expected to make more money (+0.47%) when the markets drops than the corporate bond is expected to lose (-0.25%), if the payer CDS is an exact short of the bond?

The main reason for this (expected) behavior is that corporate bonds have a credit spread risk factor that is positively correlated to the markets and an interest rate risk factor that is negatively correlated with the markets. When the markets are falling, the rate sensitivity helps immunize some of the pain, which is not present in the CDS. Therefore the CDS is expected to make more money. Conversely, when the markets are up the reverse happens: CDS is expected to lose more money (-0.66%) than the bond makes (+0.45%).

We can easily eliminate the interest rate effect described above by adding a payer swap to the portfolio:

The resulting behavior for SP500 shocks would be:

We have largely eliminated the behavior (note small value for grey bars).

Capturing these correlated effects via visualizations can be a powerful metaphor to engage an existing and/or prospective client. You can find some code that replicates these results using our PortEngine API here. Check the symbology of fixed income securities here.

We are happy to announce the release of our fixed income asset class library. It is comprised of 7 new instruments, all sharing common risk elements:

The new fixed income asset classes and their respective symbology prefix are:

Convertibles (CONV:) Exchangeables (EXCH:) Term Debt (LOAN:) Corporate Bonds (CORP:) Government Bonds (GOV:) Credit Default Swaps (CDS:) Interest Rate Swaps (SWAP:)

The above schematic illustrates the risk factors that are simulated for each security. For example: for convertibles, we simulate rate risk, spread risk and convertibility risk (underlying stock risk). We also need to simulate FX risk when the security currency is different from the base currency of the portfolio (FX risk not shown in the schematic above).

Some salient features for the remaining securities:

For corporate bonds we simulate rate and spread risk (convertibility risk is greyed out). For Government bonds we simulate rate risk and keep country spreads constant. Term loans have limited rate rate risk and no convertibility. CDS has pure spread (credit) risk. Interest rate swaps has pure rate risk.

The prefix inside each box is used for specify the security. For example: CONV:AMD 20250901 2.5 140 78.0 is our symbology for Advanced Micro Device's convertible expiring on September 2025, with a 2.5% coupon, priced at $140 and with a strike price of $78. See all supported symbology here.

These 7 new securities are fully integrated with the existing 10, namely: FX forwards, FX options, index options, future options, equity options, futures, global stocks, ETFs, mutual funds and indices. Furthermore our multi-asset, multi-currency calculation engine captures the correlated nature of all these assets.

Most money managers currently lack an efficient way to test the impact of new trades prior to executing them. The various systems available in the market are large deployments that were not designed for these type of on-the-flight, exploratory analytics.

Thus, if you want to quickly test how the addition of those SPY put contracts might affect the portfolio downside in a market shock, as a for instance, you might be out of luck.

In what follows we will show how what-if analysis can be performed effortlessly in Everysk Dashboards, in just 3 steps:

Step 1: Create a clone of the portfolio

In My Portfolios tab, drag and drop the portfolio you are interested to perform what-if analysis to the central panel. Then, in the right panel, you will see a new icon to create the clone:

Step 2: Edit the clone

When you click the button from Step 1, an editor with all properties and positions will open (see figure below):

Here you can quickly change many properties of the original portfolio, such as name, date, base currency and net asset value. Additionally, you can easily modify the contents of the original portfolio as follows:

To remove positions, just select them and click on the trash icon at the end of the list. To change a position quantity, just edit the Contracts/Shares field(s) To add a new position, just click on the button +Add Position

The image above illustrates a portfolio with 100 shares of Apple and 100 shares of Facebook that has been cloned. A new hedge was added to the portfolio, consisting of 1 at-the-money put option on SPY.

Step 3: Compare Original and Clone

Finally you can superimpose the original and clone with a template such as Stress Test Comparison

The unhedged portfolio is the light green and the portfolio with the put option is the dark green. Portfolio/Wealth managers can quickly ascertain the expected behavior of the trade just before executing it. All the process takes a few seconds from beginning to end.