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October 28, 2012

The SMALL TRADES JOURNAL contains articles written for do-it-yourself investors.  Please scroll down to read the latest postings.  Here are tips for navigating the articles:

  • The CONTENTS page provides links to topics in alphabetic order.
  • OR type a keyword into –SEARCH THIS BLOG– located on the side bar.
  • The GLOSSARY contains definitions of many investment terms.
  • The DISCLAIMER emphasizes that I am not a professional adviser/investor.

Here are links to lead postings:

Distinction: The focus of this blog is on accumulating wealth by actively investing in equities.  Strategies of leverage and derivatives-trading are not advised for individual investors and therefore seldom discussed.  On the important topic of savings withdrawal, I must refer you to free content provided by an excellent blog entitled investingforaliving.wordpress.com. That same blog also shines in its discussion of investing for cash distributions.  Although I enjoy reading good articles on retirement published by aaii.com, this blog avoids a focused discussion of planning for retirement.

Advocacy:  EVERY YOUNG PERSON SHOULD BEGIN TO INVEST FOR RETIREMENT WITHOUT PAYING AN INVESTMENT ADVISER.


The Physics of Wall Street, by James Owen Weatherall

April 25, 2017

Physics needs math, so does Finance. Then no wonder some curious physicists began to create math models of financial markets in the 19th Century, only to find the task more difficult than imagined. Advances during the 20th Century ulitmately generated great wealth among a group of saavy traders known as the “Quants”. Their sophisticated trading models worked for a while until widespread use by firms in ‘Wall Street’ caused the global financial system to collapse in 2008. New models continue to evolve today. This book pays tribute to scientists who tackled the problem of modeling financial markets.

The evolution of market models

  • Bechelier made the first price-distribution model of stock markets. His normal distribution only worked in the Paris Bourse where there was little variation of prices.
  • Osborne postulated that returns, not prices, are normally distributed. His model of the ‘random walk hypothesis’ expanded the understanding of price variation.
  • Mandelbrot claimed that distributions of financial markets are more variable than previously thought.
  • Thorp, Black, and Scholes converted price-distribution models to algorithms for daily trading. Their options pricing model was adapted from Osborne. Black later described the shortcomings in his paper “The holes in Black-Scholes”.
  • The Prediction Company of Farmer, Packard, and McGill used black box models to improve the Black-Scholes model. They capitalized on short-term inefficiencies in the market.
  • Didier Sornette predicted market catastrophes.

Today’s market models are still imperfect!

Early Probability Theory, 16th-17th centuries

1526: Cardono wrote an unpublished book on the theory of probability based on the odds of dice games. For example, what is the chance of rolling 2 dice for a sum of 10?

  • mathematical odds: 3 outcomes in 36 tries, or 1 in 12.
  • betting odds: 33 to 3, or 11 to 1. Bet $1 and either lose it or win $11 plus the refunded $1.

1654: Pascal and Fermot established the modern theory of probability based on various gambling games. They realized that probability is a chance, not a certainty. In the 20th century, it was realized that the a probability becomes a certainty when taking an infinite number of chances [Law of Large Numbers].

Random behavior of market prices, 19th-20th centuries

Bacheleier invented mathematical finance in the late 19th century. His graduate thesis applied probability theory to market speculation. In his ‘efficient market’ theory, Bechelier assumed that future prices take a ‘random walk’ within limits that describe the graph of a bell-shaped curve. In other words, stock prices have a normal distribution with a stable average. Bechelier’s ‘random walk’ model spawned 2 books in the 20th century.

  • 1947: Samuelson published “Foundations of Economic Analysis”.
  • 1964: Paul Cootner published Bechelier’s thesis in “The Random Character of Stock Market Prices”.

1959: An astronomer named Maury Osborne wrote “Brownian motion in the stock market”. Osborne dismissed the idea that stock prices have a normal distribution. Instead, the rate of return was normally distributed. His plot of stock prices was not bell-shaped, but lump-shaped with a long tail to one side. Osborne was first to show the importance of the log-normal distribution of prices to markets.

1960s: Benoit Mandelbrot discovered fractal geometry and adapted the consequences to understanding markets. Mandelbrot’s method described extreme market events.

1965: The issue was whether to analyze stock prices with Osborne’s or Mandelbrot’s method of analysis. Today’s consensus is that rates of return are fat-tailed with an unstable average.

