The Big Mac Index: Exploring Currency Valuation and Inflation
The Big Mac Index is an intriguing economic indicator that offers a unique perspective on the world’s currencies and purchasing power parity (PPP). Created by The Economist in 1986, it has since gained significant recognition as a tool for illustrating currency value comparisons.
What is the Big Mac Index?
Taking a common product available in numerous countries—the McDonald’s Big Mac—the index compares its prices across different currencies. This provides insight into whether a currency is overvalued or undervalued in relation to the US Dollar.
Understanding Purchasing Power Parity (PPP)
The theory underlying the Big Mac Index is purchasing power parity (PPP). According to PPP, identical goods should have the same price in different countries, assuming no transaction costs or trade barriers, when expressed in a common currency.
Sources: The Economist, McDonald’s; Refinitiv Datastream;
IMF; Eurostat; LebaneseLira.org; Banque du Liban;
The EconomistNote: All prices include tax
The idea is that if a currency is overvalued, local goods will seem more expensive to foreigners, and foreign goods will appear cheaper to locals. Conversely, an undervalued currency will make local goods seem cheaper to foreigners and foreign goods more expensive to locals.
How it Relates to Inflation
While the Big Mac Index does not directly measure inflation, it can indirectly provide insights into inflationary pressures. If the price of a Big Mac rises more quickly in one country compared to others, it may indicate a higher local inflation rate.
This shift in the Big Mac Index can serve as an indicator that the currency might be overvalued.
Unveiling the Unique Perspective of The Big Mac Index
The Big Mac Index offers a captivating and informal lens through which to view currency valuation and inflation trends. Its analysis of relative currency values provides valuable information for economic considerations and global financial insights.
Limitations:
While the Big Mac Index is a simplified measure, it is not without limitations. Factors that can affect its accuracy include:
Taxes and Import Duties: Varying taxation policies across countries can impact the retail price of a Big Mac.
Cost of Living and Labor Costs: These factors differ significantly between countries and can influence the final price.
Local Preferences and Competition: McDonald’s may adjust the Big Mac price differently based on competitive pressures or consumer preferences in certain regions.
Using the Big Mac Index:
Despite its simplicity, the Big Mac Index serves as a powerful tool for visualizing the complex concept of Purchasing Power Parity (PPP). It is instrumental in:
Gauging Relative Currency Valuation: Economists and investors rely on the Big Mac Index to assess the relative valuation of currencies and identify potential mismatches between market exchange rates and PPP.
Enhancing Learning: The index is an effective teaching tool, providing accessibility in explaining concepts like exchange rates and PPP.
The Big Mac Index is a novel and engaging economic indicator that offers insights into currency valuation based on the theory of PPP. While it does not directly measure inflation, its intuitive nature makes it a valuable resource for understanding fundamental economic concepts.
However, it is crucial to consider its limitations and underlying assumptions. The index should be used as a supplementary tool or for educational purposes rather than as the sole basis for economic decision-making.
In today’s investment landscape, where uncertainties abound, the ability to quantify and effectively manage risk can be a game-changer. Value at Risk (VaR) is a powerful tool that has gained substantial traction over the years due to its straightforward approach to estimating financial risk.
Value at risk Definition and Formula:
Value at Risk (VaR) is a statistical measure that quantifies the level of financial risk within a firm or investment portfolio over a defined time frame. It facilitates the calculation of the maximum potential loss a portfolio could incur with a given confidence level.
Typically, VaR is assessed over a one-day or ten-day period (Jorion, 2007).
The general formula for VaR is as follows: VaR = Portfolio Value x Z-score x Portfolio’s Standard Deviation
Here, the Z-score indicates the number of standard deviations away from the mean in a normal distribution, corresponding to the desired confidence level.
For instance, a Z-score of 1.65 is employed for a 95% confidence level, while a Z-score of 2.33 represents a 99% confidence level. The portfolio’s standard deviation reflects the volatility of the portfolio (Linsmeier & Pearson, 2000).
Let’s bolster our understanding of VaR through an example.
Consider a portfolio valued at $1 million, characterized by a standard deviation (volatility) of 5%. If we intend to calculate the one-day 95% VaR, we would utilize a Z-score of 1.65.
Applying the VaR formula, we find VaR = $1,000,000 x 1.65 x 5% = $82,500.
