1/ Trend Factor: Any Economic Gains from Using Information over Investment Horizons? (Han, Zhou, Zhu)

"A trend factor using multiple time lengths outperforms ST reversal, momentum, and LT reversal, which are based on the three price trends separately."

papers.ssrn.com/sol3/papers.cf…
2/ This resembles combining multiple measures of ST reversal, momentum, and LT reversal (forecasts determined by walking forward rather than using signs from the full sample).

Unlike normal moving average signals, these are *cross-sectional.* More below:
3/ Unsurprisingly, the Trend factor formed by this approach outperforms benchmarks in terms of both Sharpe ratio and tail metrics. It's combining momentum with two factors that are negatively correlated to it AND using multiple specifications.

More here:
4/ "Average return and volatility of the trend factor are both higher in recession periods. However, the Sharpe ratio is virtually the same.

"Interestingly, all of the factors still have positive average returns.

"Momentum experiences the greatest increase in volatility."
5/ "In terms of maximum drawdown and the Calmar ratio, the trend factor performs the best.

"The trend factor is correlated with the short-term reversal factor (35%), long-term reversal factor (14%), and the market (20%) but is virtually uncorrelated with the momentum factor."
6/ "The trend factor has a higher return during recessions, while momentum earns a small positive return b/c the short leg is a bit more negative than the long leg. Similar conclusions hold for expansions. This is why the two factors are not correlated over the entire sample."
7/ "The trend factor outperforms the momentum factor not only in terms of average returns, but also in terms of downside risks during months when the momentum factor performs poorly."
8/ "The hypothesis is strongly rejected that the trend factor is inside the mean-variance frontier of the short-term reversal, momentum, and long-term reversal factors for all three periods."
9/ "Overall, the trend factor, which utilizes price information across all the investment horizons, seems to place more emphasis on short-term price patterns than on the intermediate and long-term ones."
10/ Table 9: "While the market beta and SMB beta are U-shaped, the HML beta is hump-shaped across the quintiles. Hence, the trend factor has a small loading on the market and insignificant SMB and HML betas in the Fama-French three factor model."
11/ "The results suggest that small stocks and low priced stocks are more trending. This may be intuitively true: large stocks have more analyst following and more investors, and hence more information transparency and faster price movements to reflect available information."
12/ "A certain smoothing of the betas is necessary to maintain the out-of-sample performance of the trend factor.

"The betas do change substantially over time, varying from positive values to negative ones."
13/ "Performance decreases as the market size increases. However, even for the largest stocks, the superior performance of the High-Low spread portfolio is stat. & economically significant.

"Performance remains largely unchanged after controlling for other firm characteristics."
14/ "ER12trd has a significant and positive coefficient, indicating that the trend signals can predict future cross section returns independent of size and B/M. This is consistent with Table 11.

"Similar results are obtained when other variables are added to the regression."
15/ "It is not an easy matter to short all the stocks in the short leg.

"We have to have detailed information on which stocks can be shorted and then estimate the costs. While our evaluation based on the BETCs shows profitability potential, it is limited in scope."
16/ "The performance of the trend factor is not driven by a few outliers but is remarkably stable over time.

"Our trend factor passes the higher bar given in Harvey, Liu, and Zhu (2016) easily with a t-statistic of 13.6 (the momentum has a value of 6.04)."
17/ "The trend factor is clearly valid internationally, yielding the best performance among all the three factors and the local market indexes."
18/ "Table 15 reports the performance of the trend forecasts under different levels of information uncertainty.

"The Fama-French alphas increases as information uncertainty increases."
19/ "The trend factor performs better in explaining the industry returns than the momentum factor.

"The trend factor improves performance in terms of explaining the cross-section returns of the six size and book-to-market portfolios."
20/ "While the momentum factor together with the CAPM is far from sufficient in explaining the hedge fund returns, the trend factor performs still slightly better than the momentum factor in that task."
21/ "While our methodology here is focused on using price trends across investment horizons, it can also be applied to examine other economic fundamentals, such as firm earnings, profitability, growth, and investment patterns, over short and long terms."
22/ Related research:

Industry Long-Term Return Reversal

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More from @ReformedTrader

14 Jan
1/ Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing (Arnott, Harvey, Kalesnik, Linnainmaa)

"Factor returns can experience downside shocks far larger than expected. In certain conditions, returns also become more correlated."