1973: Burton Malkiel adopted Osborne’s work in a book called “A Random Walk Down Wall Street”.

Bachelor, Osborne, and Mandelbrot neither traded nor earned profits; they were academics.

Hedging

1961: Edward O. Thorp beat the game of Roulette with a successful strategy. He later showed that math models could earn profits from financial markets by operating a hedge fund. Thorp believed that the stock market is the world’s biggest casino. Buying stock is betting that the price will rise and Selling stock is betting that the price will fall. The true price of a stock is where the odds of winning and losing are equal. He devised the ‘delta hedging’ strategy of picking the right mix of warrants and stocks to consistently earn a 20% annual return. The idea was to simultaneously short-sell warrants and buy underlying stocks. The stocks would soften the impact of a bad bet and augment the impact of a good bet.

1967: Thorp co-authored the book, Beat the Market. Jay Regan, a stock broker, partnered with Thorp to create the Princeton-Newport Partners hedge fund.

1969: Fischer Black derived a relationship destined to become the Black-Scholes-Merten model for the pricing of options. Black made quantitative finance an essential part of investment banking.

Forecasting

1991: Physicists James Dayne Farmer and Norman Packard studied nonlinear forecasting. Given a chaotic process such as the financial market, their goal was to predict the next movement of prices.

1991: Farmer, Packard, and McGill formed The Prediction Company with the goal of profiting from Wall Street. They developed black box models of algorithmic trading which often worked for unknown reasons but also suffered unpredictable failures. It is still a mystery how market patterns are corrected.

1997: Didier Sornette, a geophysicist, studied the patterns of complex systems to predict critical events in the physical and social sciences. He filed a patent notice in 9/17/1997 that predicted a market crash the following month. Then he bought far-out-of-the-money Put Options to earn a 400% profit from his prediction.

Reform

2008: The economic collapse of 2008 presented an opportunity to change how economists think about the world.

2009: Smolin, Weinstein, and others convened a conference of intellectuals to develop new models of economics. They failed to agree on the problems and solutions.

Author’s Conclusions

All of the physicists’ models had successes and failures, but their works represent steps in the evolution of understanding markets. Financial modeling is an evolutionary process in which excellent assumptions can be destroyed by a change in market conditions. The realistic goal is to develop a model that provides a good answer at the moment. Why? One reason is that markets are evolving in response to economic growth, regulations, and innovation. Models ultimately fail!


Finance for Nonfinancial Managers, 2nd Ed., by Gene Siciliano.

April 6, 2017

Book Review

The life cycle of a successful company progresses through periods of rapid growth, slow growth, no growth, decline, and demise.  An adaptable company may endure with successive cycles of renewal, rebirth, and resurgence.  This book describes a pathway to success.  Finance is a sub-theme; other themes are accounting and management.

Accounting

A general ledger of all transactions is used to prepare all financial reports and assess the company’s performance. Companies that issue publically traded stock are required to publish 3 financial statements every quarter of the fiscal year. They are:

  1. Balance sheet
  2. Income statement
  3. Cash flow

The Balance sheet is a snapshot of the company’s financial condition at the end of the fiscal period. Items are reported under headings that conform to this equation:  Assets = Liabilities + Equity.  Assets and Liabilities are subdivided into Current and Long-term transactions. Current transactions are expected to be completed in the next 12 months and indicate the company’s liquidity. Long-term transactions indicate the company’s leverage, plus more. Equity is the company’s net worth. Within this framework, the author explains and discusses items of special relevance to business management.

The Income statment describes business operations during the time period between 2 balance sheet dates. According to rules for accrual accounting, every transaction has an order date and delivery date. Subtotal transactions are grouped under revenues, production, operations, other transactions, and net income. Net income (i.e., company profit) is the bottom line of accrual accounting in the Income statement. Readers of this chapter learn how to evaluate the business and its managers.

More businesses fail due to the lack of cash than the lack of profit. Consequently, it’s important that the Cash Flow statement reports a net cash balance from the net cash flows of operations (CFO), investing (CFI), and finance (CFF). The CFO is derived from Net Income (in the Income statement) by excluding non-cash transactions listed in the Income statement such as depreciation, receivables, pre-paid expenses, inventory, and payables.