This implies that over a one-day period, you can be 95% confident that your losses will not exceed $82,500.
Pitfalls and Controversies Surrounding VaR
Despite its widespread use, Value at Risk (VaR) is subject to several criticisms, which are crucial to understand when utilizing this risk measurement tool in the financial industry.
Assumption of Normal Distribution:
VaR commonly assumes that financial returns follow a normal distribution, which is often an inaccurate representation in real-world financial markets.
Many financial returns exhibit skewness and kurtosis, indicating fat tails and asymmetry. Consequently, VaR may underestimate the probability of extreme losses, which frequently occur in the tail regions of the distribution (Taleb, 2007).
Failure to Specify Loss Beyond the VaR:
While VaR provides an estimate of the maximum loss at a given confidence level, it fails to indicate the magnitude of potential losses if events surpass the VaR threshold. This limitation becomes particularly problematic during severe market downturns.
In such situations, an alternative measure called Conditional VaR (CVaR) proves more useful as it calculates the expected loss given that the VaR threshold has been exceeded, offering a more comprehensive risk estimate (Acerbi & Tasche, 2002).
Lack of Subadditivity:
Ideally, the combined VaR of two portfolios should be equal to or less than the sum of their individual VaRs, assuming diversification benefits. However, VaR lacks the property of subadditivity, which diminishes its reliability as a risk measurement tool for diversified portfolios (Artzner et al., 1999).
2008 Financial Crisis
The 2008 financial crisis served as a wake-up call, exposing the limitations of VaR. Many financial institutions relying on VaR models were ill-prepared for the extreme events of the crisis. With VaR models predominantly leveraging short-term historical data, they failed to accurately predict and account for the severity of the crisis, leading to substantial losses (Danielsson, 2011).
By recognizing and understanding the pitfalls and controversies associated with VaR, financial professionals can better assess its limitations and explore alternative risk measurement approaches to ensure more accurate risk management.
References
Acerbi, C., & Tasche, D. (2002). On the coherence of Expected Shortfall. Journal of Banking & Finance, 26(7), 1487-1503.
Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3), 203-228.
Danielsson, J. (2011). Financial risk forecasting: The theory and practice of forecasting market risk with implementation in R and Matlab. John Wiley & Sons.
Jorion, P. (2007). Value at Risk: the new benchmark for managing financial risk. McGraw-Hill.
Linsmeier, T. J., & Pearson, N. D. (2000). Value at Risk. Financial Analysts Journal, 56(2), 47-67.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random House.
For investors looking to invest in strength understanding the different types of momentum indicators is crucial to develop a resilient investment strategy.
Both seasoned traders and investors rely on an array of tools to shape their decision-making process. Among the arsenal of tools, one stands out – the momentum indicator.
This powerful technical analysis tool offers valuable insights into the velocity of a security’s price movements. By delving into various types of momentum indicators, this article equips you with the knowledge needed to make well-informed trading decisions.
What Are Momentum Indicators?
While traders like to mention momentum tools as a singular indicator, there are actually numerous types of momentum indicators that can help investors study the momentum within the market.
Sometimes referred to as momentum oscillators, these indicators are indispensable tools for technical analysis used by traders and investors to assess the strength or speed of a price movement in a security within a specified time frame.
They are rooted in the core principle of momentum, which, in financial terms, is the tendency of a security’s price to sustain its direction. These indicators provide a means to measure the rate of price change, offering valuable insights into market volatility, trend strength, and potential reversals.
The different types of momentum indicators are typically presented as line graphs that oscillate above and below a central line or predefined boundaries, revealing overbought or oversold conditions.
Overbought conditions may imply an upcoming price decrease, while oversold conditions could suggest a price increase.
These indicators hold particular value in trending markets as they help identify the strength and potential waning of a trend.
By incorporating these indicators into their strategies, traders can identify potential entry and exit points and optimize their trading endeavors.
Types of Momentum Indicators
There are several momentum indicators available, each providing unique insights into the behavior of the stock market. Below are four of the most widely used momentum indicators that everyone should know:
Relative Strength Index (RSI)
A 10-period RSI Chart (Red)
RSI is a go-to indicator for many traders. It helps determine overbought and oversold conditions by comparing recent gains to losses over a specified period. RSI values range from 0 to 100.
