ADAM BAIN AND THE PRICE MOMENTUM STRATEGY In February 1995, Adam Bain, investment advisor in the London, Ontario branch of RBC Dominion Securities Inc. (RBC DS), was considering whether or not to implement a price momentum strategy for his clients. Trend and Cycle, DS’s technical research department, had recently circulated a copy of a study which described a simple price momentum model and referred to its “startling results” based on back testing the strategy over a 15 year period.
The Trend and Cycle group had long promoted the importance of price momentum and relative strength to potential clients. Bain needs to determine whether the proposed model was “too good to be true” or, if it did not look promising, how he would go about implementing such a strategy for his clients. RBC Dominion Securities Inc. RBC DS had its roots in an investment firm established in 1901. In 1987, Dominion Securities was acquired by Canada’s largest financial institution, the Royal Bank of Canada.
RBC DS was a fill service international investment bank with headquarters in Toronto, London and New York, with offices in more than 150 locations including Boston, Hong Kong, Paris and Tokyo. RBC DS currently operated in over 80 offices across Canada, employed 900 investment advisors, and serviced over 300,000 individual clients. A RBC DS brochure described the firm’s objectives as follows: Our constant objective is developing and maintaining the highest degree of client confidence.
The confidence earned by providing clients with: timely, accurate financial information; reasoned appropriate investment strategies; conservative, investment grade financial products; the unfailing, rigid control of our safekeeping and segregation procedures for securities over which we have custody; and our effective, hands-on executive, operations and control management structure. Trend and Cycle Department The Trend and Cycle department had provided proprietary quantitative analysis tools for RBC DS investment advisors for over 20 years. Its objective was to assist in the implementation of technically based investment decisions.
For example, it provided charting tools to analyze thousands of price series in Canada and the US, including stocks, bonds, currencies, commodities, and mutual funds. It could provided “trend phase lines” that indicated which of four phases (analogous to business cycle peaks, troughs, expansions, and recessions) a particular security appeared to be in. The Trend and Cycle department often distributed newsletters to RBC DS investment advisors highlighting interesting internal research results. Adam Bain Bain graduated from the University of Windsor with a Bachelor of Science degree in 1985 and a Bachelor of Commerce degree in 1987.
In that year, he was employed by RBC DS for a very short period before joining with an associate to form a small publishing company focusing on publications for professionals such as doctors and lawyers. In 1991, he returned to DS as an investment advisor. He was currently enrolled in the Canadian Investment Management (CIM) program. The CIM program involved two years of course work in a variety of areas including investment policy, asset allocation, risk management, security valuation, money markets, bond trading, business cycles, foreign exchange markets, and federal government financing.
Bain was also involved in a number of organizations including London Community Foundation (which provided grant money to local non-profit organizations), University Hospital, and the Grand Theatre. Since August 1993, Bain had written a weekly column for the local newspaper, The London Free Press (see Exhibit 1 – sample of article around January 1995). His column involved two parts. The first part reviewed the recent relative performance of a portfolio of stocks with ties to the London community compared to the overall market (as measured by the Toronto Stock Exchange 300 index).
The second part alerted readers to interesting investing ideas or commented on recent stock market trends. Bain currently had approximately 250 clients with an average portfolio size of around $100,000 in his clients’ accounts. His client base had grown primarily due to referrals, often from Royal Bank branch managers. His primary source of income was through commissions charged on products sold. For example, typical stock commissions ranged from 1. 5 to 2. 5 percent depending on the size of the transaction.
Bain’s clients’ portfolios included equities (both common and preferred) as well as fixed income securities and small amounts of cash (typically “parked” on a short term basis before being allocated to fixed income or equities). Typical portfolios were approximately 60% equities and 40% fixed income, 70% domestic and 30% international. Approximately one third of equity investments were through mutual funds. Approximately 25% of client assets were included in tax sheltered Registered Retirement Savings Plans (RRSPs). As of 1991, Bain’s clients were primarily over age 70.
As of 1995, his client base had evolved to become much younger, with a median age of around 50. his clients were dominated by professionals. Bain summed up his investment philosophy as follows: I strive to provide my clients with the best possible investment advice and service by employing proven investment strategies and techniques in order to achieve superior investment returns over the long term. I keep my business simple and straightforward in order to ensure that clients understand and easily participate in our ongoing investment relationship.
