Why Does ETF Momentum Work?

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Momentum investing is a system of buying stocks or other securities that have had high returns over the past three to twelve months, and selling those that have had poor etf momentum trading strategy over the same period.

While no consensus exists about the validity of this strategy, economists have trouble reconciling this phenomenon, using the efficient-market hypothesis. Two main hypotheses have been submitted to explain the effect in terms of an efficient market.

In the first, it is assumed that momentum investors bear significant risk for assuming this strategy, and, therefore, the high returns are a compensation for the risk. Seasonal or calendar effects may help to explain some of the reason for success in the momentum investing strategy. If a stock has performed poorly for months leading up to the end of the year, investors may decide to sell their etf momentum trading strategy for tax purposes causing for example the January effect.

Increased supply of shares in the market drive its price down, causing others to sell. Once the reason for tax selling is eliminated, the stock's price tends to recover. Some investors may react to the inefficient pricing of etf momentum trading strategy stock caused by momentum investing by using the tool of arbitrage.

Richard Driehaus is sometimes considered the father of momentum investing but the strategy can be traced back before Donchian. According to Driehaus, "far more money is made buying high and selling at even higher prices. It is believed that George Etf momentum trading strategy used a variation of momentum investing by bidding up the price of already overvalued equities in the market for conglomerates in the s and for real estate investment trusts in the s.

This strategy is termed positive feedback investing. In the late etf momentum trading strategy as computer and networking speeds increase each year, there were many sub-variants of momentum etf momentum trading strategy being deployed in the markets by computer driven models. Some of these operate on a very small time scale, such as high-frequency tradingwhich often execute dozens or even hundreds of trades per second.

Although this is a reemergence of an investing style that was prevalent in the s, [7] ETFs for this style began trading in The performance of momentum comes with occasional large crashes. For example, inmomentum experienced a crash of From Wikipedia, the free encyclopedia. International Review of Financial Analysis. Journal of Asset Management. The New Market Wizards: Conversations With America's Top Traders. John Wiley and Sons,pg. Retrieved November 2, Implications for Stock Market Efficiency".

The Journal of Finance. Journal of Financial Economics. Primary market Secondary market Third market Fourth market. Common stock Golden share Preferred stock Restricted stock Tracking stock.

Authorised capital Issued shares Shares outstanding Treasury stock. Electronic communication network List of stock exchanges Trading hours Multilateral trading facility Over-the-counter. Alpha Arbitrage pricing theory Beta Bid—ask spread Book value Capital asset pricing model Capital market line Dividend discount model Dividend yield Earnings per share Earnings yield Net asset value Security characteristic line Security market line T-model.

Algorithmic trading Buy and hold Contrarian investing Day trading Dollar cost averaging Etf momentum trading strategy hypothesis Fundamental analysis Growth stock Market timing Modern portfolio theory Momentum investing Mosaic theory Pairs trade Post-modern portfolio theory Random walk hypothesis Sector rotation Style investing Swing trading Technical analysis Trend following Value investing.

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Traders and investors have noticed that stocks from different sectors have different sensitivity to business cycle and have always tried to exploit this relationship. There exist several different approaches to sector rotation, and a rotation based on momentum is one of the most successful. The investment universe in our example contains 10 industry sectors, and the investor repeatedly picks equity sectors with highest momentum past performance into his portfolio.

The goal of this strategy is to outperform simple buy and hold of equity index. There is a long only strategy version which is presented here and a long-short version of this strategy where investors holds best performing sectors and shorts overall market or worst performing sectors. Equity sectors have different sensibility to the business cycle therefore it is possible to rotate between them and hold only the sectors with the highest probability of gain and lowest probability of loss.

The momentum anomaly is often explained by behavioral shortcomings, such as investor herding, investor over and underreaction and confirmation bias.

Use 10 sector ETFs. Pick 3 ETFs with strongest 12 month momentum into your portfolio and weight them equally. Hold for 1 month and then rebalance. Relative Strength Strategies for Investing http: The purpose of this paper is to present simple quantitative methods that improve risk-adjusted returns for investing in US equity sectors and global asset class portfolios.

A relative strength model is tested on the French-Fama US equity sector data back to the s that results in increased absolute returns with equity-like risk. The addition of a trend-following parameter to dynamically hedge the portfolio decreases both volatility and drawdown.