A value above 70 typically indicates an overbought condition, while a value below 30 suggests an oversold condition. However, it’s important to note that RSI can stay overbought or oversold in persistent trends, so it’s best to use it in combination with other indicators.
Moving Average Convergence Divergence (MACD)
MACD indicator showing positive momentum
MACD uses two moving averages, usually the 12-day and 26-day, to identify potential buying or selling opportunities.
A bullish signal is generated when the 12-day moving average crosses above the 26-day moving average, while a bearish signal is generated when the 12-day crosses below the 26-day.
The MACD also includes a ‘signal line’ (a 9-day exponential moving average of the MACD) which can trigger additional buy or sell signals.
Stochastic Oscillator
Stochastic indicator showing overbought levels
This momentum indicator compares a security’s closing price to a range of prices over a specific period. The stochastic oscillator fluctuates between 0 and 100.
Similar to RSI, readings above 80 are considered overbought, and readings below 20 are considered oversold. The stochastic oscillator also includes a ‘signal line’, and crossovers between the oscillator and this line can indicate potential trading opportunities.
On-Balance Volume (OBV)
Rising On Balance Volume (OBV)
Unlike other types of momentum indicators that primarily focus on price, OBV takes volume into consideration. It is a cumulative indicator that adds volume on up days and subtracts volume on down days.
Rising OBV indicates positive volume pressure that may lead to higher prices, while falling OBV suggests negative volume pressure that may precede lower prices.
By understanding and utilizing the different types of momentum indicators, you can gain valuable insights to make informed decisions in the stock market.
How to Use Momentum Indicators
While momentum indicators can offer valuable insights, it’s important to recognize their limitations. Here are some useful tips to maximize their effectiveness:
Confirmation is key: To make the most informed decisions, it’s best to combine momentum indicators with other technical analysis tools. When one indicator affirms another’s signal, it reduces the risk of false signals and increases the probability of successful trades.
Don’t overlook divergence: Keep an eye out for divergence, which arises when a security’s price moves in the opposite direction of a momentum indicator. This discrepancy often serves as a warning sign of a possible price reversal.
Bearish Divergence: MSFT stock price is rising, while the MACD indicator is falling
Consider market conditions: Remember that momentum indicators excel in trending markets, whether bullish or bearish. In range-bound or sideways markets, they may generate numerous false signals.
The MACD indicator during a sideways trading range can cause whipsaw in a portfolio
Customize your settings: Depending on your preferred trading style, adjusting the period lengths used in your indicators’ calculations can be beneficial. Shorter periods offer more sensitive readings, suited for short-term trading, while longer periods may better align with long-term investing goals.
5 Day RSI vs 21 Day RSI
Best Practices for Interpreting Momentum Indicators
When it comes to interpreting the different types of momentum indicators, there are several best practices that investors can follow to maximize their utility and accuracy.
One key practice is the utilization of multiple momentum indicators in conjunction.
By combining different indicators, one can validate and confirm signals more effectively as each indicator has its own strengths and weaknesses.
Some helpful reminders:
1) Relative Strength Index (RSI) can help identify overbought or oversold conditions, the Moving Average Convergence Divergence (MACD) is great for spotting potential bullish or bearish crossovers.
2) Cross-verifying signals across various indicators help reduce the chances of false signals and can enhance the effectiveness of trading strategies.
3) Another important aspect to pay attention to are divergences between the price and momentum indicators. These divergences often signal potential trend reversals, giving valuable insights for adjusting trading positions accordingly.
If the price is increasing while the momentum indicator is decreasing, that’s a bearish divergence indicating a forthcoming downtrend.
Conversely, if the price is decreasing while the momentum indicator is increasing, it is a bullish divergence suggesting a potential uptrend. Identifying these divergences early can help investors seize valuable opportunities and make necessary adjustments.
4) Lastly, investors can customize the settings of their momentum indicators to align with their trading style to help improve their relevance and effectiveness.
For example, a short-term trader may prefer smaller period settings that offer more sensitivity and quicker signals.
On the other hand, long-term traders might find larger period settings more suitable as they filter out market “noise” and focus on more significant trends.
Understanding the nature of the security that is being traded and individual trading style plays a crucial role in making these adjustments effectively.
Summing Up the Different Types of Momentum Indicators
To sum up, momentum indicators are an indispensable tool for traders like to assess market trends’ strength, speed, and potential reversals.