The Price Momentum Study The price momentum study that had been brought to Bain’s attention had appeared recently in Canadian Investment Review, a publication aimed at enlightened practicing managers in the investment community. The publication included contributions from both practitioners as well as academics. Exhibit 2 includes a copy of the study. The study presented a simple model which ranked stocks based on their price momentum over the past four quarters, with twice the weight given to the most recent quarter’s performance.
The strategy involved buying the top 10 stocks and holding them for the next quarter, then rebalancing based on the most recent momentum rankings. Exhibit 3 includes a copy of a commentary by Keith Ambacchtsheer, a respected pension consultant and consulting editor of Canadian Investment Review. In his critique, Ambachtsheer attributed much of the success of momentum strategies to his observation that Canadian investment professionals tended to act only when it was “fashionable” to dos so, resulting in buying and selling securities too late to provide superior returns.
He argued that, for this reason, momentum based strategies would continue to perform well in the future and concluded that “a momentum fund based on the rebalancing rules set out in this article would likely be a winner”. Implementation Concerns Bain read these articles with keen interest. However, before he considered implementing such a strategy for his clients and, perhaps, for himself, he had a number of concerns related to the momentum study. He wanted to understand all of the assumptions that went into the model.
He also wanted to understand whether the results presented a reasonable picture of what investors could expect in the future from such a strategy since he was concerned with having clients with unreasonably optimistic expectations. If he were to replicate such a strategy, there were also a number of technical details he would have to address. For example, what was the minimum (and maximum) dollar amount he would accept from any one client? Should he allow clients to exit at any time or should he encourage relatively long term investing in the strategy? Given that the strategy involved frequent (i. e. quarterly) trading, how should he charge commissions – in particular, for any rebalancing? He also wondered what “unusual circumstances” might arise and how he would deal with them. As he pondered the strategy, he wondered whether he should implement any changes in the proposed model and what impact it might have on the performance and riskiness of the strategy. He also wondered which of his clients might be interested in the strategy and how best to approach them with the idea. If the strategy appeared to be a winner, he wanted to act as soon as possible. He wanted to present his branch manager with his plan by arly next week. Exhibit 1: Adam Bain’s London Free Press Column, January 9, 1995 London Stock Portfolio Adam Bain A local index of publicly traded companies with significant operations in the London area. It is designed to give us a sense of how local companies are doing relative to the rest of the market. The LSP was up 141. 33 on the week. This week the rise was magnified by the updated exchange rate used in the model. The lower Canadian dollar has dramatically increased the value of US stocks in the portfolio, something that will now be updated quarterly. Emco had a solid week on no news.
The market was not disturbed by Beatrice foods suit against Adult Diary. The TSE 300 was down 42. 25. The market got off to an ugly start as gold shares fell more than 7% on bullion weakness. This may be a good opportunity to buy some rather inexpensive gold shares. STOCKJan 5Change Cara$3. 35$0. 15 Ford$28. 38$0. 50 Westopast$22. 38$0. 13 Labatt$19. 13($0. 13) Trojan Tech$9. 75$0. 25 Adult Diary$17. 00($0. 13) McCormick$18. 38$0. 00 MARKET FOCUS: bullish thoughts During the past few weeks the media has been filled with predictions and reflections on the markets.
Here are a few thoughts about the US market from the US investment house Smith Barney/Shearson. Thought 1: The returns on the S&P 500 have been only average the last three years. In the last 50 years there has only been one other similar period (1946-1948); this was followed by two years of +18. 6% and 31. 4% performance. Thought 2: In the past 50 years the average gain in presidential pre-election years for the S&P 500 has been 20. 6%. Thought 3: In the past 50 years the average gain in the fifth year of a decade was 29. 8%.