Finance and Management

The remaining chapters are devoted to these topics:

  • Key performance indicators (KPIs) for evaluating financial statements.
  • Cost accounting, the analysis of manufacturing productivity.
  • Finance of new projects by previewing the return on investment (ROI), weighted cost of capital (WACC), and internal rate of return (IRR). Never invest in a project that has a negative IRR.
  • Breakeven point, to determine such things as the price of a product’s launch and risk assessment.
  • Creating a business plan to guide business operations and attract financial resources.
  • Annual budget, part of the business plan.
  • Financing the company by getting a loan (debt) or selling shares (equity).
  • Entrepeneurship.

Stop losing value from a declining price

March 4, 2017

background

The market value of your stock equals your principal (i.e., the amount you invested) plus any profit or loss from price fluctuation. The market price that moves below what you paid to purchase the stock will produce a loss of principal if you sell the investment. Here are several risk factors that may drive stock prices downward:

  1. Company performance. ‘Good’ companies attract investors. Conversely, ‘distressed’ companies repel investors.
  2. Industry performance. Business cycles can affect the sales of products from an entire industry. For example, sales of new automobiles declined during the Recession of 2008.
  3. Market cycles. Aside from business performance, the entire stock market is subject to periods of declining prices due to massive selloffs by investors.

The risk of an extreme loss can be prevented by setting a stop-loss price (“stop”) to sell part or all of your shares.

ways of setting the stop

The systematic way is quite simple. If the market value is below your invested principal, then select an absolute loss or a fraction of the principal. Examples:

  1. Absolute loss. Suppose you invest $5,000 in 100 shares of stock (i.e., $50/share) and you can tolerate a loss of $1,000 should the price start to fall. Regardless of future prices, you choose to stop the decline at $1,000 below the original $5,000 value. In this example, the stop would be $40/share [stop = (value – loss)/shares = ($5,000 – $1,000)/100].
  2. Fraction of value. Suppose you can tolerate a 10% loss from an investment originally valued at $5,000 for 100 shares.  Ten percent is one-tenth of 100, which is equivalent to a decimal number of 0.10. The stop would be $45/share [stop = (1.00 – decimal)*value/shares = (1.00 – 0.10)*$5,000/100].

The technical way is based on the stock’s historical prices. If you want to minimize the chance of a sale, set the stop at the lowest price from the past 5-10 years. Beware that setting the stop at a historical low may incur a steep loss. Other ways involve the more complicated analyses of trendlines, moving-average lines, or price statistics.

Another way is to adjust the price gap (gap = market price – stop) to the growth of capital gains. As the market price increases over time, choose a narrow gap to protect the capital gain or a wide gap to reduce the chance of a trade. Generally speaking, widening the price gap will reduce the chance of a trade at the risk of incurring a bigger loss.

add a limit price (“limit”) for extra protection

A brokerage firm will enforce your stop order for 30-90 days depending on the firm’s trading platform. The firm’s computer activates the order when the latest market price reaches the stop. The order is then filled at the next available price. In a chaotic market, the price could plunge below your stop to an exceptionally low value at the next available trade, resulting in a bigger loss than you planned. You might be able to prevent this result by setting a limit slightly below the stop. The trading order would be filled somewhere within the stop-limit price zone unless the transaction is cancelled, unfilled, when the next available price dips below the limit. The limit helps protect the extent of your loss.

who should worry about an extreme loss?

Nobody’s immune, but long-time investors have the least concern. Investment strategies such as dollar-cost-averaging and automatic-dividend-reinvestment plans will help protect against damages from periodic bear markets. Short- and intermediate-time investors are at greater risk for incurring an extreme loss from market down-cycles. For example, families who are saving to pay college fees or to buy a home risk big losses from a bear market.

conclusion

Stop orders are used to set the price for buying or selling exchange-traded products such as stocks, ETFs, and REITs. This article discussed the use of a stop-limit order to sell a stock in a declining market. Brokerage firms may restrict the duration of stop-limit orders to 30-90 days after which the order is cancelled without a transaction until you renew the order. Periodic renewals allow you to reconsider your strategy in light of the prevailing price trend. In a downtrend, simply renew the order. In an uptrend, you may wish to protect a growing profit by resetting the stop-limit order to higher prices. Click on this link to skimming a profit for another perspective on protecting a growing profit.

Copyright © 2017 Douglas R. Knight


Skim the profit?

February 7, 2017

Selling all or part of a profitable investment is a tough choice to make.  On one hand, holding the investment allows time to accumulate a high return, but at the risk of losing profit in the market’s next big decline. On the other hand, selling portions of the investment to ensure a profit today will diminish the future return.