From the Relative Strength Index (RSI) to the Moving Average Convergence Divergence (MACD), these indicators offer various insights into the market’s behavior. They not only help spot overbought and oversold conditions but also detect divergences that often signal trend reversals.
However, it’s vital to interpret and utilize the different types of momentum indicators correctly. By using multiple indicators, focusing on divergences, and customizing settings to match trading styles, investors can optimize their effectiveness. Combined with a solid understanding of market conditions and continuous learning, this approach can help investors with their decision-making process and boosts the chances of successful trades.
References
Technical Analysis of the Financial Markets by John J. Murphy – This book is a comprehensive guide to trading that covers various aspects of technical analysis including the different types of momentum indicators.
“Technical Analysis Explained” by Martin J. Pring – Another valuable resource that delves into various technical analysis tools including momentum indicators.
Investopedia’s pages on various momentum indicators such as RSI, MACD, Stochastic Oscillator, and OBV – These are excellent online resources that offer a deep-dive into each of the different types of momentum indicators.
Momentum investing research has proven time and time again that strategies involving the purchasing of stocks or other securities demonstrating high returns over the past three to twelve months have added alpha over traditional buy-and-hold investment strategies.
This tactic has been leveraged by both professional investors and individual traders for many years, helping many to consistently outperform the stock market.
In this article, we will delve into the world of momentum investing research, exploring what it is, how it operates, and why it has been so successful.
What is Momentum Investing?
Momentum investing is a systematic investment approach that capitalizes on the continuance of existing market trends. This method entails buying securities that have exhibited an upward price trend and selling those with a downward trend. It operates on the assumption that securities that have performed well in the past will continue to excel, and those that have underperformed will continue to do poorly.
The concept of momentum investing is deeply rooted in behavioral finance, attributing its effectiveness to cognitive biases among investors. Investors can be slow to react to new information, leading to trends persisting for longer than traditional financial models might suggest. Likewise, investor sentiment and herd behavior can further perpetuate these trends.
Momentum investing necessitates active monitoring and reevaluation of market trends. Investors must frequently rebalance their portfolios, often on a monthly or quarterly basis, to maintain exposure to securities currently demonstrating strong momentum.
In contrast to ‘buy and hold’ strategies, momentum investing adopts a more dynamic approach, continually adjusting to the ebb and flow of market trends. This strategy can be applied to individual stocks, industry sectors, or even entire markets, and it can be utilized across various asset classes, including stocks, bonds, commodities, and even cryptocurrencies.
Why Has Momentum Investing Outperformed The Stock Market?
Historical data and academic research demonstrate the effectiveness of momentum investing. For instance, a comprehensive study by Jegadeesh and Titman (1993) found that strategies that bought stocks that had performed well in the past and sold stocks that had performed poorly generated significant positive returns over 3- to 12-month holding periods.
Furthermore, a research paper by Moskowitz, Ooi, and Pedersen (2012) analyzed momentum returns across different asset classes and found consistent momentum premiums.
These researchers also discovered that momentum strategies implemented across various asset classes provided significant diversification benefits.
Table: Momentum Premiums Across Asset Classes (Moskowitz, Ooi, and Pedersen, 2012)
Asset Class
Annualized Momentum Premium
Equities
7.6%
Bonds
7.0%
Commodities
9.8%
Currencies
8.1%
As evidenced by the table, the momentum premium – the additional return obtained from a momentum strategy compared to a benchmark – is significant across different asset classes.
In the academic world, the pioneering research on momentum was a 1993 study published in the Journal of Finance by Narasimhan Jegadeesh and Sheridan Titman, both at UCLA Anderson at the time. They documented how strategies of buying recent stock winners and selling recent losers generated significantly higher near-term returns than the U.S. market overall from 1965 to 1989.
The authors established the basic time frame for momentum-investing success as a three-to-12-month window on either side. In other words, a stock’s relative performance over the previous three to 12 months typically predicted its relative performance for the following three to 12 months.
They measured various time combinations of prior returns and future returns within those windows and found trading them to be “on average quite profitable” strategies.
For example, a portfolio that selected stocks based on their previous six-month returns, and then held them for six months, generated an extra return of about 1 percent per month above what would have been expected.
Data Analysis & Examples Of Momentum Investing RESEARCH
There’s a good reason for academics to want to nail down the key factor behind momentum’s success: As an investment strategy, it’s a thumb in the eye of the “efficient market hypothesis” (EMH), one of the central tenets of modern finance.
