{"id":4217,"date":"2019-03-28T14:20:36","date_gmt":"2019-03-28T14:20:36","guid":{"rendered":"http:\/\/quantpedia.com\/quantpedia-update-28th-march-2019\/"},"modified":"2019-08-22T05:49:07","modified_gmt":"2019-08-22T05:49:07","slug":"quantpedia-update-28th-march-2019","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-28th-march-2019\/","title":{"rendered":"Quantpedia Update – 28th March 2019"},"content":{"rendered":"

\n\tNew strategies:<\/u><\/strong><\/p>\n

\t#423 – Industry Herding and Momentum<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> Monthly
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> ETFs, stocks, funds
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period:<\/strong> 1980-2008
\n\tIndicative performance:<\/strong> 14.43%
\n\tEstimated volatility:<\/strong> 30.70%
\n\tSource paper:<\/strong><\/p>\n

\n\tYan, Zhipeng and Zhao, Yan and Sun, Libo Alice: Industry Herding and Momentum<\/strong>
\n\thttps:\/\/ssrn.com\/abstract=3309787<\/a>
\n\tAbstract:
\n\tTheoretical models on herd behavior predict that under different assumptions, herding can bring prices away (or towards) fundamentals and reduce (or enhance) market efficiency. In this article, we study the joint effect of herding and momentum at the industry level. We find that the momentum effect is magnified when there is a low level of investor herding. Herd behavior in investors helps move asset prices towards fundamentals, enhance market efficiency and reduce the momentum effect. A trading strategy taking a long position in winner industries and a short position in loser industries when the herding level is low can generate significant returns.<\/p>\n

\n\t#424 – Long-Run Reversal in Commodity Returns<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> Yearly
\n\tMarkets traded: <\/strong>commodities
\n\tInstruments used for trading:<\/strong> futures, CFDs
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 1900-2017
\n\tIndicative performance:<\/strong> 16.03%
\n\tEstimated volatility:<\/strong> 19.37%
\n\tSource paper:<\/strong><\/p>\n

\n\tZaremba, Adam and Bianchi, Robert J. and Mikutowski, Mateusz: Long-Run Reversal in Commodity Returns: Insights from Seven Centuries of Evidence<\/strong>
\n\thttps:\/\/ssrn.com\/abstract=3314834<\/a>  
\n\tAbstract:
\n\tWe perform the longest study of long-run reversal in commodity returns ever conducted. Using a unique dataset of prices of 52 agricultural, industrial, and energy commodities, we examine the price behaviour for the years 1265 to 2017. The findings reveal a strong and robust long-run reversal effect. The returns of the past one to three years negatively predict subsequent performance in the cross-section of returns. The long-run reversal effect is present in both agricultural and non-agricultural commodity returns across all centuries and is independent of market states. The long-run reversal cannot be explained by macroeconomic risks. The phenomena is elevated in more volatile commodities and in periods of high return dispersion.<\/p>\n

\tNew research papers related to existing strategies:<\/strong><\/u><\/p>\n

\t#118 – Time Series Momentum Effect<\/strong><\/p>\n

\n\tCho, Ham, Kim, Ryu: Time-Series Momentum in the Chinese Commodity Futures Market<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3311479<\/a>
\n\tAbstract:
\n\tThis study examines time-series momentum in the Chinese commodity futures market. The findings show that a time-series momentum strategy performs best with a one-month look-back period and a one-month holding period. Furthermore, this strategy outperforms passive long and cross-sectional momentum strategies in the Chinese futures market based on Sharpe ratios, risk-adjusted excess returns, and cumulative returns. But highly volatile market characteristic with many speculative investors limits the period in which time-series momentum is maintained. Our findings suggest that the anomaly is observed in international asset markets, including Chinese commodity futures, and support the implication that speculators profit from time-series momentum strategy is the expense of hedgers.<\/p>\n

\n\t#128 – Innovative Efficiency Effect in Stocks
\n\t#363 – Technology Momentum
\n\t#414 – Patent-to-Market Equity Factor<\/strong><\/p>\n

\n\tJha: Innovation and Industry Selection<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3313212<\/a>
\n\tAbstract:
\n\tWe use a novel dataset of company-level innovation measures to identify the most innovative industries based on counts of their applications for foreign worker visas and their patent applications and grants. We are able to build portfolios which overweight these innovative industries and which generate economically significant excess returns, especially in the 2013-2018 period, with low turnover. The results do not appear to be fully explained by risk factors, and the same innovation measures do not predict returns at the single stock level.<\/p>\n

