Tag Archives: etfs

Larry Williams’ Principals And Insight Into Becoming A Better Trader

Larry Williams is a well-known trader and newsletter writer in the stock trading space. He has over 40 years of experience in the market and has written numerous books including Trade Stocks and Commodities with the Insiders: Secrets of the COT Report and How I Made One Million Dollars … Last Year … Trading Commodities . There is something to be learned from someone who has been in the markets for 40 years and been extremely successful. We were extremely lucky to be privy to a recent interview Larry Williams was a part of. Below are some notes we’ve gathered from the conversation: 1) Fundamental and technical analysis both work, however they will only work under the right market conditions whether it be a bull or bear market. For example, in the latter stages of a bullish market, as a buyer, you might find companies with low P/E ratio to be few and far between. Therefore, if you stick with fundamental analysis, you will most probably miss out on buying opportunities you’d otherwise find through technical analysis. In technical analysis, your focus is more on supply and demand in what is most likely a shorter time frame versus how well a company is fundamentally performing over the long haul. 2) For commodities, retail traders like to buy strength, but commercials like to buy weakness because the cost is less. Our interpretation of this is that most successful traders buy strength because of human behavior. People see an underlying asset like a derivative of oil go up, they jump on it for fear of missing out even if the prices jump and then more people jump on it. Until of course the prices become too ridiculously high and then people try and sell to lock in their profits. Commercial companies that use commodities like to buy at low prices because it keep their cost of goods sold lower. If revenues are constant and you reduce costs then you’d have better margins. 3) Most indicators are redundant, RSI (Relative Strength Index) and STO (Stochastic Oscillator) are essentially the same. There are a lot of things to look at, but when using an indicator understand the purpose of the indicator you are using. There are a lot indicators out there that essentially do the same thing. Both the RSI and STO both help to determine overbought and oversold conditions. While there are evidently cases when regardless of whether or not a stock or index is overbought, prices continue to print higher. The key is not to have too many, keep it simple, and don’t use the same overlapping indicators. 4) Trade your personality, find the system that fits you and lifestyle. Can you trade during work or at home? Do a personality check. One thing I’ve learned through trading in the stock markets for about 10 years now is that you have to trade your personality. Take someone else’s trading plan and trying to trade against that typically doesn’t work out unless the both of you have the same personality. Each of us have different risk tolerance and financial needs. You should only trade with what you are willing to lose and not only that but you have to be comfortable with actually losing that amount. Market Related Information When interest rates go up, stocks have historically been hit hard in the short term, but you’ll want to buy that weakness. The logic behind this is that when rates begin to go up, more people will feel goosed into borrowing and that leveraged money will go into consumption and production. Market tops are typically well formed and structured thereby also taking a long time to develop. On the other hand, market bottoms are based on crashes and plummet on panic. How many positions should you hold? Any more than 4 positions is a lot of multi-tasking. For Larry Williams, 3-4 positions is plenty. Any more than that require too much multi-tasking. In addition, he typically puts on a 2%-4% risk of total trading capital on each trade . Losing four consecutive trades at 4% risk would be a 16% drawdown. What is the biggest lesson Larry has learned from trading? He learned to be humble when you are winning and learning from other people. All highly successful traders are a little unsure of themselves, so they never bet big. None of these successful individuals have had high levels of emotional response to things and therefore don’t get emotionally rattled. What are the four steps to making a trade? Find condition, find the entry, set your target, create trailing stops. What are some other interesting tips and tidbits? 1) Conditional traders look at conditions, seasonality and overlay technicals. 2) Trading should be like combo lock where you need to get a number of factors going your way.