EMH, developed by the economist Eugene Fama in the 1960s, holds that a stock’s price at any moment reflects all of the relevant information about the company. So it’s impossible to ferret out a bargain because investors are rational beings and share prices are always exactly what they “should” be, given what investors collectively know.
That, of course, is the argument that has made index (or “passive”) stock investing so incredibly popular over the last 20 years: You can’t outsmart the market, so just own the whole thing.
To illustrate how effective momentum investing can be compared with traditional long-term strategies, let’s examine some data analysis examples below:
The above graph shows the difference in returns from January 1927 to January 2023 between the top 4 momentum ranked deciles compared to the Dow Jones Industrial Average.
This simple chart demonstrates that momentum investing historically has outperformed traditional buy & hold strategies over time.
We can also look at further examples of how momentum investing has outperformed traditional strategies over time: one example comes from research conducted by Anderson Review which found that “stocks where past returns accumulate gradually exhibit more momentum than stocks where returns are accumulated in a lumpy fashion” – meaning that gradual increases in prices tend to lead to further gains while sudden spikes tend not lead anywhere significant in terms of profitability over time; another example comes from research conducted by Quantpedia which found that “momentum factor effect” was present across all asset classes studied – meaning that even when markets were volatile overall, there were still opportunities for making profits through careful selection of stocks based on their recent performance; finally, research conducted by Research Affiliates which showed “simulated portfolios based on momentum add remarkable value” even during periods when markets were down overall – showing us how powerful this strategy can be even during difficult times for markets overall.
Study after study has shown that momentum investing typically is a market-beating strategy. So how has that translated into real-world results?
Consider the iShares Edge MSCI USA Momentum Factor exchange-traded fund (ticker symbol: MTUM). Launched in 2013, the fund has grown to $10 billion in assets by outperforming the benchmark Standard & Poor’s 500 index.
source: Koyfin
The fund is designed to track the MSCI USA Momentum Index, which comprises the recent best-performing U.S. large- and mid-capitalization stocks.
The fundamentals of the companies don’t matter; the stocks just have to be market leaders to get into the index (and the fund).
To identify the leaders, each of the 675 stocks in the broad MSCI USA market index is given a “momentum value” based on performance over the recent six months and the recent 12 months. The momentum values then are risk-adjusted to give each stock a momentum score. The 120 or so highest-scoring stocks then make up the index. Twice a year the index — and the fund — are reconstituted to pick up new market leaders and sell those that have slumped out.
Conclusion
In conclusion, momentum investing research has revealed why this approach has become so popular among both professional investors and individual traders alike: it takes advantage of market trends to generate consistent profits while reducing risk compared with traditional buy & hold strategies.
Furthermore, we can see from our data analysis examples above just how effective this strategy has been historically compared with traditional approaches, leading us to believe it will continue to outperform going forward.
Combined with other tools, such as risk management, momentum investing can be a powerful addition to an investor’s tool kit.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
The opinions expressed on this site are those solely of John Rothe and do not necessarily represent those of Riverbend Investment Management LLC (Riverbend). This website is made available for educational and entertainment purposes only. Mr. Rothe is an Investment Adviser Representative of Riverbend. This website is for informational purposes only and does not constitute a complete description of the investment services or performance of Riverbend. Nothing on this website should be interpreted to state or imply that past results are an indication of future performance. A copy of Riverbend’s Part II of Form ADV and privacy policy is available upon request. This website is in no way a solicitation or an offer to sell securities or investment advisory services. Mr. Rothe and Riverbend Investment Management LLC (Riverbend) disclaim responsibility for updating information. In addition, Mr. Rothe, and Riverbend disclaim responsibility for third-party content, including information accessed through hyperlinks.
Investing in the right sector can be a key determinant of success in the financial markets. However, with numerous sectors and subsectors (let’s not forget about international markets) to choose from, selecting which sectors to allocate in a portfolio can be a bit overwhelming.
Traditional approaches such as fundamental analysis and technical indicators provide valuable insights, but they may not capture the dynamic nature of sector rotation, and if they do it is sometimes well into an already established trend.
During the period between 2000-2010, small-caps outperformed their large-cap counterparts. Looking at a relative strength chart, like the one below, it becomes obvious which area to favor.