Thought 4: There have only been two other years in the past 50 that were both a fifth year and a pre-election year (’55 and ’75); the average gain was 34. 3%. Thought 5: The average annual return on the Dow for the past 50 years has been 11%. That means that the Dow could be at 7000 by the year 2000. (The Dow is 3850 at this writing. ) Bain is an investment advisor with RBC Dominion Securities Inc. , London. Exhibit 2: Price Momentum Study Back to the Future Investors searching for those stocks likely to beat the market may have to look no further than the past performance of a stock
By Stephen forester, Anoop Prihar and John Schmitz Any investor looking for superior returns would certainly like to know which stocks to choose to beat the market. But can the market be beaten? The validity of stock market efficiency has been hotly debated for decades. Proponents of market efficiency argue that prices fully and immediately reflect all relevant information about a stock and investors cannot earn superior returns by buying or selling securities based on, say, past trends in prices. In other words, stock prices simply follow random walks.
Detractors from this view point to over-reaction by investors, who tend to over-inflate prices above intrinsic values when a stock is “hot”, then drive prices down when the same stock falls out of grace. Most investors are not concerned with this debate, but rather with a much simpler question “Can I identify stocks which are more likely to rise in price (for whatever reason) than to fall in price? ” Without entering the market efficiency debate, evidence suggests that in the past 15 years, certain portfolios of Canadian stocks have not behaved in a totally random manner.
This article will outline a study that documents a simple rule based on price momentum that predicts which 10 stocks )from a sample of 100 largest capitalization Canadian stocks) have the best outlook for outperforming the market over the next quarter and which 10 stocks may be poised to under perform the market. The potential financial rewards from the following model are quite impressive. Riding the Wave Price momentum models are based on the premise that carefully studying historical prices can provide clues to the future trend of a security’s price.
As information and awareness about a particular stock gradually become known, markets form either positive or negative views – or outlooks – about the stock. This outlook tends to feed on the security’s price, driving it higher (in the case of positive views) or lower (in the case of negative views) than its true intrinsic value. What a price momentum model does is attempt to identify stocks that appear to be riding a wave. As the wave begins to weaken, the model identifies new stocks that appear to be riding the next wave.
This process enables the portfolio to be upgraded (adding and dropping stocks) to ensure maximum returns are earned. Price momentum or “relative strength” models tend to come in a variety of forms, from simple to quite sophisticated. For example, a simple model may build a portfolio based on a rank of stock price changes over the past month. A sophisticated model, on the other hand, may form a portfolio based on a rank of stock returns in excess of returns implied by the capital asset pricing model, using an exponentially decaying weight regression applied to past data. Regardless of the particular model, the principles are the same: Identify stocks whose price appear to have risen substantially (relative to other stocks) over a recent period; •Add these stocks to a portfolio, replacing stocks within the portfolio which have shown less momentum; and •Continue to hold them as long as they continue to exhibit price strength or momentum Data and Methodology The sample consists of all stocks which are part of the Toronto Stock Exchange (TSE) 100 Index and monthly return data (price changes as well as dividends) over the 1977 to 1992 period was obtained from the TSE/University of Western Ontario database.
The TSE 100 was chosen since it best represents the socks contained in Canadian institutional portfolios and avoids many of the smaller and less liquid stocks that are part of the broader TSE 300 Total Return Index. Naturally, the TSE 100 would be the most appropriate benchmark to represent the overall “market”, however, returns on the index were not available for the sample period since the index was started in October 1993. As a result, the TSE 300 Index (which also includes dividends) was used as the benchmark. Table 1: Not left to chance
Even after discounting a 2% transaction cost, returns for a portfolio of 10 TSE 100 stocks with the best outlook for outperforming the markets performed consistently stronger over 60 quarters than the “worst outlook” portfolio. Best Outlook PortfolioBest Outlook Less Transaction CostsWorst Outlook PortfolioTSE Total Returns Quarter Mean10. 3%8. 3%2. 7%3. 3% Quarter Standard Deviation13. 7%13. 7%13. 2%8. 6% Annual Mean41. 2%33. 2%10. 9%13. 1% Annual Standard Deviation27. 4%27. 4%26. 4%17. 3% Portfolio ? 1. 091. 091. 201. 00
Number of Quarters with Returns Greater than 050 of 6045 of 6035 of 6045 of 60 Number of Quarters with Returns Greater than TSE46 of 6041 of 6025 of 60- The particular price and momentum model chosen is a relatively simple one based on a model developed almost a decade ago, which worked well on US data over the 15 year period between 1969 and 1984. Consequently, this study represents useful out of sample test from that model, since half of this study’s sample period is more recent and the universe of stocks is entirely different.