\tAnd four additional related research papers have been included into existing free strategy reviews during last 2 weeks:<\/strong><\/u><\/p>\n

\n\tA New Look on Shiller's CAPE Ratio<\/p>\n

\tJivraj, Shiller: The Many Colours of CAPE<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3258404<\/a>
\n\tAbstract:
\n\tCampbell & Shiller's [1988] Cyclically-Adjusted Price to Earnings ratio (CAPE) has both its advocates and critics. Currently, the debate is on the validity of the high CAPE ratio for US stock markets in forecasting lower future returns, with CAPE currently at 31.21. We investigate the efficacy and validity of CAPE from several different perspectives. First, we run multiple-horizon predictability regressions for CAPE versus its peers and find that CAPE consistently displays economic and statistical significance far better than any of its peers. Second, we explore alternative constructions of CAPE based on other proxies for earnings motivated by the work of findings by Siegel [2016] using NIPA profits. We find that original CAPE is still best when comprehensively and fairly reviewing the other proxies, even for NIPA profits. Third, we assess how to practically use CAPE in both an asset allocation and relative valuation setting. We demonstrate a novel use of CAPE for asset allocation programmes as well as discuss relative valuation exercises for country, sector and single stock rotation.<\/p>\n

\n\tThree Insights from Research Related to #5 – Carry Trade Strategy:<\/p>\n

\tBurnside, Cerrato, Zhang: Foreign Exchange Order Flow as a Risk Factor<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3275356<\/a>
\n\tAbstract:
\n\tThis paper proposes a set of novel pricing factors for currency returns that are motivated by microstructure models. In so doing, we bring two strands of the exchange rate literature, namely market-microstructure and risk-based models, closer together. Our novel factors use order flow data to provide direct measures of buying and selling pressure related to carry trading and momentum strategies. We find that they appear to be good proxies for currency crash risk. Additionally, we show that the association between our order-flow factors and currency returns differs according to the customer segment of the foreign exchange market. In particular, it appears that financial customers are risk takers in the market, while non-financial customers serve as liquidity providers.<\/p>\n

\tHsu, Taylor, Wang: The Profitability of Carry Trades: Reality or Illusion?<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3158101<\/a>
\n\tAbstract:
\n\tWe carry out a large-scale investigation of the profitability of carry trades, using foreign exchange data for 48 countries spanning a period from 1983 to 2016 and employing a stepwise test to counter data-snooping bias. We find that, while we can confirm previous findings that the carry trade is profitable over this long period when a specific carry-trade strategy is selected based on the whole data set, even after controlling for data snooping, when we split the sample into sub-periods, the best carry-trade strategy in one sub-period is generally not profitable in the next sub-period. This finding holds true even when we include learning strategies and stop-loss strategies. Our findings thus highlight the instability of carry trades over long periods and their limitation in the sense that it is hard to predict their performance based on several years of data and therefore to choose a profitable carry-trade strategy ex ante.<\/p>\n

\tSakemoto: Currency Carry Trades and the Conditional Factor Model<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3210768<\/a>
\n\tAbstract:
\n\tThis study employs a conditional factor model in order to investigate the time-varying profitability of currency carry trades. To that end, I estimate conditional alphas and betas on the popular dollar and carry factors through the use of a nonparametric approach. The empirical results illustrate that the alphas and betas vary over time. Furthermore, I find that the alpha of a high interest rate currency portfolio increases in a trough in a business cycle and in a state of high market uncertainty. However, the beta on the dollar factor decreases in these market conditions, suggesting that investors reduce the foreign currency risk exposure.<\/p>\n","protected":false},"excerpt":{"rendered":"

\n\tTwo new strategies have been added:<\/p>\n

\t#423 – Industry Herding and Momentum
\n\t#424 – Long-Run Reversal in Commodity Returns<\/strong><\/p>\n

\n\tTwo new related research papers have been included into existing strategy reviews. And four additional related research papers have been included into existing free strategy reviews during last few weeks.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-4217","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4217","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=4217"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4217\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=4217"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=4217"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=4217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}