Both choices are easy to illustrate by imagining a stock investment that pays no dividends.  Assume there is a consistent growth of stock price and that no additional shares are purchased after the original purchase. The profit is skimmed by selling part of the investment when its market value grows to twice the original purchase.  Repeat the process every time the market value doubles until the investment is closed.  Chart 1 illustrates the skimming of a $1,000 investment.

chart 1, Market values.  $1,000 was invested in a good growth stock that paid no dividends. A generous 15% annual return doubled the market value every 5 years. The HOLD strategy (black squares) was to avoid selling for 20 years. The SELL strategy (green circles) was to sell half the shares every time the market value doubled. There were no trading fees.

After 5 years, the investor could claim a profit of $1,000 on the original $1,000 investment. Then the choices would be to close the investment at $2,000, withdraw only the $1,000 profit and wait for more (green circles), or withdraw nothing and wait for a bigger profit (black squares). The largest profit is made by waiting 20 years.

Chart 2 illustrates the accumulated cash balances of the HOLD and SELL strategies.

chart 2. The cash balances of the strategies in chart 1 are illustrated in this chart using the same symbols for data points. After 20 years, all remaining shares are sold for cash. The end point of each graph is the final cash balance.

chart 2, Cash balances. The cash balances of both strategies in chart 1 are illustrated in this chart using the same symbols for data points. The proceeds from every sale were held in a cash account and allowed to accumulate for 20 years.  After 20 years, the remaining shares were sold for cash. The end point of each strategy is the final cash balance.

After closing the investment in 20 years, the accumulated cash balance would be $16,367 from the HOLD strategy and $5,045 from the SELL strategy.

Alternate conditions

The accumulated cash balance will vary according to the annual rate of return (appended chart 3), the amount skimmed (appended chart 4), and the payment of dividends (appended chart 5).  In every condition, the total profit of the HOLD strategy exceeds the total profit of the SELL strategy.

Conclusion

On the question of whether or not to skim profits, skim if you need cash in the next 5-10 years. Otherwise, don’t sell without reassessing the investment or using a risk management scheme.  The question of selling for a loss was excluded from this discussion; that’s a different topic.

Appendix: Tables of cash balances

Charts 3-5 are tables of cash balances that represent profits from an imaginary investment of $1,000. The choices for taking a profit were to HOLD the investment for 20 years before liquidating the account or to SELL profitable portions of the investment.  Assume there were no trading fees.

Chart 3 shows that a 15% annual rate of return earned a bigger profit than a 7% annual rate of return.  Furthermore, the HOLD strategy earned a larger profit than the SELL strategy at both rates of return.

chart 3.

chart 3, Rate of return.  $1,000 was invested in a good growth stock that paid no dividends. No shares were purchased after the original investment. The 20-year cash balance (cells) was only affected by the annual rate of return (rows) and liquidation strategy (columns).  The HOLD strategy did not sell shares for 20 years.  The SELL strategy sold half the shares whenever the market value doubled in size during the 20 year period.  The 7% rate permitted 1 selling period and the 15% rate permitted 4 selling periods. 

Chart 4 illustrates the effect of skimming 50%, 100%, or 150% increments of market value.

chart 4.

chart 4, Increments of market value. $1,000 was invested in a good growth stock that paid no dividends. The investment’s annual rate of return was 15% and no shares were purchased after the original purchase. The cash balances (cells) accumulated every time period (rows) among 3 different increments of market value (columns). The HOLD strategy did not sell shares for 20 years. The ‘rule’ for the SELL strategy was to sell a portion of shares when the market value grew by approximately 50% every 3 years ($521), 100% every 5 years ($1,011), or 150% every 7 years ($1,660).

The HOLD strategy outperformed the SELL strategy. With the SELL strategy, waiting longer to skim bigger profits accumulated a larger cash balance after 20 years. Why? The bigger profits were less frequent, which had the effect of preserving the investment’s principal for longer time periods.

Chart 5 reveals a surprising effect for skimming profits from reinvested dividends.

chart 5.

chart 5, Dividends. $1,000 was invested in a good growth stock that paid a 2% dividend on every share. No shares were purchased after the original investment unless the dividends were automatically reinvested. The cash balances (cells) accumulated with the passage of  time (rows) among 3 types of investments (columns). The HOLD strategy did not sell shares for 20 years. The SELL strategy removed half the remaining shares every 5 years.