The model ranks stocks based on the weighted average of their past four quarters of price changes (returns examined in this study included dividend as well, but this should not have a big impact on results). The most recent 3 month return is weighted twice as heavily as the return of the other three quarters making up the past 12 months. The premise here is that investors are looking for “well-established” trends over the last year, but are particularly concerned with the most recent performance. Based on the weighted annual return, two portfolios are formed.
The “best outlook” portfolio consists of the stocks with the top 10 weighted annual returns over the last four quarters, while the “worst outlook” portfolio consists of the bottom 10 stocks. The performance of each portfolio (the average returns of the 10 stocks within each portfolio) over the subsequent quarter is then recorded. If the model is a good predictor of future performance, the performance of the “best outlook” portfolio should exceed that of the overall stock market. In addition, the performance of the “worst outlook” portfolio should be less than the overall market.
This procedure is then repeated each quarter over a 15 year period (first quarter 1978 to fourth quarter 1992). At the beginning of each quarter, according to the strategy, each of the 10 stocks in both the “best outlook” and “worst outlook” portfolios are equally weighted. If a particular stock is not sold at the end of the quarter, it is rebalanced so that its dollar weighting equals the other nine stocks in the portfolio. As an example, of the strategy consider an investor as of Dec 31, 1991. Based on weighted returns for the quarters of 1991, the “best outlook” top 10 tocks included (in order): Cott International, Magna Corp, SHL System House, Newbridge Networks, Rogers Communications, Canadian Natural Resources, Elan Energy, Bombardier, Canadian Occidental Petroleum, and the Bank of Nova Scotia. Seven of these stocks were carry-overs from the previous quarter, while three (SHL System House, Canadian Natural Resources and Bombardier) were new additions. The average return of these stocks over the subsequent quarter (Jan 1, 1992 to March 31, 1992) was 31. 7%, compared to a market return of -2. 1%. Over the same period, the portfolio of “worst outlook” stocks had a 3. % return. Not Just by Chance A summary of the results presented in Table 1 shows that the model was, indeed, successful. The average quarterly return for the “best outlook” portfolio of stocks was 10. 3% or 41. 2% annualized. These returns were much higher than the quarterly (annual) returns on the TSE index of 3. 3% (13. 1%), and the quarterly (annual) returns for the “worst outlook” stocks of 2. 7% (10. 9%). Both the “best outlook” and “worst outlook” portfolio exhibited risk (as measured by the standard deviation of returns and portfolio beta) greater than the market. Back to the Future
However, even on a simple reward to risk basis (average return divided by standard deviation), the “best outlook” portfolio was approximately twice as good as the market with a ratio of 1. 5 versus 0. 76. Transaction costs for the “best outlook” portfolio were also considered. In order to be conservative, a 2% per quarter transaction cost was assumed on each stock, whether the stock was sold off or simply rebalanced relative to the other stocks in the portfolio. Therefore, the effective transaction cost is actually higher than 2% per quarter because, on average, only 5. 4 new stocks were added (and old stocks dropped) each quarter.
Even after these commissions, the “best outlook” portfolio return was 8. 3% per quarter, or 33. 2% on an annual basis. Using t-tests to test the statistical significance of the results, the probability that the “best outlook” portfolio return, both before and after transaction costs, is statistically greater than the TSE portfolio return exceeds 99. 9%. The probability that the “worst outlook” portfolio return is statistically less than that of the TSE is 69. 5%. Therefore, the before and after transaction cost “best outlook” portfolio return is statistically and economically higher than the TSE return.
Conversely, the “worst outlook” portfolio return is not statistically lower than the TSE return at traditional significance levels but many would agree that its return is substantially lower than the TSE return in an economic sense. The robustness of the results was examined. Over the sample period the market return was positive during 75% of the quarters. As predicted by the model, returns for the “best outlook” portfolio were positive more frequently – 83% of the time before transaction costs and 75% after commissions.