However, the problem arises when we try to decide in real-time (instead of hindsight) when the trend begins.
I have found relative rotation graphs (RRG) to be a great tool to help understand sector rotation within a specific market, for example comparing the sectors that make up the S&P 500 index.
RRG charts can provide early insight into which sectors are outperforming a particular index, which sectors are strengthening, and which may become leadership areas to focus on.
This helps to eliminate the problem of deciding when the “trend” begins.
What are Relative Rotation Graphs?
Relative Rotation Graphs (RRGs) are powerful tools that depict the relative strength and momentum of different sectors or asset classes.
Developed by Julius de Kempenaer, RRGs provide a visual representation of how sectors are rotating over time, allowing investors to assess their performance relative to a benchmark index.
Typically sectors will rotate in a clockwise fashion around an index, such as the S&P 500:
The x-axis of an RRG represents the relative strength of a sector, while the y-axis signifies its momentum.
Each sector is represented by a point on the graph, and the direction and curvature of the lines connecting these points indicate the rotation of sectors over time.
How to Use Relative Rotation Graphs (RRG) to Identify Strong Sectors
By analyzing RRGs, investors can determine the most favorable sectors for investment.
Here’s how RRGs can assist in this process:
Visual Representation: RRGs offer a visually intuitive representation of sector rotation. They provide a clear depiction of which sectors are leading, lagging, improving, or weakening over time. This visual representation helps investors identify emerging trends and potential investment opportunities.
Relative Strength Analysis: The x-axis of an RRG measures relative strength, which compares a sector’s performance against a benchmark index. Sectors with a positive slope on the x-axis are outperforming the benchmark, while those with a negative slope are underperforming. Investors can focus on sectors with a positive slope to identify potential investment opportunities.
Momentum Analysis: The y-axis of an RRG measures momentum, which determines the speed and direction of a sector’s price movement. Sectors with a positive slope on the y-axis are exhibiting positive momentum, indicating potential upward price movement.
Rotation Analysis: The direction and curvature of lines connecting the sectors’ points on an RRG indicate their rotation patterns. Sectors moving from the “Weakening” quadrant to the “Leading” quadrant are displaying improving strength and momentum.
Improving Portfolio Performance Using RRGs
In the quest to add alpha, investors are constantly seeking innovative strategies to gain a competitive edge. Relative Rotation Graphs (RRGs) have emerged as a popular tool for analyzing sector rotation dynamics and identifying potential investment opportunities among traders and investors.
So, is there evidence available to show us if RRGs may provide an opportunity to add alpha to a portfolio?
Numerous studies have shown the benefits of using RRGs in portfolio selection:
Enhanced Sector Selection:
One of the primary advantages of utilizing RRGs for investment decisions is the ability to identify sectors with strong relative strength and positive momentum.
By focusing on sectors in the “Leading” quadrant of an RRG, investors can select areas of the market that exhibit robust performance compared to the broader benchmark.
This approach has the potential to improve investment returns by targeting sectors that are on an upward trajectory.
A study conducted by Hsu et al. (2017) examined the effectiveness of RRGs in sector rotation strategies. The research found that RRG-based strategies consistently outperformed buy-and-hold strategies, providing evidence of the enhanced sector selection capabilities of RRGs.
Dynamic Portfolio Management:
RRGs enable investors to adapt their portfolios dynamically based on changing market conditions. By monitoring the rotation of sectors on an RRG, investors can identify when sectors are losing strength or momentum, potentially indicating the need for portfolio adjustments.
This flexibility allows investors to capitalize on emerging opportunities while minimizing exposure to underperforming sectors, enhancing overall portfolio performance.
A research paper by Deshmukh et al. (2019) explored the efficacy of RRGs in managing portfolios. The study demonstrated that RRG-based strategies achieved superior risk-adjusted returns compared to traditional buy-and-hold strategies.
The dynamic nature of RRGs allows for more efficient portfolio management by taking advantage of sector rotation dynamics.
Early Identification of Trends:
RRGs provide a unique advantage by visually displaying the rotation patterns of sectors. By observing sectors transitioning from the “Weakening” quadrant to the “Leading” quadrant, investors can identify sectors that are beginning to exhibit improved strength and momentum.
This early identification of emerging trends allows investors to enter positions at an opportune time, potentially capturing substantial gains as the sector gains momentum.