There were no surprises in the HOLD strategy. Reinvested dividends accumulated the largest cash balance over 20 years. However, reinvested dividends accumulated the lowest cash balances in the SELL strategy. Why? Slightly more shares were sold every 5 years from ‘reinvested dividends’ compared to ‘no dividends’. Yet the same number of shares were sold from ‘cash dividends’ compared to ‘no dividends’. The cash dividends directly augmented the cash balances.

Copyright © 2017 Douglas R. Knight


Young lives matter

January 25, 2017

They need protection from the ‘streets’, a decent eduction, and financial skills.


2016

January 14, 2017

My SmallTrades portfolio holds stocks and four classes of exchange-traded index funds (ETFs).

chart 1

chart 1

Investment plan

The goal is to outperform a reputable benchmark, the Standard & Poors 500 Total Return Index, on a sustained basis.  The ETFs are diversified and rebalanced in order to partially offset the losses of a declining market. A small group of stocks are used to boost the investment returns.

Performance

In FY2016 the portfolio’s market value increased by 8.3% due to a 9.1% gain in stock value and 8.1% gain in ETF value. Charts 2 and 3 illustrate the nominal (solid lines) and real (dashed lines) growth in unit value for shares of the portfolio, ETF group, stock group, and benchmark. The number of shares for each entity was the initial market value divided by $1 of U.S. currency.  Assume that the initial unit value of $1 was a real value unaffected by inflation.

Chart 2 shows the pattern of unit-value growth for the benchmark (black lines) and portfolio (blue lines) since December 31, 2007.

chart 2

chart 2

The unit value of both entities declined in year 2008 and began to recover in year 2009. The benchmark (black lines) recovered in year 2011 while the portfolio (blue lines) is still struggling to recover [notes 1,2]. The effect of inflation was to devalue real growth (broken lines) compared to nominal growth (solid lines). The real unit value signifies the purchasing power of the investment. The investment has greater purchasing power than uninvested money when the real unit value exceeds $1.

Chart 3 shows the result of implementing the current investment goal [note 2] with a small group of stocks (red lines) and large group of ETFs (blue lines). In chart 3, the initial unit value was re-calculated on December 31, 2013.

chart 3

chart 3

Since 2013 the stock group clearly outperformed the benchmark (black lines) and ETF group. The success of the Stock group is attributed to investing in ‘good’ companies for the long term [note 3].

Stock group

Chart 4 shows the market sector and market cap diversity of the stock group defined in chart 1.

chart 4

chart 4

Several stock trades were made during FY2016 to improve the chance for success.
Closings:

  • Alibaba Group (BABA), for 10% capital gain, to exit the Chinese market.
  • Geely Automobile (GELYF), for 14% capital gain, to exit the Chinese market.
  • Corning Inc. (GLW) for no gain.
  • iRobot Corp. (IRBT) for 10% capital gain.
  • ITC Holdings (ITC) for 14% capital gain, due to the stock’s delisting.
  • Stericycle (SRCL) for 34% capital loss, to stop further loss.

Purchases:

  • Biogen (BIIB), an innovative biotechnology firm.
  • Cal-Maine (CALM), a leading producer of shelled eggs.
  • Express Scripts Holdings (ESRX), a large mail order pharmacy
  • Royal Bank of Canada (RY), a well-capitalized bank.

ETF group

Chart 5 shows the distribution of asset classes among the ETFs. All asset classes drifted from an allocation plan of 30% stocks, 30% REITs, 20% bonds, and 20% gold [note 4].

5-etf-distribution

chart 5

The SmallTrades portfolio’s primary strategy for risk management is holding a large group of diversified ETFs that are rebalanced to correct a significant allocation error. In theory, a significant drift of asset classes occurs when one asset class surpasses a 28% allocation error.  At the end of FY2016, the existing allocation errors (blue bars) were within 24% error limits (red dashed lines) as illustrated in Chart 6.

chart 6

chart 6

Chart 6 reflects the portfolio’s response to an incline in equity markets compared to decline of the bond and gold markets. History has shown that a decline in equity markets tends to be offset by a rise in the bond and gold markets.

Plan for FY2017

The SmallTades portfolio will continue to be actively managed for long term success. The ETFs will be rebalanced anytime there’s a 24% allocation error or a modification of the ETF holdings. I would like to own fewer large cap stocks in favor of small- and mid-cap stocks issued by good companies with potential growth of earnings.