However, returns for the portfolio with the “worst outlook” were positive only 58% of the time. Intuitively, one would expect that a portfolio formation strategy that lacked nay predictive capability would only outperform the market, by chance, in 50% of all quarters. However, comparisons on a quarterly basis indicate that the “best outlook” portfolio outperforms the TSE 300 Index 77% of the time before transaction costs (68% after these costs), while the “worst outlook” portfolio outperforms the index only 42% of the time.
Binomial tests confirm that the “best outlook” portfolio performance both before and after transaction costs (as measured by the frequency of outperforming the market) is significantly greater (at the 99% confidence level) than what would be expected to occur simply by chance. Furthermore, a binomial test indicates that the “worst outlook” portfolio performance is significantly worse (at the 90% confidence level) than what would be expected by chance. Therefore, the results are not simply attributable to a small number of quarters in which the “best outlook” portfolio performed strongly and the “worst outlook” portfolio performed poorly.
Table 2: Keeping the momentum Far from behaving randomly, the price momentum models illustrate that equity returns clock in consistently on a year to year basis. Only once over a 15 year period did the “best outlook” portfolio, before and after transaction costs, underperform each the “worst outlook” portfolio and the TSE Total Return Index. YearBest Outlook Portfolio ReturnBest Outlook Less Transaction CostsWorst Outlook Portfolio ReturnTSE Total Return 197845. 335. 032. 229. 7 197982. 370. 134. 144. 8 198095. 782. 819. 930. 1 981-17. 0-23. 85. 0-10. 3 198218. 39. 4-3. 45. 5 198349. 939. 413. 035. 5 198417. 78. 930. 5-2. 4 198545. 335. 026. 225. 1 198658. 347. 2-1. 79. 0 198759. 748. 221. 15. 9 198821. 712. 7-16. 411. 1 198960. 048. 925. 221. 4 19904. 7-3. 4-22. 0-14. 8 199183. 271. 030. 812. 0 199285. 773. 212. 0-1. 4 Average47. 437. 010. 013. 4 How the price momentum model performs on a year by year basis is illustrated in Table 2. The yearly returns shown are the compounded quarterly returns resulting from the various portfolio selection strategies.
It is apparent that the “best outlook” portfolio, before and after transaction costs, outperform in 14 of 15 years the TSE Total Return Index portfolio as well as the “worst outlook” portfolio. Conversely, the “worst outlook” portfolio – before transaction costs- outperforms the TSE portfolio in only 7 of 15 years. This further illustrates that the “best outlook” portfolio results are not simply a consequence of a sustained but isolated incidence of luck over a portion of the sample time period. The momentum model, in fact, seems to produce fairly consistent and correct predictions over the whole sample eriod. The results are equally dramatic after controlling risk. In Table 3, the various returns are adjusted for risk by calculating both the Treynor index and the Sharpe ratio for each portfolio. The Treynor index measures portfolio returns in excess of the risk free interest rate per unit of systematic market risk. Systematic market risk is measured by the portfolio’s beta. The “best outlook” portfolio’s Treynor index is 11. 7 times that of the TSE 300 portfolio before transaction costs and 8. 6 times after transaction costs.
Furthermore, the TSE portfolio’s Treynor index is 16. 1 times the Treynor index of the “worst outlook” portfolio. Table 3: Accounting for risk The “best outlook” portfolio less transaction costs continued to rake in superior returns when adjusted for risk, proved by using either the Treynor index, which measures systematic risk of the portfolio beta, or the Sharpe ratio, to calculate total risk measured by standard deviation of returns. Treynor IndexSharpe Ratio Best Outlook Portfolio6. 9740. 5550 Best Outlook Less Transaction Costs5. 1420. 093 Worst Outlook Portfolio0. 037. 00034 TSE Total Return Buy-and-Hold Portfolio0. 5950. 0606 The Sharpe ratio, on the other hand measures portfolio returns in excess of the risk free interest rate per unit of total risk. Total portfolio risk is measured by standard deviation of portfolio returns. The “best outlook” portfolio’s Sharpe ratio is 8. 1 times that of the TSE portfolio before transaction costs and 5. 9 times after transaction costs. The TSE portfolio’s Sharpe ratio is 20. 2 times the Sharpe ratio of the “worst outlook” portfolio.