Be Careful When Investing In Low-Beta Stocks

Summary Beta is a common measure of a stock’s risk, and investing in low-beta stocks (low-risk stocks) has become a highly popular investment strategy among institutional investors today. Our new research shows that betas significantly change over time and seem to depend on the stocks’ ownership structure and how frequently the stocks trade. Low-beta stocks are often thinly traded; when investors buy into low-beta stocks, both their prices and betas increase. The opposite can occur when investors try to exit. Increases in institutional ownership breadth and the stocks’ turnover temporarily increase the stocks’ CAPM beta. The figure below shows the regression coefficients when future changes in stocks’ betas are regressed on changes in ownership breadth and turnover. Solid lines give the regression coefficients; dashed lines present 95% confidence intervals. Source: P. Jylhä, M. Suominen and T. Tomunen, “Beta Bubbles,” working paper 2015 Betting against beta All MBA and finance students learn in their basic finance courses the Capital Asset Pricing Model (CAPM), a theory largely attributable to the Nobel Prize winner William Sharpe. This theory states that riskier assets in equilibrium should earn higher returns, and that the relevant measure for a stock’s risk should be its “beta,” a measure of the stock’s systematic risk. Technically, a stock’s beta equals its correlation with the stock market index, scaled by the ratio of its volatility to the market index volatility. All well in theory, but in practice the CAPM has failed miserably. In real life, stocks with the higher risk measures, i.e., the high-beta stocks, have over the recent decades systematically earned lower returns than the low-beta stocks. In fact, investing in low-beta stocks has become a highly popular investment strategy in the financial market, one that is today aggressively marketed to all major institutional investors. Be careful: Betas do not measure what you think they measure In a recent working paper “Beta Bubbles,” written together with Petri Jylhä from Imperial College and Tuomas Tomunen from Columbia University, we suggest a potential reason why the logically well-motivated CAPM fails to work in practice. Most importantly, we show that the stock’s beta in reality seems to measure not only the stock’s level of risk, but also how frequently it is being traded. We study the US stock markets (NYSE and NASDAQ) starting from 1970s, and calculate the stocks’ betas annually from daily data using the Scholes-Williams (1977) method. We find that the low-beta stocks are commonly held by few passive long-term investors. These stocks have low average turnover; in fact on nearly 70% of the days, their trading volume is less than 0.1% of the stocks’ market capitalization. Intuitively, these stocks are so rarely traded that they rarely co-move with the market . This does not necessarily mean that the low-beta stocks are less risky, just that the traditional risk measure beta fails to measure their risk. The low-beta stocks are more prone to jumps, i.e. large market revaluations of their value. High-beta stocks, in turn, are owned by active, short-horizon investors that continuously trade and monitor the market. These short-horizon investors’ entry and exit from the stock market seems also to occur in tandem with the returns of the entire market. For both reasons, stocks owned by short-horizon investors co-move highly with the market. As the high-beta stocks are also more widely held, their risks are more evenly distributed amongst investors and the investors require less return from holding them. Hence the poor future returns to the high-beta stocks. Importantly, the stocks’ betas change over time as the stocks’ popularity changes. For instance, 20% of the stocks in the lowest-beta decile (the 10% of the stocks with the lowest beta) had an above median beta in the previous year. When a stock goes out of fashion and institutions sell the stock, we find that its beta declines. When a stock becomes popular among the active institutional investors, its beta rises. Low beta bets make sense – but prudence required The stocks in the lowest-beta decile are on average thinly owned and thinly traded. As many of them are unpopular among investors today, they, in principle, make up for great investments. However, as the low-beta investing has now become a popular investment theme, there is large risk that an investor investing in low-beta stocks today is in for a big surprise. The investor may find that the prices of low-beta stocks run up as he tries to take positions in these stocks. After all, these are commonly illiquid and thinly-traded stocks. Secondly, they may find that these stocks’ betas increase, as according to our working paper, the betas are a function of the investor population and the betas increase as the number of investors increase. Thirdly, the investors investing today in low-beta stocks should be expecting that these stocks’ prices drop vastly when all the investors following the low-beta investment theme today eventually try to get rid of their former low (now higher) beta stocks. So indeed, there is reason to be careful with your low-beta bets. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Why Comparing Returns Is A Bad Way To Choose An Investment Manager