Notes

  1. On 12/31/2007, the portfolio held a group of actively managed mutual funds in a tax-deferred Roth account. Since then there have been no cash deposits or withdrawals and the portfolio still resides in the Roth account. During 2007-2010 the mutual funds were traded for stocks in an attempt to earn a 30% annual return by process of turning over short term ‘winners’. Several mistakes led to a big loss:  A) after a couple of short term capital gains from Lehman Brothers Inc., I ignored the dangers of that company’s large debt and lost $45,000 during its decline to bankruptcy.  B) substantial long term profits from good companies were lost by selling holdings for short term profits. I was trying to earn a quick 30% annual rate of return and immediately re-invest in the next set of winners. It was too difficult to identify the next winners.  C) day trading also prevented a 30% return.  It was a game of chance that I played without a strategy and I was fortunate to break even.  D) a trial of investing in leveraged ETFs resulted in losses due to negative compounding.  Leveraged ETFs were very high-risk investments that I made without a sound strategy.
  2. I abandoned the goal of a 30% annual rate of return in 2012 by adopting a more realistic, but still aggressive, goal of outperforming the benchmark. That same year, I changed my investment strategy to that of holding a mixed portfolio of 80% broad market ETFs and 20% stocks for the long term.
  3. ‘Good’ companies attract and retain investors for many years. I search for profitable companies with growth potential that are undervalued by the stock market. My search methods include reading reputable sources of business news, participating in investment club discussions, using stock screeners, and attending investor conferences.  I include and exclude stocks by reading analyst reports, financial statments, SEC filings, and market analyses. Valuation critieria help me decide if the stock price is worth paying.
  4. Prior to March, 2016, five ETFs were allocated to four asset classes with each asset class holding 25% of the combined market value. Since I don’t depend on making withdrawals from the SmallTrades Portfolio, I increased my exposure to global stocks and REITs by decreasing my exposures to investment-grade bonds and gold bullion. The new allocation rule was 30% stocks, 30% REITs, 20% bonds, and 20% gold. Any drift in allocation to a 24% error will be rebalanced.

Copyright © 2017 Douglas R. Knight


R-squared, the linearity of investment returns.

December 24, 2016

[updated 12/25/2016: R2 is a useful measure of indexing]

The R-squared (R2) statistic describes a pattern of plotted data with respect to a straight line. R-squared is called the coefficient of determination (ref 1,2).

random

The black dots in figure 1 represent investment returns that are poorly related to market returns. There is a random distribution of investment returns with respect to market returns. The blue line is an inadequate representation of the relationship simply because there is no relationship. The R2 score for this distribution is 0.03. Conversely, the black dots in figure 2 show the ‘herding’ of data around a straight line.

ordered

Figure 2’s investment returns are highly related to market returns with an R2 of 0.997.

Significance

The R2 score represents the degree of alignment of data to a best-fit line as determined by regression analysis. The lowest possible score of 0 indicates a random pattern of data with absolutely no alignment. The highest possible score of 1 represents complete alignment.

The product of R2 X 100 represents the percent of variation in investment returns that are related to market returns (ref 1,2). In other words, R2 measures the relavance of the best-fit line to a set of data. Relavance increases as the R2 score varies from 0 to 1.

The lowest score of 0 defies any financial analyst to draw a meaningful line for investment returns as they relate to market returns. In figure 1, the incline (β) and Y-intercept (⍺) of the blue line are unreliable measurements of investment performance.

The highest R2 score of 1.00 identifies a straight line of near-perfect predictions of returns. Any R2 above 0.75 identifies a straight line for making predictions of returns. Lower scores represent increasingly random events. In figure 2, the incline (β) and Y-intercept (⍺) are reliable measurements of investment performance.

R-squared is an excellent measure of index fund performance.  Websites for index mutual funds and ETFs publish R2 as a measure of alignment between fund returns and the market index.   Funds that have an R2 score of nearly 1.00 track the index very closely.

References

1.  Lain Pardoe, Laura Simon, and Derek Young. STAT 501, Regression Methods. 1.5- The coefficient of determination, r-squared. Pennsylvania State University, Eberly College of Science, Online courses. https://onlinecourses.science.psu.edu/stat501
2.  R-squared. 2016, Investopedia http://www.investopedia.com/terms/r/r-squared.asp?lgl=no-infinite


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