Finally, by comparing the “wealth relatives” of the various strategies, one can see the substantial financial gain that can be achieved following the model (below). The investor is assumed to start with $1,000 at the beginning of 1978. The value of the TSE Total Return Index portfolio grows to %5,602 by the end of 1992. Excluding transaction costs, the “worst outlook” portfolio grows to only $3,173. Yet the “best outlook” portfolio, after adjusting for commission charges, is worth $75,066 by the end of 1992. Momentum Judges the Beauty Contest
The strength of the results is surprising – price momentum models do seem to have worked in Canada between 1978 and 1992. there is no indication that the market is “adjusting” to this effect over time, since some of the best results have been in the more recent periods. Nor do the results indicate that the momentum portfolio returns are a result of extreme differences in risk from the market portfolio. And while we thought a strategy of shorting the “worst outlook” portfolio may have been profitable, it does not appear to be the case since the “worst outlook” portfolio’s average returns are greater than zero.
All told, momentum appears to be stronger on the upside rather than the downside. The results of the study also support the notion of “over-reaction” on both the positive and negative side. At a minimum, they suggest investors should include some measure of momentum when assessing whether to buy or sell particular stocks. As economist John Maynard Keynes has noted, the stock market is like a beauty contest where the object of the investor is not to pick the “best looking” stock but rather to pick the stock that the investor feels the market will think is the best looking.
Price momentum can help determine which stocks the market finds appealing – for whatever reason. Source: Canadian Investment Review, Winter 1994-1995 Exhibit 3: Price Momentum Study Commentary The Price is Right If a momentum based strategy can consistently clobber the TST 300 for 15 years, what does that tell us about professional investors in Canada? By Keith Ambachtsheer Denial. That was my first reaction to the article written by Steven Foerster, Anoop Prihar and John Schmitz in this issue (page 9) when it hit my desk.
A simple, easily replicated, momentum based strategy outperforming the TSE 300 Total Return Index by a net 2,000 basis points annually over the last 15 years? No Way. I searched for errors in the methodology but there were none that I could find. Ah, I thought, maybe this astounding result was due to one or two big “hits” over the 15 year investment period (1978-1992). So I read the article again, looking to see if the researchers had slipped in a few penny stocks that happened to rise to $10 per share. No, that wasn’t the case either.
Their securities selection universe ws limited to the large cap, liquid TSE 100 index. Also their strategy outperformed the TST 300 in 14 of the 15 years. Not exactly a random outcome. As for transaction costs, they charged their strategy a seemingly hefty 800 basis points per year to support an annual turnover rate of about 200%. No logic gap there, either. After further reflection, a second reaction began to surface. If the study reflects reality, what does it tell us about professional investing in Canada?
I use the word “professional” because the typical (in other words, price setting) investor seems to have been one who is professional rather than retail these last 15 years. This is a reasonable assumption, given that the return for the typical institutional stock portfolio matches that of the TSE 300 rather closely over such long evaluation periods. Simply put, the study tells us that by jumping on price trend early and aggressively, and then selling when price momentum dissipates, anybody could have outperformed the lion’s share of Canada’s investment professionals by a mile over the last decade and a half.
That’s because the behavior of those very same investment professionals, and the people who hire them, created the incredible profit opportunity uncovered in this article. Investment managers should be rock-solid, steady-as-you-go, lean-against-the-wind, stick-to-your-knitting, don’t-do-anything-out-of-the-ordinary people, right? Well, those are exactly the kind of people who will only trade a stock once it has become fashionable to do so – exactly the kind of people you want to trade with if you want to make money.
They are destined to buy and sell too little too late their entire professional lives. So, even thought this amazing profit opportunity has been uncovered, it will disappear only if the attitudes and behavior that created the opportunity disappear. Bet against it, though. A momentum fund based on the rebalancing rules set out in the article would likely be a big winner. Whether someone has the chutzpah to start one is another question altogether. It would simply be too un-Canadian, eh. Keith Ambachtsheer is the consulting editor for the Canadian Investment Review.