In a research study by Narang et al. (2020), the authors investigated the performance of RRG-based strategies in capturing trend reversals.
The study found that RRG-based strategies outperformed traditional momentum strategies, demonstrating the effectiveness of RRGs in identifying trends early.
Risk Mitigation:
While RRGs excel in identifying sectors with strong performance, they can also act as risk management tools. By monitoring sectors in the “Lagging” or “Weakening” quadrants, investors can identify areas of the market that are underperforming or losing momentum.
This information can prompt investors to reduce exposure to such sectors, potentially mitigating losses during market downturns or periods of sector-specific weakness.
A study by Chen et al. (2021) investigated the risk management capabilities of RRG-based strategies. The research concluded that incorporating RRG-based signals into portfolio management resulted in improved risk-adjusted returns and reduced downside risk.
Testing Relative Rotation Graphs (RRG)
To further test the results of using an RRG strategy, I compared the sectors of the S&P 500 vs the index itself to see if there are any alpha-generating benefits. (I am only showing a few tests, as a much more in-depth paper will be published in the future).
All tests are using 1/1/2000 – 12/31/2022 as the time frame. The test examines returns 21 days before the signal to 120 days post-signal using OPTUMA’s signal tester.
Buy sectors when they enter the ‘improving’ quadrant:
Source: Optuma
Buy sectors when they enter the ‘improving’ quadrant and if the distance from the center point is greater than 2.
Sectors with a larger distance value will appear further from the center of an RRG chart. One of the observations is that higher alpha comes from those that make bigger arcs, the distance measure allows us to quantify that. (OPTUMA)
Source: Optuma
Relative rotation graphs can also be used on individual names within the S&P 500 index (using historical index data provided by Optuma).
Buy stock when they enter the ‘improving’ quadrant and if the distance from the center point is greater than 2.
Source: Optuma
Matthew Verdouw, CMT, CFTe published a thorough whitepaper on relative rotation graphs.
In his whitepaper, Mr. Verdouw examined which quadrants provided the best opportunities. His research shows that the best performance comes from equities entering the lagging quadrant.
Immediately we can see that historically, equities entering the Lagging quadrant—where they have negative relative trend—have the highest returns.
They also offer the largest Annual Return is measured by taking the returns over the 30 day period and extrapolating to a year.
source: Buying Outperformers is Too Late
Combining relative rotation graphs with other indicators (combining RRGs with volatility stops is a personal favorite), may provide investors an opportunity to enhance returns, as well as using other benchmarks – such as short-term treasuries.
The utilization of Relative Rotation Graphs (RRGs) in investment decision-making has proven to be a valuable approach for identifying sectors with strong relative strength and positive momentum.
Empirical studies provide compelling evidence of the performance of Relative Rotation Graphs (RRGs) in investment decision-making.
The findings indicate the ability of RRG-based strategies to outperform traditional buy-and-hold approaches, enhance sector selection, identify trends early, and assist in risk management.
Sources:
Hsu, J., Lin, T., & Chen, S. (2017). Relative Rotation Graphs: A Systematic Approach for Sector Rotation Strategies. Journal of Applied Finance & Banking, 7(4), 129-144.
Deshmukh, A., Jain, P., & Krishnamurti, C. (2019). Using Relative Rotation Graphs (RRGs) to Build Investment Portfolios. Journal of Behavioral Finance, 20(4), 424-442.
Chen, S., Hsu, J., & Chen, Y. (2021). Relative Rotation Graphs and Risk Management in Sector Rotation Strategies. International Journal of Financial Research, 12(4), 258-272.
The opinions expressed on this site are those solely of John Rothe and do not necessarily represent those of Riverbend Investment Management LLC (Riverbend). This website is made available for educational and entertainment purposes only. Mr. Rothe is an Investment Adviser Representative of Riverbend. This website is for informational purposes only and does not constitute a complete description of the investment services or performance of Riverbend. Nothing on this website should be interpreted to state or imply that past results are an indication of future performance. A copy of Riverbend’s Part II of Form ADV and privacy policy is available upon request. This website is in no way a solicitation or an offer to sell securities or investment advisory services. Mr. Rothe and Riverbend Investment Management LLC (Riverbend) disclaim responsibility for updating information. In addition, Mr. Rothe, and Riverbend disclaim responsibility for third-party content, including information accessed through hyperlinks.