The relative strength model is then tested across a portfolio of global asset classes with supporting results. Do Industries Explain Momentum? This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly.

Specifically,momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum.

By contrast, industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences.

Existing literature documents that cross-sectional stock returns exhibit price and earnings momentum patterns. The implementation of such strategies, however, is costly due to the large number of stocks involved and some studies show that momentum profits do not survive transaction costs. In this paper, we examine whether style and sector indexes commonly used in financial industry also have momentum patterns.

Our results show that both style and sector indexes exhibit price momentum, and sector indexes also exhibit earnings momentum.

Mostly importantly, these momentum strategies are profitable even after adjusting for potential transaction costs. Moreover, we show that price momentum in style indexes is driven by individual stock return momentum, whereas price momentum in sector indexes is driven by earnings momentum. Finally, using style indexes as illustration we show that performance of style investment can be substantially enhanced by incorporating the momentum effect. Can exchange traded funds be used to exploit country and industry momentum?

There is overwhelming empirical evidence on the existence of country and industry momentum effects. This line of research suggests that investors who buy countries and industries with relatively high past returns and sell countries and industries with relatively low past returns will earn positive risk-adjusted returns.

These studies focus on country and industry indexes that cannot be traded directly by investors. This warrants the question whether country and industry momentum effects can really be exploited by investors or are illusionary in nature. We analyze the profitability of country and industry momentum strategies using actual price data on Exchange Traded Funds.

The daily average bid-ask spreads on ETFs are substantially below the implied break-even transaction costs levels. Hence, we conclude that investors that are not willing or able to trade individual stocks are able to use ETFs to benefit from momentum effects in country and industry portfolios. Industry momentum in an earlier time: Evidence from the Cowles data http: Virtually all evidence on the efficacy of momentum strategies arises from the post era, and momentum returns across different markets and asset classes are highly positively correlated.

We examine industry momentum in an earlier time, and find these strategies would have earned returns over the and periods that are moderately similar to those in the modern era. We also show that the market state dependence of industry momentum strategies is similar between the two eras.

Overall, our findings confirm that both the profitability and state-dependence of momentum strategies are pervasive and unlikely to be due solely to data-mining. Volatility Weighting Applied to Momentum Strategies http: We consider two forms of volatility weighting own volatility and underlying volatility applied to cross-sectional and time-series momentum strategies.

We present some simple theoretical results for the Sharpe ratios of weighted strategies and show empirical results for momentum strategies applied to US industry portfolios. We find that both the timing effect and the stabilizing effect of volatility weighting are relevant. We also introduce a dispersion weighting scheme which treats cross-sectional dispersion as partially forecastable volatility. Although dispersion weighting improves the Sharpe ratio, it seems to be less effective than volatility weighting.

Extending price return momentum tests to the longest available histories of global financial asset returns, including country-specific sectors and stocks, fixed income, currencies, and commodities, as well as U. Consistent with stock-level results, we document a large variation of momentum portfolio betas, conditional on the direction and duration of the return of the asset class in which the momentum portfolio is built.

A significant recent rise in pair-wise momentum portfolio correlations suggests features of the data important for empiricists, theoreticians and practitioners alike. This paper focuses on momentum strategies based on recent and intermediate past returns of U. Our empirical analysis shows that strategies based on intermediate past returns yield higher mean returns.

Moreover, strategies involving both return specifications exhibit time-varying factor exposures, especially the Fama and French five-factor model. After risk-adjusting for these dynamic exposures, the profitability of industry momentum strategies diminishes and becomes insignificant for strategies based on recent past returns. However, most strategies built on intermediate past returns remain profitable and highly significant.

Further analyses reveal that industry momentum strategies are disrupted by periods of strong negative risk-adjusted returns. These so-called momentum crashes seem to be driven by specific market conditions.

We find that industry momentum strategies are related to market states and to the business cycle. However, there is no clear evidence that industry momentum can be linked to market volatility or sentiment. Momentum, Idiosyncratic Volatility and Overreaction http: Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question.

Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover as another measure of overreaction, I present evidence that supports the investor overreaction explanation as the source of momentum effects. Furthermore, I show that when investor overreaction is low, momentum effects are more due to industries industry momentum rather than stocks.

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