papers.ssrn.com/sol3/papers.cf…
2/ * July 1963 to June 2018
* Factors 1-6 are the most popular academic factors, followed by factors 7-14, then the 33 factors in the "other" group
* Long/short Fama-French portfolio construction (this leads to factors being dollar-neutral rather than beta-neutral)
3/ "When the market does great (months >1σ above the mean), most factors do not; when the market moves sideways, most factors deliver positive returns; and when the market falls >1σ, most factors are at their best. Similarly, on average, factors have higher premia in recessions."
Read 14 tweets
13 Jan
1/ To the extent that findings can be falsified but not verified, there is no scientific consensus around specific models and findings. (This is what you find when you dig into the journals: scientists deeply disagree about what is presented as 'science' by the media.)
2/ There can be consensus around what *doesn't* work: what has failed to replicate over and over again.

Our default stance (null) should be no correlation and no causality. The replication crisis may result from a tendency to believe too quickly.

3/ There are also philosophical problems with the idea of falsification itself. What does it take to falsify a claim? If the slightest inconsistency is enough, then all theories are false.

How we weight and evaluate evidence is assumption-dependent.
Read 10 tweets
12 Jan
1/ What Happens with More Funds than Stocks? (Madhavan, Sobczyk, Ang)

"Funds differ meaningfully in terms of individual stock holdings, and we examine the factor exposures of the typical fund and the cross section of holdings of different funds."

papers.ssrn.com/sol3/papers.cf…
2/ * Data on fund holdings are from Morningstar
* Sample: Jan. 1, 2007 to Dec. 31, 2018
* Only funds with at least 80% of holdings from the Russell 3000 universe are considered
* Mean AUM-weighted expense ratio for mutual funds (ETFs) is 70 bps (14.2 bps) as of Q4 2018
3/ "The holdings of ETFs and active mutual funds across U.S. stocks can be efficiently summarized by approximately 10 canonical funds.

"There is more commonality explained by the first few canonical funds for active mutual funds than for ETFs."
Read 9 tweets
12 Jan
1/ Research group meetings: Pros and cons

These are the ones where a paper/book/project is openly discussed and criticized.

Their dynamic is present in very few places (scientific research, theological debate, close friendships). IMO, most organizations have no idea it exists.
2/ Pro: They are great if you avoid groupthink. Always have someone other than the boss present so that dissent doesn't become associated with career risk.

Pro: This setting flattens organizations. If you have an idea, speak up: don't bother raising your hand.
3/ Pro: A good boss loves getting criticized here. If you get in a friendly argument with him in a safe setting and win, you've shown you can add something to the organizations' research efforts.

Pro: It's hard for people to get away with fudging ideas. Always present evidence!
Read 9 tweets
12 Jan
1/ When Diversification Fails (Page, Panariello)

"To fully appreciate extreme correlations, we take an in-depth look at stock-to-credit, stock-to–hedge fund, stock-to-private asset, stock-to-factor, and stock-to-bond correlations during tail events."

troweprice.com/content/dam/id…
2/ "For each pair, we simulated two normal distributions using empirical full-sample correlations, means, & volatilities."

"Empirical correlation profiles differ substantially from their normally-distributed counterparts: International diversification works only on the upside."
3/ "We found similar results across risk assets.

"We use bond returns net of duration-matched U.S. Treasuries ('excess returns') to isolate credit risk factors.

"Across the board, left-tail correlations are much higher than right-tail correlations."
Read 13 tweets
10 Jan
1/ Institutional Gold (Parikh)

"Gold’s correlations are sensitive to the investor’s horizon and time period. We discuss the difficulties of estimating correlations (especially for long horizons) and the importance of measuring estimation uncertainty."

papers.ssrn.com/sol3/papers.cf…
2/ * Futures include cash returns
* Rolls to contract with the most favorable carry or (if carry is negative) to a simulated gold ETF with 40 bps/y expenses
* Using this roll strategy has 7.1%/year returns vs. 4.8% for rolling monthly

More on this here:
3/ "Gold might serve as a long-term hedge against economic slowdowns, was the only asset to have negative correlation to equity at the 90th percentile for 5y horizon returns, and (of all the assets) was the most diversifying to Treasuries."
Read 8 tweets

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