Summary Short-term or recent returns give little information about future returns, and they increase the odds you’ll make a bad decision. Far too often, investors put significant weight on short-term performance, in many cases by choosing the investment with the highest recent investment return. This tends to actually produce future underperformance. The better way to choose an investment manager is to look at service, fit, and investor returns. The greatest trick the stock market ever pulled was convincing investors that historical returns are predictive. They aren’t. In fact, historical returns not only give you very little information about future returns, but they can also increase the odds you’ll make a bad decision. We often see this bias in investors. Both reporters and prospective customers often ask us, “What are your returns?” I cringe when I hear this. Out of all the questions you should be asking, this one should be low on the list. There are far more informative and useful questions to ask, once you know what’s in our portfolio . To be fair, there are aspects of the answer that can be helpful. Returns can give you an idea of the size of upswings and drawdowns, and how the portfolio relates to other asset classes. But in a passive, index-tracking portfolio, such as Betterment’s, you shouldn’t expect to see market alpha in our performance. When properly benchmarked, we are the benchmark. The other common mistake people make is comparing our portfolio to another over a short period of time. If, after six months, our portfolio has a lower return, they’ll often ask, “Why should I use you if your returns are worse?” Far too often, investors put too much weight on small sample, recent historical performance, choosing the investment with the highest investment return. How deceptive can this be? Our interactive tool below shows that this method leads to astonishingly high odds that they’ll underperform both in absolute and risk-adjusted terms in the future. How the Data Deceives You might not realize it, but when you look at historical returns, you’re doing a statistical analysis. Any set of historical returns comprises a sample of behavior over a certain period. Any inferences you make about what they tell you of the future should be balanced by placing them into context of how variable they are. And when you do that, two clear issues arise. Fooled by Randomness The first is being “fooled by randomness,” a phrase coined by Nicholas Nassim Taleb, a risk analyst and statistician. When you choose the highest returning of two correlated investments using a small sample of historical data, the odds are incredibly high that you picked the wrong fund. The randomness of small samples overwhelms the truth. Let’s work through some examples. We’ll use hypothetical portfolios with return probabilities we know for certain, because we’ve created them through simulation, and see how well the short-term data mimics the long-term truth. These are not Betterment portfolios. Portfolio A will have a mean annual return of 6% and a volatility of 14%. Portfolio B has a mean return of 6.5% and annual volatility of 13%. The portfolios will also have a 0.90 correlation to each other-most stock funds have higher correlations. By both measures of absolute return and risk-adjusted return, Portfolio B is better. Yet over the first randomly simulated six-month period, Portfolio A came out ahead. One 6-Month Simulation (click to enlarge) How often does the worse portfolio come out ahead over a short time period? In this case, we’ll call them C and D, with the same parameters. Let’s look at running 1,000 of such simulations over a six-month period. How often does Portfolio D, who should be the winner, come out ahead? Many Simulations Over 6 Months (click to enlarge) The answer is so close to 50% as to be indistinguishable from it. In fact, we can increase the differences in expected returns and this remains true. Let’s give Portfolio D a mean return of 8% and Portfolio C a mean return of 6%. Both have 14% volatility. The significantly higher return Portfolio D will still lose over 40% of the time over a six-month period. Many Simulations Over 6 Months (click to enlarge) While the odds are just better than 50/50 in the short term, they have big consequences in the long term. Here are the distributions of 20-year outcomes for those same portfolios: Many Simulations Over 20 Years (click to enlarge) The randomness in half-year returns results in choosing the wrong portfolio about half the time, even with large difference in return. You might as well save yourself the time and expense and flip a coin. Over long periods of time (20 years), and with large differences in average returns, the odds of picking the correct choice do increase. But you may be surprised how long it can take. For portfolios with a 1% return difference, by 20 years you still have about a one-in-four chance of picking the portfolio that will have worse underlying returns over even longer periods of time. Chance of Choosing Worse Portfolio Based on Performance Return Difference 3 months 6 months 1 Year 5 Years 10 Years 20 Years 0.50% 49% 48% 48% 42% 40% 37% 1.0% 47% 46% 44% 36% 32% 26% 2.0% 44% 43% 37% 26% 16% 9% Each cell based on 3,000 simulated cumulative returns of better portfolio (8% return) versus a benchmark portfolio with a mean return of 6% and 14% volatility. Correlation of 0.90 between portfolios. To be clear, there are statistical tools you can use to improve your odds of picking the right portfolio, but most investors aren’t professional statisticians. They just go by the cumulative returns over a short period of time. Performance Chasing Is Worse Than Random If the low odds of correctly choosing a better portfolio above didn’t convince you, it’s even worse than that. Empirically, choosing the best funds, a strategy called performance chasing, is likely to reduce your returns. The graph below comes from an excellent research paper from Vanguard. It shows the returns achieved by investing in the best fund in each asset class, compared to a buy-and-hold strategy. Performance chasing-picking investment based on recent performance-produced worse returns of about -2% to -3.5%. Buy-and-Hold Superior to Performance Chasing, 2004-2013 (click to enlarge) If every year, you picked the investment manager with above average returns over the past 12 months, you’d end up underperforming an investor who stuck with the passive index-tracking manager. The Right Things to Consider If recent investment performance is such a poor way to choose an investment manager, how should you select one? Use a set of clear principles that are likely to be true in the future: Monetary Cost: A certain drag on returns, if the service doesn’t deliver value above cost. Consider commissions, trade fees, and assets under management (AUM) fees. Non-Money Costs: How much time and and effort does it take for you to use it well? Does it have a high time or stress cost for you to get the most out of it? Services Offered: Do the services offered make you better off? Does it do things for you which you wouldn’t do yourself? Does it help you make better decisions? Does it make some of those decisions for you, automatically? Experience: Is it easy to use? Do you enjoy using it? Philosophy Fit: Consider its investment philosophy, and if it is parallel to yours. Some funds seek to deviate from the index and cost more, some seek to track it passively. Tax Management: Returns will likely not take into account actual value-adds , such as tax loss harvesting. You won’t have received a comparison tax bill that allows you to compare after-tax returns across services; it will be up to you to compare them. Behavior Management: Does the service have a proven track record of reducing the behavior gap? When choosing an investment manager, the key isn’t to focus on investment performance; it’s to focus on service, fit, and investor returns. Information in this article represents the opinion of the author. No statement in this article should be construed as advice to buy or sell a security. The author does not know your particular objectives for returns or constraints upon investing. All investors are encouraged to do their own research before making any investment decision.